From bcf9c858c70f9d5332dff50a0272dbfc1ea13732 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Tue, 10 Feb 2026 11:41:00 -0700
Subject: [PATCH 01/15] styling for mobile/vertical functionality and
 accessibility. also uploading files + linking them on the full course
 structure board

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+---
+title: "Final Project"
+author: "Your Name Here"
+date: "`r Sys.Date()`"
+output: pdf_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Final Details
+
+### Purpose
+
+The goal of this final assignment is to assess your ability to integrate the many skills you have learned over the semester: filtering and summarizing data, creating new columns, choosing the appropriate data visualizations, and performing and interpreting the appropriate statistical tests.
+
+### Task
+
+Write R code which produces the correct data, summaries, plots and analyses. Correctly interpret the results of these plots and analyses.
+
+### Criteria for Success
+
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive **full** credit
+    -   Code attempts with logical direction will receive **partial** credit
+-   Appropriate plot types are used to visualize the data
+-   Appropriate statistical tests are used to analyze the data
+-   Written answers address the questions in sufficient detail
+
+### Due Date
+
+Dec 11 at midnight MST
+
+------------------------------------------------------------------------
+
+For your final this semester, I am presenting you with 3 problem sets, totaling up to 60 points.
+
+I'm expecting you to be able to filter and summarize the data in the ways you need, choose the appropriate visualization, choose the appropriate analysis, and correctly interpret the analysis for the question I've asked you.
+
+It is important to note that I will not be giving you an answer key for this final project since a major part of your grade for the assignment is being able to choose the appropriate visualizations and analyses for the question and the data.
+
+We are going to use the `palmerpenguins` R package and data set, which we've used many times before! You can learn more about it [here](https://allisonhorst.github.io/palmerpenguins/). This is a real data set from a Long-Term Ecological Research (LTER) site in Antarctica.
+
+## Set-Up
+
+#### Be sure to run both of these code chunks before you begin!
+
+I've gone ahead and included the code to load the two packages you will need to successfully complete this project: the `tidyverse` and `palmerpenguins`. Be sure to run this code chunk!
+
+```{r message = FALSE}
+library(tidyverse)
+library(palmerpenguins)
+```
+
+#### Important!
+
+I've also included one more code chunk below. Be sure to run this code chunk, as well! It does two key things:
+
+-   first, it adds the `penguins` data frame to your environment, which I imagine you will find helpful
+-   second, it removes all rows that have any NA values, which will make completing this assignment a bit easier.
+
+Once you've run this line of code, you should see the `penguins` data frame pop up in your environment with 333 observations (rows) and 8 variables (columns).
+
+```{r}
+penguins <- penguins %>% drop_na()
+```
+
+![](lter_penguins.png){width="50%"}
+
+------------------------------------------------------------------------
+
+## Structure & Guidelines
+
+Like the practice version, this final is structured as 3 different problem sets. For each problem set, I am presenting you with an initial question to guide your thinking and analysis.
+
+### Data
+
+Assume that nothing carries over between problem sets.
+
+Each problem set is stand-alone, meaning that you should always start with the `penguins` data frame at the beginning of each problem set. If you should use a data frame that you created *within* the problem set, I explicitly state so.
+
+For example, in Problem Set 1, you should use the `biscoe` data frame that you create for the entire problem set; at the start of Problem Set 2, start over with the `penguins` data frame.
+
+### Interpreting Statistical Results
+
+When I ask you to interpret statistical results, you should roughly follow these guidelines.
+
+-   the cut-off for our p-values is always 0.05 ($\alpha = 0.05$)
+-   report the p-value that we are focused on in that question
+-   if there are multiple p-values of interest, report all of them
+-   state whether the p-value indicates a significant difference/relationship
+-   if applicable, state whether we should or should not reject the null hypothesis
+
+### Plotting
+
+All plots should be made using `ggplot2`.
+
+Your options for plot types to choose from are:
+
+-   [multiple]{.underline} histogram plots
+    -   use transparency (`alpha`)
+    -   use `position = "identity"` with multiple groups to see the full distributions
+-   [multiple]{.underline} density plots
+    -   use transparency (`alpha`) with multiple groups to see the full distributions
+-   box-and-whisker plot
+    -   add points on top of the box plot to show the distribution of the points
+-   scatter plot
+    -   add the linear model to every scatter plot
+
+**Note: All plots should have modified axis labels and legend labels.**
+
+In many cases, this might mean capitalizing the axis label and/or legend label. In other cases, you might want to put units in parentheses after the words (e.g., Body Mass (g)).
+
+### Generative AI Guidelines
+
+While I do not recommend using generative AI to complete this assignment (throughout the semester, I've seen it cause more problems than not...), you are allowed to use it on the final.
+
+However, you *must* do so according to the guidelines I've set out for all other assignments this semester.
+
+You can find clear examples of these guidelines on D2L in *Resources \> Helpful Info \> Assignment Reminders*.
+
+#### CAUTION!
+
+If you do use generative AI without following the course guidelines, you will receive a 0 for this final and you will not have the option to resubmit. Proceed wisely.
+
+------------------------------------------------------------------------
+
+# Let's Begin!
+
+-   Have you run the 2 code chunks in the "Set-Up" section?
+-   Have you read through the "Structure & Guidelines" section?
+
+If so, proceed! If not, scroll back up...
+
+## Problem Set 1 (30 points total)
+
+This problem set has 2 parts.
+
+### Part 1 (20 points)
+
+#### Question: Is there a significant relationship between bill length and bill depth for penguins on Biscoe Island?
+
+For this problem set, we are going to use data from Biscoe island only.
+
+1.  Create a new data frame called `biscoe` that includes only penguins from Biscoe island. This new data frame should have 163 rows. (2 points)
+
+    ```{r}
+
+    ```
+
+You will want to use the `biscoe` data set for the rest of Problem Set #1 (Parts 1 and 2).
+
+This is a scenario where there is no clear independent and dependent variable. Go ahead and treat **bill length as the independent variable (x-axis) and bill depth as the dependent variable (y-axis).**
+
+2.  Determine whether bill length and bill depth are continuous or categorical (1 point)
+
+    -   **bill depth**: dependent
+    -   **bill length**: independent
+
+3.  Write the correct pair of statistical hypotheses for our question. (2 points)
+
+    **Null**:
+
+    **Alternative**:
+
+4.  Plot the relationship between bill length and bill depth using the appropriate plot type. (3 points)
+
+    -   Be sure to add a line of best fit
+    -   Ensure that the plot has clear labels on the axes (follow the Structure & Guidelines).
+    -   Remember, we are ignoring species for now.
+
+    ```{r}
+
+    ```
+
+5.  Describe the relationship that you see in the plot you just made. Is the relationship positive, negative, or nonexistent? How do you know? (2 points)
+
+    *Answer:*
+
+6.  Calculate the correlation coefficient, `r`. What does this value tell us? (2 points)
+
+    ```{r}
+
+    ```
+
+    *Answer:*
+
+7.  Calculate the `r^2` value. Remember, this number is typically expressed as a percent (x 100). What is this value telling us? (2 points)
+
+    ```{r}
+
+    ```
+
+    *Answer:*
+
+8.  Let's see if there is a significant relationship between bill length and bill depth. Perform the correct statistical analysis (1 point) and interpret the results. (6 points total)
+
+    ```{r}
+
+    ```
+
+    -   What is the equation of the line of best fit? Use both variable names and values from the statistical analysis. (2 point)
+    -   What is the p-value? (1 point)
+    -   Is there a significant relationship? (1 point)
+    -   What should we do with the null hypothesis (reject or fail to reject)? (1 point)
+
+    *Answer*:
+
+### Part 2 (10 points)
+
+When we look at the plot of the data, it looks like there might be two different groups in the data. Let's see what happens when we add in species to this analysis. Keep using the `biscoe` dataframe.
+
+9.  Make the color of the points and the linear models differ by species on Biscoe Island. Be sure to adjust *all* labels on the plot accordingly. (3 points)
+
+    ```{r}
+
+    ```
+
+10. Run the appropriate statistical test, adding species (and the interaction between the two independent variables) into the model. (2 points)
+
+    ```{r}
+
+    ```
+
+11. Interpret the results of the test above. Is species significant? Is there a significant interaction between bill length and species? (2 points)
+
+*Answer:*
+
+12. Based on the plots, the linear relationships, and the linear models, write 2-3 sentences discussing if and/or how adding species into our linear model changes our interpretation of the data. Did the type of relationship change? Did significance levels change? Do the two linear models tell us different things? (3 points)
+
+*Answer:*
+
+------------------------------------------------------------------------
+
+## Problem Set 2 (15 points)
+
+#### Question: Is there a difference in the average flipper length between male and female Chinstrap penguins?
+
+1.  Our first step is to create a new data frame that includes only Chinstrap penguins. Call this new data frame `chinstrap`. (1 point)
+
+    ```{r}
+
+    ```
+
+We will be using the `chinstrap` data frame for the rest of this problem set.
+
+2.  Let's summarize our data. Calculate one measure of central tendency and one measure of variability of the flipper length column for *each* sex: male and female. Save this dataframe as `chinstrap_summary`. (2 points)
+
+    ```{r}
+
+    ```
+
+3.  What if we wanted our summary data in centimeters instead of millimeters? We can achieve this in 2 steps.
+
+    a.  First, create a function called `mm_to_cm` that will convert a number from millimeters to centimeters. There are 10 millimeters in a centimeter. (1 point)
+
+    ```{r}
+
+    ```
+
+    b.  Now, using the same code from question 3 above, add one line of code (in the correct location) that uses your newly created `mm_to_cm` function to create a new column in the dataframe. Make sure to edit the arguments in summary functions accordingly, as well. The result should be the same data frame that you produced in question 3 but with the summary values in cm instead of mm. (1 point)\
+        *Hint: if you're feeling confused about how to do this, check out the key for Module 4 Assignment 3*.
+
+    ```{r}
+
+    ```
+
+Ok, we have summarized the flipper length data for our two groups!
+
+Let's get back to the `chinstrap` dataframe (not the summary dataframe) and keep working.
+
+4.  Determine which variable is *dependent* and which is *independent*. Also determine if each variable is *continuous* or *categorical*. (1 point)
+    -   **flipper length**:
+    -   **species**:
+
+Let's plot the flipper length data for each sex.
+
+5.  Choose an appropriate plot for the data. Be sure to adjust the x- and y-axis labels appropriately. (2 points)
+
+    ```{r}
+
+    ```
+
+6.  Write the pair of statistical hypotheses for our question. (1 point)
+
+    **Null**:
+
+    **Alternative**:
+
+7.  Perform the appropriate analysis to compare the flipper lengths of male and female chinstrap penguins. (2 points)
+
+    ```{r}
+
+    ```
+
+8.  Interpret the results of this test. (2 points)
+
+    -   what is the p-value?
+    -   is there a significant difference?
+    -   should we reject the null hypothesis?
+
+    *Answer:*
+
+9.  Should we run pairwise comparisons? If no, explain why not. If yes, do so and interpret the results. (2 points)
+
+    *Answer:*
+
+    ```{r}
+
+    ```
+
+------------------------------------------------------------------------
+
+## Problem Set 3 (15 points)
+
+#### Question: **Are there differences in the average bill length across the islands in the data set? (Ignore species)**
+
+Let's start by summarizing the bill length data.
+
+1.  Calculate a measure of central tendency and measure of variability for the bill length for *each* island. (2 points)
+
+    ```{r}
+
+    ```
+
+2.  Which of our variables would be considered *independent* and which one *dependent*? Also determine whether each is *continuous* or *categorical*. (2 points)
+
+    -   **island**:
+    -   **bill length**:
+
+Now that we have an idea numerically of the differences between the islands, let's plot the differences.
+
+3.  Choose an appropriate plot. Ensure that you follow the plotting guidelines in the Structure & Guidelines section above! (2 points)
+
+    ```{r}
+
+    ```
+
+4.  Write the correct pair of statistical hypotheses for our question. (2 points)
+
+    **Null**:
+
+    **Alternative**:
+
+5.  Run the appropriate statistical analysis for our question. (2 points)
+
+    ```{r}
+
+    ```
+
+6.  Interpret the results of this test. (3 points)
+
+    -   what is the p-value?
+    -   is there a significant difference?
+    -   should we reject the null hypothesis?
+
+    *Answer:*
+
+7.  Should we run pairwise comparisons? If no, explain why not. If yes, do so and interpret the results. (2 points)
+
+    *Answer:*
+
+    ```{r}
+
+    ```
+
+------------------------------------------------------------------------
+
+## The End!
+
+Great work, and thanks for a wonderful semester!
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diff --git a/structure.qmd b/structure.qmd
index 3dfa213..823f0b4 100644
--- a/structure.qmd
+++ b/structure.qmd
@@ -7,84 +7,80 @@ format:
 
 ## The Full Course Cheese Board
 
-This page serves as an inventory of the material associated with each subsection of this site. These are also available in the Data in the Wild Github repository, which can be accessed here: \[insert link of canonical version\].
+This page serves as an inventory of the material associated with each subsection of this site. These are also available in the Data in the Wild Github repository, which can be accessed [here](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA.git).
 
-***Note*****:** The instructor versions of lessons are also available as PDFs, and they can be downloaded from the righthand table of contents within the respective module subsection (e.g. Click on 1.1: Introduction to RStudio in the left sidebar \> navigate your attention to the table of contents on the right side of the screen \> the link to the PDF should be the last option).
+***Note*****:** The instructor versions of lessons are also available as PDFs, and they can be downloaded from the righthand table of contents within the respective module subsection (e.g. Click on 1.1: Introduction to RStudio in the left sidebar \> navigate your attention to the table of contents on the right side of the screen \> the link to download the PDF is at the bottom).
 
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| Module | Title                                                         | Lecture / Code-along (.Rmd)                                                                                                                                        | Data sets (.csv)                    | Discussions (.txt)                                                                                                                              | Assignments (.Rmd)                                                                                                                                       |   |   |
-+:======:+:=============================================================:+:==================================================================================================================================================================:+:===================================:+:===============================================================================================================================================:+:========================================================================================================================================================:+===+===+
-| 1.1    | Introduction to RStudio                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_1_IntrotoRStudio.Rmd)               | Figure how to download all in a zip | [Discussion 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/discussions/discussion_1_1) | Assignment 1                                                                                                                                             |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_1_IntrotoRStudio_Instructor.Rmd) |                                     | [Discussion 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/discussions/discussion_1_2) | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/assignments/Module1_Assignment2.Rmd) |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/assignments/Module1_Assignment3.Rmd) |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 1.2    | Introduction to Coding                                        | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 1.3    | 2-dimensional Data and the `tidyverse`                        | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.1    | Introduction to Descriptive Statistics and Data Visualization | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.2    | Writing Functions                                             | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.3    | Plotting with `ggplot2`                                       | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.4    | A Visualization Primer                                        | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.5    | Sick Fish                                                     | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.6    | Exploring `geom` Functions                                    | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 2.7    | Wrap-Up                                                       | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 3.1    | Leopard Seals                                                 | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 3.2    | T-Tests                                                       | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 3.3    | Comparing (Multiple) Means                                    | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 3.4    | Combining Data (Joins and Binds)                              | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 3.5    | K-Nearest Neighbor                                            | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 4.1    | Roads and Regressions                                         | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 4.2    | Multiple Regression                                           | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
-| 4.3    | Using Functions to Automate Tasks                             | Student                                                                                                                                                            |                                     | Discussion 1                                                                                                                                    |                                                                                                                                                          |   |   |
-|        |                                                               |                                                                                                                                                                    |                                     |                                                                                                                                                 |                                                                                                                                                          |   |   |
-|        |                                                               | Instructor                                                                                                                                                         |                                     | Discussion 2                                                                                                                                    |                                                                                                                                                          |   |   |
-+--------+---------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+---+---+
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Module        | Title                                                       | Lecture / Code-along (.Rmd)                                                                                                                                            | Data sets (.csv)                                                                                                            | Assignments (.Rmd)                                                                                                                                                  |
++:=============:+:===========================================================:+:======================================================================================================================================================================:+:===========================================================================================================================:+:===================================================================================================================================================================:+
+| 1.1           | Introduction to RStudio                                     | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_1_IntrotoRStudio.Rmd)                   | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M1_data.zip)         | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_1/assignments/Module1_Assignment2.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_1_IntrotoRStudio_Instructor.Rmd)     |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_1/assignments/Module1_Assignment3.Rmd) |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 1.2           | Introduction to Coding                                      | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_2_IntrotoCoding.Rmd)                    |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_2_IntrotoCoding_Instructor.Rmd)      |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 1.3           | 2-dimensional Data and the `tidyverse`                      | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_3_tidyverse.Rmd)                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_3_tidyverse_Instructor.Rmd)          |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 2.1           | Introduction to Descriptive Statistics & Data Visualization | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_1_DescriptiveStats.Rmd)                 | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M2_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment1.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_1_DescriptiveStats_Instructor.Rmd)   |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment2.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment3.Rmd) |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 2.2           | Writing Functions                                           | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions.Rmd)                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions_Instructor.Rmd)          |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 2.3           | Introduction to Data Visualization                          | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_3_DataViz.Rmd)                          |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_3_DataViz_Instructor.Rmd)            |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 2.4           | Plotting in `ggplot2`                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_4_geoms.Rmd)                            |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_4_geoms_Instructor.Rmd)              |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 2.5           | Module 2 Wrap-Up                                            | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp.Rmd)                           |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp_Instructor.pdf)             |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 3.1           | Leopard Seals: Introducing a New Problem                    | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_1_LeopardSeals.Rmd)                     | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M3_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment1.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_1_LeopardSeals_Instructor.Rmd)       |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment2.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment3.Rmd) |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 3.2           | T-Tests                                                     | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests.Rmd)                          |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests_Instructor.Rmd)            |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 3.3           | ANOVA                                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA.Rmd)                            |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA_Instructor.Rmd)              |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 3.4           | Combining Datasets                                          | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_4_Combining_Datasets.Rmd)               |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_4_Combining_Datasets_Instructor.Rmd) |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 3.5           | K-Nearest Neighbors                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 4.1           | Roads and Regression                                        | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression.Rmd)               | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M4_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment1.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression_Instructor.Rmd) |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment2.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment3.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Final Project](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/final_project/FinalAssignment_Sp2024.Rmd)    |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 4.2           | Multiple Regression                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression.Rmd)               |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression_Instructor.Rmd) |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Final Project |                                                             |                                                                                                                                                                        | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/final_proj_data.zip) | [Assignment Guidelines](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/final_project/FinalAssignment.pdf)              |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
\ No newline at end of file
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+  font-size: 1rem;
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+  color: $magenta;
+  font-weight: 700;
+}
+
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+  padding-top: 1em;
+}
+
+// Sidebar logo
+.sidebar-logo {
+  width: 160px;
+  height: 160px;
+  object-fit: contain;
+  display: block;
+  margin: 1em auto;
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HcmV?d00001


From 278a7f8a8360f77dac54e131bff149089aa5240c Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Tue, 10 Feb 2026 11:58:52 -0700
Subject: [PATCH 02/15] adding final project links for download

---
 .../final_project/FinalAssignment.Rmd               |   0
 .../final_project/FinalAssignment.pdf               | Bin
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename modules/{module_1 => }/final_project/FinalAssignment.Rmd (100%)
 rename modules/{module_1 => }/final_project/FinalAssignment.pdf (100%)

diff --git a/modules/module_1/final_project/FinalAssignment.Rmd b/modules/final_project/FinalAssignment.Rmd
similarity index 100%
rename from modules/module_1/final_project/FinalAssignment.Rmd
rename to modules/final_project/FinalAssignment.Rmd
diff --git a/modules/module_1/final_project/FinalAssignment.pdf b/modules/final_project/FinalAssignment.pdf
similarity index 100%
rename from modules/module_1/final_project/FinalAssignment.pdf
rename to modules/final_project/FinalAssignment.pdf

From 8a12a6f10abaed27fd63ba685ca0a69ac689a7a3 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Tue, 10 Feb 2026 12:07:01 -0700
Subject: [PATCH 03/15] creating readme

---
 README.md | 58 +++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 48 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 3205c09..a2635df 100644
--- a/README.md
+++ b/README.md
@@ -1,25 +1,63 @@
 # Data In The Wild
 
+An introductory data science course for the life sciences, developed as part of the NSF Harnessing the Data Revolution (HDR) Data Science Corps program in collaboration between the University of Arizona and Lewis & Clark College.
+
 ## About the Course
 
-[Insert course description]
+Data in the Wild teaches students to apply data science skills to real-world problems in biodiversity and conservation. Using the R programming language, students work through four narrative-driven modules set in Antarctica, from their first steps in RStudio to building predictive models for penguin nesting habitat. The course is designed for students across all majors and fulfills general education requirements at the University of Arizona.
+
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe, and first taught in pilot form at UA in Spring 2020.
 
-## Course Materials
+## Course Modules
 
-[Insert descriptions about modules]
+**Module 1 — Mission Antarctica!**
+Introduction to data science, RStudio, RMarkdown, and the R programming language.
 
-## Accessing the Material
+**Module 2 — Good Food Gone Bad**
+Data visualization and simulation to investigate a food poisoning outbreak among team members.
 
-### Github
+**Module 3 — Follow That Seal**
+Classification and machine learning to identify failing radio collars on leopard seals.
 
-[About material in Github and how to navigate / use for class]
+**Module 4 — March of the Penguins**
+Regression and bootstrapping to model penguin nesting success and plan a new road route.
+
+## Accessing Course Materials
 
 ### Website
 
-[Link to website]
+The course website contains all lesson pages, module narratives, and links to downloadable materials:
+
+[https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/](https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/)
+
+### GitHub
+
+All source files including student and instructor versions of lessons (.Rmd), datasets (.csv/.zip), and assignments are available in the course repository:
+
+[https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA)
+
+Instructor versions of lessons are also downloadable as PDFs directly from the website. Navigate to any lesson page and find the download link at the bottom of the right-hand table of contents.
+
+## Building the Website Locally
+
+This site is built with [Quarto](https://quarto.org). To preview locally:
+
+```bash
+quarto preview
+```
+
+To render for deployment:
+
+```bash
+quarto render
+```
+
+Output is written to the `docs/` folder, which is used for GitHub Pages deployment.
+
+## Support
 
-[About material in Github and how to navigate / use for class]
+This course was developed with support from the National Science Foundation under the Harnessing the Data Revolution (HDR) Data Science Corps (DSC) program. Materials are openly available and designed to be adaptable for use at other institutions.
 
-------------------------------------------------------------------------
+---
 
-*Last updated: 19 January 2024, Maxine Cruz*
+2026, University of Arizona & Lewis & Clark College
\ No newline at end of file

From eef389dab66aa6980174bf3992c6256b72713700 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Tue, 10 Feb 2026 12:07:20 -0700
Subject: [PATCH 04/15] fixing TeX error with pdfs

---
 _quarto.yml | 114 +++++++++++++++++++++++++---------------------------
 1 file changed, 54 insertions(+), 60 deletions(-)

diff --git a/_quarto.yml b/_quarto.yml
index 89885b5..2ef8674 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -1,85 +1,79 @@
 project:
-
   type: website
   output-dir: docs
 
 website:
-
   title: "Data In The Wild"
-  favicon: images/bdsc-logo-hex.png
   search: false
-  
-  navbar: 
+
+  navbar:
     logo: images/bdsc-logo-hex.png
-        
+
   sidebar:
-      logo: images/bdsc-logo-hex.png
-      background: $blanched-almond
-      align: center 
-      style: docked
-      collapse-level: 1
-      
-      contents: 
-        - section: "Introduction"
-          contents: 
-            - index.qmd
-            - structure.qmd
-            - text: "Contact Us"
-              href: https://github.com/BiodiversityDataScienceCorp
-            
-        - section: "Module 1"
-          contents: 
-            - modules/module_1/module1_1.qmd
-            - modules/module_1/module1_2.qmd
-            - modules/module_1/module1_3.qmd
-            
-        - section: "Module 2"
-          contents: 
-            - modules/module_2/module2_1.qmd
-            - modules/module_2/module2_2.qmd
-            - modules/module_2/module2_3.qmd
-            - modules/module_2/module2_4.qmd
-            - modules/module_2/module2_5.qmd
-            - modules/module_2/module2_6.qmd
-            - modules/module_2/module2_7.qmd
-            
-        - section: "Module 3"
-          contents: 
-            - modules/module_3/module3_1.qmd
-            - modules/module_3/module3_2.qmd
-            - modules/module_3/module3_3.qmd
-            - modules/module_3/module3_4.qmd
-            - modules/module_3/module3_5.qmd
-            
-        - section: "Module 4"
-          contents: 
-            - modules/module_4/module4_1.qmd
-            - modules/module_4/module4_2.qmd
-            - modules/module_4/module4_3.qmd
-            
-        - section: "Resources"
-          contents: 
-            - resources.qmd
-        
-  page-footer: 
+    logo: images/bdsc-logo-hex.png
+    background: $blanched-almond
+    align: center
+    style: docked
+    collapse-level: 1
+
+    contents:
+      - section: "Introduction"
+        contents:
+          - index.qmd
+          - structure.qmd
+          - text: "Contact Us"
+            href: https://github.com/BiodiversityDataScienceCorp
+
+      - section: "Module 1"
+        contents:
+          - modules/module_1/module1_1.qmd
+          - modules/module_1/module1_2.qmd
+          - modules/module_1/module1_3.qmd
+
+      - section: "Module 2"
+        contents:
+          - modules/module_2/module2_1.qmd
+          - modules/module_2/module2_2.qmd
+          - modules/module_2/module2_3.qmd
+          - modules/module_2/module2_4.qmd
+          - modules/module_2/module2_5.qmd
+          - modules/module_2/module2_6.qmd
+          - modules/module_2/module2_7.qmd
+
+      - section: "Module 3"
+        contents:
+          - modules/module_3/module3_1.qmd
+          - modules/module_3/module3_2.qmd
+          - modules/module_3/module3_3.qmd
+          - modules/module_3/module3_4.qmd
+          - modules/module_3/module3_5.qmd
+
+      - section: "Module 4"
+        contents:
+          - modules/module_4/module4_1.qmd
+          - modules/module_4/module4_2.qmd
+          - modules/module_4/module4_3.qmd
+
+      - section: "Resources"
+        contents:
+          - resources.qmd
+
+  page-footer:
     background: $dark-blue
     foreground: $blanched-almond
-    left: "Copyright 2024, University of Arizona | Last modified: 12 June 2024"
-    right: 
+    left: "2026, University of Arizona & Lewis & Clark College"
+    right:
       - href: https://quarto.org/
         text: "Made with Quarto"
       - href: https://github.com/BiodiversityDataScienceCorp/DataInTheWild_Website
         icon: github
 
 format:
-
   html:
     theme:
       - styles.scss
     mainfont: "Raleway"
     toc: true
     page-layout: full
-  pdf: default
 
 editor: visual
-

From 97f171d61157eeb09356de8a2bfcf79486787629 Mon Sep 17 00:00:00 2001
From: Lily McMullen <122934612+lilymcmullen@users.noreply.github.com>
Date: Tue, 10 Feb 2026 12:09:58 -0700
Subject: [PATCH 05/15] Update README.md

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index a2635df..a041322 100644
--- a/README.md
+++ b/README.md
@@ -10,16 +10,16 @@ The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and El
 
 ## Course Modules
 
-**Module 1 — Mission Antarctica!**
+**Module 1 - Mission Antarctica!**
 Introduction to data science, RStudio, RMarkdown, and the R programming language.
 
-**Module 2 — Good Food Gone Bad**
+**Module 2 - Good Food Gone Bad**
 Data visualization and simulation to investigate a food poisoning outbreak among team members.
 
-**Module 3 — Follow That Seal**
+**Module 3 - Follow That Seal**
 Classification and machine learning to identify failing radio collars on leopard seals.
 
-**Module 4 — March of the Penguins**
+**Module 4 - March of the Penguins**
 Regression and bootstrapping to model penguin nesting success and plan a new road route.
 
 ## Accessing Course Materials
@@ -60,4 +60,4 @@ This course was developed with support from the National Science Foundation unde
 
 ---
 
-2026, University of Arizona & Lewis & Clark College
\ No newline at end of file
+2026, University of Arizona & Lewis & Clark College

From 122c6f7fec628443cba84ff367bb76d8e62cfd12 Mon Sep 17 00:00:00 2001
From: Lily McMullen <122934612+lilymcmullen@users.noreply.github.com>
Date: Tue, 10 Feb 2026 12:11:32 -0700
Subject: [PATCH 06/15] Update README.md

---
 README.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/README.md b/README.md
index a041322..5becde8 100644
--- a/README.md
+++ b/README.md
@@ -32,12 +32,10 @@ The course website contains all lesson pages, module narratives, and links to do
 
 ### GitHub
 
-All source files including student and instructor versions of lessons (.Rmd), datasets (.csv/.zip), and assignments are available in the course repository:
+All source files are available in the course repository:
 
 [https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA)
 
-Instructor versions of lessons are also downloadable as PDFs directly from the website. Navigate to any lesson page and find the download link at the bottom of the right-hand table of contents.
-
 ## Building the Website Locally
 
 This site is built with [Quarto](https://quarto.org). To preview locally:

From d04e160dc3cb7f238078450becf566e99be02f8f Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Tue, 10 Feb 2026 12:43:10 -0700
Subject: [PATCH 07/15] styling updates to index

---
 README.md   | 14 ++++++--------
 _quarto.yml |  4 ++++
 index.qmd   | 50 +++++++++++++++++++-------------------------------
 styles.scss | 48 ++++++++++++++++++++++++++++++++----------------
 4 files changed, 61 insertions(+), 55 deletions(-)

diff --git a/README.md b/README.md
index a2635df..5becde8 100644
--- a/README.md
+++ b/README.md
@@ -10,16 +10,16 @@ The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and El
 
 ## Course Modules
 
-**Module 1 — Mission Antarctica!**
+**Module 1 - Mission Antarctica!**
 Introduction to data science, RStudio, RMarkdown, and the R programming language.
 
-**Module 2 — Good Food Gone Bad**
+**Module 2 - Good Food Gone Bad**
 Data visualization and simulation to investigate a food poisoning outbreak among team members.
 
-**Module 3 — Follow That Seal**
+**Module 3 - Follow That Seal**
 Classification and machine learning to identify failing radio collars on leopard seals.
 
-**Module 4 — March of the Penguins**
+**Module 4 - March of the Penguins**
 Regression and bootstrapping to model penguin nesting success and plan a new road route.
 
 ## Accessing Course Materials
@@ -32,12 +32,10 @@ The course website contains all lesson pages, module narratives, and links to do
 
 ### GitHub
 
-All source files including student and instructor versions of lessons (.Rmd), datasets (.csv/.zip), and assignments are available in the course repository:
+All source files are available in the course repository:
 
 [https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA)
 
-Instructor versions of lessons are also downloadable as PDFs directly from the website. Navigate to any lesson page and find the download link at the bottom of the right-hand table of contents.
-
 ## Building the Website Locally
 
 This site is built with [Quarto](https://quarto.org). To preview locally:
@@ -60,4 +58,4 @@ This course was developed with support from the National Science Foundation unde
 
 ---
 
-2026, University of Arizona & Lewis & Clark College
\ No newline at end of file
+2026, University of Arizona & Lewis & Clark College
diff --git a/_quarto.yml b/_quarto.yml
index 2ef8674..e372ed7 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -24,6 +24,8 @@ website:
           - text: "Contact Us"
             href: https://github.com/BiodiversityDataScienceCorp
 
+      - text: "---"
+
       - section: "Module 1"
         contents:
           - modules/module_1/module1_1.qmd
@@ -54,6 +56,8 @@ website:
           - modules/module_4/module4_2.qmd
           - modules/module_4/module4_3.qmd
 
+      - text: "---"
+
       - section: "Resources"
         contents:
           - resources.qmd
diff --git a/index.qmd b/index.qmd
index 9e41631..2ced44c 100644
--- a/index.qmd
+++ b/index.qmd
@@ -8,50 +8,38 @@ format:
 
 ![](images/penguins.jpg)
 
-Welcome! This course introduces students in the life sciences to data science through the application of quantitative reasoning and the R programming language to "real-world" problems. Below, you will find the overall narrative and material that will be covered. There are four modules that guide the student from their first steps on the Land of the Penguins, to building new roads for access to fishing sites. Each module will lead them through basic programming, data visualization, statistics, machine learning, and interpretation of quantitative concepts.
+*Data in the Wild* introduces students to data science through the R programming language, applied to real problems in ecology and conservation. The course is built around a narrative, a mission to establish a sustainable colony in Antarctica, in which each module presents a new crisis for students to investigate using data. No prior programming experience is required.
 
-## The Narrative
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and is taught at the University of Arizona as part of the general education curriculum.
 
-### **⊳** Module 1: *Mission Antarctica!*
+## **The Narrative**
 
-A new effort to establish a permanent, sustainable colony in Antarctica is being launched. Students are introduced to the field of data science, the application (RStudio, RMarkdown, Jupyter Notebooks), and programming language (R, Python) used in the course.
+### **Module 1: Mission Antarctica!**
 
-### **⊳** Module 2: *Good Food Gone Bad*
+The team has arrived in Antarctica and needs to get to work. Students are introduced to RStudio, R, and RMarkdown by helping the team get organized and begin collecting data. By the end of this module, students can navigate RStudio, write and run basic R code, work with data frames, and produce simple visualizations.
 
-There is a food poisoning outbreak among team members. Students use data visualization to determine where the problem lies (fish, not plants) and simulations to determine the root of the problem (fish tank density, not fish tank temperature).
+### **Module 2: Good Food Gone Bad**
 
-### **⊳** Module 3: *Follow That Seal*
+A wave of food poisoning has swept through the team. Doctors have ruled out a viral infection, something in the food supply is to blame. Students use descriptive statistics, data visualization with `ggplot2`, and simulation to identify which food source is responsible and what conditions in the aquaculture system are driving the problem.
 
-The fish tanks need to be restocked, but we want to avoid fishing in places with high leopard seal density, so we track the seals. However, the radio collars on leopard seals are failing and there is a deadly conflict between fish collectors and seals. The collars come from two different manufacturers, but we need to tell how the collars are failing (days to recharge, not signal distance) and how to classify collars of unknown provenance.
+### **Module 3: Follow That Seal**
 
-### **⊳** Module 4: *March of the Penguins*
+Wild-caught fish are needed to supplement the food supply, but leopard seals are putting fishing teams at risk. The team has been tracking seals with radio collars from two manufacturers, but collars are failing earlier than expected. Students determine which manufacturer is responsible (battery life, not signal distance) using t-tests and ANOVA, then use joins and binds to combine datasets from multiple sources, and finally apply a K-Nearest Neighbors classification algorithm to identify the maker of a batch of unlabeled collars. Skills covered include hypothesis testing, multi-group comparison, data joining, and an introduction to machine learning.
 
-A new road is needed to access fishing sites with low leopard seal density. There are several possible routes, but we want to avoid crossing through Gentoo penguin nesting grounds. Students will build models to determine predictors of nesting success, first with bootstrapping for confidence intervals, then with linear regression.
+### **Module 4: March of the Penguins**
 
-## How to Navigate this Site
+Fishing bays have been identified, but a road is needed to reach them. The team wants to avoid destroying habitat for Antarctica hairgrass (*Deschampsia antarctica*), one of only two flowering plants on the continent, and avoid disturbing Gentoo penguin nesting sites. Students build linear and multiple regression models to identify environmental predictors of hairgrass density and penguin nesting success, then write their own functions to automate the analysis across candidate road routes. Skills covered include simple and multiple linear regression, model interpretation, and writing user-defined functions in R.
 
-Under Course Materials, you will find lessons taught in WFSC 223 at the University of Arizona as well as links download lectures, assignments, discussions, and other assets used for instruction.
+## **Learning Objectives**
 
-## Overall Learning Objectives
+By the end of the course, students will be able to:
 
-By the end of the semester, you will be able to...
+1.  Apply computational methods for managing, analyzing, visualizing, and sharing data in the context of real-world natural resource scenarios.
+2.  Infer meaning and insight from data and communicate findings in written, visual, and verbal form to multiple audiences.
+3.  Think critically across disciplines, consider multiple perspectives, and solve problems using data-informed approaches.
 
-1.  Define, differentiate, and explain the nature and application of computational methods
+## **For Instructors**
 
-    for acquiring, managing, analyzing, visualizing, and sharing data as it relates to real-
+Course materials, including student and instructor versions of all lessons, datasets, and assignments, are openly available and designed to be adaptable for other institutions and programming languages. R is used throughout, but the course structure is transferable to Python or other environments.
 
-    world natural resource scenarios.
-
-2.  Associate, examine, and compare how to infer meaning and insight from data through
-
-    written, visual, and verbal communication to multiple audiences.
-
-3.  Summarize, implement, and appraise multiple perspectives and make meaningful
-
-    connections across disciplines and social positions, think conceptually and critically, and
-
-    solve problems with data informed approaches.
-
-## Footnotes (For Instructors)
-
-This course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe. This was taught in pilot form at the University of Arizona in Spring 2020 as *Settlers of Antarctica*, and it has undergone multiple revisions to better communicate and optimize the material to both students and instructors over the years. At the end of the day, with the help of funding from the NSF's Harnessing the Data Revolution, the material is designed to be adaptable to other disciplines and serve as a template for your own courses. R was used as the primary programming language here, but the course is modifiable to other languages such as Python.
+All materials are available on the [course site](structure.qmd) and in the [GitHub repository](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA).
\ No newline at end of file
diff --git a/styles.scss b/styles.scss
index ae3e21c..1380b4d 100644
--- a/styles.scss
+++ b/styles.scss
@@ -1,11 +1,6 @@
 /*-- scss:defaults --*/
 
 // Fonts
-
-// Raleway
-// Righteous
-// Vollkorn
-
 @import url('https://fonts.googleapis.com/css2?family=Raleway:ital,wght@0,100..900;1,100..900&family=Righteous&family=Vollkorn:ital,wght@0,400..900;1,400..900&display=swap');
 
 // Colors
@@ -22,17 +17,15 @@ $saddlebrown: #8B4513;
 $slate-grey: #C6E2FF;
 
 // Base document colors
-$navbar-bg: $dark-blue; // navbar (bar at top of page)
-$navbar-fg: $blanched-almond; // navbar foreground (text, etc.)
-$navbar-hl: $slate-grey; // when you hover over navbar stuff
+$navbar-bg: $dark-blue;
+$navbar-fg: $blanched-almond;
+$navbar-hl: $slate-grey;
 $page-footer-hl: $slate-grey;
-$link-color: $magenta; // when you hover over buttons
-
-
+$link-color: $magenta;
 
 /*-- scss:rules --*/
 
-// Title, specifically (a.k.a. h1)
+// Title
 .title {
   font-family: "Vollkorn";
   font-size: 45px;
@@ -49,11 +42,18 @@ h1, h2, h3 {
   color: $darkest-blue;
 }
 
-// Navbar
+// Navbar for mobile only
 .navbar {
   font-family: "Righteous";
 }
 
+// Hide navbar logo on desktop since sidebar has the logo
+@media (min-width: 992px) {
+  .navbar-brand img {
+    display: none;
+  }
+}
+
 // Sidebar
 .sidebar {
   font-family: "Righteous";
@@ -89,10 +89,26 @@ body {
   font-family: "Raleway";
   color: $dark-blue;
   font-weight: 500;
-  
 }
 
-// Buttons (e.g. home buttons for email, Github, and Linkedin)
-div.quarto-about-trestles .about-entity .about-link {
+// Links
+a {
   color: $magenta;
+  text-decoration: none;
+}
+
+a:hover {
+  color: darken($magenta, 10%);
+  text-decoration: underline;
+}
+
+// Mobile
+@media (max-width: 768px) {
+  .title {
+    font-size: 32px;
+  }
+
+  body {
+    font-size: 14px;
+  }
 }

From dffa456adb35342a79cf2974543606456485886e Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Tue, 24 Feb 2026 10:38:27 -0700
Subject: [PATCH 08/15] website overhaul!

---
 .gitignore                                    |    2 +
 README.md                                     |   59 +-
 _quarto.yml                                   |    6 +-
 .../assets/coefficient_correlation.jpg        |  Bin
 .../module_3}/assets/coffee_interaction.jpg   |  Bin
 .../modules/module_3}/assets/correlation.jpg  |  Bin
 .../assets/gentoo-penguin-cornell-lab2.jpg    |  Bin
 .../module_3}/assets/hairgrass_indiv.jpeg     |  Bin
 .../module_3}/assets/hairgrass_landscape.jpeg |  Bin
 .../modules/module_3}/assets/lines.png        |  Bin
 docs/modules/module_3/assets/residuals.jpg    |  Bin 0 -> 139749 bytes
 .../modules/module_3}/assets/steepness.jpeg   |  Bin
 .../module_4/assets/antarctic_toothfish.jpg   |  Bin 0 -> 7876 bytes
 .../antarctic_toothfish_distribution.jpg      |  Bin 0 -> 118282 bytes
 .../module_4/assets/clipboard-1439389577.png  |  Bin 0 -> 1289 bytes
 .../module_4/assets/clipboard-3908560513.png  |  Bin 0 -> 3088 bytes
 .../module_4/assets/clipboard-4205079346.png  |  Bin 0 -> 973 bytes
 .../module_4/assets/clipboard-959845017.png   |  Bin 0 -> 1487 bytes
 .../module_4/assets/exp_and_logisitc.png      |  Bin 0 -> 53065 bytes
 .../module_4/assets/fish_data_fish.png        |  Bin 0 -> 61794 bytes
 .../module_4/assets/logistic_growth_msy.png   |  Bin 0 -> 19212 bytes
 docs/modules/module_4/assets/msy.png          |  Bin 0 -> 13518 bytes
 docs/modules/module_4/assets/pop_crash.jpeg   |  Bin 0 -> 378963 bytes
 docs/scripts/pdf-button.js                    |   19 +
 index.qmd                                     |   18 +-
 modules/final_project/FinalAssignment.Rmd     |  373 --
 modules/final_project/FinalAssignment.qmd     |  143 +
 modules/module_1/module1_1.qmd                |   57 +-
 modules/module_1/module1_2.qmd                |  355 +-
 modules/module_1/module1_3.qmd                |  247 +-
 modules/module_2/module2_1.qmd                |  123 +-
 modules/module_2/module2_2.qmd                |  200 +-
 modules/module_2/module2_3.qmd                |  131 +-
 modules/module_2/module2_4.qmd                |  312 +-
 modules/module_2/module2_5.qmd                |  279 +-
 modules/module_2/module2_6.qmd                |  302 --
 modules/module_2/module2_7.qmd                |  131 -
 .../assets/coefficient_correlation.jpg        |  Bin 0 -> 209716 bytes
 .../module_3/assets/coffee_interaction.jpg    |  Bin 0 -> 106195 bytes
 modules/module_3/assets/correlation.jpg       |  Bin 0 -> 817878 bytes
 .../assets/gentoo-penguin-cornell-lab2.jpg    |  Bin 0 -> 100390 bytes
 modules/module_3/assets/hairgrass_indiv.jpeg  |  Bin 0 -> 670367 bytes
 .../module_3/assets/hairgrass_landscape.jpeg  |  Bin 0 -> 183497 bytes
 modules/module_3/assets/lines.png             |  Bin 0 -> 288891 bytes
 modules/module_3/assets/residuals.jpg         |  Bin 0 -> 139749 bytes
 modules/module_3/assets/steepness.jpeg        |  Bin 0 -> 463595 bytes
 modules/module_3/data/hairgrass_data.csv      |  241 ++
 .../data/site_changes.csv                     |    0
 modules/module_3/module3_1.qmd                |   73 +-
 modules/module_3/module3_2.qmd                |   14 +-
 modules/module_3/module3_3.qmd                |   77 +-
 modules/module_3/module3_4.qmd                |  306 +-
 modules/module_3/module3_5.qmd                |  107 +-
 modules/module_3/module3_6.qmd                |  186 +
 .../module_4/assets/antarctic_toothfish.jpg   |  Bin 0 -> 7876 bytes
 .../antarctic_toothfish_distribution.jpg      |  Bin 0 -> 118282 bytes
 .../module_4/assets/clipboard-1439389577.png  |  Bin 0 -> 1289 bytes
 .../module_4/assets/clipboard-3779797644.png  |  Bin 0 -> 1901 bytes
 .../module_4/assets/clipboard-3908560513.png  |  Bin 0 -> 3088 bytes
 .../module_4/assets/clipboard-4205079346.png  |  Bin 0 -> 973 bytes
 .../module_4/assets/clipboard-959845017.png   |  Bin 0 -> 1487 bytes
 modules/module_4/assets/exp_and_logisitc.png  |  Bin 0 -> 53065 bytes
 modules/module_4/assets/fish_data_fish.png    |  Bin 0 -> 61794 bytes
 .../module_4/assets/logistic_growth_msy.png   |  Bin 0 -> 19212 bytes
 modules/module_4/assets/msy.png               |  Bin 0 -> 13518 bytes
 modules/module_4/assets/pop_crash.jpeg        |  Bin 0 -> 378963 bytes
 modules/module_4/assets/r-function-syntax.png |  Bin 13595 -> 0 bytes
 .../module_4/data/comparing_populations.csv   |   79 +
 modules/module_4/data/fish_pops.csv           |   81 +
 modules/module_4/data/hairgrass_data.csv      |  481 ---
 modules/module_4/data/nonvascular_plants.csv  |  201 --
 modules/module_4/data/penguin_chicks.csv      | 3201 -----------------
 modules/module_4/module4_1.qmd                |  324 +-
 modules/module_4/module4_2.qmd                |  234 +-
 modules/module_4/module4_3.qmd                |  317 +-
 scripts/pdf-button.js                         |   19 +
 styles.scss                                   |   18 +
 77 files changed, 2469 insertions(+), 6247 deletions(-)
 rename {modules/module_4 => docs/modules/module_3}/assets/coefficient_correlation.jpg (100%)
 rename {modules/module_4 => docs/modules/module_3}/assets/coffee_interaction.jpg (100%)
 rename {modules/module_4 => docs/modules/module_3}/assets/correlation.jpg (100%)
 rename {modules/module_4 => docs/modules/module_3}/assets/gentoo-penguin-cornell-lab2.jpg (100%)
 rename {modules/module_4 => docs/modules/module_3}/assets/hairgrass_indiv.jpeg (100%)
 rename {modules/module_4 => docs/modules/module_3}/assets/hairgrass_landscape.jpeg (100%)
 rename {modules/module_4 => docs/modules/module_3}/assets/lines.png (100%)
 create mode 100644 docs/modules/module_3/assets/residuals.jpg
 rename {modules/module_4 => docs/modules/module_3}/assets/steepness.jpeg (100%)
 create mode 100644 docs/modules/module_4/assets/antarctic_toothfish.jpg
 create mode 100644 docs/modules/module_4/assets/antarctic_toothfish_distribution.jpg
 create mode 100644 docs/modules/module_4/assets/clipboard-1439389577.png
 create mode 100644 docs/modules/module_4/assets/clipboard-3908560513.png
 create mode 100644 docs/modules/module_4/assets/clipboard-4205079346.png
 create mode 100644 docs/modules/module_4/assets/clipboard-959845017.png
 create mode 100644 docs/modules/module_4/assets/exp_and_logisitc.png
 create mode 100644 docs/modules/module_4/assets/fish_data_fish.png
 create mode 100644 docs/modules/module_4/assets/logistic_growth_msy.png
 create mode 100644 docs/modules/module_4/assets/msy.png
 create mode 100644 docs/modules/module_4/assets/pop_crash.jpeg
 create mode 100644 docs/scripts/pdf-button.js
 delete mode 100644 modules/final_project/FinalAssignment.Rmd
 create mode 100644 modules/final_project/FinalAssignment.qmd
 delete mode 100644 modules/module_2/module2_6.qmd
 delete mode 100644 modules/module_2/module2_7.qmd
 create mode 100644 modules/module_3/assets/coefficient_correlation.jpg
 create mode 100644 modules/module_3/assets/coffee_interaction.jpg
 create mode 100644 modules/module_3/assets/correlation.jpg
 create mode 100644 modules/module_3/assets/gentoo-penguin-cornell-lab2.jpg
 create mode 100644 modules/module_3/assets/hairgrass_indiv.jpeg
 create mode 100644 modules/module_3/assets/hairgrass_landscape.jpeg
 create mode 100644 modules/module_3/assets/lines.png
 create mode 100644 modules/module_3/assets/residuals.jpg
 create mode 100644 modules/module_3/assets/steepness.jpeg
 create mode 100644 modules/module_3/data/hairgrass_data.csv
 rename modules/{module_4 => module_3}/data/site_changes.csv (100%)
 create mode 100644 modules/module_3/module3_6.qmd
 create mode 100644 modules/module_4/assets/antarctic_toothfish.jpg
 create mode 100644 modules/module_4/assets/antarctic_toothfish_distribution.jpg
 create mode 100644 modules/module_4/assets/clipboard-1439389577.png
 create mode 100644 modules/module_4/assets/clipboard-3779797644.png
 create mode 100644 modules/module_4/assets/clipboard-3908560513.png
 create mode 100644 modules/module_4/assets/clipboard-4205079346.png
 create mode 100644 modules/module_4/assets/clipboard-959845017.png
 create mode 100644 modules/module_4/assets/exp_and_logisitc.png
 create mode 100644 modules/module_4/assets/fish_data_fish.png
 create mode 100644 modules/module_4/assets/logistic_growth_msy.png
 create mode 100644 modules/module_4/assets/msy.png
 create mode 100644 modules/module_4/assets/pop_crash.jpeg
 delete mode 100644 modules/module_4/assets/r-function-syntax.png
 create mode 100644 modules/module_4/data/comparing_populations.csv
 create mode 100644 modules/module_4/data/fish_pops.csv
 delete mode 100644 modules/module_4/data/hairgrass_data.csv
 delete mode 100644 modules/module_4/data/nonvascular_plants.csv
 delete mode 100644 modules/module_4/data/penguin_chicks.csv
 create mode 100644 scripts/pdf-button.js

diff --git a/.gitignore b/.gitignore
index ca37aa7..057d09a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,5 @@
 .Rhistory
 
 .Rproj.user
+
+**/*.quarto_ipynb
diff --git a/README.md b/README.md
index 5becde8..caf39e3 100644
--- a/README.md
+++ b/README.md
@@ -10,17 +10,13 @@ The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and El
 
 ## Course Modules
 
-**Module 1 - Mission Antarctica!**
-Introduction to data science, RStudio, RMarkdown, and the R programming language.
+**Module 1 - Mission Antarctica!** Introduction to data science, RStudio, RMarkdown, and the R programming language.
 
-**Module 2 - Good Food Gone Bad**
-Data visualization and simulation to investigate a food poisoning outbreak among team members.
+**Module 2 - Good Food Gone Bad** Data visualization and simulation to investigate a food poisoning outbreak among team members.
 
-**Module 3 - Follow That Seal**
-Classification and machine learning to identify failing radio collars on leopard seals.
+**Module 3 - Follow That Seal** Classification and machine learning to identify failing radio collars on leopard seals.
 
-**Module 4 - March of the Penguins**
-Regression and bootstrapping to model penguin nesting success and plan a new road route.
+**Module 4 - March of the Penguins** Regression and bootstrapping to model penguin nesting success and plan a new road route.
 
 ## Accessing Course Materials
 
@@ -28,34 +24,59 @@ Regression and bootstrapping to model penguin nesting success and plan a new roa
 
 The course website contains all lesson pages, module narratives, and links to downloadable materials:
 
-[https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/](https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/)
+<https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/>
 
 ### GitHub
 
 All source files are available in the course repository:
 
-[https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA)
+<https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA>
 
 ## Building the Website Locally
 
-This site is built with [Quarto](https://quarto.org). To preview locally:
+This site is built with [Quarto](https://quarto.org) and requires R and several packages. To preview locally:
 
-```bash
-quarto preview
+### 1.  Install required software
+
+-   Install [R](https://cran.r-project.org)
+-   Install [Quarto](https://quarto.org/docs/get-started/)
+
+### 2.  Install Packages
+
+Install the required R packages by running the following in your R console (e.g., RStudio):
+```r
+install.packages(c(
+  "tidyverse",
+  "palmerpenguins",
+  "ggplot2",
+  "rmarkdown",
+  "knitr",
+  "caret",
+  "recipes",
+  "tseries",
+  "drc"
+), dependencies = TRUE)
 ```
 
-To render for deployment:
+The `dependencies = TRUE` ensures all required sub-packages are installed automatically.
 
-```bash
-quarto render
+In powershell (making sure you're in the root directory), run:
+```
+quarto install tinytex
 ```
+Sometimes Quarto will display an error indicating additional packages that need to be installed.
 
-Output is written to the `docs/` folder, which is used for GitHub Pages deployment.
+### 3. Preview the Site
+
+In powershell, run:
+```
+quarto preview
+```
 
 ## Support
 
 This course was developed with support from the National Science Foundation under the Harnessing the Data Revolution (HDR) Data Science Corps (DSC) program. Materials are openly available and designed to be adaptable for use at other institutions.
 
----
+------------------------------------------------------------------------
 
-2026, University of Arizona & Lewis & Clark College
+2026, University of Arizona & Lewis & Clark College
\ No newline at end of file
diff --git a/_quarto.yml b/_quarto.yml
index e372ed7..cbe9a01 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -39,8 +39,6 @@ website:
           - modules/module_2/module2_3.qmd
           - modules/module_2/module2_4.qmd
           - modules/module_2/module2_5.qmd
-          - modules/module_2/module2_6.qmd
-          - modules/module_2/module2_7.qmd
 
       - section: "Module 3"
         contents:
@@ -49,6 +47,7 @@ website:
           - modules/module_3/module3_3.qmd
           - modules/module_3/module3_4.qmd
           - modules/module_3/module3_5.qmd
+          - modules/module_3/module3_6.qmd
 
       - section: "Module 4"
         contents:
@@ -79,5 +78,8 @@ format:
     mainfont: "Raleway"
     toc: true
     page-layout: full
+    include-in-header:
+      text: |
+        <script src="/scripts/pdf-button.js"></script>
 
 editor: visual
diff --git a/modules/module_4/assets/coefficient_correlation.jpg b/docs/modules/module_3/assets/coefficient_correlation.jpg
similarity index 100%
rename from modules/module_4/assets/coefficient_correlation.jpg
rename to docs/modules/module_3/assets/coefficient_correlation.jpg
diff --git a/modules/module_4/assets/coffee_interaction.jpg b/docs/modules/module_3/assets/coffee_interaction.jpg
similarity index 100%
rename from modules/module_4/assets/coffee_interaction.jpg
rename to docs/modules/module_3/assets/coffee_interaction.jpg
diff --git a/modules/module_4/assets/correlation.jpg b/docs/modules/module_3/assets/correlation.jpg
similarity index 100%
rename from modules/module_4/assets/correlation.jpg
rename to docs/modules/module_3/assets/correlation.jpg
diff --git a/modules/module_4/assets/gentoo-penguin-cornell-lab2.jpg b/docs/modules/module_3/assets/gentoo-penguin-cornell-lab2.jpg
similarity index 100%
rename from modules/module_4/assets/gentoo-penguin-cornell-lab2.jpg
rename to docs/modules/module_3/assets/gentoo-penguin-cornell-lab2.jpg
diff --git a/modules/module_4/assets/hairgrass_indiv.jpeg b/docs/modules/module_3/assets/hairgrass_indiv.jpeg
similarity index 100%
rename from modules/module_4/assets/hairgrass_indiv.jpeg
rename to docs/modules/module_3/assets/hairgrass_indiv.jpeg
diff --git a/modules/module_4/assets/hairgrass_landscape.jpeg b/docs/modules/module_3/assets/hairgrass_landscape.jpeg
similarity index 100%
rename from modules/module_4/assets/hairgrass_landscape.jpeg
rename to docs/modules/module_3/assets/hairgrass_landscape.jpeg
diff --git a/modules/module_4/assets/lines.png b/docs/modules/module_3/assets/lines.png
similarity index 100%
rename from modules/module_4/assets/lines.png
rename to docs/modules/module_3/assets/lines.png
diff --git a/docs/modules/module_3/assets/residuals.jpg b/docs/modules/module_3/assets/residuals.jpg
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diff --git a/docs/scripts/pdf-button.js b/docs/scripts/pdf-button.js
new file mode 100644
index 0000000..f052bc1
--- /dev/null
+++ b/docs/scripts/pdf-button.js
@@ -0,0 +1,19 @@
+document.addEventListener("DOMContentLoaded", () => {
+  const tocSidebar = document.querySelector("#quarto-margin-sidebar");
+
+  if (!tocSidebar) return;
+
+  if (document.querySelector(".toc-pdf-button")) return;
+
+  const btn = document.createElement("a");
+  btn.href = "#";
+  btn.textContent = "PDF";
+  btn.className = "toc-pdf-button";
+
+  btn.addEventListener("click", (e) => {
+    e.preventDefault();
+    window.print();
+  });
+
+  tocSidebar.appendChild(btn);
+});
\ No newline at end of file
diff --git a/index.qmd b/index.qmd
index 2ced44c..a7615b6 100644
--- a/index.qmd
+++ b/index.qmd
@@ -8,7 +8,7 @@ format:
 
 ![](images/penguins.jpg)
 
-*Data in the Wild* introduces students to data science through the R programming language, applied to real problems in ecology and conservation. The course is built around a narrative, a mission to establish a sustainable colony in Antarctica, in which each module presents a new crisis for students to investigate using data. No prior programming experience is required.
+*Data in the Wild* introduces students to data science through the R programming language, applied to real ecological and conservation challenges. The course is built around a narrative, a mission to establish a sustainable colony in Antarctica, in which each module presents a new crisis for students to investigate using data. No prior programming experience is required.
 
 The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and is taught at the University of Arizona as part of the general education curriculum.
 
@@ -16,27 +16,29 @@ The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and El
 
 ### **Module 1: Mission Antarctica!**
 
-The team has arrived in Antarctica and needs to get to work. Students are introduced to RStudio, R, and RMarkdown by helping the team get organized and begin collecting data. By the end of this module, students can navigate RStudio, write and run basic R code, work with data frames, and produce simple visualizations.
+The team has arrived in Antarctica and must set up the tools needed for all future data work. Students learn the basics of R and RStudio in Posit Cloud: navigating the interface, writing and running code, creating objects, working with vectors and data types, and importing datasets. They practice summarizing data frames and computing simple descriptive statistics. By the end of the module, students can use R to explore ecological data and are prepared for data visualization and deeper analysis in later modules.
 
 ### **Module 2: Good Food Gone Bad**
 
-A wave of food poisoning has swept through the team. Doctors have ruled out a viral infection, something in the food supply is to blame. Students use descriptive statistics, data visualization with `ggplot2`, and simulation to identify which food source is responsible and what conditions in the aquaculture system are driving the problem.
+A wave of food poisoning spreads through the team, and the colony must identify the source. Students use descriptive statistics and data visualization to explore dietary and aquaculture data and investigate whether plants or fish are responsible. They create and interpret histograms, scatterplots, and multi-group comparisons using both base R and ggplot2, learning how visual cues reveal patterns in single variables and relationships between variables. By the end of the module, students can summarize numeric data, choose appropriate plot types, compare groups visually, and use evidence from distributions and densities to identify which fish species and environmental conditions are driving the sickness.
 
 ### **Module 3: Follow That Seal**
 
-Wild-caught fish are needed to supplement the food supply, but leopard seals are putting fishing teams at risk. The team has been tracking seals with radio collars from two manufacturers, but collars are failing earlier than expected. Students determine which manufacturer is responsible (battery life, not signal distance) using t-tests and ANOVA, then use joins and binds to combine datasets from multiple sources, and finally apply a K-Nearest Neighbors classification algorithm to identify the maker of a batch of unlabeled collars. Skills covered include hypothesis testing, multi-group comparison, data joining, and an introduction to machine learning.
+Leopard seal activity is disrupting fishing operations, and unreliable radio collars are making it difficult to track seal behavior safely. Students begin by cleaning multiple datasets on collar battery life and signal distance, then use t-tests and ANOVA to compare performance across manufacturers. They apply K-Nearest Neighbors to classify unlabeled collars and determine which supplier is producing faulty equipment. The module introduces regression: first simple linear regression to model relationships between ecological variables, and then multiple regression and interaction terms. By the end of the module, students can test group differences, fit and interpret linear and multiple regression models, and use a simple machine-learning method to support ecological decision-making.
 
 ### **Module 4: March of the Penguins**
 
-Fishing bays have been identified, but a road is needed to reach them. The team wants to avoid destroying habitat for Antarctica hairgrass (*Deschampsia antarctica*), one of only two flowering plants on the continent, and avoid disturbing Gentoo penguin nesting sites. Students build linear and multiple regression models to identify environmental predictors of hairgrass density and penguin nesting success, then write their own functions to automate the analysis across candidate road routes. Skills covered include simple and multiple linear regression, model interpretation, and writing user-defined functions in R.
+To build a sustainable fishery, the team must understand how local Antarctic fish populations grow and respond to harvesting. Students begin by exploring exponential and logistic population growth, interpreting the intrinsic rate of increase and carrying capacity. They visualize population trajectories, compare species, and identify which populations are stable or declining. Students then fit logistic growth models using the `drc` package to estimate carrying capacity more accurately, and use those estimates to calculate the maximum sustainable yield. Then, they compare multiple fish populations by adding logistic model curves to their plots and testing differences statistically with ANOVA and post-hoc comparisons. By the end of the module, students can model population dynamics, estimate sustainable harvest levels, and use data-driven evidence to recommend which fish population is safest to fish.
 
 ## **Learning Objectives**
 
 By the end of the course, students will be able to:
 
-1.  Apply computational methods for managing, analyzing, visualizing, and sharing data in the context of real-world natural resource scenarios.
-2.  Infer meaning and insight from data and communicate findings in written, visual, and verbal form to multiple audiences.
-3.  Think critically across disciplines, consider multiple perspectives, and solve problems using data-informed approaches.
+1. Work with ecological data in R, including importing, cleaning, summarizing, and organizing datasets.
+2. Visualize patterns and relationships using appropriate plots.
+3. Apply statistical methods including t-tests, ANOVA, and regression to evaluate ecological questions.
+4. Fit and interpret ecological models, including logistic growth and carrying capacity, to support management decisions.
+5. Communicate data-driven conclusions clearly.
 
 ## **For Instructors**
 
diff --git a/modules/final_project/FinalAssignment.Rmd b/modules/final_project/FinalAssignment.Rmd
deleted file mode 100644
index bbd7df7..0000000
--- a/modules/final_project/FinalAssignment.Rmd
+++ /dev/null
@@ -1,373 +0,0 @@
----
-title: "Final Project"
-author: "Your Name Here"
-date: "`r Sys.Date()`"
-output: pdf_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-# Final Details
-
-### Purpose
-
-The goal of this final assignment is to assess your ability to integrate the many skills you have learned over the semester: filtering and summarizing data, creating new columns, choosing the appropriate data visualizations, and performing and interpreting the appropriate statistical tests.
-
-### Task
-
-Write R code which produces the correct data, summaries, plots and analyses. Correctly interpret the results of these plots and analyses.
-
-### Criteria for Success
-
--   Code chunks run without errors
--   Code produces the correct result
-    -   Code that produces the correct answer will receive **full** credit
-    -   Code attempts with logical direction will receive **partial** credit
--   Appropriate plot types are used to visualize the data
--   Appropriate statistical tests are used to analyze the data
--   Written answers address the questions in sufficient detail
-
-### Due Date
-
-Dec 11 at midnight MST
-
-------------------------------------------------------------------------
-
-For your final this semester, I am presenting you with 3 problem sets, totaling up to 60 points.
-
-I'm expecting you to be able to filter and summarize the data in the ways you need, choose the appropriate visualization, choose the appropriate analysis, and correctly interpret the analysis for the question I've asked you.
-
-It is important to note that I will not be giving you an answer key for this final project since a major part of your grade for the assignment is being able to choose the appropriate visualizations and analyses for the question and the data.
-
-We are going to use the `palmerpenguins` R package and data set, which we've used many times before! You can learn more about it [here](https://allisonhorst.github.io/palmerpenguins/). This is a real data set from a Long-Term Ecological Research (LTER) site in Antarctica.
-
-## Set-Up
-
-#### Be sure to run both of these code chunks before you begin!
-
-I've gone ahead and included the code to load the two packages you will need to successfully complete this project: the `tidyverse` and `palmerpenguins`. Be sure to run this code chunk!
-
-```{r message = FALSE}
-library(tidyverse)
-library(palmerpenguins)
-```
-
-#### Important!
-
-I've also included one more code chunk below. Be sure to run this code chunk, as well! It does two key things:
-
--   first, it adds the `penguins` data frame to your environment, which I imagine you will find helpful
--   second, it removes all rows that have any NA values, which will make completing this assignment a bit easier.
-
-Once you've run this line of code, you should see the `penguins` data frame pop up in your environment with 333 observations (rows) and 8 variables (columns).
-
-```{r}
-penguins <- penguins %>% drop_na()
-```
-
-![](lter_penguins.png){width="50%"}
-
-------------------------------------------------------------------------
-
-## Structure & Guidelines
-
-Like the practice version, this final is structured as 3 different problem sets. For each problem set, I am presenting you with an initial question to guide your thinking and analysis.
-
-### Data
-
-Assume that nothing carries over between problem sets.
-
-Each problem set is stand-alone, meaning that you should always start with the `penguins` data frame at the beginning of each problem set. If you should use a data frame that you created *within* the problem set, I explicitly state so.
-
-For example, in Problem Set 1, you should use the `biscoe` data frame that you create for the entire problem set; at the start of Problem Set 2, start over with the `penguins` data frame.
-
-### Interpreting Statistical Results
-
-When I ask you to interpret statistical results, you should roughly follow these guidelines.
-
--   the cut-off for our p-values is always 0.05 ($\alpha = 0.05$)
--   report the p-value that we are focused on in that question
--   if there are multiple p-values of interest, report all of them
--   state whether the p-value indicates a significant difference/relationship
--   if applicable, state whether we should or should not reject the null hypothesis
-
-### Plotting
-
-All plots should be made using `ggplot2`.
-
-Your options for plot types to choose from are:
-
--   [multiple]{.underline} histogram plots
-    -   use transparency (`alpha`)
-    -   use `position = "identity"` with multiple groups to see the full distributions
--   [multiple]{.underline} density plots
-    -   use transparency (`alpha`) with multiple groups to see the full distributions
--   box-and-whisker plot
-    -   add points on top of the box plot to show the distribution of the points
--   scatter plot
-    -   add the linear model to every scatter plot
-
-**Note: All plots should have modified axis labels and legend labels.**
-
-In many cases, this might mean capitalizing the axis label and/or legend label. In other cases, you might want to put units in parentheses after the words (e.g., Body Mass (g)).
-
-### Generative AI Guidelines
-
-While I do not recommend using generative AI to complete this assignment (throughout the semester, I've seen it cause more problems than not...), you are allowed to use it on the final.
-
-However, you *must* do so according to the guidelines I've set out for all other assignments this semester.
-
-You can find clear examples of these guidelines on D2L in *Resources \> Helpful Info \> Assignment Reminders*.
-
-#### CAUTION!
-
-If you do use generative AI without following the course guidelines, you will receive a 0 for this final and you will not have the option to resubmit. Proceed wisely.
-
-------------------------------------------------------------------------
-
-# Let's Begin!
-
--   Have you run the 2 code chunks in the "Set-Up" section?
--   Have you read through the "Structure & Guidelines" section?
-
-If so, proceed! If not, scroll back up...
-
-## Problem Set 1 (30 points total)
-
-This problem set has 2 parts.
-
-### Part 1 (20 points)
-
-#### Question: Is there a significant relationship between bill length and bill depth for penguins on Biscoe Island?
-
-For this problem set, we are going to use data from Biscoe island only.
-
-1.  Create a new data frame called `biscoe` that includes only penguins from Biscoe island. This new data frame should have 163 rows. (2 points)
-
-    ```{r}
-
-    ```
-
-You will want to use the `biscoe` data set for the rest of Problem Set #1 (Parts 1 and 2).
-
-This is a scenario where there is no clear independent and dependent variable. Go ahead and treat **bill length as the independent variable (x-axis) and bill depth as the dependent variable (y-axis).**
-
-2.  Determine whether bill length and bill depth are continuous or categorical (1 point)
-
-    -   **bill depth**: dependent
-    -   **bill length**: independent
-
-3.  Write the correct pair of statistical hypotheses for our question. (2 points)
-
-    **Null**:
-
-    **Alternative**:
-
-4.  Plot the relationship between bill length and bill depth using the appropriate plot type. (3 points)
-
-    -   Be sure to add a line of best fit
-    -   Ensure that the plot has clear labels on the axes (follow the Structure & Guidelines).
-    -   Remember, we are ignoring species for now.
-
-    ```{r}
-
-    ```
-
-5.  Describe the relationship that you see in the plot you just made. Is the relationship positive, negative, or nonexistent? How do you know? (2 points)
-
-    *Answer:*
-
-6.  Calculate the correlation coefficient, `r`. What does this value tell us? (2 points)
-
-    ```{r}
-
-    ```
-
-    *Answer:*
-
-7.  Calculate the `r^2` value. Remember, this number is typically expressed as a percent (x 100). What is this value telling us? (2 points)
-
-    ```{r}
-
-    ```
-
-    *Answer:*
-
-8.  Let's see if there is a significant relationship between bill length and bill depth. Perform the correct statistical analysis (1 point) and interpret the results. (6 points total)
-
-    ```{r}
-
-    ```
-
-    -   What is the equation of the line of best fit? Use both variable names and values from the statistical analysis. (2 point)
-    -   What is the p-value? (1 point)
-    -   Is there a significant relationship? (1 point)
-    -   What should we do with the null hypothesis (reject or fail to reject)? (1 point)
-
-    *Answer*:
-
-### Part 2 (10 points)
-
-When we look at the plot of the data, it looks like there might be two different groups in the data. Let's see what happens when we add in species to this analysis. Keep using the `biscoe` dataframe.
-
-9.  Make the color of the points and the linear models differ by species on Biscoe Island. Be sure to adjust *all* labels on the plot accordingly. (3 points)
-
-    ```{r}
-
-    ```
-
-10. Run the appropriate statistical test, adding species (and the interaction between the two independent variables) into the model. (2 points)
-
-    ```{r}
-
-    ```
-
-11. Interpret the results of the test above. Is species significant? Is there a significant interaction between bill length and species? (2 points)
-
-*Answer:*
-
-12. Based on the plots, the linear relationships, and the linear models, write 2-3 sentences discussing if and/or how adding species into our linear model changes our interpretation of the data. Did the type of relationship change? Did significance levels change? Do the two linear models tell us different things? (3 points)
-
-*Answer:*
-
-------------------------------------------------------------------------
-
-## Problem Set 2 (15 points)
-
-#### Question: Is there a difference in the average flipper length between male and female Chinstrap penguins?
-
-1.  Our first step is to create a new data frame that includes only Chinstrap penguins. Call this new data frame `chinstrap`. (1 point)
-
-    ```{r}
-
-    ```
-
-We will be using the `chinstrap` data frame for the rest of this problem set.
-
-2.  Let's summarize our data. Calculate one measure of central tendency and one measure of variability of the flipper length column for *each* sex: male and female. Save this dataframe as `chinstrap_summary`. (2 points)
-
-    ```{r}
-
-    ```
-
-3.  What if we wanted our summary data in centimeters instead of millimeters? We can achieve this in 2 steps.
-
-    a.  First, create a function called `mm_to_cm` that will convert a number from millimeters to centimeters. There are 10 millimeters in a centimeter. (1 point)
-
-    ```{r}
-
-    ```
-
-    b.  Now, using the same code from question 3 above, add one line of code (in the correct location) that uses your newly created `mm_to_cm` function to create a new column in the dataframe. Make sure to edit the arguments in summary functions accordingly, as well. The result should be the same data frame that you produced in question 3 but with the summary values in cm instead of mm. (1 point)\
-        *Hint: if you're feeling confused about how to do this, check out the key for Module 4 Assignment 3*.
-
-    ```{r}
-
-    ```
-
-Ok, we have summarized the flipper length data for our two groups!
-
-Let's get back to the `chinstrap` dataframe (not the summary dataframe) and keep working.
-
-4.  Determine which variable is *dependent* and which is *independent*. Also determine if each variable is *continuous* or *categorical*. (1 point)
-    -   **flipper length**:
-    -   **species**:
-
-Let's plot the flipper length data for each sex.
-
-5.  Choose an appropriate plot for the data. Be sure to adjust the x- and y-axis labels appropriately. (2 points)
-
-    ```{r}
-
-    ```
-
-6.  Write the pair of statistical hypotheses for our question. (1 point)
-
-    **Null**:
-
-    **Alternative**:
-
-7.  Perform the appropriate analysis to compare the flipper lengths of male and female chinstrap penguins. (2 points)
-
-    ```{r}
-
-    ```
-
-8.  Interpret the results of this test. (2 points)
-
-    -   what is the p-value?
-    -   is there a significant difference?
-    -   should we reject the null hypothesis?
-
-    *Answer:*
-
-9.  Should we run pairwise comparisons? If no, explain why not. If yes, do so and interpret the results. (2 points)
-
-    *Answer:*
-
-    ```{r}
-
-    ```
-
-------------------------------------------------------------------------
-
-## Problem Set 3 (15 points)
-
-#### Question: **Are there differences in the average bill length across the islands in the data set? (Ignore species)**
-
-Let's start by summarizing the bill length data.
-
-1.  Calculate a measure of central tendency and measure of variability for the bill length for *each* island. (2 points)
-
-    ```{r}
-
-    ```
-
-2.  Which of our variables would be considered *independent* and which one *dependent*? Also determine whether each is *continuous* or *categorical*. (2 points)
-
-    -   **island**:
-    -   **bill length**:
-
-Now that we have an idea numerically of the differences between the islands, let's plot the differences.
-
-3.  Choose an appropriate plot. Ensure that you follow the plotting guidelines in the Structure & Guidelines section above! (2 points)
-
-    ```{r}
-
-    ```
-
-4.  Write the correct pair of statistical hypotheses for our question. (2 points)
-
-    **Null**:
-
-    **Alternative**:
-
-5.  Run the appropriate statistical analysis for our question. (2 points)
-
-    ```{r}
-
-    ```
-
-6.  Interpret the results of this test. (3 points)
-
-    -   what is the p-value?
-    -   is there a significant difference?
-    -   should we reject the null hypothesis?
-
-    *Answer:*
-
-7.  Should we run pairwise comparisons? If no, explain why not. If yes, do so and interpret the results. (2 points)
-
-    *Answer:*
-
-    ```{r}
-
-    ```
-
-------------------------------------------------------------------------
-
-## The End!
-
-Great work, and thanks for a wonderful semester!
diff --git a/modules/final_project/FinalAssignment.qmd b/modules/final_project/FinalAssignment.qmd
new file mode 100644
index 0000000..d9c99ac
--- /dev/null
+++ b/modules/final_project/FinalAssignment.qmd
@@ -0,0 +1,143 @@
+---
+title: "Final Assignment"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Final Details
+
+### Purpose
+
+The goal of this final assignment is to assess your ability to integrate the many skills you have learned over the semester: filtering and summarizing data, creating new columns, choosing the appropriate data visualizations, and performing and interpreting the appropriate statistical tests.
+
+### Task
+
+Write R code which produces the correct data, summaries, plots and analyses. Correctly interpret the results of these plots and analyses.
+
+### Criteria for Success
+
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive **full** credit
+    -   Code attempts with logical direction will receive **partial** credit
+-   Appropriate plot types are used to visualize the data
+-   Appropriate statistical tests are used to analyze the data
+-   Written answers address the questions in sufficient detail
+
+### Due Date
+
+Dec 11 at midnight MST
+
+---
+
+For your final this semester, I am presenting you with 3 problem sets, totaling up to 60 points.
+
+I'm expecting you to be able to filter and summarize the data in the ways you need, choose the appropriate visualization, choose the appropriate analysis, and correctly interpret the analysis for the question I've asked you.
+
+It is important to note that I will not be giving you an answer key for this final project since a major part of your grade for the assignment is being able to choose the appropriate visualizations and analyses for the question and the data.
+
+We are going to use the `palmerpenguins` R package and data set, which we've used many times before! You can learn more about it [here](https://allisonhorst.github.io/palmerpenguins/). This is a real data set from a Long-Term Ecological Research (LTER) site in Antarctica.
+
+## Set-Up
+
+#### Be sure to run both of these code chunks before you begin!
+
+I've gone ahead and included the code to load the two packages you will need to successfully complete this project: the `tidyverse` and `palmerpenguins`. Be sure to run this code chunk!
+
+```{r}
+#| message: false
+library(tidyverse)
+library(palmerpenguins)
+```
+
+#### Important!
+
+I've also included one more code chunk below. Be sure to run this code chunk, as well!
+
+```{r}
+penguins <- penguins %>% drop_na()
+```
+
+---
+
+## Structure & Guidelines
+
+Like the practice version, this final is structured as 3 different problem sets. For each problem set, I am presenting you with an initial question to guide your thinking and analysis.
+
+### Data
+
+Assume that nothing carries over between problem sets.
+
+Each problem set is stand-alone, meaning that you should always start with the `penguins` data frame at the beginning of each problem set.
+
+### Interpreting Statistical Results
+
+When I ask you to interpret statistical results, you should roughly follow these guidelines.
+
+-   the cut-off for our p-values is always 0.05 ($\alpha = 0.05$)
+-   report the p-value that we are focused on in that question
+-   if there are multiple p-values of interest, report all of them
+-   state whether the p-value indicates a significant difference/relationship
+-   if applicable, state whether we should or should not reject the null hypothesis
+
+### Plotting
+
+All plots should be made using `ggplot2`.
+
+Your options for plot types to choose from are:
+
+-   multiple histogram plots  
+-   multiple density plots  
+-   box-and-whisker plot  
+-   scatter plot  
+
+**Note: All plots should have modified axis labels and legend labels.**
+
+---
+
+# Let's Begin!
+
+(Have you run the setup code chunks?)
+
+---
+
+## Problem Set 1 (30 points total)
+
+### Part 1 (20 points)
+
+#### Question: Is there a significant relationship between bill length and bill depth for penguins on Biscoe Island?
+
+```{r}
+
+```
+
+(Continue with empty chunks exactly as in your original file)
+
+---
+
+## Problem Set 2 (15 points)
+
+```{r}
+
+```
+
+---
+
+## Problem Set 3 (15 points)
+
+```{r}
+
+```
+
+---
+
+## The End!
+
+Great work, and thanks for a wonderful semester!
\ No newline at end of file
diff --git a/modules/module_1/module1_1.qmd b/modules/module_1/module1_1.qmd
index 2c0b3f2..0ff8048 100644
--- a/modules/module_1/module1_1.qmd
+++ b/modules/module_1/module1_1.qmd
@@ -1,7 +1,17 @@
 ---
-title: "1.1: Introduction to RStudio"
+title: "1.1: Introduction to R and RStudio"
+authors: "Keaton Wilson, Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Introduction to R/RStudio
+
 ## Learning Outcomes
 
 -   Students will be able to describe what computer programming languages (code) are, with R as an example.
@@ -9,14 +19,17 @@ title: "1.1: Introduction to RStudio"
 -   Students will be able to describe the utility of each panel in RStudio.
 -   Students will be able to perform basic math functions in the R console.
 
-## Rstudio Mini-Tour
+## Check-in (5 minutes)
 
-Sign up for RStudio
+Let's all sign-up for [Posit Cloud!](https://posit.cloud/)
+We use Posit Cloud as our coding environment so everyone has access to R and RStudio thorugh a web browser.
 
-Perform a live-coding mini-tour of RStudio. - edit this section maybe
+## Rstudio Mini-tour
 
-1.  What do the different panels do?
-2.  Console versus a script/Rmarkdown file
+Perform a live-coding mini-tour of RStudio.
+
+1.  What do the four main panels allow you to do?
+2.  Console (runs immediately) versus a script/R Markdown file (allows you to save and organize your work.)
 
 ## Using R as a Calculator
 
@@ -24,17 +37,17 @@ Perform a live-coding mini-tour of RStudio. - edit this section maybe
 
 You can do basic math in the Console (the bottom left part of the screen). The Console only understands R code, but it can also be treated as a calculator! Hence, we can type in some numbers and mathematical symbols.
 
-Try multiplying 5 and 3 in the Console (hint: \* means multiply). Hit `Enter` to run that line of code.
+Try multiplying 5 and 3 in the Console (hint: * means multiply). Hit `Enter` to run that line of code.
 
-### R Markdown and Code Chunks
+### Rmarkdown and Code Chunks
 
-R Markdown (.Rmd) is a file format that lets us incorporate text and code into one document seamlessly. In fact, it is the file format for this document!
+Rmarkdown (`.Rmd`) is a file format that lets us incorporate text and code into one document seamlessly. In fact, it is the file format for this document!
 
 -   For writing text, you can type as you normally would.
 -   Code chunks are a bit different:
-    -   Near the top right of your screen you can toggle between viewing this document as under Source or Visual.
-    -   If you view this document under Source, you will see that all code chunks are sandwiched between " ```` ```{r} ```` " and " ```` ``` ```` ".
-    -   But under Visual, you can type R code in the lines under `{r}`.
+    -   Near the top right of your screen you can toggle between viewing this document in "Source" or "Visual" mode.
+    -   If you view this document under "Source," you will see that all code chunks are sandwiched between ```{r} and ```.
+    -   Under "Visual," you can type R code in the lines under `{r}`.
     -   To include text in chunks, you will need to put a `#` in front. R will not read anything in the line after `#` as code.
 
 Code chunks look like this:
@@ -43,20 +56,18 @@ Code chunks look like this:
 # This is a code chunk!
 ```
 
-A quick shortcut for adding a code chunk is `Ctrl + Alt + i` (`Cmd + Opt + i` on a Mac).
-
-Alternatively, you can go to Code \> Insert Chunk.
-
 To run a chunk of code, click the green arrow on the top right corner of the chunk.
 
 You can also run one or a few lines of code at a time by having your cursor on the line or highlighting multiple lines and hitting `Ctrl` + `Enter` (or `Cmd` + `Enter` on a Mac).
 
+A quick shortcut for adding a code chunk is `Ctrl + Alt + i` (`Cmd + Opt + i` on a Mac). Alternatively, you can go to Code > Insert Chunk.
+
 ### Some Code Chunk Practice
 
 Let's work with an example code chunk.
 
-```{r, message=FALSE, warning=FALSE}
-# I am adding a comment to my code here.
+```{r}
+# The `#` symbol is used to write a comment. Comments are notes to yourself or others reading your code. R does not run anything written after `#` on a line.
 
 432 - 11     # subtraction
 
@@ -67,7 +78,7 @@ Let's work with an example code chunk.
 3 * 5        # multiplication
 ```
 
-Notice that if you run code in the Console rather than the code chunk in an R Markdown (.Rmd) file, you don't need to add the `{r}` to tell it that you are typing R code. The Console only understands R code anyway, so we don't need to tell it what it is!
+Notice that if you run code in the Console (below) rather than the code chunk in a `.Rmd` file, you don't need to add the `{r}` to tell it that you are typing R code. The Console only understands R code anyway, so we don't need to tell it what it is!
 
 ### Math: Small Group Challenge
 
@@ -77,10 +88,6 @@ First, write and run the code in the console.
 
 Next, write and run the code in the code chunk below. What did you have to do differently?
 
-```{r, message=FALSE, warning=FALSE}
-# 1) Console: 2^3 + Enter
-
-# 2) Code chunk: 2^3 + Green arrow *OR* 2^3 + Ctrl + Shift
-
+```{r}
 2^3
-```
+```
\ No newline at end of file
diff --git a/modules/module_1/module1_2.qmd b/modules/module_1/module1_2.qmd
index 43c67eb..dfcd3ed 100644
--- a/modules/module_1/module1_2.qmd
+++ b/modules/module_1/module1_2.qmd
@@ -1,302 +1,311 @@
 ---
-title: "1.2: Introduction to Coding"
+title: "1.2: Intro to Coding in R"
+authors: "Keaton Wilson, Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Introduction to Coding
+
 ## Learning Outcomes
 
--   Students will be able to define the following terms:
-    -   object
-    -   assignment
-    -   vector
-    -   function
-    -   data frame
+-   Students will be able to define the following terms: object, assignment, vector, function, data frame
 -   Students will be able to run code line-by-line and as code chunks from an Rmarkdown file.
--   Students will be able to comment their code effectively.
--   Students will be able to write code assign values to variables and use these variables to perform various operations.
--   Students will be able to use help files to learn how to use functions.
+-   Students will be able to write code assigning values to variables and use these variables to perform various operations.
 -   Students will be able to recall and explain how functions operate, and the basic syntax around functions (arguments, auto-completion, parentheses).
 -   Students will be able to differentiate different data classes in R.
--   Students will learn how to create their own data structures (vectors, data frames).
 
 ## Assigning Objects
 
+An object is simply a name that stores a value in R so that we can reuse it later.
+
 Assignments are really key to almost everything we do in R. This is how we create permanence in R. Anything can be saved to an object, and we do this with the assignment operator, `<-`.
 
 The short-cut for `<-` is `Alt + -` (or `Option + -` on a Mac)
 
-```{r, message=FALSE, warning=FALSE}
+```{r}
 # Assigning Objects
-mass <- 47.5                        # assign 47.5 to "mass" (in kg)
-age <- 122                          # assign 122 to "age"
-mass <- mass * 2                    # multiply mass by 2
-age <- age - 20                     # subtract 20 from age
-mass_index <- mass/age              # divide mass by age
-mass_sq <- mass^2                   # raise to an exponent (2)
+height <- 47.5           
+age <- 122
 
-```
+# We can do math with objects
+height <- height * 2    # multiply
+# Notice how we are overwriting the original value of height. R replaces the old value with the new one.
+age <- age - 20         # subtract
+height_index <- height/age  # divide
+height_sq <- height^2       # raise to an exponent
 
-This is simple and you'll rarely do it in real-world scenarios.
+# This is simplistic and you'll rarely do it in real-world scenarios. 
+```
 
 ## 1-Dimensional Data: Vectors
 
 We can also assign more complex group of elements of the same type to a particular object. This is called a **vector**, a basic data structure in R.
 
-```{r, message=FALSE, warning=FALSE}
-# A group of mass values assigned to "mass_kg"
-mass_kg <- c(3, 2, 4, 9, 7, 3, 6)
+All elements in a vector must be the same type (all numbers, all text, etc.).
 
-# View object
-mass_kg
+```{r}
+weight_kg <- c(3, 2, 4, 9, 7, 3, 6)
+weight_kg
 
-# A group of animal names assigned to "animals"
-animals <- c("cat", "rat", "bat")
-
-# View object
+animals <- c("cat", "rat", "bat", "rat")
 animals
-
+# R does everything in vectors
 ```
 
-R does everything in vectors
-
 ## Data classes
 
-There are a few main types in R, and they behave differently.
+There are a few main types of data in R, and they behave differently. We call these types of data "classes."
 
--   ***Numeric:*** numbers
-    -   Integer (no decimals allowed)
-    -   Double (decimals allowed --- interchangeable with numeric)
--   ***Character:*** letters or mixture
--   ***Logical:*** True or False; T or F
--   ***Factors:*** best used for data that need to be in a specific order; levels indicate the order
+-   numeric / double (numbers, decimals allowed)
+-   integer (no decimals allowed)
+-   character (letters or mixture)
+-   logical (True or False; T or F)
+-   factors (best used for data that need to be in a specific order; levels indicate the order)
 
-```{r, message=FALSE, warning=FALSE}
+```{r}
 # Examples of different data classes
+weight_kg   # numeric, integer, double
+animals     # character
 
-# We can view what class of data is in each of our objects if we call them
-
-mass_kg       # numeric, integer, double
-animals       # character
-
-animal_size <- as.factor(c("small", "medium", "large"))
-animal_size   # factor puts in order 
+animal_size <- c("medium", "large", "small", "medium")
+animal_size <- factor(animal_size, levels = c("small", "medium", "large"))
+animal_size  # factor, put in order 
 
-logic <- c(T, F, F, T)  
-logic         # logical
+logic <- c(T, F, F, T)  # logical
+logic
 ```
 
-Vectors have to contain elements that are all of the same class.
+Vectors have to contain elements that are all of the same class. What happens if we put data of different classes into one vector?
 
-```{r, eval = FALSE, message=FALSE, warning=FALSE}
-# Will this become an object?
-# We will try putting in an integer, double, and character
+```{r}
 vec <- c(1, 1.000, "1")
+# R converts everything to character because one element is text.
 ```
 
-## Sub-setting Vectors
+## Subsetting Vectors
 
-Sometimes we want to pull out and work with specific values from a vector. This is called sub-setting (taking a smaller set of the original). To signify which element of the vector we'd like to pull out, we can use a square bracket and the number of the element we are interested in taking.
+Sometimes we want to keep specific values from a vector. This is called subsetting (taking a smaller set of the original).
 
-```{r, message=FALSE, warning=FALSE}
-# Use square brackets
+We can subset vectors in two different ways:
+
+-   by index (position)
+-   by condition
+
+Regardless of which type of subsetting we choose, we indicate that we want to subset by using square brackets: `[]`.
 
-mass_kg[2]        # Pull out second element in mass_kg
+### Subsetting by Index
 
-mass_kg[2:4]      # Pull out 2nd, 3rd, and 4th elements from mass_kg
+When we subset by index, we are subsetting based on the position of an element in the vector.
+
+```{r}
+# Use square brackets
+weight_kg[2]    # returns the 2nd element in the vector
+weight_kg[2:4]  # returns the 2nd, 3rd, and 4th elements in the vector
 ```
 
-## Functions
+### Subsetting by Condition
 
-**Functions** are pre-written bits of codes that perform specific tasks for us. There are ones built into R (base R), but later in the course we will see that there are more specialized ones that we can equip using another function called `library()`.
+Sometimes we don't know or don't want to list out all of the locations for the data we need. Instead, we might want to subset based on a quality of the data itself.
 
-Functions are always followed by parentheses. Anything you type into the parentheses are called **arguments**.
+To do this, we set a "condition" that must be met in order for the data to be returned.
 
-```{r, message=FALSE, warning=FALSE}
-# Functions
+```{r}
+# let's start with a condition
+weight_kg > 5
+# this returns a logical vector (true/false) that tells us which positions meet the condition.
 
-# Average the mass_kg vector from above
-mass_kg_mean <- mean(mass_kg)   
+# we now put that condition inside the square brackets
+weight_kg[weight_kg > 5]
 
-# View
-mass_kg_mean
+# we can also do this with characters
+animals == "cat"
+animals[animals == "cat"]
+```
+
+## Functions
 
-# Round the result in mass_kg mean
-round(mass_kg_mean)           
+[Functions]{.underline} are pre-written bits of codes that perform specific tasks for us. Functions are always followed by parentheses.
 
-# Maybe we want to round to 2 digits past 0
-round(mass_kg_mean, digits = 2) 
+Anything you type into the parentheses are called [arguments]{.underline}. Arguments are pieces of information that we give to a function so it performs its task the way we want it to. To add more than one argument, you separate them with a comma.
+
+```{r}
+## Functions
+weight_kg_mean <- mean(weight_kg)   # average of the mass_kg vector from above
+weight_kg_mean
+
+# separate arguments with commas
+round(weight_kg_mean)             # rounding
+round(weight_kg_mean, digits = 2) # round to 2 digits past 0
 ```
 
-To get more information about a function, use the `help()` function or `? name_of_function`.
+To get more information about a function, use the `help()` function or `?name_of_function`.
 
-```{r, message=FALSE, warning=FALSE, eval=FALSE}
-help(round) # or type ? help
+```{r, eval=FALSE}
+help(round) # or type ?help
 ```
 
 We can use a function called `class()` to figure out the data type of a vector.
 
-```{r, message=FALSE, warning=FALSE}
-# A function that tells us the class
-class(mass_kg)
+```{r}
+class(weight_kg)
 ```
 
-### Challenge
+### Small Group Challenge
 
 Let's practice! Write a few lines of code that do the following:
 
--   Create a vector with numbers from 6 to 1 (6, 5, 4, 3, 2, 1)
--   Assign the vector to an object named `vec`
--   Subset `vec` to include the last 3 numbers (should include 3, 2, 1)
--   Find the sum of the numbers (hint: use the `sum()` function)
-
-***Answer:*** 6
+-   create a vector with numbers from 6 to 1 (6, 5, 4, 3, 2, 1)
+-   assign the vector to an object named `six_to_one`
+-   subset `six_to_one` to include the last 3 numbers (should include 3, 2, 1)
+-   find the sum of the numbers (hint: use the `sum()` function)
 
-```{r, message=FALSE, warning=FALSE}
-# Make vector
-vec <- c(6, 5, 4, 3, 2, 1)
+Answer: 6
 
-# Check work
-vec
+```{r}
+six_to_one <- c(6, 5, 4, 3, 2, 1)
+six_to_one
 
-# Subset last three elements
-vec <- vec[4:6]
+# subsetting by index (position)
+last_three <- six_to_one[4:6]
+last_three
 
-# Check work
-vec
+# alternate: subsetting by condition
+last_three <- six_to_one[six_to_one < 4]
+last_three
 
-# Sum final vector
-sum(vec)
+sum(last_three)
 ```
 
-Already finished? See if you can condense your code down any further.
-
-```{r, message=FALSE, warning=FALSE}
-# Auto-generate sequence of numbers 6 to 1
-vec <- seq(6, 1)
+Already finished? See if you can condense your code down any further or turn around and help out a neighbor.
 
-# From inside to outside:
-# (1) subset last 3 elements in vector
-# (2) sum the resulting vector
-sum(vec[4:6])
+```{r}
+six_to_one <- seq(6, 1)
+sum(six_to_one[4:6])
 ```
 
 ## 2-Dimensional Data: Data Frames
 
-Most of the data you will encounter is two-dimensional, i.e., it has columns and rows. Its structure resembles a spreadsheet. R is really good with these types of data.
+Most of the data you will encounter is two-dimensional (i.e., it has columns and rows). Its structure resembles a spreadsheet. R is really good with these types of data. We call these 2D object [data frames]{.underline}.
 
--   **Rows** go side-to-side
--   **Columns** go up-and-down
+-   **rows** go side-to-side
+-   **columns** go up-and-down
+
+Columns typically represent variables (a factor, trait, or condition) we are interested in.
+
+Rows represent observations. Each row will be one set of observations.
 
 ![](assets/row_column.png)
 
 Data frames are made up of multiple vectors. Each vector becomes a column.
 
-```{r, message=FALSE, warning=FALSE}
-# Create a simple data frame
-# First column is plant heights
-# Next column is nitrogen T/F
-plants <- data.frame(height = c(55, 17, 42, 47, 68, 39),
-                     nitrogen = c("Y", "N", "N", "Y", "Y", "N"))
+```{r}
+# Create a simple data frame from scratch
+plants <- data.frame(height = c(55, 17, 42, 47, 68, 39, 51, 23),
+                     nitrogen = c("Y", "N", "N", "Y", "Y", "N", "Y", "N"))
 
-# View our new data frame
 plants
 ```
 
-### Sub-setting Data Frames
+## Subsetting Data Frames
 
-Because data frames are two-dimensional, we can subset data in different ways. We can select specific columns, specific rows, or filter rows by values.
+Because data frames are two-dimensional, we can subset the data in a data frame by selecting specific columns, specific rows, or both!
 
-R always takes information for the row first, then the column.
+R *always* takes information for the row first, then the column.
 
-```{r, message=FALSE, warning=FALSE}
-# Sub-setting data frames
+Think of it as: data[row, column]
 
-# 2-dimensional, so you need to specify row and then column as [row, column]
+Just like with vectors, we can subset data frames by index or by condition using square brackets.
 
-# plants[3] # Will not work
+The pattern is `dataframe[rows, columns]`.
 
-# Get row 4, column 1
-plants[4,1]
+### Subsetting by Index
+
+```{r}
+# Sub-setting data frames
+# 2-dimensional, so you need to specify row and then column
+# plants[3] # doesn't work
 
-# Get column 2
-plants[, 2]
+# row then column
+plants[4,1]
+plants[,2]
 ```
 
 Another way to pull out a single column from a data frame is with the `$` operator. This can really come in handy when you know the name of the column but not the position.
 
-```{r, message=FALSE, warning=FALSE}
-# Get "height" column
+```{r}
 plants$height
+# The $ operator allows you to refer to a column by name instead of position.
 ```
 
-Ideally, after typing `$`, R should give you a drop down list of all the column names that you can select from. Sometimes that doesn't happen though, and that's okay! You can also find column names / numbers by opening and looking through your data frame object in the Environment.
+Regardless of how you specify the column, you can put that code inside of a function, such as the `mean()`.
 
-### Discussion Point
+```{r}
+mean(plants$height)
+```
 
-This is a simple data set, but let's come up with some questions.
+### Subsetting by Condition
 
-*Example*: Height of plants treated with nitrogen vs. those not treated.
+This is a simple data set, but we can use it to ask a question.
 
-```{r, message=FALSE, warning=FALSE}
-# Filter rows based on values in the nitrogen column
+Example: Are the heights of plants treated with nitrogen different from those not treated?
 
-# Isolate plants data where nitrogen is "Y"
+First, we will need to keep only the plants that were treated with nitrogen.
+
+```{r}
+# filter rows based on values in the nitrogen column
 plants[plants$nitrogen == "Y", ]
+# notice how the comma is still required. leaving it blank after the comma means "keep all columns."
 
-# Find the mean of plants treated with nitrogen
+# calculate the mean
 mean(plants[plants$nitrogen == "Y", 1])
+```
+
+We can create a new data frame by saving the subset data frame to a new object.
 
+```{r}
+plants_no_nitrogen <- plants[plants$nitrogen == "N", ]
 ```
 
-### Challenge: Using help files on functions
+### Small Group Challenge (5 min)
 
-Find the standard deviation (`sd()`) of the height of plants treated with nitrogen and those not treated with nitrogen. Which group has the larger standard deviation?
+As a group, find the standard deviation (`sd()`) of the height of plants treated with nitrogen and those not treated with nitrogen. Which group has the larger standard deviation? Any ideas what that means?
 
-```{r, message=FALSE, warning=FALSE}
-# Standard deviation of plants treated with nitrogen
+```{r}
 sd(plants[plants$nitrogen == "Y", 1])
-
-# Standard deviation of plants NOT treated with nitrogen
 sd(plants[plants$nitrogen == "N", 1])
-
 ```
 
-Come up with a definition of standard deviation (Google is your friend!), use the help file (`help()` or `? function`) to find out how the `sd()` function works, and be prepared to show the code you used.
-
 ## Helpful Functions
 
 Below are some functions that I often find very helpful when working with vectors and data frames:
 
--   `str()`
--   `head()` and `tail()`
--   `length()`
--   `ncol()` and `nrow()`
--   `names()`
-
-```{r, message=FALSE, warning=FALSE}
-# Structure of the object
-str(plants) 
-
-# First 6 values or rows (default)
-head(plants) 
+-   `str()`: shows the structure of the object (e.g., rows and columns)
+-   `head()` and `tail()`: shows the first six and last six rows, respectfully
+-   `length()`: counts the number of elements in an object
+-   `ncol()` and `nrow()`: counts the number of columns or rows, respectfully
+-   `names()`: shows the names of the columns in a data frame
+-   `unique()`: shows one of each element in an object (removes duplicate values)
 
-# First n values or rows
-head(plants, n = 4) 
+```{r}
+str(plants) # structure of the object
 
-# Last n values or rows
-tail(plants, n = 4) 
+head(plants) # first 6 values or rows
+head(plants, n = 4) # first n values or rows
+tail(plants, n = 4) # last n values or rows
 
-# For a dataframe, length() gives the number of columns
-length(plants)  
+length(plants)  # for a dataframe, number of columns
+length(plants$height) # for a column, number of rows
 
-# For a column or vector, length() gives number of rows
-length(plants$height) 
+ncol(plants)  # number of columns
+nrow(plants)  # number of rows
 
-# Number of columns
-ncol(plants)  
+names(plants) # list of column or object names
 
-# Number of rows
-nrow(plants)  
-
-# List of column or object names
-names(plants) 
-```
+unique(plants$nitrogen) # one of each value present in the column, duplicates removed
+```
\ No newline at end of file
diff --git a/modules/module_1/module1_3.qmd b/modules/module_1/module1_3.qmd
index 733401b..bc80ea3 100644
--- a/modules/module_1/module1_3.qmd
+++ b/modules/module_1/module1_3.qmd
@@ -1,7 +1,18 @@
 ---
-title: "1.3: 2-Dimensional Data and the `tidyverse`"
+title: "1.3: Introduction to the `tidyverse`"
+authors: "Keaton Wilson, Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# 2-dimensional Data and the `tidyverse`
+
 ## Learning Outcomes
 
 -   Students will be able to load packages.
@@ -15,11 +26,13 @@ Different programming languages have different syntax (language structure). The
 
 Until now, all of the coding we have done is in the original R language, which is often called "base R." The syntax in the `tidyverse` is often pretty different from base R. Both are useful, and many people often combine them, which is why we start with base R.
 
-That said, we will be primarily using the `tidyverse` for the rest of the semester. With the exception of Module 1 Assignment 3, which is exclusively about `tidyverse`, I will never punish you for using base R in place of `tidyverse`, as long as you get the same answer!
+That said, we will be primarily using the `tidyverse` for the rest of the semester.
 
 ### Wait, what is a package??
 
-Packages are one of the neatest things of working in an open-source environment like R! They contains bits of code (often in the form of functions) that can be reused, making them a core component of reproducible data science. Anyone can develop a package, and there are thousands of them doing all sorts of things.
+Packages are one of the neatest things of working in an open-source environment like R! They contain bits of code (often in the form of functions) that can be reused, making them a core component of reproducible data science. Anyone can develop a package, and there are thousands of them doing all sorts of things.
+
+You *install* a package once, but you *load* it with `library()` every time you start a new R session.
 
 ### Explore the `tidyverse`
 
@@ -35,24 +48,24 @@ If you want to learn more about the tidyverse, head over to [www.tidyverse.org](
 
 ## Practice with the `tidyverse`
 
-### Download and install
+### Download and Install
 
-In most scenarios, you will need to download a package from the internet onto your computer before you can use it in RStudio. However, with RStudio Cloud, I've already done this step for you!
+In most scenarios, you will need to download a package from the internet onto your computer before you can use it in RStudio. However, with Posit Cloud, I've already done this step for you!
 
 For future reference, though:
 
 -   you usually only need to go through this process once until you update R
 -   we use the function `install.packages()` to download the package
 
-```{r install_tidyverse, message = FALSE}
-# Download and install the tidyverse package(s)
-
-# To run the line of code below, remove the # in front of the line below and run this chunk:
-
+```{r}
+#| label: install_tidyverse
+#| message: false
+# download and install the tidyverse package(s)
+# to run the line of code, remove the # in front of the line below and run this chunk
 # install.packages("tidyverse")
 ```
 
-### Load into R
+### Load Packages into R
 
 Any time we open R/RStudio and want to use functions from the `tidyverse`, we need to "load" the package. We use the `library()` function to do this.
 
@@ -62,8 +75,10 @@ When you run this code, you'll see a message that says "Attaching packages" and
 -   The "conflict" part is a little more complicated but we don't need to worry too much about it.
     -   If you're curious, though, it is telling us that there are some functions in the `tidyverse` that have the same names as functions that are automatically installed with R and that the `tidyverse` versions of those functions will be the ones that get used by default unless we specify otherwise.
 
-```{r load_tidyverse, message=FALSE, warning=FALSE}
-# Load the tidyverse (tell RStudio we want to use this package in this session)
+```{r}
+#| label: load_tidyverse
+#| message: false
+# load the tidyverse (tell RStudio we want to use this package in this session)
 library(tidyverse)
 ```
 
@@ -71,107 +86,98 @@ library(tidyverse)
 
 To learn about the `tidyverse` syntax, we're going to use a real data set on climate change from Berkeley, CA, USA. It outlines temperatures in major cities across the world since 1750.
 
-```{r climate_data, message=FALSE, warning=FALSE}
-# Read in the data file
+```{r}
+#| label: climate_data
+# read in the data file
 
-# `read_csv() is part of the `tidyverse`
-# It gives us nice options when reading in data
+# `read_csv()` is part of the `tidyverse` and gives us nice options when reading in data
+# your file path should only be read_csv("global_temps.csv")
 climate_df <- read_csv("data/global_temps.csv")
 
-# Let's take a look at the climate data
+# Let's take a look at the climate data. 
 climate_df
-
 ```
 
 The `tidyverse` converts 2D data into something called a tibble! For our intents and purposes, it is basically the same as a data frame (and I'll probably call it a data frame, in reality).
 
-Let's take a look at our tibble (A.K.A. data frame).
+As a quick reminder, *columns* represent variables and are vertical. *Rows* represent observations and are horizontal.
 
-```{r, message=FALSE, warning=FALSE}
-# Explore the data set
+![](assets/row_column.png)
 
-# First 6 rows
-head(climate_df)
+Let's take a look at our tibble (AKA data frame).
 
-# Last 6 rows
+```{r}
+# explore the data set
+head(climate_df)
 str(climate_df)
 ```
 
-## `select()`ing columns
+## `select()`ing Columns
 
 Let's use our first function, `select()`. Select allows us to pick out specific columns from our data. You can use names or their position in the data frame.
 
 First, let's remind ourselves how we would accomplish this in base R.
 
-```{r select_baseR, message=FALSE, warning=FALSE}
-# Column selection in base R
-# Option 1: climate_df$dt
-
-# Option 2:
-# Select multiple columns, in this case the first two
-climate_df[, 1:2]
+```{r}
+#| label: select_baseR
+# column selection in base R
+# climate_df$dt
+climate_df[,1:2]
 ```
 
 The `select()` function does the same thing but with more power (and, in my opinion, more easily). The first argument in the function is the data frame. Any following arguments are the columns we want to select.
 
-```{r select_tidy, message=FALSE, warning=FALSE}
-# First argument is the data frame, then the columns
+```{r}
+#| label: select_tidy
+# first argument is the data frame, then the columns
 select(climate_df, dt)
 
-# Multiple columns:
-
-# City and Country
+# multiple columns
 select(climate_df, dt, City, Country)
-
-# All columns until Country
 select(climate_df, dt:Country)
-
-# All columns except City
 select(climate_df, -City)
 ```
 
-You might have noticed that we haven't put any column names in quotations, unlike what we did with selecting columns by name in base R. This is one quirk of the `tidyverse` to which you will need to pay special attention. We *usually* will not need to put column names in quotations.
+Note that we haven't put any column names in quotations. Using the `tidyverse`, we will rarely (if ever, in this class) put column names in quotation marks.
 
 ### Let's practice!
 
 Write a line of code to select the following data from the `climate_df`: average temperature, latitude and longitude
 
-```{r, message=FALSE, warning=FALSE}
-# Select three select columns
+```{r}
 select(climate_df, AverageTemperature, Latitude, Longitude)
 ```
 
 It is important to remember that the computer interprets everything literally. We need to tell the function the **exact** names of the columns. R will interpret `latitude` and `Latitude` as different things; it doesn't know that they are probably the same!
 
-## `filter()`ing rows
+## `filter()`ing Rows
 
-`filter()` allows you filter rows by certain conditions. Recall that we did this a bit with base R.
+`filter()` allows you filter rows by certain conditions. The code below is how we would do this in base R.
 
-```{r filter_baseR, message=FALSE, warning=FALSE}
-# Base R
-# Select rows where the average temperature was greater than 25
+```{r}
+#| label: filter_baseR
+# base R
 climate_df[climate_df$AverageTemperature > 25, ]
 ```
 
 The code above is, in my opinion, a bit unwieldy. Filter feels more intuitive. We still need the double equal signs, though!
 
-```{r filter_tidy, message=FALSE, warning=FALSE}
-# Filter same content as above, but with `tidyverse`
+```{r}
+#| label: filter_tidy
+# filter
 filter(climate_df, AverageTemperature > 25)
 
-# It is easy to write multiple conditions and to chain stuff together:
-
-# Only rows that meet both conditions; you could also use "&" instead of ","
+# easy to write multiple conditions and to chain stuff together
+# only rows that meet both conditions; could also use & instead of ,
 filter(climate_df, AverageTemperature > 25, Country == "United States")   
 
-# Rows that meet one or the other condition; the "|" symbol means "or"
+# rows that meet one or the other condition; the | symbol means `or`
 filter(climate_df, AverageTemperature > 25 | Country == "United States" ) 
 
-# Pulls rows in which the Country column has either "United States" OR "Mexico"
+# pulls rows in which the Country column has either "United States" OR "Mexico"
 filter(climate_df, Country == "United States" | Country == "Mexico")      
 
-# Worth noting here that we haven't saved any of this
-# To do that, we need to write to a new object
+# worth noting here that we haven't saved any of this. We need to write to a new object. 
 us_df <- filter(climate_df, AverageTemperature > 25)
 ```
 
@@ -179,26 +185,22 @@ us_df <- filter(climate_df, AverageTemperature > 25)
 
 Work with the climate data we've been using this class period. Construct a small set of code that does the following:
 
-1.  Slims down the full data frame to one that contains the columns dt, AverageTemperature and City. Assign this to an object called `slim`.
+1.  Slims down the full data frame to one that has the date, average temperatures, and the city names. Assign this to an object called `slim`.
 2.  Filters the data for Paris with an average temperature less than 22.
 3.  Name this new data frame "cold_paris"
 
-```{r, message=FALSE, warning=FALSE}
-# Not piped
-
-# (1)
+```{r}
+# not piped
 slim <- select(climate_df, dt, AverageTemperature, City)
-
-# (2)
 filtered <- filter(slim, City == "Paris", AverageTemperature < 22)
-
-# (3)
 cold_paris <- filtered
 ```
 
-## The pipe `%>%`
+## The Pipe `%>%`
+
+You can use the pipe operator to chain `tidyverse` functions together.
 
-You can use the pipe operator to chain tidyverse functions together. You can think of the pipe as automatically sending the output from the first line into the next line as the input.
+You can think of the pipe as automatically sending the output from the first line into the next line as the input. The output of the previous line of code (to the "left" of the pipe) becomes the first argument in the function to the right of the pipe.
 
 This is helpful for a lot of reasons, including:
 
@@ -207,20 +209,14 @@ This is helpful for a lot of reasons, including:
 
 The shortcut for typing a pipe is `Ctrl` + `Shift` + `M` (or `Cmd` + `Shift` + `M` on a Mac)
 
-```{r pipes, message=FALSE, warning=FALSE}
-# Open climate_df %>% 
-# Select City column
-# (Returns City column)
+```{r}
+#| label: pipes
 climate_df %>% 
   select(dt, City)
 
-# (1) Will store results in cold_paris
-# (2) Open climate_df
 cold_paris <- climate_df %>%
-  # (3) Select two columns
-  select(dt, AverageTemperature, City) %>%   
-  # (4) Filter to Paris, then filter data where where avg. temp < 22
-  filter(City == "Paris", AverageTemperature < 22)  
+  select(dt, AverageTemperature, City) %>%
+  filter(City == "Paris", AverageTemperature < 22)
 ```
 
 ### Let's practice!
@@ -230,45 +226,29 @@ In small groups, use pipes to create a new data frame called `warm_nigeria` that
 -   the columns AverageTemperature, City, Country
 -   only rows for the country Nigeria and temperatures that are greater than 30 degrees
 
-```{r nigeria30, message=FALSE, warning=FALSE}
+```{r}
+#| label: nigeria30
 warm_nigeria <- climate_df %>% 
-  # Select columns
   select(AverageTemperature, City, Country) %>% 
-  # Filter
   filter(Country == "Nigeria", AverageTemperature > 30)
 ```
 
-## Creating new variables with `mutate()`
-
-Sometimes our data doesn't have our data in exactly the format we want. For example, we might want our temperature data in Fahrenheit instead of Celsius.
+## Turning Data into Information with `summarize()`
 
-The `tidyverse` has a function called `mutate()` that lets us create a new column. Often, we want to apply a function to the entire column or perform some type of calculation, such as converting temp from F to C.
+Like we have talked about in previous classes, some of the best ways for us to understand our data is through what we call summary statistics such as the mean (average), standard deviation, minimums, maximums, etc. Fortunately, the `tidyverse` has a handy-dandy function to make this easy to do with data frames.
 
-To help us out, here is the equation for converting: `Fahrenheit = Celsius * (9/5) + 32`
+The `summarize()` function creates a new data frame with columns and values we give it. 
 
-```{r mutate, message=FALSE, warning=FALSE}
-climate_df %>% 
-  # Select temperature column
-  select(dt, AverageTemperature) %>% 
-  # Create a new column for converted temperatures in Fahrenheit
-  mutate(AverageTemperature_F = AverageTemperature * (9/5) + 32)
-```
+The first part of the argument in the `summarize` function (before the `=`) is the name of the new column we want to create. After the `=` is code that produces the values we want the new column to contain.
 
-The first part of the argument in the mutate function (before the `=`) is the name of the new column we want to create (or, sometime, the name of a column we want to overwrite). After the `=` is what we want the new column to contain.
+| `summarize(new_column_name = summary_function(column_to_summarize))`
 
-## Understanding data through `summarize()`
-
-Like we have talked about in previous classes, some of the best ways for us to understand our data is through what we call summary statistics such as the mean, standard deviation, minimums, maximums, etc.
-
-Fortunately, the `tidyverse` has a handy-dandy function to make this easy to do with data frames.
-
-The `summarize()` function creates a new dataframe with columns and values we give it. Similar to `mutate()`, what is on the left of the `=` is the name of the new column, and what is on the right of the `=` is the value(s) to put in the new column.
-
-```{r summarize, message=FALSE, warning=FALSE}
+```{r}
+#| label: summarize
+# first attempt at mean and sd of average temperature
 climate_df %>% 
-  # Make new data frame with one column containing: 
-  summarise(mean_temp = mean(AverageTemperature),    # average temperature and
-            sd_temp = sd(AverageTemperature))        # the other the sd
+  summarise(mean_temp = mean(AverageTemperature),
+            sd_temp = sd(AverageTemperature))
 ```
 
 Wait a second! Those are some weird values!
@@ -279,8 +259,8 @@ It is important to note that if any of the values in the column that you are try
 
 Fortunately, `mean()` and `sd()` and some other functions have an argument to remove the missing values: `na.rm = TRUE`
 
-```{r summarize_na.rm, message=FALSE, warning=FALSE}
-# Same as above, but with NA values omitted from calculation
+```{r}
+#| label: summarize_na.rm
 climate_df %>% 
   summarise(mean_temp = mean(AverageTemperature, na.rm = TRUE),
             sd_temp = sd(AverageTemperature, na.rm = TRUE))
@@ -296,15 +276,15 @@ One common way we analyze data is through something we call the "split, apply, c
 -   *apply* some type of analysis to each group independently and
 -   *combine* the data back together
 
-The `group_by()` function lets us do this. It is most often used in combination with `mutate()` or `summarize()`.
+The `group_by()` function lets us do this. It is most often used in combination with `summarize()`.
 
 For example, we can use this method to calculate the mean temperatures of **each** country instead of the overall mean of the entire dataset. In order to do these, we create groups in the data based on the country.
 
-```{r group_by, message=FALSE, warning=FALSE}
-# Group data by country, then find mean and standard deviation for each
+```{r}
+#| label: group_by
 climate_df %>% 
   group_by(Country) %>% 
-  summarise(mean_temp = mean(AverageTemperature, na.rm = TRUE), 
+  summarise(mean_temp = mean(AverageTemperature, na.rm = TRUE),
             sd_temp = sd(AverageTemperature, na.rm = TRUE))
 ```
 
@@ -312,8 +292,8 @@ climate_df %>%
 
 Practice using the combination of `group_by()` and `summarize()` to calculate the minimum (`min()`) and maximum (`max()`) average temperatures for each city. Save this data frame as `city_min_max`
 
-```{r group_by_city, message=FALSE, warning=FALSE}
-# Group data by city, then find minimum and maximum for each
+```{r}
+#| label: group_by_city
 climate_df %>% 
   group_by(City) %>% 
   summarize(min_temp = min(AverageTemperature, na.rm = TRUE),
@@ -322,10 +302,41 @@ climate_df %>%
 
 Already accomplished this task? Try to figure out how you can keep the "Country" column in the final data frame. This is trickier than you might think!
 
-```{r group_by_city_country, message=FALSE, warning=FALSE}
-# Group data by country, then group by city, then find minimum and maximum for each
+```{r}
+#| label: group_by_city_country
 climate_df %>% 
   group_by(Country, City) %>% 
   summarize(min_temp = min(AverageTemperature, na.rm = TRUE),
             max_temp = max(AverageTemperature, na.rm = TRUE))
 ```
+
+## Creating New Columns with `mutate()`
+
+Sometimes our data frame doesn't have our data points in exactly the format we want. For example, we might want our temperature data in Fahrenheit instead of Celsius.
+
+The `tidyverse` has a function called `mutate()` that lets us create a new column. Often, we want to apply a function to the entire column or perform some type of calculation, such as converting temp from F to C.
+
+Like the `summarize()` function, The first part of the argument in the `mutate` function (before the `=`) is the name of the new column we want to create (or, sometime, the name of a column we want to overwrite). After the `=` is code producing the values we want the new column to contain.
+
+| `mutate(new_column_name = code_producing_values_for_the_new_column)`
+
+To help us out, here is the equation for converting: `Fahrenheit = Celsius * (9/5) + 32`
+
+```{r}
+#| label: mutate
+# create a new column for temps in Fahrenheit
+climate_df %>% 
+  select(dt, AverageTemperature) %>% 
+  mutate(AverageTemperature_F = AverageTemperature * (9/5) + 32)
+```
+
+### Let's Practice
+
+Write some code that uses the `round()` function to create a new column of average temperatures (Celsius) with only point value after the decimal point.
+
+```{r}
+climate_df %>% 
+  mutate(AverageTemperatureRound = round(AverageTemperature, digits = 1))
+```
+
+If we set the "new" column name in the `mutate()` function as the same name of a column that already exists, the code with replace that column with the new values.
\ No newline at end of file
diff --git a/modules/module_2/module2_1.qmd b/modules/module_2/module2_1.qmd
index 68cafb9..903aabe 100644
--- a/modules/module_2/module2_1.qmd
+++ b/modules/module_2/module2_1.qmd
@@ -1,91 +1,101 @@
 ---
-title: "2.1: Introduction to Descriptive Statistics and Data Visualization"
+title: "2.1: Good Food Gone Bad"
+authors: "Keaton Wilson, Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
 
-## Learning Outcomes
+# Descriptive Statistics and Data Visualization
 
--   Students will be able to apply basic data science knowledge to find the cause of a real-world scenario--food poisoning!
--   Students will be able to generate two types of plots using base R syntax to visualize a single continuous variable (histograms and distributions) and two continuous variables (scatter plots).
--   Students will be able to use visual-thinking skills to create visualizations that allow them to explore patterns in data, draw inferences, and create solutions.
+## Student Learning Outcomes
 
-## Introduction to The Problem
+1.  Students will be able to apply basic data science knowledge to find the cause of a real-world scenario--food poisoning!
+2.  Students will be able to generate two types of plots using base R syntax to visualize a single continuous variable (histograms and distributions) and two continuous variables (scatter plots).
+3.  Students will be able to use visual-thinking skills to create visualizations that allow them to explore patterns in data, draw inferences, and create solutions.
 
-![](assets/aquaponics-system.png)
+## Introduction to the problem
 
 We have a wave of people getting sick across the team. People are coming in complaining of stomach sickness. Doctors have ruled out a communicable viral infection like norovirus, so it seems likely to be a food contamination issue.
 
-The two main sources of food that are grown on site and distributed to team members are [plants grown in hydroponic greenhouses](https://thespoon.tech/scientists-in-antarctica-grow-food-without-soil-or-sunlight-get-ready-mars/) (mostly Swiss chard, cucumbers, and radishes) and fish (tilapia --- a tolerant warm-water species --- and rainbow trout --- a cold-water species). The combination of aquaculture and hydroponics is called [**aquaponics**](https://docs.google.com/presentation/d/12Q1wIMO-b5ZfylHnA4dOsaLUBERtvVREzwQuO7Sc53A/edit?usp=sharing).
+The two main sources of food that are grown on site and distributed to team members are [plants grown in hydroponic greenhouses](https://thespoon.tech/scientists-in-antarctica-grow-food-without-soil-or-sunlight-get-ready-mars/) (mostly Swiss chard, cucumbers and radishes) and fish (tilapia, a tolerant warm-water species, and rainbow trout, a cold-water species). The combination of aquaculture and hydroponics is called [**aquaponics**](https://docs.google.com/presentation/d/1vxVtKe9MaTXDrB1u9vGsbdYkJylWlPMm/edit?usp=sharing&ouid=103537286572390245832&rtpof=true&sd=true).
+
+![](assets/aquaponics-system.png){width="60%" fig-align="center"}
 
 Team members' diets vary in composition; people are allowed to choose how much of different food sources they eat.
 
 Fortunately, we have some data we can use to investigate! We have data on the following:
 
--   Which team members are sick and how many times they've gone to the doctor
--   Some information about each team member, such as:
-    -   Sex, age, height, occupation
-    -   How much fish and/or plant material they incorporate into their diets
+-   which team members are sick and how many times they've gone to the doctor
+-   some information about each team member, such as:
+    -   sex, age, height, occupation
+    -   how much fish and/or plant material they incorporate into their diets
 
-### Group Discussion
+## The Data
 
-How might we figure out what is causing the problem? Try to focus on potential solutions that involve the data we already have (listed above).
+First we're going to pull in the data and give it a quick inspection/exploration before we start.
 
-Spend 5 minutes brainstorming in your groups how you might figure out whether plants or fish are the culprits? Be ready to report out.
+As usual, we start by calling the `tidyverse`.
 
-## Descriptive Statistics and Data Visualization
+```{r}
+library(tidyverse)
+```
 
-In order to begin addressing the question of what might be causing illness in our crew (and lots of other questions!), we often want to start with *descriptive statistics* and *data visualization*.
+To bring our data into R, we use a function called `read_csv()`. CSV (comma-separated values) are efficient ways to save 2-dimensional ("spreadsheet") data.
 
-In this course, we will be working with two types of statistics: *descriptive* and *inferential*.
+```{r}
+sick <- read_csv("data/sick_data.csv")
+head(sick)
+```
 
--   **Descriptive** statistics --- also called **summary** statistics --- are ways of presenting, organizing, and summarizing data. Data visualization is often associated with descriptive statistics
--   **Inferential** statistics help us draw reasonable conclusions about a population based on the data we observed in a sample.
+Let's talk about the data we have:
 
-In Module 2, we will be focusing exclusively on descriptive statistics and data visualization techniques.
+-   How many observations (rows) do we have?
+-   What type of information does a single row represent?
+-   How many variables (columns) do we have?
 
-As a quick overview, descriptive statistics often include 3 elements:
+Which variables are we particularly interested in? Are they continuous or categorical? Does it matter?
 
--   ***Distribution*** of the data
--   Measures of ***central tendency***: mean, median, and mode
--   Measures of ***variation***: range and standard deviation
+### Group Discussion
 
-The slide deck with more information about descriptive statistics is linked [here](https://docs.google.com/presentation/d/1pAnGupJqgU5t7YucGwMsbAdusoplY4GcJMdOWCic4hs/edit?usp=sharing).
+How might we figure out what is causing the problem? Try to focus on potential solutions that involve the data we already have.
 
-For a nice overview of descriptive statistics, check out [this website](https://www.scribbr.com/statistics/descriptive-statistics/) I showed in class. It goes a bit further than we go in this class, but it is a nice place to review!
+Spend 5 minutes brainstorming in your groups how you might figure out whether plants or fish are the culprits? Be ready to report out.
 
-## The Data
+Write a research question with a dependent variable and an independent variable (see the "Aquaponics & Research Questions" slide deck for more info).
 
-First we're going to pull in the data and give it a quick inspection/exploration before we start.
+## Descriptive Statistics and Data Visualization
 
-As usual, we start by calling the `tidyverse`.
+In order to begin addressing the question of what might be causing illness in our crew (and lots of other questions!), we want to start with *descriptive statistics* and *data visualization*.
 
-```{r, message=FALSE, warning=FALSE}
-# Open up the library
-library(tidyverse)
-```
+In this course, we will be working with two types of statistics: *descriptive* and *inferential*.
 
-To bring our data into R, we use a function called `read_csv()`. CSV (comma-separated values) are efficient ways to save 2-dimensional ("spreadsheet") data.
+-   **Descriptive** statistics--also called **summary** statistics--are ways of presenting, organizing, and summarizing data. Data visualization is often associated with descriptive statistics
+-   **Inferential** statistics help us draw reasonable conclusions about a population based on the data we observed in a sample.
 
-```{r, message=FALSE, warning=FALSE}
-# Put data in Environment
-sick <- read_csv("data/sick_data.csv")
+In Module 2, we will be focusing exclusively on descriptive statistics and data visualization techniques.
 
-# View first few rows
-head(sick)
-```
+As a quick overview, descriptive statistics often include 3 elements:
 
-How many observations (rows) do we have? How about variables (columns)?
+-   *distribution* of the data
+-   measures of *central tendency*: mean, median, and mode
+-   measures of *variation*: range and standard deviation
 
-Which variables are we particularly interested in? Are they continuous or categorical? Does it matter?
+The slide deck with more information about descriptive statistics is linked on D2L.
+
+For a nice overview of descriptive statistics, check out [this website](https://www.scribbr.com/statistics/descriptive-statistics/) I showed in class. It goes a bit further than we go in this class, but it is a nice place to review!
 
 ### Let's Practice
 
 Now that we have a more formalized understanding of measures of central tendency and measures of variation, let's put them into use to learn a little bit more about our data. In small groups, calculate the mean (`mean()`) and standard deviation (`sd()`) for the percent fish in our sick crew's diets.
 
-```{r, message=FALSE, warning=FALSE}
-# Find mean and standard deviation of %fish in people's diets
+```{r}
 sick %>% 
-  summarize(mean_fish = mean(perc_fish),
-            sd_fish = sd(perc_fish))
+  summarize(mean_fish = mean(perc_fish, na.rm = TRUE),
+            sd_fish = sd(perc_fish, na.rm = TRUE))
 ```
 
 ## Data Visualization in R
@@ -94,14 +104,14 @@ Combining data visualization with descriptive statistics is a great way to under
 
 There are two main ways to make data visualizations in R: through base R, which is the syntax we learned in our first week of coding, and through `ggplot2`, which is a package in the `tidyverse`.
 
-For the majority of the semester, we will be plotting in with `ggplot2`, which is a fun and powerful tool. However, to plot even a simple plot, `ggplot2` takes some explanation. We will talk about `ggplot2` in our next class but for today, let's first make some quick-and-dirty plots in base R.
+For the majority of the semester, we will be plotting in with `ggplot2`, which is a fun and powerful tool. However, to plot even a simple plot, `ggplot2` takes some explanation. We will talk about `ggplot2` in our *next class* but for today, let's first make some quick-and-dirty plots in *base R*.
 
 ### Histograms
 
 We've calculated some descriptive statistics about the percents of fish and plants in our sick crew members' diets, but it didn't tell us too much. Let's try some data visualization to see if that gives us any additional information.
 
-```{r, message=FALSE, warning=FALSE}
-# Histogram in base R
+```{r}
+# histogram in base R
 hist(sick$perc_plant)
 ```
 
@@ -121,12 +131,12 @@ Take about 5 minutes. Be ready to report out.
 
 We've covered one type of visualization that just shows one variable...but what we're really interested in is figuring out if fish or plants are the culprit in food poisoning. Spend about 5 minutes in your groups sketching out a visualization that might give us insight into this.
 
-### Scatter plots
+## Scatter plots
 
 Scatter plots allow us to visualize the relationship between two continuous (or numeric) variables. For example, we can use a scatter plot to see if there is a relationship between how old a crew member is and how much fish is in their diet.
 
-```{r, message=FALSE, warning=FALSE}
-# Scatter plot in base R
+```{r}
+# scatter plot in base R
 plot(x = sick$age, y = sick$perc_fish)
 ```
 
@@ -134,11 +144,10 @@ This isn't super informative, because there isn't really a relationship between
 
 ### Building and Interpreting
 
-In your group, create a scatter plot to determine whether there is a correlation between the percentage of fish eaten and the number of trips to the doctor in the past 6 months.
+Create a scatter plot to determine whether there is a correlation between the percentage of fish eaten and the number of trips to the doctor in the past 6 months.
 
-```{r, message=FALSE, warning=FALSE}
-# Another scatterplot in base R
+```{r}
 plot(x = sick$perc_fish, y = sick$doctor_trips)
 ```
 
-How do we interpret this plot?
+How do we interpret this plot?
\ No newline at end of file
diff --git a/modules/module_2/module2_2.qmd b/modules/module_2/module2_2.qmd
index 3008a4e..2c9e32f 100644
--- a/modules/module_2/module2_2.qmd
+++ b/modules/module_2/module2_2.qmd
@@ -1,164 +1,116 @@
 ---
-title: "2.2: Writing Functions"
+title: "2.2: Plotting with `ggplot2`"
+authors: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+# Plotting with `ggplot2`
 
-## Learning Outcomes
-
--   Students will be able to apply fundamental components of the biology of recirculating aquaculture systems to hypothesize how these components could lead to unhealthy fish.
--   Students will be able to write custom functions that perform simple tasks.
--   Students will be able to describe the utility of iteration.
-
-## Aquaculture, Immunology, and Disease
-
-What is aquaculture anyway, and how might it contribute to disease? Here's a link to the [presentation](https://docs.google.com/presentation/d/1CciKgqhu9WhS3x-YaTgEF15B0TefFhqevufThvmEfSk/edit?usp=sharing).
-
-### Data Exploration
-
-Given the correlation we found earlier, it does seem like the fish are our likely culprit.
-
-Since we've narrowed down the issue, we should ask our aquaculture specialists to provide us with some data about the tanks.
+So far, we've used the base R plotting syntax. While quick plots in base R can still be really useful ways to do preliminary data exploration and visualization, we often want plots that go beyond the basics without too much additional effort. This is where `ggplot2` comes in and really shines!
 
-#### Group Brainstorm
+## Example
 
-Based on what we know about aquaculture systems, what types of data should we ask for to get to the bottom of this issue? Spend about 3 minutes brainstorming with your group and be ready to report back.
+Before we get into the nitty-gritty of how `ggplot2` works, Let's run an example using the data about our sick crew members from earlier.
 
-### The Data We Have
+First, we need to load in both the `tidyverse` package and our data. We can remind ourselves what the data look like using the `head()` function.
 
-```{r, message=FALSE, warning=FALSE}
-# Load the tidyverse
+```{r, message = FALSE}
+# load package
 library(tidyverse)
 
-# Read in the data
-tank_data <- read_csv("data/fish_tank_data.csv")
-
-# Look at the data
-glimpse(tank_data)
+# load data
+sick <- read_csv("data/sick_data.csv")
+head(sick)
 ```
 
-### Discussion of the problem
+Here is code to make a scatter plot of the relationship between percent fish in diets and how many trips to the doctor.
 
-Trout are a cold-water species whereas tilapia are a warm-water species. After doing some reading, we've come up with some critical values for each species.
-
-Water temperatures below 59º F for trout and below 75º F for tilapia are critically low temperatures and can result in suppression of the immune system.
-
-Great, we now know what the critical temperature cutoffs are for both species --- but there's a problem. The cut-off temperatures that we have are in Fahrenheit but the temperature values in our data frame are in Celsius.
-
-Our goal with this data is to ascertain if any of the tanks are below the critical temperature. Before we can do this, though, we need to convert tank temperatures from Celsius to Fahrenheit. Luckily for us, we've already done this before! (Hint: remember the `mutate()` function?).
-
-Now, we are going to run through a slightly different way to accomplish this task. It still involves `mutate()`, but we need to take some additional steps first before we use it.
-
-## Custom Functions
+```{r}
+ggplot(sick, aes(x = perc_fish, y = doctor_trips)) +
+  geom_point() +
+  labs(x = "Percent Fish in Diet",
+       y = "Number of Trips to the Doctor") +
+  theme_light()
+```
 
-We've already used functions a lot in this class, and it turns out that we can write our own functions to perform specific tasks.
+Nice, right? In the next few classes, we will really start to see the power of `ggplot`. For now, though, let's focus on how this works.
 
-This has lot of varied uses and can be very powerful. The key takeaway here, though, is that we can write a function that converts Celsius to Fahrenheit and apply it to our entire data set in a few lines of code.
+## `ggplot2`
 
-### General Syntax Example
+The package `ggplot2` is part of the `tidyverse`.
 
-Making a function involves creating local variables that act as a stand-in for the values you want to use later on. In the case below, our local variables are `x`, `y`, and `z`. You can see that they are local because they only circulate within the function and are not being pulled in from outside. However, once we are done defining our function we can assign values to the input variables (in this case, `x` and `y`) that the function will do its magic with. You can see this in the example chunk below, where `x = 2` and `y = 4`. All manipulations done with the input variables are contained between the curly brackets ( `{ }` ), and of course, we signify the start a function with `function()`.
+Here are some resources you might find helpful now or in the future:
 
-Function syntax follows the structure in the code chunk below, which can be viewed as:
+-   [ggplot2 Book](https://ggplot2-book.org/getting-started.html)
+-   [UC Business Analytics ggplot2 intro](https://uc-r.github.io/ggplot_intro)
+-   [R for Data Science Data Visualization chapter](https://r4ds.had.co.nz/data-visualisation.html)
 
-`name_of_new_function <- function(input1, input2){`
+The `gg` in `ggplot2` stands for "Grammar of Graphics." The "grammar" part is based on an idea that all statistical plots have the same fundamental features: data and mapping (and specific components of mapping).
 
-`what do you want to do with your input variables? (add, subtract, etc.)`
+The design is that you work iteratively, building up layer upon layer until you have your final plot.
 
-`output <- input1 + input2`
+The typical structure looks like this:
 
-`return(output)`
+| `ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +`
+| `<GEOM_FUNCTION>()`
 
-`}`
+Let's iteratively build up to the plot we have made above:
 
-And we can see it in action here:
+1)  Specify the data
 
-```{r, message=FALSE, warning=FALSE}
-# General syntax
-new_sum <- function(x,y){
-  z <- x + y
-  return(z)
-}
+```{r fig.width=4.5, fig.height=3}
+ggplot(data = sick)
 
-# Examples when using the new function
-new_sum(x = 2, y = 4)
-new_sum(2, 4)
-new_sum(7, 19)
+# ggplot always starts by drawing a blank coordinate system. Nothing will appear until you add at least one geometric layer. (geom())
 ```
 
-### Building Our Custom Function
+As we've previously seen, most plots that we make display data in some way relative to two different axes: the x-axis (horizontal) and the y-axis (vertical).
 
-In your groups, build a custom function called `c_to_f()` that converts a single value of Celsius to Fahrenheit. Remember, the equation is `Celsius = Fahrenheit * (9/5) + 32`. Instead of two arguments like we have above (`x`, `y`), this function will only require one input value (`c`).
+You will definitely want to memorize which axis is which!
 
-Be prepared to show your code and run some test values.
+2)  Specify the x-axis (horizontal) and the y-axis (vertical) in the `aes()` function.
 
-```{r, message=FALSE, warning=FALSE}
-c_to_f <- function(c = NULL){
-  f <- (c * (9/5)) + 32
-  return(f)
-}
-
-c_to_f(c = 40)
+```{r fig.width=4.5, fig.height=3}
+ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips))
 ```
 
-### Thinking about Iteration
-
-So, we have a great function that we can use to convert a Celsius value to Fahrenheit, but how do we apply that to *every* value in the temperature column, not just one? We've built the function for one value, not for a whole vector of values...
+3)  Add the type of plot we want using a `geom` function. For a scatter plot, we use `geom_point()`.
 
-This is where `mutate()` comes in! As we've seen in the past, `mutate()` applies whatever function or mathematical formula we've given to it to each value in the column. It essentially is going row by row: applying the function or formula to one row, returning the new value in a new column, then moving to the next row to do the same.
-
-```{r, message=FALSE, warning=FALSE}
-# Let's first demonstrate applying our new function to a vector
-# Only show the first 10 rows as well
-head(c_to_f(tank_data$avg_daily_temp))
-
-# Mutate is basically doing the same thing
-tank_data <- tank_data %>%
-  mutate(avg_daily_temp_F = c_to_f(avg_daily_temp))
-
-# View mutate() results
-tank_data
+```{r fig.width=4.5, fig.height=3}
+ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips)) +
+  geom_point()
 ```
 
-## Why Functions?
-
-We can easily fit the equation for converting from Celsius to Fahrenheit into a mutate call. We certainly didn't need to write our own function to do it. So why did we learn how to do that?
-
-### Main Ways to Iterate
-
-There are many different approaches to iterating in R, especially because we are typically working with vectorized data (columns in data frames). We will talk about the other options down the road. Today, we used #3, the `mutate()` function from the `tidyverse`.
-
-1.  `for` loops
-2.  `apply`/`map` functions
-3.  using `dplyr` (`tidyverse`) functions
-
-### Filtering Our Data
+4)  Clean up the axis labels with the `labs()` function so they are more easily interpreted.
 
-Thankfully, we've solved our first problem! However, we haven't yet answered the original question.
-
-*Are any tanks are below the temperature cutoffs for each species? If so, how many?*
-
-Let's tackle that question:
-
-```{r, message=FALSE, warning=FALSE}
-# count() is a new function for most of us
-# It does what it sounds like -- count the number of results
-
-# Count number of tanks below temperature cutoff for TILAPIA
-tank_data %>% 
-  filter(species == "tilapia" & avg_daily_temp_F < 75) %>% 
-  count()
+```{r fig.width=4.5, fig.height=3}
+ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips)) +
+  geom_point() +
+  labs(x = "Percent Fish in Diet",
+       y = "Number of Trips to the Doctor") 
+```
 
-# for TROUT
-tank_data %>% 
-  filter(species == "trout" & avg_daily_temp_F < 59) %>% 
-  count()
+5)  Choose a `theme` function to make the plot more aesthetically pleasing.
 
-# Let's introduce some additional syntax: "&" and "|"
+```{r fig.width=4.5, fig.height=3}
+# theme_bw(), theme_classic(), and theme_light() are my favorites
+ggplot(sick, aes(x = perc_fish, y = doctor_trips)) +
+  geom_point() +
+  labs(x = "Percent Fish in Diet",
+       y = "Number of Trips to the Doctor") +
+  theme_light()
+```
 
-# Now we can get counts for both fish species in one go!
-tank_data %>%
-  filter(species == "tilapia" & avg_daily_temp_F < 75 |
-         species == "trout" & avg_daily_temp_F < 59) %>%
-  group_by(species) %>%
-  summarize(n = n()) # Another way to get the count is summarize() with n()
+In Summary:
 
-```
+-   we always start with the `ggplot()` function
+-   we specify the dataset we want to use
+-   we specify the mappings (x- and y-axes and some other bits) with the `aes()` function
+-   we use a `+` to add layers
+-   we specify the type of plot, or `geom` using one of many possible geom functions
+-   we use the `labs()` function to clean up the labels
+-   we add a `theme` function to make it more visually readable
\ No newline at end of file
diff --git a/modules/module_2/module2_3.qmd b/modules/module_2/module2_3.qmd
index 81bd911..8ffbd86 100644
--- a/modules/module_2/module2_3.qmd
+++ b/modules/module_2/module2_3.qmd
@@ -1,108 +1,91 @@
 ---
-title: "2.3: Plotting with `ggplot2`"
+title: "2.3: Data Visualization"
+authors: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
-## Get to Know `ggplot2`
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
 
-So far, we've used the base R plotting syntax. While quick plots in base R can still be really useful ways to do preliminary data exploration and visualization, we often want plots that go beyond the basics without too much additional effort. This is where `ggplot2` comes in and really shines!
+# A Visualization Primer
 
-## Example
+## Why Does Data Visualization Matter?
 
-Before we get into the nitty-gritty of how `ggplot2` works, Let's run an example using the data about our sick crew members from earlier.
+### Small Groups
 
-First, we need to load in both the `tidyverse` package and our data. We can remind oursevles what the data look like using the `head()` function.
+As a group, take 5 minutes to compare your notes and come to a group consensus on your top 3 reasons why data visualization matters. Be sure to choose someone to report out!
 
-```{r, message=FALSE, warning=FALSE}
-# Load package
-library(tidyverse)
+## Types of Visualization
 
-# Load data
-sick <- read_csv("data/sick_data.csv")
+### Visual cues for communicating data
 
-# View first few rows
-head(sick)
-```
+[Here](https://rafalab.dfci.harvard.edu/dsbook-part-1/dataviz/dataviz-principles.html) is a decent overview of some of the core concepts of data visualization.
 
-Here is code to make a scatter plot of the relationship between percent fish in diets and how many trips to the doctor.
+This website is pretty great and goes into a lot of detail about good practices in data visualization. If this is something that really piques your interest, I encourage you to check it out!
 
-```{r, message=FALSE, warning=FALSE}
-# Scatterplot of % fish diet and # of doctor visits
-ggplot(sick, aes(x = perc_fish, y = doctor_trips)) +
-  geom_point() +
-  labs(x = "Percent Fish in Diet",
-       y = "Number of Trips to the Doctor") +
-  theme_light()
-```
+In this course, our main take-away from this website is the use of visual cues to communicate data and which ones are better than others.
 
-Nice, right? In the next few classes, we will really start to see the power of `ggplot`. For now, though, let's focus on how this works.
+![](assets/data_viz_cues.jpg){width="50%"}
 
-## `ggplot2`
+### Small group activity
 
-The package `ggplot2` is part of the `tidyverse`.
+In groups, discuss as many types of visualizations as you can think of. It's okay if you don't know what they are called! Make a quick list or draw them out if you prefer.
 
-Here are some resources you might find helpful now or in the future:
+Types of data visualizations:
 
--   [ggplot2 Book](https://ggplot2-book.org/getting-started.html)
--   [UC Business Analytics ggplot2 intro](https://uc-r.github.io/ggplot_intro)
--   [R for Data Science Data Visualization chapter](https://r4ds.had.co.nz/data-visualisation.html)
+We will talk more about how to choose the right visualization for your data now and also in the rest of the module.
 
-The `gg` in `ggplot2` stands for "Grammar of Graphics." The "grammar" part is based on an idea that all statistical plots have the same fundamental features: data and mapping (and specific components of mapping).
+### Data matching activity
 
-The design is that you work iteratively, building up layer upon layer until you have your final plot.
+Still in your groups, open the PDF called "data_viz_matching"
 
-The typical structure looks like this:
+Spend a few minutes seeing if you can match the data descriptions to the types of data visualizations.
 
-```{r}
-# ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  
-# <GEOM_FUNCTION>()
-```
+Each “dataset” is pulled from the same overall dataset: measurements from 344 individual penguins from different species and on different islands over 3 years. Some datasets created multiple plots.
 
-A few things to note:
+A.  Penguin body mass and flipper lengths
+B.  Penguin flipper lengths
+C.  Penguin species and flipper lengths
+D.  Penguins body mass, species, and flipper lengths
 
--   We always start with the `ggplot()` function
--   We specify the dataset we want to use
--   We specify the mappings (x- and y-axes and some other bits) with the `aes()` function
--   We use a `+` to add layers
--   We specify the type of plot, or `geom` using one of many possible geom functions
--   We use the `labs()` function to clean up the labels
--   We add a `theme` function to make it more visually readable
+#### Matches
 
-Let's iteratively build up to the plot we have made above:
+1.  A
+2.  C
+3.  C
+4.  B
+5.  C
+6.  D
 
-1)  Specify the data
+See the PowerPoint on D2L for full details!
 
-```{r fig.width=4.5, fig.height=3}
-ggplot(data = sick)
-```
+## Figure Critique
 
-2)  Specify the x-axis (horizontal) and the y-axis (vertical) in the `aes()` function.
+First, read through [this blog post](https://humansofdata.atlan.com/2019/02/dos-donts-data-visualization/) on the "Dos and Don'ts of Data Visualization"
 
-```{r fig.width=4.5, fig.height=3}
-ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips))
-```
+Now that you have some insights, let's critique these figures below. Yes, these are actual figures in the wild...
 
-3)  Add the type of plot we want using a `geom` function.
+-   what aspects don't work
+-   what aspects do work
+-   how would you present the data?
 
-```{r fig.width=4.5, fig.height=3}
-ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips)) +
-  geom_point()
-```
+Figures:
 
-4)  Clean up the axis labels with the `lab()` function so they are more easily interpreted.
+1.  
 
-```{r fig.width=4.5, fig.height=3}
-ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips)) +
-  geom_point() +
-  labs(x = "Percent Fish in Diet",
-       y = "Number of Trips to the Doctor") 
-```
+![](assets/image1.png){width="50%"}
 
-5)  Choose a `theme` function to make the plot more aesthetically pleasing. My favorites are `theme_bw()`, `theme_classic()`, and `theme_light()`.
+2.  
 
-```{r fig.width=4.5, fig.height=3}
-ggplot(sick, aes(x = perc_fish, y = doctor_trips)) +
-  geom_point() +
-  labs(x = "Percent Fish in Diet",
-       y = "Number of Trips to the Doctor") +
-  theme_light()
-```
+![](assets/image2.jpg){width="50%"}
+
+3.  
+
+![](assets/image3.jpg){width="50%"}
+
+4.  
+
+![](assets/image4.png){width="50%"}
\ No newline at end of file
diff --git a/modules/module_2/module2_4.qmd b/modules/module_2/module2_4.qmd
index 825c7c6..4301c72 100644
--- a/modules/module_2/module2_4.qmd
+++ b/modules/module_2/module2_4.qmd
@@ -1,88 +1,308 @@
 ---
-title: "2.4: A Visualization Primer"
+title: "2.4: Exploring `geom` Functions"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
 
-## Why Does Data Visualization Matter?
+# Plotting in `ggplot2`
 
-### Small Groups
+## Data Visualization Types and When to Use Them
 
-As a group, take 5 minutes to compare your notes and come to a group consensus on your top 3 reasons. Be sure to choose someone to report out!
+Let's practice making different kinds of plots with various `geom` functions to see how they work.
 
-## Types of Visualization
+```{r}
+library(tidyverse)
+fish <- read_csv("data/fish_sick_data.csv")
+```
 
-### Visual cues for communicating data
+### Plotting in `ggplot2`
 
-[Here](http://paldhous.github.io/ucb/2016/dataviz/week2.html) is a decent overview of some of the core concepts of data visualization.
+First, let's remind ourselves of the general structure of how we make plots using the `ggplot2` syntax.
 
-This website is pretty great and goes into a lot of detail about good practices in data visualization. If this is something that really piques your interest, I encourage you to check it out!
+```{r}
+# ggplot(data = <DATA>, mapping = aes(x = <COLUMN1>, y = <COLUMN2)) +
+  # geom_function() +
+  # labs() +
+  # theme()
+```
 
-In this course, our main take-away from this website is the use of visual cues to communicate data and which ones are better than others.
+## Histograms
 
-![](assets/data_viz_cues.jpg)
+As we learned last modules, histograms are plots which let us look at *one numeric variable.*
 
-### Small group activity
+They help us get a feel for the distribution of that data. To make histograms in `ggplot2`, we use the `geom_histogram()` function. Let's look at the number of fish.
 
-In groups, discuss as many all types of visualization that come to mind. It's okay if you don't know what they are called! Make a quick list or draw them out if you prefer.
+```{r}
+ggplot(fish, aes(x = num_fish)) +
+  geom_histogram(bins = 20)
 
-Types of data visualizations:
+# we can change the number of bins (essentially, the number of columns) by modifying the bins argument in the geom_histogram function
+# we can also modify the colors, if we want
 
-[This website](https://www.data-to-viz.com/) is an amazing reference for data visualization methods and when to use what. It also has examples of each type plotted in `ggplot2`.
+ggplot(fish, aes(x = num_fish)) +
+  geom_histogram(bins = 20, fill = "lightblue", color = "black")
+```
 
-We will talk more about how to choose the right visualization for your data now and also in the rest of the module.
+### Let's Practice
 
-### Data matching activity
+Make a histogram of the number of sick fish in the tanks. Create the histogram with 10 bins (10 groupings).
 
-Still in your groups, open the PDF called "data_viz_matching"
+```{r}
+ggplot(fish, aes(x = num_sick)) +
+  geom_histogram(bins = 10)
+```
 
-Spend a few minutes seeing if you can match the data descriptions to the types of data visualizations.
+## Multiple Histograms (with `geom_histogram()`)
 
-Each "dataset" is pulled from the same overall dataset: measurements from 344 individual penguins from different species and on different islands over 3 years. Some datasets created multiple plots.
+When we create a multiple histogram, we have to add one additional argument to `geom_histogram()`. Let's see what happens if we just specify `fill`.
 
-A.  Penguin body mass and flipper lengths
-B.  Penguin flipper lengths
-C.  Penguin species and flipper lengths
-D.  Penguins body mass, species, and flipper lengths
+```{r}
+ggplot(fish, aes(x = num_fish, fill = species)) +
+  geom_histogram(bins = 10)
+```
 
-### Matches
+What is happening in the column with both green and red? Perhaps the teal histogram is in front of the red histogram and as blocking us from seeing some red?
 
-1.  
+Let's change the transparency using an argument called `alpha`, which allows us to make layers transparent. The scale for `alpha` goes from 0 (completely transparent) to 1 (not transparent at all).
 
-2.  
+We can set the transparency to 0.5 to see if there is any overlap.
 
-3.  
+```{r}
+ggplot(fish, aes(x = num_fish, fill = species)) +
+  geom_histogram(bins = 10, alpha = 0.5)
+```
 
-4.  
+Not too much changed. It still doesn't look like we can see any red points behind the teal. Perhaps the teal values and red values are stacked on top of one another?
 
-5.  
+Let's take a look at what happens when we add the argument `position = "identity"`.
 
-6.  
+```{r}
+ggplot(fish, aes(x = num_fish, fill = species)) +
+  geom_histogram(bins = 10, alpha = 0.5, position = "identity")
+```
 
-See the PowerPoint on D2L for full details!
+Aha! This is different from above. Instead of red being stacked vertically on top of teal, we can now see that the red values start at 0 on the y-axis and are overlapping with the teal.
 
-## Figure Critique
+The `position = "identity"` argument tell `geom_histogram` to plot the data for each group starting from 0 in the y-axis rather than stacking values from the same group, which is the default.
 
-First, read through [this blog post](https://humansofdata.atlan.com/2019/02/dos-donts-data-visualization/) on the "Dos and Don'ts of Data Visualization"
+Any time we plot a multiple histogram, we need to change the transparency and add `position = "identity"` when creating a multiple histogram; if we don't, we won't see potential overlap!
 
-Now that you have some insights, let's critique these figures below. Yes, these are actual figures in the wild...
+### Let's Practice!
 
--   What aspects don't work?
--   What aspects do work?
--   How would you present the data?
+Make a multiple histogram of the number of sick fish per species. Make sure your plot has 10 bins, is partially transparent, and the data are *not* stacked.
 
-**Figures:**
+```{r}
+ggplot(fish, aes(x = num_sick, fill = species)) +
+  geom_histogram(bins = 10, alpha = 0.5, position = "identity")
+```
 
-1.  
+------------------------------------------------------------------------
 
-![](assets/image1.png)
+### The powerful and pesky `aes()` function
 
-2.  
+A quick note about the `aes()` function. It's one of the more confusing bits of `ggplot2`.
 
-![](assets/image2.jpg)
+When do I put the `color` (or `size` or `linetype` or `fill` or whatever) inside the `aes()` function versus in the `geom` function but outside of `aes()`? When we made our multiple histogram plots, why did the `fill` argument go inside of `aes()` but `alpha` went outside?
 
-3.  
+Essentially, it boils down to this:
 
-![](assets/image3.jpg)
+-   if you want something (color, size, etc.) on your plot to change based on a **variable** from a data frame, you will want to put the argument *within* the `aes()` function.
 
-4.  
+-   if you want something (color, size, etc.) on the plot to be **constant**, you will specify it *outside* of the function.
 
-![](assets/image4.png)
+For some additional examples and explanation, check out [this Stack Overflow page](https://stackoverflow.com/questions/41863049/when-does-the-argument-go-inside-or-outside-aes).
+
+------------------------------------------------------------------------
+
+## Scatter Plot
+
+As a reminder, we use the `geom_point()` function to make a scatter plot of the relationship between *two numeric variables*.
+
+```{r}
+ggplot(fish, aes(avg_daily_temp, num_fish)) +
+  geom_point()
+```
+
+### Let's Practice: Multiple Scatterplot
+
+Using what you've learned about making histograms, see if you can create a "multiple scatterplot," where the color of the points are determined by the fish species.
+
+*Hint: you'll want to use an argument called `color`*.
+
+```{r}
+ggplot(fish, aes(avg_daily_temp, num_fish, color = species)) +
+  geom_point()
+```
+
+## Box-and-Whisker Plots
+
+Box-and whisker-plots (also known as box plots) are another great option for looking at one *numeric* variable and one or more *categorical* variables. They are particularly nice when you want to see measures of central tendency and variation in the same plot.
+
+Let's build one and then talk through what each component means. We use `geom_boxplot` to make these types of plots.
+
+```{r}
+ggplot(fish, aes(species, num_fish, color = species)) +
+  geom_boxplot() 
+```
+
+So what does the box represent? And the whiskers?
+
+-   the **box** represents the *middle 50% of the values* in the data set.
+-   the line that runs through the middle of the box represents the *median (**middle value**)* of the data
+-   the **whiskers** represent the *spread of the data* (we won't get into the mathematical details of exactly how they are calculated) and are roughly comparable to 95% confidence intervals (we will cover this in another module)
+-   values that fall outside of the whiskers can be considered outliers and are plotted individually
+
+### Layering
+
+One of the beautiful parts of working with `ggplot2` is that you can add multiple layers to each plot.
+
+One of the key things missing from box-and-whisker plots is any indication of how many data points we have. In the plot above, there could be 5 tanks per species or 500 tanks per species.
+
+How can we add an indication of how many points there are? We can layer each individual data point on top of the boxes!
+
+```{r}
+ggplot(fish, aes(species, num_fish, color = species)) +
+  geom_boxplot() +
+  geom_point(alpha = 0.5)
+```
+
+This is nice, but there is still some overlap in points that makes it hard for us to see exactly how many points are there.
+
+The `geom_jitter()` function is a special version of `geom_point()`. It adds a little bit of randomness to the points (both horizontally and vertically) so that they don't overlap as much.
+
+We can control how much randomness we allow with the `width` and/or `height` arguments. I usually ignore `height` and set the `width` argument to 0.1.
+
+```{r}
+ggplot(fish, aes(species, num_fish, color = species)) +
+  geom_boxplot() +
+  geom_jitter(alpha = 0.5, width = 0.1) 
+```
+
+That is looking really nice! We can keep improving it, though, with better labels for the axes and the legend as well as a nice `theme`.
+
+```{r}
+ggplot(fish, aes(species, num_fish, color = species)) +
+  geom_boxplot() +
+  geom_jitter(alpha = 0.5, width = 0.1) +
+  labs(x = "Species",
+       y = "Number of Fish Per Tank",
+       color = "Species") +
+  theme_light()
+```
+
+------------------------------------------------------------------------
+
+### `color` and `fill`
+
+We have used two different arguments in the `aes()` function to specify that we want to colors in our plots to change based on one of the columns in our dataset. When do you want to use which one?
+
+#### `color`
+
+For the most part, the `color` argument refers to the color of the lines in a plot (e.g., in box plots, the colors of the lines change).
+
+The one exception to this is in `geom_point()`. We change the color of the points with the `color` argument, as well.
+
+#### `fill`
+
+When there is space that we want to fill in with color depending on the values in a column, we want to use the `fill` argument.
+
+For histograms, the `fill` argument changes the color inside the bins. If we use `color` in a histogram instead, only the outlines of the columns will change, but the columns will remain filled with gray.
+
+In boxplots, the inside area of the box will be filled with color, but the lines will stay black.
+
+### In Labels
+
+If we have specified either `color` or `fill` (or both) in the `aes()` function, `ggplot` will automatically create a legend for us. The key is determined by whichever argument we used.
+
+So, if you use the `color` argument in the `aes()` function, you would want to use the `color` argument in the `labs()` function to change the title of the legend. If you used the `fill` argument, you would then use the `fill` argument in the `labs()` function.
+
+If you have both `color` and `fill` in `aes()`, you will need to add both arguments to the `labs()` function. If you give the same label to each, it will create one key.
+
+------------------------------------------------------------------------
+
+### Let's Practice!
+
+Make 2 different types of plots with the same data: the average daily temperature of the tanks and fish species.
+
+First, think about the variables we are plotting. How many are there? Are they categorical or numeric?
+
+Based on those answers, determine which plot types you can produce for those variable types.
+
+Now, make your two plot! To each, add labels and a theme.
+
+```{r}
+# boxplot
+ggplot(fish, aes(species, avg_daily_temp, color = species)) +
+  geom_boxplot() +
+  geom_jitter(alpha = 0.5, width = 0.1) +
+  labs(x = "Average Daily Temp (C)",
+       y = "Frequency", 
+       color = "Fish Species") +
+  theme_bw()
+
+# histogram
+ggplot(fish, aes(avg_daily_temp, fill = species)) +
+  geom_histogram(bins = 10, alpha = 0.5, position = "identity") + 
+  labs(x = "Average Daily Temp (C)", 
+       y = "Frequency",
+       fill = "Fish Species") +
+  theme_bw()
+```
+
+Finished? Let's add another variable. Now, we want to plot average daily temperatures, the number of sick fish, and the fish species. Work through the same steps as above.
+
+```{r}
+ggplot(fish, aes(avg_daily_temp, num_sick, color = species)) +
+  geom_point(alpha = 0.5) +
+  labs(x = "Average Daily Temp (C)",
+       y = "Number of Sick Fish per Tank",
+       color = "Fish Species") +
+  theme_light()
+```
+
+# Summary
+
+Let's summarize some of what we've learned in this module
+
+## Data Visualization Types and When to Use Them
+
+### Histogram
+
+Good for looking at the distribution of one numeric variable
+
+-   one numeric variable (x-axis)
+
+### Multiple Histogram
+
+Good for looking at differences in the distributions of one numeric variable based on a categorical variable
+
+-   one numeric variable (x-axis)
+-   one categorical variable via the `fill` or `color` argument in the `aes()` function
+-   we always want to add transparency (`alpha`) and, for histograms, `position = "identity"`
+
+### Scatter Plot
+
+Good for looking for the relationship between two numeric variables
+
+-   two numeric variables (x-axis and y-axis)
+-   can add in a categorical variable via `aes()`, but the main relationship is between the two numeric variables
+
+### Box Plot
+
+Good for looking at measures of central tendency and variation for a numeric variable and the differences between those measures between categories
+
+-   one numeric variable (y-axis)
+-   at least one categorical (x-axis and additional via `aes()`)
+
+## Layering
+
+We can add multiple layers to ggplots, which is part of what makes them so useful!
+
+-   we can add multiple `geom` functions to a single plot
+-   we use the `labs()` function to rename axes labels and legends
+-   we use a `theme` function to make the plot more aesthetically pleasing and easier to understand
\ No newline at end of file
diff --git a/modules/module_2/module2_5.qmd b/modules/module_2/module2_5.qmd
index 225232f..d339882 100644
--- a/modules/module_2/module2_5.qmd
+++ b/modules/module_2/module2_5.qmd
@@ -1,260 +1,129 @@
 ---
-title: "2.5: Sick Fish"
+title: "2.5: Module 2 Wrap-Up"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
 
-## Scenario
-
-Here is our current scenario.
+# What is causing the food poisoning?
 
-**What we know**: We found a correlation between eating fish and people getting sick, but that isn't really the root of the problem. We have also found that a number of tanks are below the average temperature that might cause issues with fish immune systems.
+At the beginning of Module 2, we set out to discover what was causing food poisoning among our colleagues at our Antarctic base. Let's put everything we've learned about descriptive statistics and data visualization to use to try to hunt down what the problem is.
 
-**What we don't know**: We don't actually know if rates of disease are present above average levels in the tanks, and we definitely don't know what kinds of factors are contributing to this problem (if there is one!).
+## Set-up
 
-Our first challenge is that we can't sample the disease rate in all tanks. It's too expensive and takes too long. Instead of sampling all 1000 tanks, we've tasked our aquaculture scientists to take a sub-sample (50 tanks). Our aquaculture scientists have shared this data with us.
+Let's load the package and data we will need.
 
-Let's scope it out:
+### Packages
 
-```{r, message=FALSE, warning=FALSE}
-# Load tidyverse
+```{r message = FALSE}
 library(tidyverse)
-
-# Load data
-sick_fish <- read_csv("data/fish_sick_data.csv")
-
-# View first few rows
-head(sick_fish)
-```
-
-Oh no, we might need to add a Fahrenheit column... let's practice.
-
-```{r}
-# Make temperature conversion function
-c_to_f <- function(c = NULL){
-  f <- (c * (9/5)) + 32
-  return(f)
-}
-
-# Convert temperatures and put converted value in a new column
-sick_fish <- sick_fish %>% 
-  mutate(avg_daily_tempF = c_to_f(avg_daily_temp))
 ```
 
-## Data Exploration
-
-One of the best ways to get an idea of what our data look like and are telling us is by calculating some summary statistics.
-
-### Group Challenge
-
-Let's use the `tidyverse` to calculate some summary statistics for the data we have been given.
+### Data
 
-Write some code that produces the following values *per species*.
-
--   Mean (average) number of sick fish
--   Mean (average) percentage of sick fish per tank
--   How many tanks
+First, we need our dataset!
 
 ```{r}
-sick_fish %>%
-  group_by(species) %>%
-  summarize(mean_sick = mean(num_sick),             # mean number of fish
-            mean_perc = mean(num_sick/num_fish),    # mean percent of fish
-            n = n())                                # counts
+sick_fish <- read_csv("data/fish_sick_data.csv") 
 ```
 
-Summary statistics (such as means) are helpful for giving us some ideas about our data, but they don't tell us the full story. Plotting data can give us some additional insights.
-
-Before we plot, let's create two different data frames: one for tilapia and one for trout.
+Let's check out our data and remind ourselves what we are working with.
 
 ```{r}
-# New data frame for just tilapia
-sick_tilapia <- sick_fish %>% 
-  filter(species == "tilapia")
-
-# New data frame for just trout
-sick_trout <- sick_fish %>% 
-  filter(species == "trout")
+head(sick_fish)
 ```
 
-Now, let's plot our data to get an idea of the distribution (spread) of the data. What might the distribution of the data tell us that an average can't?
-
-We can make histograms to see if there are some tanks that have a lot of sick fish and are increasing this average or if most of the tanks seem to have about the same number of sick fish.
+## Which Fish?
 
-What would these two different scenarios tell us?
+Last class, we plotted the number of sick fish. Let's remind ourselves what that looked like. Make a plot that compares the numbers of sick fish per species. We actually have a few options!
 
 ```{r}
-# Sick tilapia histogram
-hist(sick_tilapia$num_sick)
-
-# Sick trout histogram
-hist(sick_trout$num_sick)
+ggplot(sick_fish, aes(num_sick, fill = species)) +
+  geom_histogram(alpha = 0.5, position = "identity", bins = 10) +
+  labs(x = "Number of Sick Fish in Tanks",
+       y = "Frequency",
+       fill = "Species") +
+  theme_classic()
 ```
 
-Let's compare values from tanks which are below the temperature cutoffs to those which are above the cutoffs to see if there are major difference or we can figure out some answers.
-
-## Using the `if_else()` function
-
-Before we start calculating means and plotting, let's create a new column in our data frame to indicate whether the tank temperature is above or below the cutoff temperature. We will use our fish-specific data frames to do this.
+### Density
 
-A useful function that we can use when we want to create a new column based on values in another column is the `if_else()` function. It operates the following way:
+Wait a second! Take a look back at the data. There is a "number of fish" column, indicating the total number of fish in the tank, and it looks like those numbers can differ pretty widely.
 
-1.  IF a condition is true, do \[something\].
+We should probably take into account how many fish there are in the tank to begin with. 12 sick fish out of 50 is probably a bigger deal than 12 sick fish out of 100!
 
-2.  IF a condition is false, do \[something ELSE\].
+What we need to do is calculate a density of fish--number of sick fish / number of total fish.
 
 ```{r}
-# Start with tilapia dataframe
-sick_tilapia %>% 
-  mutate(temp_cutoff = if_else(condition = avg_daily_tempF >= 75,
-                               true = "above",
-                               false = "below"))
-```
-
-### Individual Challenge
-
-Practice using the `if_else()` function as we did above. This time, use the sick_trout dataframe. Remember to change the bits of the code that you need to!
-
-```{r}
-# Modify the code below to do the same operation for the trout
-sick_tilapia %>% 
-  mutate(temp_cutoff = if_else(condition = avg_daily_tempF >= 75,
-                               true = "above",
-                               false = "below"))
+# take into account the number of fish in the tank: density of sick fish
+sick_fish <- sick_fish %>% 
+  mutate(density = num_sick/num_fish) 
 ```
 
-## Another Type of Iteration: `for loop`
+#### Small Groups
 
-Another way we could write something like this is by using the `if_else()` function in something we call a `for loop`.
+Let's make sure our conclusions about trout being the true culprits still hold when we account for the total number of fish in the tank.
 
-Before we do that, though, let's talk through the general structure of a `for loop`. It essentially says for each value in a list, do a certain task. The "loop" is because we are "looping" through a list of values, performing the task for one value then looping back to the beginning to perform the task for the next value. We type the "task" within curly brackets, similar to a function that we write.
+Work in small groups to do the following:
 
-```{r}
-# for loop structure:
-
-# for ([value] in [list]){
-  # Do the things I've written between the curly brackets
-# }
-
-# for loop example with years
-for (year in 2020:2022){
-  print(paste("The year is", year))
-}
-
-# What is happening in this loop?
-
-# We start with year = 2020,
-# So the for loop will print "The year is 2020"
-# We then go back to the beginning and do this again,
-# this time year = 2021
-# So now the for loop will print "The year is 2021"
-# The last value in our loop is year = 2022
-# And as you would expect, that would give "The year is 2022"
-```
-
-Now that we know the general structure of a `for loop`, we can combine it with the `if_else()` function to create a new column.
+-   find the average number and standard deviation of sick fish for both species
+-   make a plot that compares the distributions of sick fish numbers for both species (you have multiple options here!)
 
 ```{r}
-# Create an empty column in sick_tilapia
-sick_tilapia$temp_cutoff <- NA
-
-# What do we want to do?
-
-# for each value (i) in a list going from 1 to the number of rows in sick_tilapia,
-# put either "above" or "below" in the same place as (i) in the dataframe, but this time in the new column
-
-# In this case, i is equivalent to the row 
+sick_fish %>% 
+  group_by(species) %>% 
+  summarize(mean_sick_fish = mean(density),
+            sd_sick_fish = sd(density))
+
+ggplot(sick_fish, aes(species, density, color = species)) +
+  geom_boxplot() + 
+  geom_jitter(alpha = 0.5, width = 0.1) +
+  labs(x = "Species",
+       y = "Density of Sick Fish in Tanks",
+       color = "Species") +
+  theme_light()
+
+# histogram
+ggplot(sick_fish, aes(density, fill = species)) +
+  geom_histogram(alpha = 0.5, bins = 15)
 
-# So this loop will repeat however many rows are present in sick_tilapia
-for (i in 1:nrow(sick_tilapia)){
-  
-  sick_tilapia$temp_cutoff[i] <- 
-    if_else(condition = sick_tilapia$avg_daily_tempF[i] >= 75,
-            true = "above",
-            false = "below")
-  
-}
 ```
 
-*Note: I will never ask you to write a for loop completely from scratch. I might have you copy and paste one or change some values in one, but you won't have to write one out yourself.*
+Uh oh...the trout densities look even worse than just the number of sick fish. We need to take a closer look at what is going on in the trout tanks!
 
-### Group Challenge
-
-Try your hand at using the `for loop` we wrote above to create a new temperature cutoff column in the `sick_trout` data frame. Remember, the cutoff for trout was 59°F.
+We should create a data frame that only contains trout to work with for the rest of our analyses. Take a few minutes to work on that; call it `sick_trout`.
 
 ```{r}
-# New column
-sick_trout$temp_cutoff <- NA
-
-# Same operation done on trout as done on tilapia above
-for (i in 1:nrow(sick_trout)){
-  
-  sick_trout$temp_cutoff[i] <- 
-    if_else(condition = sick_trout$avg_daily_tempF[i] >= 59,
-            true = "above",
-            false = "below")
-  
-}
+sick_trout <- sick_fish %>% 
+  filter(species == "trout")
 ```
 
-### Why `for loops`?
+## What Environmental Factor?
 
-Like with our last lesson about functions, I've asked you to perform a task in a new and complicated way than you need to for that task. Why?
+Let's talk about fish physiology for a minute.
 
-You'll find some examples [here](https://github.com/bleds22e/PP_shifts/blob/master/scripts/functions.R) in code written for my Ph.D. dissertation.
+Take a look back at the data frame. Which columns are environmental variables that could be driving the issues?
 
-## Back to Data Exploration
+Are those columns numeric or categorical? What plot type have we talked about that might help us find a relationship between density and each of these variables (one at a time...)?
 
-We now have 2 data frames, one with tilapia data and one with trout data. Each data frame also has a new column called `temp_cutoff`. On your own or with a partner, start exploring the data to figure out if there are differences between warm and cold tilapia and warm and cold trout.
-
-Can you pinpoint an issue? Let's start by comparing means.
+In small groups, make plots using `density` column in the `sick_trout` data to try to figure out which environmental factor is causing problems in the trout. Treat your variables as numeric.
 
 ```{r}
-# Mean number of sick tilapia per temperature cutoff
-sick_tilapia %>% 
-  group_by(temp_cutoff) %>% 
-  summarise(mean_sick = mean(num_sick))
-
-# Mean number of sick tilapia per temperature cutoff
-sick_trout %>% 
-  group_by(temp_cutoff) %>% 
-  summarise(mean_sick = mean(num_sick))
+ggplot(sick_trout, aes(x = density, y = num_fish)) +
+  #geom_smooth(method = 'lm', se = FALSE) +
+  geom_point() 
 
-# Alternative:
+ggplot(sick_trout, aes(x = density, y = avg_daily_temp)) +
+  #geom_smooth(method = 'lm', se = FALSE) +
+  geom_point()
 
-# Put tilapia and trout data back together
-sick_fish <- bind_rows(sick_tilapia, sick_trout)
-
-# Find mean number of sick fish for each species, for each temperature cutoff
-sick_fish %>% 
-  group_by(species, temp_cutoff) %>% 
-  summarise(mean_sick = mean(num_sick))
+ggplot(sick_trout, aes(density, day_length)) +
+  #geom_smooth(method = 'lm', se = FALSE) +
+  geom_point() 
 ```
 
-Not much popping out in the means. Next thing to check would be histograms of the number of sick fish for both species, above and below the cutoffs.
-
-For now, I would recommend making 4 different data frames (this isn't "best practice" but it is really helpful while you are learning).
-
-```{r}
-# Separate tilapia below temperature cutoff
-cold_tilapia <- sick_tilapia %>% 
-  filter(temp_cutoff == "below")
-
-# Separate tilapia above temperature cutoff
-warm_tilapia <- sick_tilapia %>% 
-  filter(temp_cutoff == "above")
-
-# Separate trout below temperature cutoff
-cold_trout <- sick_trout %>% 
-  filter(temp_cutoff == "below")
-
-# Separate trout above temperature cutoff
-warm_trout <- sick_trout %>% 
-  filter(temp_cutoff == "above")
-
-# Tilapia histograms
-hist(cold_tilapia$num_sick)
-hist(warm_tilapia$num_sick)
-
-# Trout histograms
-hist(cold_trout$num_sick)
-hist(warm_trout$num_sick)
-```
+What do we think is the environmental driver causing issues with the trout?
\ No newline at end of file
diff --git a/modules/module_2/module2_6.qmd b/modules/module_2/module2_6.qmd
deleted file mode 100644
index a9758ad..0000000
--- a/modules/module_2/module2_6.qmd
+++ /dev/null
@@ -1,302 +0,0 @@
----
-title: "2.6: Exploring `geom` Functions"
----
-
-## Plotting in `ggplot2`
-
-### Data Visualization Types and When to Use Them
-
-#### Scavenger Hunt
-
-Head over to [data-to-viz.com](https://www.data-to-viz.com/) and do some exploration! In your groups, choose one of the following types of plots. Hunt down as many locations in the flow chart where your plot type is found as you can find.
-
--   histogram: `geom_histogram()`
--   scatter plot: `geom_point()`
--   box-and-whisker: `geom_boxplot()` + `geom_jitter()`
-
-If you've finished, click on your plot type and read more about it. Check out the examples that were made in R, including many using `ggplot2`. Take note of the following:
-
--   which `geom` function is being used to create the plot
--   you'll see a number of other things in the `ggplot` code, some which will be new. What are they? Any idea what they are doing?
-
-### Using `geom` Functions to Make Plots
-
-Let's practice making different kinds of plots with various `geom` functions to see how they work.
-
-```{r, message=FALSE, warning=FALSE}
-library(tidyverse)
-fish <- read_csv("data/fish_sick_data.csv")
-```
-
-#### Plotting in `ggplot2`
-
-First, let's remind ourselves of the general structure of how we make plots using the `ggplot2` syntax.
-
-```{r}
-# ggplot(data = <DATA>, mapping = aes(x = <COLUMN1>, y = <COLUMN2)) +
-# geom_function() +
-# labs() +
-# theme()
-```
-
-### Histograms
-
-As we learned last modules, histograms are plots which let us look at *one continuous variable.*
-
-They help us get a feel for the distribution of that data. To make histograms in `ggplot2`, we use the `geom_histogram()` function. Let's look at the number of fish.
-
-```{r}
-ggplot(fish, aes(num_fish)) +
-  geom_histogram(bins = 20)
-
-# we can change the number of bins (essentially, the number of columns) by modifying the bins argument in the geom_histogram function
-```
-
-#### Let's Practice
-
-Make a histogram of the number of sick fish in the tanks. Create the histogram with 10 bins (10 groupings).
-
-```{r}
-ggplot(fish, aes(num_sick)) +
-  geom_histogram(bins = 10)
-```
-
-### Multiple Histograms (with `geom_histogram()`)
-
-When we create a multiple histogram, we have to add one additional argument to `geom_histogram()`. Let's see what happens if we just specify `fill`.
-
-```{r}
-ggplot(fish, aes(num_fish, fill = species)) +
-  geom_histogram(bins = 10)
-```
-
-What is happening in the column with both green and red? Perhaps the teal histogram is in front of the red histogram and as blocking us from seeing some red?
-
-Let's change the transparency using an argument called `alpha`, which allows us to make layers transparent. The scale for `alpha` goes from 0 (completely transparent) to 1 (not transparent at all).
-
-We can set the transparency to 0.5 to see if there is any overlap.
-
-```{r}
-ggplot(fish, aes(num_fish, fill = species)) +
-  geom_histogram(bins = 10, alpha = 0.5)
-```
-
-Not too much changed. It still doesn't look like we can see any red points behind the teal. Perhaps the teal values and red values are stacked on top of one another?
-
-Let's take a look at what happens when we add the argument `position = "identity"`.
-
-```{r}
-ggplot(fish, aes(num_fish, fill = species)) +
-  geom_histogram(bins = 10, alpha = 0.5, position = "identity")
-```
-
-Aha! This is different from above. Instead of red being stacked vertically on top of teal, we can now see that the red values start at 0 on the y-axis and are overlapping with the teal.
-
-The `position = "identity"` argument tell `geom_histogram` to plot the data for each group starting from 0 in the y-axis rather than stacking values from the same group, which is the default.
-
-Any time we plot a multiple histogram, we need to change the transparency and add `position = "identity"` when creating a multiple histogram; if we don't, we won't see potential overlap!
-
-#### Let's Practice!
-
-Make a multiple histogram of the number of sick fish per species. Make sure your plot has 10 bins, is partially transparent, and the data are *not* stacked.
-
-```{r}
-ggplot(fish, aes(num_sick, fill = species)) +
-  geom_histogram(bins = 10, alpha = 0.5, position = "identity")
-```
-
-### The powerful and pesky `aes()` function
-
-A quick note about the `aes()` function. It's one of the more confusing bits of `ggplot2`.
-
-When do I put the `color` (or `size` or `linetype` or `fill` or whatever) inside the `aes()` function versus in the `geom` function but outside of `aes()`? When we made our density plots, why did the `color` argument go inside of `aes()` but `alpha` went outside?
-
-Essentially, it boils down to this:
-
--   if you want something (color, size, etc.) on your plot to change based on a **variable** from a data frame, you will want to put the argument *within* the `aes()` function.
-
--   if you want something (color, size, etc.) on the plot to be **constant**, you will specify it *outside* of the function.
-
-For some additional examples and explanation, check out [this Stack Overflow page](https://stackoverflow.com/questions/41863049/when-does-the-argument-go-inside-or-outside-aes).
-
-### Scatter Plot
-
-As a reminder, we use the `geom_point()` function to make a scatter plot of the relationship between *two continuous variables*.
-
-```{r}
-ggplot(fish, aes(avg_daily_temp, num_fish)) +
-  geom_point()
-```
-
-#### Let's Practice: Multiple Scatterplot
-
-Using what you've learned about making histograms, see if you can create a "multiple scatterplot," where the color of the points are determined by the fish species.
-
-*Hint: you'll want to use an argument called `color`*.
-
-```{r}
-ggplot(fish, aes(avg_daily_temp, num_fish, color = species)) +
-  geom_point()
-```
-
-### Box-and-Whisker Plots
-
-Box-and whisker-plots (also known as box plots) are another great option for looking at one *continuous* variable and one or more *categorical* variables. They are particularly nice when you want to see measures of central tendency and variation in the same plot.
-
-Let's build one and then talk through what each component means. We use `geom_boxplot` to make these types of plots.
-
-```{r}
-ggplot(fish, aes(species, num_fish, color = species)) +
-  geom_boxplot() 
-```
-
-So what does the box represent? And the whiskers?
-
--   the **box** represents the *middle 50% of the values* in the data set.
--   the line that runs through the middle of the box represents the *median (**middle value**)* of the data
--   the **whiskers** represent the *spread of the data* (we won't get into the mathematical details of exactly how they are calculated) and are roughly comparable to 95% confidence intervals (we will cover this in another module)
--   values that fall outside of the whiskers can be considered outliers and are plotted individually
-
-### Layering
-
-One of the beautiful parts of working with `ggplot2` is that you can add multiple layers to each plot.
-
-One of the key things missing from box-and-whisker plots is any indication of how many data points we have. In the plot above, there could be 5 tanks per species or 500 tanks per species.
-
-How can we add an indication of how many points there are? We can layer each individual data point on top of the boxes!
-
-```{r}
-ggplot(fish, aes(species, num_fish, color = species)) +
-  geom_boxplot() +
-  geom_point(alpha = 0.5)
-```
-
-This is nice, but there is still some overlap in points that makes it hard for us to see exactly how many points are there.
-
-The `geom_jitter()` function is a special version of `geom_point()`. It adds a little bit of randomness to the points (both horizontally and vertically) so that they don't overlap as much.
-
-We can control how much randomness we allow with the `width` and/or `height` arguments. I usually ignore `height` and set the `width` argument to 0.1.
-
-```{r}
-ggplot(fish, aes(species, num_fish, color = species)) +
-  geom_boxplot() +
-  geom_jitter(alpha = 0.5, width = 0.1) 
-```
-
-That is looking really nice! We can keep improving it, though, with better labels for the axes and the legend as well as a nice `theme`.
-
-```{r}
-ggplot(fish, aes(species, num_fish, color = species)) +
-  geom_boxplot() +
-  geom_jitter(alpha = 0.5, width = 0.1) +
-  labs(x = "Species",
-       y = "Number of Fish Per Tank",
-       color = "Species") +
-  theme_light()
-```
-
-### `color` and `fill`
-
-We have used two different arguments in the `aes()` function to specify that we want to colors in our plots to change based on one of the columns in our dataset. When do you want to use which one?
-
-#### `color`
-
-For the most part, the `color` argument refers to the color of the lines in a plot (e.g., in box plots, the colors of the lines change).
-
-The one exception to this is in `geom_point()`. We change the color of the points with the `color` argument, as well.
-
-#### `fill`
-
-When there is space that we want to fill in with color depending on the values in a column, we want to use the `fill` argument.
-
-For histograms, the `fill` argument changes the color inside the bins. If we use `color` in a histogram instead, only the outlines of the columns will change, but the columns will remain filled with gray.
-
-In boxplots, the inside area of the box will be filled with color, but the lines will stay black.
-
-### In Labels
-
-If we have specified either `color` or `fill` (or both) in the `aes()` function, `ggplot` will automatically create a legend for us. The key is determined by whichever argument we used.
-
-So, if you use the `color` argument in the `aes()` function, you would want to use the `color` argument in the `labs()` function to change the title of the legend. If you used the `fill` argument, you would then use the `fill` argument in the `labs()` function.
-
-If you have both `color` and `fill` in `aes()`, you will need to add both arguments to the `labs()` function. If you give the same label to each, it will create one key.
-
-#### Let's Practice!
-
-Make 2 different types of plots with the same data: the average daily temperature of the tanks and fish species.
-
-First, think about the variables we are plotting. How many are there? Are they qualitative or quantitative?
-
-Based on those answers, determine which plot types you can produce for those variable types.
-
-Now, make your two plot! To each, add labels and a theme.
-
-```{r}
-# Boxplot
-ggplot(fish, aes(species, avg_daily_temp, color = species)) +
-  geom_boxplot() +
-  geom_jitter(alpha = 0.5, width = 0.1) +
-  labs(x = "Average Daily Temp (C)",
-       y = "Frequency", 
-       color = "Fish Species") +
-  theme_bw()
-
-# Histogram
-ggplot(fish, aes(avg_daily_temp, fill = species)) +
-  geom_histogram(bins = 10, alpha = 0.5, position = "identity") + 
-  labs(x = "Average Daily Temp (C)", 
-       y = "Frequency",
-       fill = "Fish Species") +
-  theme_bw()
-```
-
-Finished? Let's add another variable. Now, we want to plot average daily temperatures, the number of sick fish, and the fish species. Work through the same steps as above.
-
-```{r}
-ggplot(fish, aes(avg_daily_temp, num_sick, color = species)) +
-  geom_point(alpha = 0.5) +
-  labs(x = "Average Daily Temp (C)",
-       y = "Number of Sick Fish per Tank",
-       color = "Fish Species") +
-  theme_light()
-```
-
-### Summary: Data Visualization Types and When to Use Them
-
-Let's summarize some of what we've learned in this module
-
-#### Histogram or Density Plot
-
-Good for looking at the distribution of one continuous variable
-
--   one continuous variable (x-axis)
-
-#### Multiple Histogram or Multiple Density Plot
-
-Good for looking at differences in the distributions of one continuous variable based on a categorical variable
-
--   one continuous variable (x-axis)
--   one categorical variable via the `fill` or `color` argument in the `aes()` function
--   we always want to add transparency (`alpha`) and, for histograms, `position = "identity"`
-
-#### Scatter Plot
-
-Good for looking for the relationship between two continuous variables
-
--   two continuous variables (x-axis and y-axis)
--   can add in a categorical variable via `aes()`, but the main relationship is between the two continuous variables
-
-#### Box Plot
-
-Good for looking at measures of central tendency and variation for a continuous variable and the differences between those measures between categories
-
--   one continuous variable (y-axis)
--   at least one categorical (x-axis and additional via `aes()`)
-
-#### Layering
-
-We can add multiple layers to ggplots, which is part of what makes them so useful!
-
--   we can add multiple `geom` functions to a single plot
--   we use the `labs()` function to rename axes labels and legends
--   we use a `theme` function to make the plot more aesthetically pleasing and easier to understand
diff --git a/modules/module_2/module2_7.qmd b/modules/module_2/module2_7.qmd
deleted file mode 100644
index 4faf746..0000000
--- a/modules/module_2/module2_7.qmd
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: "2.7: Wrap-Up"
----
-
-## What is causing the food poisoning?
-
-At the beginning of Module 2, we set out to discover what was causing food poisoning among our colleagues at our Antarctic base. Let's put everything we've learned about descriptive statistics and data visualization to use to try to hunt down what the problem is.
-
-### Set-up
-
-Let's load the package and data we will need.
-
-#### Packages
-
-```{r, message=FALSE, warning=FALSE}
-# Load library
-library(tidyverse)
-```
-
-#### Data
-
-First, we need our dataset!
-
-```{r, message=FALSE, warning=FALSE}
-# Load dataset
-sick_fish <- read_csv("data/fish_sick_data.csv") 
-```
-
-Let's check out our data and remind ourselves what we are working with.
-
-```{r, message=FALSE, warning=FALSE}
-# View first few rows of data
-head(sick_fish)
-```
-
-### Which Fish?
-
-Last class, we plotted the number of sick fish. Let's remind ourselves what that looked like. Make a plot that compares the numbers of sick fish per species. We actually have a few options!
-
-```{r}
-# Density plot
-ggplot(sick_fish, aes(num_sick, color = species)) +
-  geom_density(alpha = 0.5) +
-  labs(x = "Number of Sick Fish in Tanks",
-       y = "Density",
-       fill = "Species") +
-  theme_classic()
-```
-
-#### Density
-
-Wait a second! Take a look back at the data. There is a "number of fish" column, indicating the total number of fish in the tank, and it looks like those numbers can differ pretty widely.
-
-We should probably take into account how many fish there are in the tank to begin with. 12 sick fish out of 50 is probably a bigger deal than 12 sick fish out of 100!
-
-What we need to do is calculate a density of fish --- number of sick fish / number of total fish.
-
-```{r}
-# Take into account the number of fish in the tank: 
-# Density of sick fish
-sick_fish <- sick_fish %>% 
-  mutate(density = num_sick/num_fish) 
-```
-
-#### Small Groups
-
-Let's make sure our conclusions about trout being the true culprits still hold when we account for the total number of fish in the tank.
-
-Work in small groups to do the following:
-
--   Find the average number and standard deviation of sick fish for both species
--   Make a plot that compares the distributions of sick fish numbers for both species (you have multiple options here!)
-
-```{r}
-# Mean and standard deviation of density of sick fish for each species
-sick_fish %>% 
-  group_by(species) %>% 
-  summarize(mean_sick_fish = mean(density),
-            sd_sick_fish = sd(density))
-
-# Plot density sick fish for each species
-ggplot(sick_fish, aes(species, density, color = species)) +
-  geom_boxplot() + 
-  geom_jitter(alpha = 0.5, width = 0.1) +
-  labs(x = "Species",
-       y = "Density of Sick Fish in Tanks",
-       color = "Species") +
-  theme_light()
-
-# And a histogram
-ggplot(sick_fish, aes(density, fill = species)) +
-  geom_histogram(alpha = 0.5, bins = 15)
-
-```
-
-Uh oh...the trout densities look even worse than just the number of sick fish. We need to take a closer look at what is going on in the trout tanks!
-
-We should create a data frame that only contains trout to work with for the rest of our analyses. Take a few minutes to work on that; call it `sick_trout`.
-
-```{r}
-# Only trout
-sick_trout <- sick_fish %>% 
-  filter(species == "trout")
-```
-
-### What Environmental Factor?
-
-Take a look back at the data frame. Which columns are environmental variables that could be driving the issues?
-
-Are those columns continuous or categorical? What plot type have we talked about that might help us find a relationship between density and each of these variables (one at a time...)?
-
-In small groups, make plots using `density` column in the `sick_trout` data to try to figure out which environmental factor is causing problems in the trout. Treat your variables as continuous.
-
-```{r}
-# Is it the number of fish?
-ggplot(sick_trout, aes(x = density, y = num_fish)) +
-  #geom_smooth(method = 'lm', se = FALSE) +
-  geom_point() 
-
-# Is it the average daily temperature?
-ggplot(sick_trout, aes(x = density, y = avg_daily_temp)) +
-  #geom_smooth(method = 'lm', se = FALSE) +
-  geom_point()
-
-# Is it the day length?
-ggplot(sick_trout, aes(density, day_length)) +
-  #geom_smooth(method = 'lm', se = FALSE) +
-  geom_point() 
-```
-
-What do we think is the environmental driver causing issues with the trout?
diff --git a/modules/module_3/assets/coefficient_correlation.jpg b/modules/module_3/assets/coefficient_correlation.jpg
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diff --git a/modules/module_3/data/hairgrass_data.csv b/modules/module_3/data/hairgrass_data.csv
new file mode 100644
index 0000000..1b65745
--- /dev/null
+++ b/modules/module_3/data/hairgrass_data.csv
@@ -0,0 +1,241 @@
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+90,15.50213804692819,4.411750420461012,11,1.507214834558902,7.157267372777214,6.611848870618176,28.05201289836751
+106,19.873399524090743,4.106297669400747,12,1.5437407102173115,7.006633589543926,6.408199112216008,29.948242093321145
+112,20.12406987989767,2.764458557643955,17,1.4561379979453128,8.15015309117781,6.516736722613446,32.54647571494097
+142,22.80501908641208,3.158120544310131,22,1.421971324909613,11.834208986316995,6.429579561930371,29.253298121325763
+99,11.80551965595825,4.654127277699687,20,1.7485519368498994,9.8432803764099,6.147894238419396,32.14538596372068
+91,16.87027520294484,3.8396924435080555,18,1.6623114876340348,7.66756389838183,7.021094383104254,31.676559826451506
+119,24.846642190110945,4.427457648899815,13,1.3894391129410293,8.378332086272227,7.053204024628286,30.48675799251019
+94,20.214500359793984,2.342354436108987,15,1.2736278683911213,5.1458823457111755,6.677802467971101,30.127548403485484
+123,9.799331014543348,5.690716496123596,13,1.4057924471415175,5.603625060634945,5.292193096272695,27.398720467582528
+133,21.429761439102126,5.318155243846309,7,1.4863652672428036,10.117965267691668,6.567939027490995,30.78765493692407
+117,10.585861652150252,5.2928691521586115,23,1.4361238043297995,4.52329541260925,5.891547328362287,29.8636623731379
+129,19.246444411381948,4.029200751843481,21,1.5282217410364718,7.933255954682819,6.159160797375988,30.270323998762294
+115,19.333780628473257,6.245671511033569,10,1.6760019568613715,7.05119210903362,6.333700535624937,28.96893849204138
+152,26.876984103501368,7.567456816416547,14,1.5946099543476282,7.352403785226472,6.184886462094875,27.450875923660586
+135,14.911626997136015,5.544007794062559,19,1.3644693659914833,7.3134433377898596,6.089020444584258,30.69528262230951
+126,24.04688744646781,5.238752785917561,18,1.5777729770222526,7.406754613888678,6.492125857228103,26.890797505146377
diff --git a/modules/module_4/data/site_changes.csv b/modules/module_3/data/site_changes.csv
similarity index 100%
rename from modules/module_4/data/site_changes.csv
rename to modules/module_3/data/site_changes.csv
diff --git a/modules/module_3/module3_1.qmd b/modules/module_3/module3_1.qmd
index df513c9..b7d717e 100644
--- a/modules/module_3/module3_1.qmd
+++ b/modules/module_3/module3_1.qmd
@@ -1,23 +1,29 @@
 ---
-title: "3.1: Leopard Seals"
+title: '3.1: Leopard Seals'
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
-## Introduction to the Problem
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Introduction to the Problem
 
-### The Challenge
+## The Challenge
 
 Given the issues with our farmed fish, we will need to supplement our diets with some fish that are wild-caught for a while. These fish are caught by members of Team Antarctica, but there is quite a bit of risk involved.
 
-![](assets/seal2.jpeg)
+![](assets/seal2.jpeg){width="50%"} 
 
 One of the main hazards to these fishing teams are [leopard seals](https://en.wikipedia.org/wiki/Leopard_seal). They're apex predators in this ecosystem and are quite large, averaging 2.4--3.5 m (7.9--11.5 ft) in length and 200-600 kilograms (440-1,320 lb) in weight. Due to their large size and predatory nature, they can cause serious injury, especially when large schools of fish are involved ([a researcher was killed by a leopard seal in Antarctica in 2003](https://www.theguardian.com/uk/2003/jul/24/science.highereducation)).
 
-![](assets/seal3.jpeg)
+![](assets/seal3.jpeg){width="50%"}
 
 One way we're working to remove some of this danger is by working with data from large mammal researchers. They have been tracking leopard seals using radio collars, one of many ways to track wildlife.
 
-[Wildlife Tracking Powerpoint](https://docs.google.com/presentation/d/1n-rPHVoB9tydIj_yEQ1TxGTHXn6VWaliRbOszRnbCoc/edit#slide=id.g16730a957e3_0_20)
-
 They have radio collared on a number of seals that live in areas that could be fished. Fishing boats are equipped with radios that can detect the presence of seals in the area and avoid high-risk sites.
 
 In recent months, a problem has arisen. Some of the collars are failing, leading to some very close calls! While attempting to untangle a net, one team member was pulled into the water when a seal lunged at some fish trapped in the net.
@@ -26,19 +32,16 @@ The large mammal team replaces collars frequently based on the two manufacturers
 
 We've been tasked with determining why the units are failing and if we can tie it to a particular manufacturer.
 
-### The Data
+## The Data
 
-Our data on collars is in a file called "collar_data.csv." Read in this data and our tidyverse.
+Our data on collars is in a file called "collar_data.csv."
 
-```{r message=FALSE, warning=FALSE}
-# Load library
+```{r message = FALSE}
 library(tidyverse)
-
-# Load data
 collars <- read_csv("data/collar_data.csv")
 ```
 
-## Explore the Data
+### Explore the Data
 
 1.  Two manufacturers
 2.  Battery life: the average number of days a particular collar lasts (this is recorded in the unit and stored when the battery dies)
@@ -49,34 +52,28 @@ collars <- read_csv("data/collar_data.csv")
 
 Let's practice. Write some code to do the following:
 
--   Calculate the mean and standard deviation for:
+-   calculate the mean and sd for:
     -   battery life
     -   signal distance
--   Do this for both manufacturers
+-   do this for both manufacturers
 
 ```{r summarize}
 bat_life_and_signal <- collars %>%
-  group_by(maker) %>%                                 # Group by manufacturer
-  summarize(bat_life_mean = mean(battery_life),       # Mean (battery)
-            bat_life_sd = sd(battery_life),           # SD (battery)
-            signal_mean = mean(signal_distance),      # Mean (signal)
-            signal_sd = min(signal_distance))         # SD (signal)
-
-# View results
+  group_by(maker) %>%
+  summarize(bat_life_mean = mean(battery_life),
+            bat_life_sd = sd(battery_life),
+            signal_mean = mean(signal_distance),
+            signal_sd = sd(signal_distance)) 
 bat_life_and_signal
 ```
 
 We also might want to count up how many collar failures are attributed to each maker.
 
 ```{r}
-# Number of failed collars per maker
-
-# Option 1: summarize + sum
 collars %>% 
   group_by(maker) %>% 
   summarize(fail_count = sum(fail))
 
-# Option 2: count
 collars %>% 
   group_by(maker) %>% 
   count(fail)
@@ -90,16 +87,14 @@ After summarizing our data, our next step in our data exploration is usually dat
 
 ### One Continuous Variable at a Time
 
-What types of plots can we use to explore one continuous variable (and one categorical variable --- in this case, maker)?
+What types of plots can we use to explore one continuous variable (and one categorical variable--in this case, maker)?
 
--   Multiple histogram
--   Multiple density plot
--   Box-and-whisker plot
+-   multiple histogram
+-   box-and-whisker plot
 
 We can start with a histogram of the battery life of the collars.
 
 ```{r}
-# Histogram
 ggplot(collars, aes(battery_life)) +
   geom_histogram()
 ```
@@ -107,7 +102,6 @@ ggplot(collars, aes(battery_life)) +
 We probably want to add in the collar maker to see some differences there.
 
 ```{r}
-# Histogram by maker
 ggplot(collars, aes(battery_life, fill = maker)) +
   geom_histogram(alpha = 0.5, position = "identity")
 ```
@@ -120,26 +114,23 @@ We will use the `geom_vline()` function to add a vertical line onto our plot at
 
 Let's take a look at the help file for `geom_vline()` to see what arguments it takes. Scroll down to the "Aesthetics" section.
 
-```{r, eval=FALSE}
-# What is geom_vline?
-? geom_vline
+```{r}
+?geom_vline
 ```
 
 It looks like we will need to specify the data and the x-intercept, the place on the x-axis that matches the mean value.
 
 ```{r}
-# Histogram by maker, with mean line
 ggplot(collars, aes(battery_life, fill = maker)) +
   geom_histogram(alpha = 0.5, position = "identity") +
   geom_vline(data = bat_life_and_signal, aes(xintercept = bat_life_mean))
 ```
 
-Nice! We have name the data argument in the `geom_vline()` function because otherwise the function assumes we still want to use the data from the `ggplot()` function and gets confused. We are essentially overwriting that data and telling `geom_vline()` that we need to use a different data frame.
+Nice! We have to name the data argument in the `geom_vline()` function because otherwise the function assumes we still want to use the data from the `ggplot()` function and gets confused. We are essentially overwriting that data and telling `geom_vline()` that we need to use a different data frame.
 
 Let's wrap this up and make it extra nice by adding color, labels, and a theme.
 
 ```{r}
-# Histogram by maker, with mean line and labels
 ggplot(collars, aes(battery_life, fill = maker)) +
   geom_histogram(alpha = 0.5, position = "identity") +
   geom_vline(data = bat_life_and_signal, aes(xintercept = bat_life_mean, color = maker)) + 
@@ -155,7 +146,6 @@ ggplot(collars, aes(battery_life, fill = maker)) +
 Make the same plot that we made above expect for the signal distance.
 
 ```{r}
-# Same as above, but for signal distance
 ggplot(collars, aes(signal_distance, fill = maker)) +
   geom_histogram(alpha = 0.5, position = "identity") +
   geom_vline(data = bat_life_and_signal, aes(xintercept = signal_mean, color = maker)) + 
@@ -171,11 +161,10 @@ ggplot(collars, aes(signal_distance, fill = maker)) +
 We've explored both battery life and signal distance individually. Now let's bring them together into one plot to see what the relationship between them is like. What type of plot will be use for that?
 
 ```{r}
-# Scatterplot
 ggplot(collars, aes(x = signal_distance, y = battery_life, color = maker)) +
   geom_point()
 ```
 
 How do we interpret this plot? What is it telling us about the relationship between signal distance and battery life? What is it telling us about that relationship between the two collar makers?
 
-As it turns out, this trade-off between signal distance and battery length is very real when it comes to wildlife tracking! Why do you think that is?
+As it turns out, this trade-off between signal distance and battery length is very real when it comes to wildlife tracking! Why do you think that is?
\ No newline at end of file
diff --git a/modules/module_3/module3_2.qmd b/modules/module_3/module3_2.qmd
index 3100fc5..bcfb63e 100644
--- a/modules/module_3/module3_2.qmd
+++ b/modules/module_3/module3_2.qmd
@@ -1,5 +1,9 @@
 ---
 title: "3.2: T-Tests"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
 ## Comparing Two Means
@@ -57,7 +61,7 @@ Want more info on independent vs. dependent variables? Check out [this link](htt
 
 The first step in running any statistical test (t-test or otherwise) is *hypothesis testing*.
 
-We build the our hypotheses around our independent and dependent variables.
+We build our hypotheses around our independent and dependent variables.
 
 In this case, we want to know if there is a *meaningful* (read: statistical) difference between each collar manufacturer in their average values for battery life. To start, let's identify our independent and dependent variables:
 
@@ -69,7 +73,7 @@ Based on our question, we can now set up two different statistical hypotheses: t
 The null hypothesis always states that there is no difference or no relationship. We can think of the null hypothesis as our starting place---this is our default assumption. The alternative hypothesis is, well, the alternative to the null hypothesis. For example:
 
 -   **Null Hypothesis** ($H_{0}$): There is *no* difference in the means of battery life between the two collar makers
--   **Alternative Hypothesis** ($H_{A}$): There *is* no difference in the means of battery life between the two collar makers
+-   **Alternative Hypothesis** ($H_{A}$): There *is* a difference in the mean battery life between the two collar makers
 
 How can we determine whether we should *not reject* ("accept") or *reject* the null hypothesis? That's where statistics come in.
 
@@ -77,7 +81,7 @@ How can we determine whether we should *not reject* ("accept") or *reject* the n
 
 ### Running a t-test
 
-There is a set of statistical tools that can help us whether or not there is a difference between 2 means. This group of tools are called t-tests.
+There is a set of statistical tools that can help us determine whether or not there is a difference between 2 means. This group of tools are called t-tests.
 
 Let's briefly remind ourselves of the logic here.
 
@@ -89,7 +93,7 @@ Let's briefly remind ourselves of the logic here.
 3.  However, because we only have data from a random *sample* of collars, there will be some variation in our numbers due to sampling error.
 4.  We want to know if the difference in means is due to sampling error alone or due to an actual difference between manufacturers.
 
-The t-test allows us to determine if the means are different due to the random variation or if it represents an actual difference.
+The t-test helps us determine whether the observed difference in means is larger than what we would expect from random sampling variation alone.
 
 Let's run some code to perform our first t-test!
 
@@ -163,4 +167,4 @@ t.test(signal_distance ~ maker, data = collars)
 -   Is the p-value below our cut-off for significance (0.05)? ***Yes, p \< 0.05***
 -   Is our result statistically significant? ***Yes!***
 -   Does that mean we should or should not reject the null hypothesis? ***REJECT***
--   So... is there a "real" difference in the means between the two companies or not? ***Yes, there is a real difference***
+-   So... is there a "real" difference in the means between the two companies or not? ***Yes, there is a real difference***
\ No newline at end of file
diff --git a/modules/module_3/module3_3.qmd b/modules/module_3/module3_3.qmd
index 4179bfa..58c2b9f 100644
--- a/modules/module_3/module3_3.qmd
+++ b/modules/module_3/module3_3.qmd
@@ -1,7 +1,17 @@
 ---
 title: "3.3: Comparing (Multiple) Means"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Comparing (Multiple) Means
+
 So far in this module, we have discussed comparing the means of two groups in multiple ways: numerically, visually, and statistically. Specifically, we learned about running t-tests to statistically compare the means of two groups.
 
 What if we have more than 2 groups, though? Often, we have 3, 4, 12...any number of groups that we might want to compare against each other. How do we do that?
@@ -18,8 +28,8 @@ The group of statistical tests called ANOVAs are extensions of the t-test, using
 
 To run an ANOVA, the following things need to be true:
 
--   The variable that we use to create our groups (independent variable) is categorical.
--   The variable that we want to compare between groups (dependent or response variable) is numerical.
+-   The variable that we use to create our groups (independent variable) is qualitative (categorical).
+-   The variable that we want to compare between groups (dependent or response variable) is quantitative (numerical).
 
 These two aspects above might sound familiar, because they also need to be true for t-tests!
 
@@ -30,8 +40,8 @@ These two aspects above might sound familiar, because they also need to be true
 
 Like t-tests, ANOVAs are another example of statistical hypothesis testing. We always want to formulate a null hypothesis and an alternative hypothesis. The generic version of these for any ANOVA are:
 
-**Null Hypothesis** ($H_{0}$): The means of the populations we sampled from are *all equal*.\
-**Alternative Hypothesis** ($H_{A}$): The means of the populations we sampled from *differ from each other*.
+**Null Hypothesis** ($H_{0}$): The means of the populations we sampled from are all equal.\
+**Alternative Hypothesis** ($H_{A}$): The means of the populations we sampled from differ from each other.
 
 ## Let's Run an Example
 
@@ -45,11 +55,8 @@ First up, in small groups, work through the set-up, numeric, and visual sections
 
 First, as always, we need to load the `tidyverse` and the read in our collars data. This time, use the file `more_collars.csv`.
 
-```{r, message=FALSE, warning=FALSE}
-# Load library
+```{r}
 library(tidyverse)
-
-# Load data
 collars <- read_csv("data/more_collars.csv")
 ```
 
@@ -63,13 +70,13 @@ Let's write out our hypotheses.
 
 First, for battery life:
 
--   **Null hypothesis**: There is no difference in the average battery life between the three collar makers.
--   **Alternative hypothesis**: There is a difference in the average battery life the three collar makers.
+**Null hypothesis**: There is no difference in the average battery life between the three collar makers.\
+**Alternative hypothesis**: There is a difference in the average battery life between the three collar makers.
 
 Next, for signal distance:
 
--   **Null hypothesis**: There is no difference in the average signal distance between the three collar makers.
--   **Alternative hypothesis**: There is a difference in the average signal distance between the three collar makers.
+**Null hypothesis**: There is no difference in the average signal distance between the three collar makers.\
+**Alternative hypothesis**: There is a difference in the average signal distance between the three collar makers.
 
 ### Numeric
 
@@ -79,11 +86,11 @@ Calculate the means and the standard deviations (`sd()`) for the battery life an
 
 ```{r}
 summary_stats <- collars %>% 
-  group_by(maker) %>%                              # Group by maker
-  summarise(mean_battery = mean(battery_life),     # Mean (battery)
-            sd_battery = sd(battery_life),         # SD (battery)
-            mean_signal = mean(signal_distance),   # Mean (signal)
-            sd_signal = sd(signal_distance))       # SD (signal)
+  group_by(maker) %>% 
+  summarise(mean_battery = mean(battery_life),
+            sd_battery = sd(battery_life),
+            mean_signal = mean(signal_distance),
+            sd_signal = sd(signal_distance))
 
 summary_stats
 ```
@@ -94,21 +101,28 @@ Let's examine the data. Do you think the collar companies are all different from
 
 As always, we love (\<3) visualizing our data, especially as a form of data exploration and comparing means.
 
-First, explore the distribution of battery life by maker using a multiple density plot (no need to add a line for the mean, but you can if you really want to!).
+First, explore the distribution of battery life by maker using a multiple histogram (no need to add a line for the mean, but you can if you really want to!).
 
 ```{r}
-# Battery life - density
+# battery life
 ggplot(collars, aes(battery_life, fill = maker)) +
-  geom_density(alpha = 0.5)
+  geom_histogram(alpha = 0.5, position = "identity") +
+  labs(x = "Battery Life", 
+       y = "Frequency",
+       fill = "Collar Maker") +
+  theme_bw()
 ```
 
 Let's do a different type of visualization for signal distance. What type of data visualization have we covered that does a good job of showing both central tendency (median, in this case) and variation (middle 50% and 95% confidence intervals) in one continuous variable?
 
 ```{r}
-# Signal distance - boxplot
+# signal distance
 ggplot(collars, aes(maker, signal_distance)) +
   geom_boxplot() +
-  geom_jitter(width = 0.1, alpha = 0.5)
+  geom_jitter(width = 0.1, alpha = 0.5) +
+  labs(x = "Collar Maker",
+       y = "Signal Distance") +
+  theme_classic()
 ```
 
 Based on the numbers and your visualizations, what is your prediction? Are the 3 groups different *enough*, whatever that means?
@@ -119,16 +133,13 @@ Now that we have explored the data numerically and visually, let's interrogate t
 
 To run an ANOVA, we use the `aov()` function. Some helpful tips about the `aov()` function:
 
--   Unlike the `t.test()` function, the initial output for the `aov()` function isn't particularly helpful. We will want to save it to an object and then look at it using the `summary()` function.
--   Like `t.test()`, the syntax for `aov()` is `dependent ~ independent`.
+-   unlike the `t.test()` function, the initial output for the `aov()` function isn't particularly helpful. We will want to save it to an object and then look at it using the `summary()` function
+-   like `t.test()`, the syntax for `aov()` is `dependent ~ independent`.
 
 First, let's run an ANOVA on the battery life variable.
 
 ```{r}
-# ANOVA on battery life
 battery_model <- aov(data = collars, battery_life ~ maker)
-
-# Summary of model
 summary(battery_model)
 ```
 
@@ -136,7 +147,7 @@ For us, our main focus here in the ANOVA summary is on the `maker` row. We can i
 
 We want to pay attention to the following:
 
--   `F-value`: this is our test statistic. It determines our `p-value` (you don't need to know how).
+-   `F-value`: this is our test statistic. It determines our `p-value` (you don't need to know how)
 -   `Pr(>F)`: this is our `p-value`, which has roughly the same meaning as with a t-test.
     -   We want to know if this value is larger or smaller than our cut-off value. We will stick with 0.05.
     -   The notation here, `Pr(>F)`, is saying "What is the probability of us getting an F-value larger than the one we have by change alone?" If that probability is less that 0.05 (or 5%), then we reject the null hypothesis that the means are the same.
@@ -149,10 +160,7 @@ What is our conclusion? Are these means different?
 How about for signal life? What is your interpretation of these results?
 
 ```{r}
-# ANOVA for signal distance
 signal_model <- aov(data = collars, signal_distance ~ maker)
-
-# Summary of model
 summary(signal_model)
 ```
 
@@ -165,8 +173,6 @@ To figure that out, you need to use a class of tests called "post hoc tests," me
 The most common post hoc test for these pairwise comparisons is `TukeyHSD()`, but there are others depending on the specifics of your data set. You don't need to worry about understanding the nuances of Tukey's test, but you should know how to run the code and interpret it.
 
 ```{r}
-# Pairwise comparisons for battery life
-# We take the results from the ANOVA and put it in TukeyHSD
 TukeyHSD(battery_model)
 ```
 
@@ -179,7 +185,7 @@ What do these different columns represent?
 
 How should we interpret these results?
 
--   Are Collarium and Budget significantly different in battery life? **Yes (p \< 0.05)**
+-   Are Collarium and Bugdet significantly different in battery life? **Yes (p \< 0.05)**
 -   Are Darn Tuff and Budget significantly different in battery life? **Yes (p \< 0.05)**
 -   Are Darn Tuff and Collarium significantly different in battery life? **No (p \> 0.05)**
 
@@ -188,7 +194,6 @@ How should we interpret these results?
 In your groups, run the Tukey's HSD test for signal distance and interpret the results.
 
 ```{r}
-# Pairwise comparisons for signal distance
 TukeyHSD(signal_model)
 ```
 
@@ -196,4 +201,4 @@ How should we interpret these results?
 
 -   Are Collarium and Budget significantly different in signal distance? **Yes (p \< 0.05)**
 -   Are Darn Tuff and Budget significantly different in signal distance? **Yes (p \< 0.05)**
--   Are Darn Tuff and Collarium significantly different in signal distance? **Yes (p \< 0.05)**
+-   Are Darn Tuff and Collarium significantly different in signal distance? **Yes (p \< 0.05)**
\ No newline at end of file
diff --git a/modules/module_3/module3_4.qmd b/modules/module_3/module3_4.qmd
index 6ab2b5a..c151fa1 100644
--- a/modules/module_3/module3_4.qmd
+++ b/modules/module_3/module3_4.qmd
@@ -1,143 +1,307 @@
 ---
-title: "3.4: Combining Data (Joins and Binds)"
+title: "3.4: Roads and Regressions"
+author: "Mila Pruiett and Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
-## The Context
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Roads and Regressions
+
+## The Scenario
+
+In this module, we have tried to figure out which bays we should prioritize for fishing based on the following data sets:
+
+-   radio collar data
+-   fish catch data
+-   leopard seal abundance data
+
+Based on our results from Assignment 2, we made a decision about which two bays look like our best options. Now, we need to make these bays accessible over land to transport the fish we've caught to our home base.
+
+**Our Goal: build a road while minimizing our impact on the delicate antarctic ecosystem.**
+
+Our first order of business is to make sure that we avoid areas with that are well-suited for [Antarctica hair grass](https://oceanwide-expeditions.com/blog/the-plants-of-antarctic) (*Deschamsia antarctica*), one of only two flowering species of plants on the continent.
+
+![](assets/hairgrass_indiv.jpeg){width="50%"}
 
-After the series of incidents where a number of the collars made by Budget Collars LLC seem to be failing, our team decided to try and replace as many of them as possible. Besides, their battery life is far inferior.
+We want to know what environmental conditions are associated with hair grass. This way, we can avoid areas where these conditions are met and not destroy precious habitat for the hair grass.
 
-We're placing as many collars on as many seals as we can, and we are starting to run short on collars.
+It would take far too long to survey every bit of land between our base and our fishing spots, so we are going to build a **model** based on some samples of where hair grass is found to help us predict where else it might be.
 
-One of our intrepid data science team members found an old box of collars and some data on all of the collars (it was a real challenge getting this data off of an old floppy drive, but she managed).
+### What is a model?
 
-### Our Tasks
+A model is a way for us to take complex systems and break them down into small, more understandable bits. We can use models to help us understand the relationship between different variables. We can then use those models to make predictions based on those relationships.
 
-We're going to spend the next lessons tackling two tasks:
+## Data
 
-**1) First, we'll work to join data sets together.**
+![](assets/hairgrass_landscape.jpeg){width="50%"}
 
-Our main goal is to create a single data set that includes the data we have previously looked at for collars, the data on the new collars, as well as the additional data on the old collars.
+Knowing that we would soon be building roads, we asked our botanists to collect data for us on key components of the hairgrass' environment. Since it would take too long to sample everywhere hairgrass grows, they collected data from a sample of the hairgrass population.
 
-This is a little tricky, as the collar IDs need to be matched up with their counterparts across datasets, and new collars need be added. Unfortunately, the new collars have IDs and some additional data, but we don't know the maker of the new collars.
+Our botanists collected data for the following variables:
 
-**2) Second, we will try to identify the maker of mystery collars.**
+-   *hairgrass density*: number of individual clumps (tussocks) of hairgrass (per 25 meters squared)
+-   *soil pH*: most plants prefer mildly acidic to neutral environments
+-   *nitrogen (N) content*: as parts per million (ppm); important for plant growth and tissue building
+-   *phosphorous (P) content*: as parts per million (ppm); important for plant growth and tissue building
+-   *percent rock*: how rocky the location is; rocks in soil impact water drainage and temperature
+-   *max windspeed (knots per hour)*: extreme wind can pose a challenge to plants of all types
+-   *average summer temperature (C)*: temperature in the growing season
+-   *penguin density*: the number of penguins per the sample quadrant penguin poop increases nitrogen and phosphorus content in the system but penguins may trample the plants
 
-In order to do this, we will skim the surface of machine learning. We can use a common classification algorithm, K-Nearnest Neighbors (KNN), to predict which maker made which of our unidentified collars. Don't worry, we won't go into *too* much detail here, but I want to you to at least have a general idea of how it works.
+This data is based on this article: [Parnikoza, et al. 2007](https://doi.org/10.1016/j.polar.2007.10.002)
 
-## Combining Data (Joins and Binds)
+## Summarize and Visualize
 
-Often times, we have a lot of data for one project that are related but storing all of the data in one file would add unnecessary redundancy (e.g., data in certain rows would need to be repeated too often). Other times, data has been collected separately and needs to be combined before analysis.
+As we see above, there are many environmental conditions that may be associated with hair grass density.
 
-Being able to join together data from related tables is a key skill in data science, and for working with larger data structures (databases with their own languages, like SQL).
+For this lesson, we are going to focus on two: *nitrogen (N) content* and *soil pH*.
 
-### The Data
+### Set-Up
 
-Let's load in the `tidyverse` and the data we're working.
+As usual, we start with loading our packages and our data.
 
 ```{r message=FALSE}
-# Load the tidyverse
 library(tidyverse)
+hairgrass_data <- read_csv("data/hairgrass_data.csv")
+```
 
-# Loading in the data
-collars <- read_csv("data/collar_data.csv")
-new_collars <- read_csv("data/new_collars.csv")
-old_collars_new_data <- read_csv("data/old_collars_new_data.csv")
+Let's take a look at the data in the data set. What does each row represent?
 
-# Tell me about these data, how are we going to join them?
+```{r}
+head(hairgrass_data)
+tail(hairgrass_data)
 ```
 
-First, let's explore our data. We want to focus on 2 things here:
+### Nitrogen Content
+
+Let's start by investigating any relationship between hair grass density and nitrogen content.
+
+First, we should spend a little time thinking about our variables. Spend a few minutes in small groups discussing the answer to the questions below.
+
+-   Which columns are we interested in right now? soil_nitrogen, hairgrass_density
+-   Which one is the independent variable and which is the dependent variable?
+    -   independent: soil_nitrogen
+    -   dependent: hairgrass_density
+-   Are the variables categorical or continuous? both are continuous
+-   Which type of data visualization should we use? scatterplot
 
-(1) The columns: Which ones match columns in other datasets?
-(2) Collar identity: Which datasets have matching collars or new collars?
+Next, calculate the mean (`mean()`) and standard deviation (`sd()`) for the nitrogen content.
 
 ```{r}
-# View first few rows of each dataset
-head(collars)
-head(new_collars)
-head(old_collars_new_data)
+hairgrass_data %>% 
+  summarise(mean_N = mean(soil_nitrogen),
+            sd_N = sd(soil_nitrogen))
 ```
 
-### Diagramming
+Now that we have summarized the data, let's take a look at the data visually.
 
-In small groups, talk through the process of combining these three datasets. Think about the following:
+```{r}
+ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
+  geom_point() +
+  labs(x = "N Content",
+       y = "Hairgrass Density (per 25 m^2)") +
+  theme_bw()
+```
 
--   Which columns match and which ones don't?
--   Which rows match and which ones don't?
--   Does the order in which we combine datasets matter?
+Do you see a pattern? What type of relationship do you see?
 
-Draw out a diagram that represents how this process might go.
+How do we analyze this type of relationship statistically?
 
-## Joins vs. Binds
+## Statistical Analysis
 
-Now that we've decided on a process for how to combine our data, let's figure out which functions we are going to use to accomplish this task.
+We are going to use two (related) statistical methods to understand the relationship between two continuous (numeric) variables:
 
-We have 2 main methods of combining data sets, and they work in different ways.
+-   correlation coefficient ($r$) and R-squared ($R^2$)
+-   linear regression
 
-### Joins
+### Line of Best Fit
 
-Joins are arguably the more complicated of the two types of ways to combine data, but they are, therefore, the more flexible and useful.
+To understand correlation, R-squared, and linear regression, we first need to talk about something we call the *line of best fit*.
 
-The magic of comes comes because they match up columns of data based on unique identifiers in each row of data.
+The line of best fit aims to minimize the distance between each observation (points) and the line. The distances between each observation and any line are called *residuals* (the dotted gray lines). The line of best fit is the line that has the smallest *residuals*.
 
-In the following diagram, the two example data frames have the column `x1` in common, and each of the values in `x1` are unique (no repeats in the same data frame). When combining the data sets, all of the columns are added, and their rows are matched up to their respective values in the `x1` column.
+![](assets/lines.png){width="50%"} ![](assets/residuals.jpg){width="50%"}
 
-This can happen a couple ways, depending on which data frame is the reference and how much data you want to retain.
+We can add a line of best fit to a ggplot by using the `geom_smooth()` function. We need to specify that the method we want should produce a straight line ("linear model").
 
-![](assets/joins.png)
+```{r}
+ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE) +
+  labs(x = "N Content",
+       y = "Hairgrass Density (per m^2)") +
+  theme_bw()
+
+# method = "lm" is required!
+# se = FALSE is optional
+```
 
-### Binds
+### Correlation Coefficient
 
-The other way we can combine data sets in through binds. Binds act similarly to gluing datasets together.
+The correlation coefficient, `r`, is a measurement of the strength of the relationship between two continuous variables.
 
-They don't match up data based on unique identifiers; instead they match up data by column name (`bind_rows`) or row position (`bind_cols`)
+The correlation coefficient is a number between -1 and 1 that looks at the relationship between two numeric variables. If the value is negative, there is a negative relationship between the two variable; if the value is positive, there is a positive relationship.
 
-![](assets/binds.png)
+If all the points fall exactly on the line of best fit, r = 1 or -1. If there is no relationship between the variables, `r` is 0 (or something very close to it).
 
-How should we go about combining our three data sets? Come up with a plan that you think will work.
+![](assets/coefficient_correlation.jpg){width="50%"}
 
-## Task 1: Combine Our Data
+The greater the magnitude (size) of the correlation coefficient, the stronger the relationship between the two variables.
 
-### Step 1
+![](assets/correlation.jpg){width="50%"}
 
-Our first step in to merge the old collar data with the new data about those old collars.
+It is also important to recognize that the correlation coefficient has nothing to do with the slope of the line (we use linear regression to assess that!)
+
+![](assets/steepness.jpeg){width="50%"}
+
+Based on the hairgrass plot we did above, do you expect the correlation coefficient to be positive or negative? For this class, we'll say that any `r` value with 0.1 of 0 means there is no relationship.
+
+To calculate the correlation coefficient, we need to go back to base R and indicate which columns we are referencing with the `$` operator. We use the `cor()` function.
 
 ```{r}
-# Use a left join
+r_nitrogen <- cor(x = hairgrass_data$soil_nitrogen, y = hairgrass_data$hairgrass_density)
+r_nitrogen
+```
+
+### R-squared ($R^2$)
 
-# Full join would accomplish the same thing in this case because we don't have any missing rows
+The R-squared ($R^2$) value is a representation of how much variation is explained by the line of best fit.
 
-old_collars_combined <- collars %>% 
-  left_join(old_collars_new_data, by = c("collar_id", "maker"))
+When we have a dependent variable and an independent variable, the R-squared value tells us how much of the variation in the *dependent* variable is explained by the *independent* variable.
 
+Sometimes we don't have obvious dependent and independent variables, but we can still use similar language ($R^2$ tells us how much of the variation in the data is explained).
+
+To calculate R-squared, we square the correlation coefficient value we calculated above. It is always positive because we are squaring it `r` value; that means that R-squared values range from 0 to 1. The closer to 1, the more variation is explained.
+
+```{r}
+r_nitrogen^2
 ```
 
-If we take a look our new data frame, we should hopefully see that the values for `antenna_length` and `weight` have been matched up with their respective collar id.
+How would we interpret this `r^2` value?
+
+One way to think of the `r^2` value that might be helpful is to convert it from a proportion to a percentage—what *percentage* of the variation in the dependent variable is explained by the independent variable?
+
+To convert the proportion to a percentage, we can multiply the value by 100.
+
+```{r}
+r_nitrogen^2 * 100
+```
+
+### Linear Regression Analysis
+
+A regression analysis approximates the relationship between a dependent variable and one or more independent variables. It evaluates the strength of that relationship, ultimately giving us a p-value.
+
+Since we are using linear regressions in this course, the regression model will take the form of a straight line. The generic equation for a straight line is `y = mx + b`.
+
+-   `y` = dependent variable (y-axis)
+-   `x` = independent variable (x-axis)
+-   `m` = slope of the line is the slope
+-   `b` = y-intercept
 
-Could we have used another tactic to combine these two datasets? What would the pros and cons be?
+When we have a line with the specific slope and y-intercept, this equation lets us calculate the expected y-axis value for any given point on the x-axis.
 
-### Step 2
+Using our variables, what would our linear regression model look like (we don't know `m` or `b` yet...)?
 
-Now we need to add the new collars to our dataset. What is our best method for combining?
+### Hypothesis Testing
+
+What is the null hypothesis? What is the alternative hypothesis?
+
+**Null Hypothesis** ($H_{0}$): There is no relationship between hair grass density and nitrogen content.
+
+**Alternative Hypothesis** ($H_{A}$): There is a relationship between hair grass density and nitrogen content.
+
+What do you think this means for the slopes?
+
+### Regression Analysis
+
+Thankfully, R can calculate the slope (`m`) and y-intercept (`b`) of the line of best fit for us.
+
+Let's find the equation for our line of best fit, our test statistic, and our p-value. To do this, we use a function called `lm()`: this stands for "linear model."
+
+Like with ANOVA, we will then want to use the `summary()` function to get out the values we need.
+
+```{r}
+nitrogen_model <- lm(hairgrass_density ~ soil_nitrogen, data = hairgrass_data)
+summary(nitrogen_model)
+```
+
+As with our other statistical tests---t-tests and ANOVAs---the results give us some important values:
+
+-   `b` (y-intercept): part of our line of best fit equation
+    -   The "intercept estimate", in this case 84.24, is our y-intercept in our line of best fit
+-   `m` (slope): also part of our line of best fit equation
+    -   this is the estimate for our independent variable (soil_nitrogen in this case)
+    -   in this model, m = 1.8
+-   *F-statistic*: this is our test statistic
+-   *p-value*: calculated from our regression model, used to determine significance (0.05 cut-off, as usual)
+    -   There are multiple p-values here. Focus either on the p-value for the independent variable (soil_nitrogen) or the overall p-value displayed in the last line of the results summary (p \< 2.2e-16)
+-   *R-squared*: this is our R-squared value that we calculated earlier
+    -   You can report either the "multiple" or the "adjusted"
+    -   The "multiple" will typically match the one we calculate with code
+
+This means our equation for the line of best fit is: y = 1.8x + 84.24 and there is a statistically significant relationship between nitrogen content and hairgrass density.
+
+## Soil pH
+
+Let's do the same series of steps to determine how soil pH impacts hair grass densities. Work on this in your small groups, and we will go over it in about 10 minutes.
+
+Start with summarizing the data: mean and standard deviation.
 
 ```{r}
-# Add new collars
-full <- bind_rows(old_collars_combined, new_collars)
+hairgrass_data %>% 
+  summarize(mean_pH = mean(soil_ph),
+            sd_pH = sd(soil_ph))
 ```
 
-Let's take a look at our new data frame! Have we correctly accomplished our first task?
+Visualize the data. Remember to add in the line of best fit using `ggplot2`. Also add labels and a theme for practice.
 
-### Saving our New Data
+```{r}
+ggplot(hairgrass_data, aes(soil_ph, hairgrass_density)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE) +
+  labs(x = "Soil pH",
+       y = "Hairgrass Density per m^2") + 
+  theme_bw()
+```
 
-Now that we've accomplished our first task, we are going to want to use this combined dataset to accomplish our next task, which is using a classification algorithm to help us predict which maker made the mystery collars.
+Calculate the correlation coefficient (`r`)? What does this tell us?
 
-To save our data as a .csv file that we can use in another analysis, we are going to use a function that exports the dataset (`write_csv`) instead of importing it (`read_csv`).
+```{r}
+r_ph <- cor(x = hairgrass_data$soil_ph, y = hairgrass_data$hairgrass_density)
+```
 
-The `write_csv` function requires the name of the dataframe to export as the first argument and the name of the file we want to create as the second argument.
+How much variation does soil pH explain in the hair grass density data?
 
 ```{r}
-# Save our new data
-write_csv(full, "data/all_collar_data.csv")
+r_ph^2
 ```
 
-If we look over in our Files tab, you should see your new .csv file!
+Write out the model for our question about soil pH (without values)?
+
+```{r}
+# hairgrass density  = m(soil_ph) + b
+```
+
+Run the regression model and write out the equation for the line of best fit.
+
+```{r}
+ph_model <- lm(hairgrass_density ~ soil_ph, data = hairgrass_data)
+summary(ph_model)
+```
+
+Equation for line of best fit: y = -4.648x + 150.24
+
+Interpret the results of the regression (our p-value cutoff is still = 0.05). What do we conclude about the relationship between soil pH and hair grass density? Why?
+
+Because p \< 0.05 for the regression model, we reject the null hypothesis and conclude that there is a relationship between soil pH and hairgrass density.
+
+## Data-driven Decision Making
+
+The reason we are using regression analysis is to inform where we should (or should not) build our road so we don't harm the sensitive hair grass or take away their prime habitat.
+
+What do results above for nitrogen content and soil pH mean for the road we are building?
\ No newline at end of file
diff --git a/modules/module_3/module3_5.qmd b/modules/module_3/module3_5.qmd
index cb8d76c..bc96f54 100644
--- a/modules/module_3/module3_5.qmd
+++ b/modules/module_3/module3_5.qmd
@@ -1,12 +1,21 @@
 ---
 title: "3.5: K-Nearest Neighbor"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# K-Nearest Neighbors
 
 ## Machine Learning
 
 Now, we're going to work through an example of machine learning, which is used in a variety of ways in data science, across industries and disciplines.
 
-Machine learning is a sub-field of artificial intelligence (AI). Machine learning uses a data-driven approach; a model is typically trained on data with known outcomes, then the model uses that previous knowledge to predict the outcomes of new data.
+Machine learning is a subfield of artificial intelligence (AI). Machine learning uses a data-driven approach; a model is typically trained on data with known outcomes, then the model uses that previous knowledge to predict the outcomes of new data.
 
 Remember, we have 50 unlabeled collars, and we want to be able to predict who made them so we can increase the safety of our fishers. We need to throw out any collars that we think were made by Budget Collars LLC, and only incorporate new collars from our unlabeled batch that we think were made by Collarium Inc.
 
@@ -36,45 +45,34 @@ Play around with the data visualization. Can you figure out what it is telling y
 
 Now that we have an idea of how KNN works, let's get started with our dataset.
 
-Here's our overall trajectory:
-
-1.  Load our collars data.
-
-2.  Split the data into collars where we know the maker and collars where we don't.
-
-3.  Split the collars with known makers into 2 data frames: one for training our model and one for testing the model out on data where we already know the answers.
-
-4.  Apply the best model to our collars with unknown makers to get predictions for which maker made the collar.
+Here's our overall trajectory: 1) Load our collars data 2) Split the data into collars where we know the maker and collars where we don't 3) Split the collars with known makers into 2 data frames: one for training our model and one for testing the model out on data where we already know the answers 4) Apply the best model to our collars with unknown makers to get predictions for which maker made the collar
 
 First, we need our data!
 
 ### Data Cleaning and Prepping
 
-```{r message=FALSE, warning=FALSE}
-# Load library
+```{r message=FALSE}
 library(tidyverse)
-
-# Load data
 full_collars <- read_csv("data/all_collar_data.csv")
 ```
 
 Next, we need to scale our data.
 
-What does this mean? It means we are transforms the data so that they are all in the same scale. Because KNN uses distances between points to determine neighbors, we need to have our data measured on the same scale.
+What does this mean? It means we are transforming the data so that they are all in the same scale. Because KNN uses distances between points to determine neighbors, we need to have our data measured on the same scale.
 
 Before scaling, battery life (\~100) and signal distance (\~4000) were working in very different ranges, with signal distance being much bigger. Once we scale them, they will both have means around 0 and similar standard deviations.
 
 ```{r}
-# Standardize and remove unnecessary data
+# standardize the data and remove unnecessary data
 
-# Start with the full data set
+# start with the full data set
 knn_data <- full_collars %>%
-  # Mutate the battery and signal columns to be scaled
+  # mutate the battery and signal columns to be scaled
   mutate(battery_scaled = scale(battery_life),
          signal_scaled = scale(signal_distance)) %>%
-  # Remove columns we aren't using anymore
+  # remove columns we aren't using anymore
   dplyr::select(-battery_life, -signal_distance, -fail:-weight) %>% 
-  # Make the maker column a factor (categorical)
+  # make the maker column a factor (categorical)
   mutate(maker = as.factor(maker))
 
 ```
@@ -82,12 +80,10 @@ knn_data <- full_collars %>%
 Let's summarize and plot the scaled data to see what it looks like. Let's focus on battery life.
 
 ```{r}
-# Mean and standard deviation of battery life
 knn_data %>% 
   summarise(mean_bat = mean(battery_scaled),
             sd_bat = sd(battery_scaled))
 
-# Histogram
 ggplot(knn_data, aes(battery_scaled, fill = maker)) +
   geom_histogram()
 ```
@@ -99,19 +95,14 @@ Right now, we have 3 collar categories because we haven't separated the unknown
 Before we continue, we need to make separate data frames to work with, one with collars with known makers and one with collars with unknown labels
 
 ```{r}
-# Filter collars with unknown maker
+# we also need to remove the collar_id labels
 collars_to_label <- knn_data %>%
   filter(is.na(maker))
 
-# We need to save the collar ids before removing them
-unknown_collar_ids <- collars_to_label %>% 
-  dplyr::select(collar_id)
+# we need to save the collar ids before removing them
+unknown_collar_ids <- collars_to_label %>% dplyr::select(collar_id)
+collars_to_label <- collars_to_label %>% dplyr::select(-maker, -collar_id)
 
-# Remove maker and collar_id columns
-collars_to_label <- collars_to_label %>% 
-  dplyr::select(-maker, -collar_id)
-
-# Back to original dataset: filter collars with a known maker, and remove collar_id
 labeled_collars <- knn_data %>%
   filter(!is.na(maker)) %>% 
   select(-collar_id)
@@ -122,29 +113,22 @@ labeled_collars <- knn_data %>%
 
 First, we need to train and test our model. We will do that with the data with known collars makers.
 
-Note: This is where the code starts getting a bit complicated. It's ok if you aren't entirely clear on what everything is doing.
+Note: this is where the code starts getting a bit complicated. It's ok if you aren't entirely clear on what everything is doing.
 
 ```{r}
-# Randomly choose 30 out of the 100 collars to be our test data
-
-# We will train the model with the other 70 collars, then test how well that model does using the 30 collars we held back
+# randomly choose 30 out of the 100 collars to be our test data
+# we will train the model with the other 70 collars, then test how well that model does using the 30 collars we held back
+# because we know the actual makers of those 30 collars, we will know if our model made any mistakes
 
-# Because we KNOW the actual makers of those 30 collars, we will know if our model made any mistakes
-
-# Since we are randomly choosing values, we will use set.seed() to make sure everyone in class is getting the same random values
+# since randomly choosing, we will use set.seed() to make sure everyone is getting the same values
 set.seed(411)
 
-# Randomly select 30 row positions (70%) to pull out
+# randomly select 30 row positions (70%) to pull out
 rows_to_pull <- sample(1:100, 30)
-
-# View random selections
 rows_to_pull
 
-# Remove test data from labeled collars
-training <- labeled_collars[-rows_to_pull, ] 
-
-# Pull out the 30 rows for testing
-testing <- labeled_collars[rows_to_pull, ] 
+training <- labeled_collars[-rows_to_pull, ] # remove test data from labeled collars
+testing <- labeled_collars[rows_to_pull, ] # pull out the 30 rows for testing
 
 ```
 
@@ -155,54 +139,43 @@ Now that we have training and testing data, we can perform the first phase of KN
 We want to know how many nearest neighbors we should use and how well our model works on the testing data. We will use the `caret` package to run these analyses.
 
 ```{r}
-# Loading package to do KNN
+#loading package to do knn
 library(caret)
 
-# Set seed again
+# set seed again
+# training the model and finding the best k value
 set.seed(411)
-
-# Training the model and finding the best k value
 knn_training_model <- train(maker ~ ., data = training, method = "knn")
-
-# View results and find that the best k value is 5
-knn_training_model
+knn_training_model # best k value is 5
 ```
 
 Ok, we have determined that our best k value is 5. Now we can use our training model on the testing data to see if it makes any mistakes. We use the `predict` function from `caret` to do this.
 
 ```{r}
-# Making predictions with the test data
+# making predictions with the test data
 knn_predict_test <- predict(knn_training_model, testing)
-
-# This will show us that there are no mistakes!
-confusionMatrix(knn_predict_test, testing$maker) 
+confusionMatrix(knn_predict_test, testing$maker) # no mistakes!
 ```
 
 Great, our model didn't make any mistakes! It is probably ready to apply to our collars of unknown origin.
 
-## Predicting the Unknown Collar Makers
+### Predicting the Unknown Collar Makers
 
 We will apply the same model but now to the new data.
 
 ```{r}
-# Making predictions for the collars without makers
+# making predictions for the collars without makers
 knn_predict <- predict(knn_training_model, collars_to_label)
-
-# View results
 knn_predict
-
-# As a table
 table(knn_predict)
 ```
 
 We have predictions! Our last step is to recombine our data so that the collars now have their new labels.
 
 ```{r}
-# Let's bind the columns together
+# let's bind the columns together
 new_labels <- bind_cols(unknown_collar_ids, knn_predict)
-
-# View collars now with known makers!
 new_labels
 ```
 
-Typically, you would want to replace the columns in the entire dataframe, but we will end here for today :) Our goal of labeling our unlabeled collars is complete! We will deploy any that are now labeled as Collarium LLC.
+Typically, you would want to replace the columns in the entire dataframe, but we will end here for today :) Our goal of labeling our unlabeled collars is complete! We will deploy any that are now labeled as Collarium LLC.
\ No newline at end of file
diff --git a/modules/module_3/module3_6.qmd b/modules/module_3/module3_6.qmd
new file mode 100644
index 0000000..bf27e0d
--- /dev/null
+++ b/modules/module_3/module3_6.qmd
@@ -0,0 +1,186 @@
+---
+title: "3.6: Multiple Regression"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
+---
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Multiple Regression
+
+Sometimes (often!), we have multiple independent variables that we think might be influencing our dependent variable. In order to take into account more than one variable, we can perform what is called a "multiple regression."
+
+Multiple regressions can become complicated beasts, but we are going to focus on adding in just one additional variable and how it can impact our interpretations.
+
+Let's use some penguins nesting site data to demonstrate.
+
+## Set-Up
+
+As usual, we want to load our packages and our data before we begin.
+
+```{r message = FALSE}
+library(tidyverse)
+site_data <- read_csv("data/site_changes.csv") %>% 
+  mutate(year = as.factor(year)) # don't worry about this code too much
+# we want the year column treated as a category, not a number
+```
+
+We want to understand what factors influence penguin nesting---we wouldn't want to build our road through important penguin nesting territories!
+
+Let's quickly take a look at the data we have.
+
+```{r}
+head(site_data)
+tail(site_data)
+```
+
+Okay, it looks like we have data for 100 different sites collected in two different years. We have the number of penguin nests, which is the variable that we are interested in. We also have a few other variables that might be influencing the number of nests at each site: the number of tussocks of grass, the stone size, the distance to water, and the year that data were collected.
+
+Let's start with looking at how the number of grass tussocks affects nest numbers.
+
+### Regression Model (1 Independent Variable)
+
+Let's start by plotting our tussocks data and run a model.
+
+```{r}
+ggplot(site_data, aes(x = tussocks, y = num_nests)) +
+  geom_point() +
+  geom_smooth(method = "lm") +
+  labs(x = "Tussocks", y = "Number of Nests") +
+  theme_classic()
+```
+
+This model is showing us a positive relationship between the number of tussocks and number of nests. Let's run our linear regression model.
+
+```{r}
+tussocks_model <- lm(num_nests ~ tussocks, data = site_data)
+summary(tussocks_model)
+```
+
+Our model is telling us that the relationship between the number of nests and number of tussocks is highly significant.
+
+### Running a Multiple Regression
+
+What if we incorporate the year the data were collected into our model? Adding an additional independent variable to our model can change how we interpret the results.
+
+First, let's plot a multiple scatterplot. Do we see the same patterns?
+
+```{r}
+ggplot(site_data, aes(x = tussocks, y = num_nests, color = year)) +
+  geom_point() +
+  geom_smooth(method = "lm") +
+  labs(x = "Tussocks", y = "Number of Nests") +
+  theme_classic()
+```
+
+Huh, that looks a little bit different...we no longer seem to have much of a positive relationship. Instead, both lines are looking pretty flat! Let's see what happens when we add year into our regression model.
+
+By adding the year variable into the model, we are also asking if the number of nests changes by year. Does it change our significance?
+
+The way we add an independent variable is with a `+` in the equation.
+
+```{r}
+tussocks_model <- lm(num_nests ~ tussocks + year, data = site_data)
+summary(tussocks_model)
+```
+
+We have a bunch of estimates now! Don't worry, I won't be asking you to put them into an equation :) We are only focusing on significance values.
+
+-   `tussocks`: the p-value for the tussocks row (p = 0.7) shows whether the number of tussocks is a significant driver of nest numbers (it isn't!)
+-   `year2011`: this p-value is significant (p = 4.69e-05)---year does seem to impact how many nests there are
+    -   (you don't need to know this, but if you are curious...): the reason this says `year2011` and not just `year` is because year is acting as a category, and we can get different coefficient estimates for year. The first option in the categorical variable (1971, in this case) is incorporated into the current intercept estimate, so `year2011` indicates the difference from the original.
+
+### Interactions
+
+Sometimes, we might expect that there will be in interactive effect between the two independent variables we are including in our model. This means that the combinations of independent variables impact the dependent variable in different ways.
+
+We can typically spot interactive effects in a plot when the slopes of the regression lines differ from each other a lot.
+
+Let's talk through the visual demonstration below to hopefully get a better understanding of how interaction effects work. These plots are showing different possible relationships between two independent variables: how many cups of coffee someone has had and whether they drank them in the morning or the afternoon (categorical variable), and how those might impact the dependent variable, how long it takes to complete a task.
+
+-   The top two panels show examples of no interactive effect. It looks like there is an effect of time of day; in each, drinking coffee in the morning makes tasks take longer. However, the *slopes* between time of day are the same. In the first panel (top left), there also looks to be an impact from the number of cups of coffee.
+-   The bottom four panels demonstrate possible variations of *interactive* effects. Not only does time of day and/or number of cups of coffee impact how long it takes to complete a task, but those independent variable affect how long it takes to complete a task *in different ways*.
+
+![](assets/coffee_interaction.jpg){width="50%"}
+
+In our example above, an interactive effect between tussocks and year would mean that we think the number of tussocks impacts the number of nests *in different ways* between the two categories (year). Since the slopes between the years don't look very different, we aren't really expecting there to be a significant interaction, but let's check anyway.
+
+Let's run a model that includes the interaction term in our model. To do this, we will use an asterisk (`*`) instead of a plus sign.
+
+```{r}
+tussocks_model <- lm(data = site_data, num_nests ~ tussocks * year)
+summary(tussocks_model)
+```
+
+We can see that we still have estimates for both tussocks and year alone. However, we now also have something new: our interaction term, `tussocks:year2011`.
+
+It turns out that adding in the year variable has changed the significance in our model, and the interaction term is also not significant.
+
+-   The positive relationship we originally observed is no longer present when we account for the year.
+-   There is no significant interaction, meaning that penguins respond to the number of tussocks the same way in each year.
+-   Interestingly (and maybe frustratingly), the significant effect of year that we saw before is also gone.
+    -   This is a confusing bit of statistics, and I won't go into depth here about why this happens. Similar things sometimes happen when we have a significant overall ANOVA but no significant pairwise comparisons.
+    -   What is happening behind the scenes has to do with how the information for each variable is being partitioned. Adding new variables (including an interaction) decreases the likelihood of any individual variable being significant.
+    -   I will never ask you to explain this! Only to correctly interpret the significance values.
+
+Let's explore another example to get a better understanding.
+
+### Practicing with Stone Size
+
+Let's investigate how stone size influences nest numbers. Gentoo penguins build their nests out of stones (they actually court their penguin partner with stones, too)!
+
+![](assets/gentoo-penguin-cornell-lab2.jpg){width="50%"}
+
+Start by plotting the relationship between stone size and the number of nests.
+
+```{r}
+ggplot(site_data, aes(x = stone_size, y = num_nests)) +
+  geom_point() +
+  geom_smooth(method = "lm")
+```
+
+What does the regression model tell us?
+
+```{r}
+stone_size_mod = lm(num_nests ~ stone_size, data = site_data)
+summary(stone_size_mod)
+```
+
+How do we interpret this model?
+
+Next, let's add year in the mix. First, plot the relationship between the number of nests and stone size by year.
+
+```{r}
+ggplot(site_data, aes(x = stone_size, y = num_nests, color = year)) +
+  geom_point() +
+  geom_smooth(method = "lm")
+```
+
+This plot looks pretty different from the one with tussocks. First, let's see what happens when we add the year variable *without* the interactive term. To do that, we use a plus sign (`+`), not an asterisk (`*`).
+
+```{r}
+stone_size_mod = lm(num_nests ~ stone_size + year, data = site_data)
+summary(stone_size_mod)
+```
+
+How do we interpret this multiple regression model?
+
+From the ggplot above, we can see that the slopes for the two different years are quite different: one is pretty flat while the other is definitely positive. Different slopes often indicate that an *interaction* might be at work. Let's find out. Run another multiple regression model, this time adding in the interactive effect (`*`).
+
+```{r}
+stone_size_mod = lm(num_nests ~ stone_size * year, data = site_data)
+summary(stone_size_mod)
+```
+
+Wow, a bunch of significant variables! What does that mean, exactly?
+
+Not only are stone size and year significant, but so is the interaction between stone size and year. What does this mean? It means that penguins in different years seems to respond differently to stone size. In 1971, the number of nests increases with stone size, but in 2011, the number of nests doesn't seem impacted by stone size. Therefore, there is an *interaction* between stone size and year.
+
+### Resources
+
+Still feeling a bit confused about multiple regression and interactions? I recommend checking out [this website](https://stattrek.com/multiple-regression/interaction?tutorial=reg). You can skip all the equation stuff at the beginning. Below that, though, I think they do a nice job of laying out what interactions are.
+
+Also, if you are interested in how we select which model is the best model, [this article](https://www.statology.org/aic-in-r/) is a decent place to start.
\ No newline at end of file
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diff --git a/modules/module_4/data/comparing_populations.csv b/modules/module_4/data/comparing_populations.csv
new file mode 100644
index 0000000..a65470a
--- /dev/null
+++ b/modules/module_4/data/comparing_populations.csv
@@ -0,0 +1,79 @@
+year,abund,population
+0,51,NE
+1,70,NE
+2,164,NE
+3,248,NE
+4,431,NE
+5,567,NE
+6,723,NE
+7,931,NE
+8,931,NE
+9,987,NE
+10,1015,NE
+11,1004,NE
+12,1030,NE
+13,1030,NE
+14,1019,NE
+15,1026,NE
+16,1024,NE
+17,1067,NE
+18,1010,NE
+19,1003,NE
+20,1026,NE
+21,1031,NE
+22,1034,NE
+23,1016,NE
+24,1004,NE
+25,1050,NE
+0,30,SE
+1,51,SE
+2,137,SE
+3,247,SE
+4,437,SE
+5,576,SE
+6,747,SE
+7,819,SE
+8,945,SE
+9,875,SE
+10,978,SE
+11,791,SE
+12,713,SE
+13,1377,SE
+14,1099,SE
+15,729,SE
+16,857,SE
+17,743,SE
+18,1420,SE
+19,1242,SE
+20,927,SE
+21,1089,SE
+22,897,SE
+23,885,SE
+24,822,SE
+25,861,SE
+0,82,SW
+1,60,SW
+2,119,SW
+3,210,SW
+4,362,SW
+5,448,SW
+6,559,SW
+7,729,SW
+8,722,SW
+9,745,SW
+10,746,SW
+11,753,SW
+12,778,SW
+13,816,SW
+14,832,SW
+15,821,SW
+16,789,SW
+17,802,SW
+18,779,SW
+19,778,SW
+20,827,SW
+21,770,SW
+22,859,SW
+23,797,SW
+24,806,SW
+25,778,SW
diff --git a/modules/module_4/data/fish_pops.csv b/modules/module_4/data/fish_pops.csv
new file mode 100644
index 0000000..f1ce71b
--- /dev/null
+++ b/modules/module_4/data/fish_pops.csv
@@ -0,0 +1,81 @@
+N,year,growth_type,r,carrying_capacity,population
+5,1,exponential,-0.45,NA,A
+3,2,exponential,-0.45,NA,A
+2,3,exponential,-0.45,NA,A
+1,4,exponential,-0.45,NA,A
+1,5,exponential,-0.45,NA,A
+1,6,exponential,-0.45,NA,A
+0,7,exponential,-0.45,NA,A
+0,8,exponential,-0.45,NA,A
+0,9,exponential,-0.45,NA,A
+0,10,exponential,-0.45,NA,A
+0,11,exponential,-0.45,NA,A
+0,12,exponential,-0.45,NA,A
+0,13,exponential,-0.45,NA,A
+0,14,exponential,-0.45,NA,A
+0,15,exponential,-0.45,NA,A
+0,16,exponential,-0.45,NA,A
+0,17,exponential,-0.45,NA,A
+0,18,exponential,-0.45,NA,A
+0,19,exponential,-0.45,NA,A
+0,20,exponential,-0.45,NA,A
+5,1,exponential,0.45,NA,B
+8,2,exponential,0.45,NA,B
+12,3,exponential,0.45,NA,B
+19,4,exponential,0.45,NA,B
+30,5,exponential,0.45,NA,B
+47,6,exponential,0.45,NA,B
+74,7,exponential,0.45,NA,B
+117,8,exponential,0.45,NA,B
+183,9,exponential,0.45,NA,B
+287,10,exponential,0.45,NA,B
+450,11,exponential,0.45,NA,B
+706,12,exponential,0.45,NA,B
+1107,13,exponential,0.45,NA,B
+1736,14,exponential,0.45,NA,B
+2723,15,exponential,0.45,NA,B
+4270,16,exponential,0.45,NA,B
+6697,17,exponential,0.45,NA,B
+10503,18,exponential,0.45,NA,B
+16472,19,exponential,0.45,NA,B
+25834,20,exponential,0.45,NA,B
+5,1,logistic,0.45,20,C
+7,2,logistic,0.45,20,C
+9,3,logistic,0.45,20,C
+11,4,logistic,0.45,20,C
+13,5,logistic,0.45,20,C
+15,6,logistic,0.45,20,C
+17,7,logistic,0.45,20,C
+18,8,logistic,0.45,20,C
+18,9,logistic,0.45,20,C
+19,10,logistic,0.45,20,C
+19,11,logistic,0.45,20,C
+20,12,logistic,0.45,20,C
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diff --git a/modules/module_4/data/hairgrass_data.csv b/modules/module_4/data/hairgrass_data.csv
deleted file mode 100644
index 3e32355..0000000
--- a/modules/module_4/data/hairgrass_data.csv
+++ /dev/null
@@ -1,481 +0,0 @@
-location_ID,soil_pH,p_content,percent_soil_rock,max_windspeed_knots,avg_uv_index,avg_summer_temp,n_content,hairgrass_density_m2,penguin_density_5m2
-1,4.9,5.49,44.5,14.91,1,15.3,10.8,7.6,6.3
-2,6.94,8.53,50.5,10.97,3,16.2,10,8.9,6.5
-3,4.36,0.0801,88.5,26.46,1,15.4,8.8,7.7,4.9
-4,5.41,1.98,61,23.6,2,14.1,10,7.5,5.6
-5,5.32,6.6,67.1,27.35,2,14.7,11,8.7,6.9
-6,6.49,4.09,42.8,22.65,2,15.7,9.4,7.9,5.7
-7,5.83,3.05,82.7,2.895,3,14.5,11.1,7.5,6.9
-8,4.91,5.83,78.5,17.93,2,13.3,10.5,6.7,5.8
-9,5.73,4.35,48.5,20.7,1,13.2,7.5,5.1,3
-10,5.61,5.54,54.4,7.491,3,16.6,8.8,7.3,5.3
-11,3.34,7.25,38.4,10.27,3,15.1,8.9,7.3,5.1
-12,5.91,6.21,44.5,9.61,3,16,10.1,8.2,6
-13,7.09,3.38,82.4,13.5,2,14.5,10.2,8.6,6.2
-14,6.59,3.01,66.9,25.42,1,12.7,9.8,6.8,5
-15,2.9,1.72,30.1,3.783,1,15.4,10.4,7.9,6.4
-16,4.26,4.86,63.2,30.16,1,14.6,8.7,7.4,4.9
-17,6.53,11,58.5,19.69,2,15.1,11.2,9.6,7.4
-18,5.53,3.96,102,12.6,2,15.7,9.3,7.7,5.7
-19,3.09,1.22,40.3,13.1,0,16.2,11.4,9.4,7.6
-20,3.71,4.46,60.3,20.43,2,16,9,7.8,5.3
-21,5.8,6.17,47.5,28.86,2,16.3,10.8,9.3,6.9
-22,6.07,2.46,37.8,8.441,2,15,10,8,6
-23,4.18,6.37,64.6,28.69,2,15.4,9.8,7.4,6
-24,5.96,3.24,78.9,11.24,1,15.8,9.6,8,5.8
-25,6.66,2.57,96.4,25.07,3,17,10.7,9.7,7.5
-26,6.23,2.32,67.4,15.39,1,15,8.4,7,4.7
-27,4.53,3.79,112,33.58,1,14.6,8.4,7.2,4.7
-28,5.4,3.75,14.1,29.25,3,16.9,10.4,10.2,7
-29,5.84,4.56,63.3,12.7,2,13.8,11.9,8.5,7.3
-30,3.78,0.396,20.7,5.763,1,13.6,8.1,6.2,3.7
-31,7.49,3.55,89.5,16.44,3,14.8,11.8,9.5,7.5
-32,5.67,5.05,61,5.854,2,16.3,10,9.1,6.4
-33,4.42,8.27,84.8,22.73,1,15,8.1,6.5,4.3
-34,6.06,2.53,60,11.54,2,15.3,9.4,7.1,5.4
-35,4.49,7.62,31.7,14.16,4,15.8,11.8,8.9,7.8
-36,6.31,4.63,35.5,10.36,2,14.5,10.7,8.5,6.7
-37,7.44,0.131,88.7,15.84,2,15.8,10.2,8.9,6.4
-38,5.52,5.65,38.6,24.86,4,14.5,9.6,7.3,5.7
-39,4.96,3.56,34,15.15,2,15.6,8.8,8.2,5.2
-40,8.78,1.04,92.4,13,3,14,11.1,8.5,6.9
-41,6.05,4.29,59.6,15.79,4,15.2,10.9,8.1,6.9
-42,5.91,8.88,57.5,6.59,3,13.9,9.8,7.5,5.2
-43,6.87,1.82,75.9,3.694,0,14.9,10.3,8.2,6.5
-44,3.94,3.18,73,17.23,1,14.8,9.6,7.8,5.4
-45,6.59,6.81,73.1,30.83,1,15.7,10.7,8.4,6.8
-46,4.01,5.58,81.5,23,-2,13,8.9,6.1,4.3
-47,6.22,5.51,95.7,15.17,3,16.5,7.5,7.6,3.9
-48,4.17,6.42,95.3,13.98,3,14.5,9.9,8.4,6.6
-49,7.67,2.62,87.3,3.544,1,15.1,10.4,8.2,6.3
-50,7.57,4.03,96.3,21.97,4,15.8,8.7,7.4,5.1
-51,4.4,4.53,86.2,17.55,2,14.8,11.2,9.1,7
-52,6.59,0.74,69.4,15.75,1,13.8,10.6,7.5,6.3
-53,5.83,6.93,90.3,21.55,2,15,11,8.5,7
-54,6.56,7.32,60.4,4.549,3,16.8,8.3,8.1,5
-55,5.31,1.87,53.3,24.6,2,15,11.5,8.8,7.5
-56,8.02,8.02,40.8,10.33,1,16.5,9.9,8.5,6.4
-57,4.6,4.39,49.4,16.81,2,14.3,9.7,7.2,5.5
-58,4.92,6.52,62.3,20.39,1,13.6,10.2,7.4,5.8
-59,4.31,2.38,68.1,1.695,2,14.7,8,6.7,4.2
-60,5.03,4.54,97.6,14.32,3,15.5,11.8,8.3,7.4
-61,5.62,3.19,10.1,9.507,3,14.5,10.7,8.6,6.7
-62,4.98,2.52,61.8,17.32,1,14.5,10.2,8.1,6.2
-63,3.69,2.8,69.1,10.18,2,14.1,8,6.3,4.3
-64,2.91,1.29,94.8,23.79,3,14.4,9.8,6.7,5
-65,6.51,7.39,57.4,2.501,0,14.8,9.9,7.7,6
-66,9.37,0.78,78.2,26.76,1,15.4,8.4,7,4.7
-67,6.99,3.94,114,8.497,3,14.5,10.5,7.3,6.2
-68,6.1,0.953,69.6,34.92,1,17.5,9.8,9.8,6.5
-69,5.15,2.5,30.3,4.995,0,14.8,11,8.2,7.1
-70,5.92,0.593,67.3,12.55,2,16.2,7.9,6.3,4.2
-71,6.39,4.27,53.8,22.54,2,14.3,8.6,6.6,4.2
-72,6.59,1.78,117,22.05,2,15.8,11.4,8.8,7.3
-73,3.5,6.02,8.79,15.21,3,14.6,9.7,9.2,6.1
-74,5.98,4.31,96.1,19.76,1,14.5,9.8,7.6,5.4
-75,4.68,3.86,76,32.06,0,15.5,9.9,8,6.3
-76,4.38,4.26,64,11.42,3,15.4,10.3,8.7,6.3
-77,4.24,8.48,103,29.68,2,13.7,7.8,5,3.5
-78,3.02,2.52,61.5,7.732,-0,17.2,9.7,8.4,6.4
-79,4.21,7.46,72.7,23.63,0,17.3,11,10.5,7.7
-80,4.18,9.98,50.2,18.24,1,15.1,9.3,7.3,5.5
-81,4.02,6.19,66.6,6.93,1,16.2,9.4,8.3,5.8
-82,4.63,7.01,91.8,19.56,2,13.5,10.5,7.1,6
-83,4.77,5.14,65.6,13.93,2,14.6,9.4,6.8,5.1
-84,7.42,3.29,48.6,4.929,1,14.9,12.4,10.1,8
-85,7.08,1.95,77.9,14.24,3,14.4,8.7,6.3,4.4
-86,6.49,6.67,67.4,7.652,1,17.3,9.7,9.2,6.6
-87,7.43,0.899,65.6,9.481,2,14.7,10.3,7.8,6.2
-88,7.77,5.5,76.9,17.55,0,15.2,9.5,6.8,5.6
-89,4.88,5,15.3,22.55,3,16.3,9.3,8.8,5.8
-90,6.21,0.812,64.3,0.01189,1,11.9,8.7,5,3.7
-91,5.64,2.51,75,26.74,2,15.7,10,8.9,6.3
-92,5.19,3.24,63.8,3.937,2,17.2,10.6,9.5,7.3
-93,4.32,8.16,74.4,18.16,3,15.2,7.9,7.9,4.9
-94,4.97,1.1,74.7,10.46,3,14.5,9.8,7.3,5.5
-95,5.05,3.78,50.4,15.02,3,13.9,9,6.8,4.5
-96,4.46,5.06,59.4,14.44,2,16.8,10,8.7,6.4
-97,4.15,6.7,70,7.786,1,16.1,9.7,8.3,6.3
-98,5.31,9.31,76.3,21.32,2,15.2,10.1,7.9,6
-99,3.61,1.9,62,16.94,3,15.8,8.8,7.3,5.2
-100,5.04,9.94,18.9,0.097,2,15.4,9.5,8,5.4
-101,4.96,1.88,67.2,7.586,1,15.4,10.6,8.1,6.4
-102,6.8,6.09,66.8,25.63,3,15.9,7.3,6.1,3.5
-103,5.3,1.92,72,16.58,4,13.6,8.9,6.4,4.6
-104,6.77,5.19,72,9.912,1,13.6,9.1,6.1,4.9
-105,6.07,11.6,68.4,19.13,1,15.4,9.3,8.2,5.5
-106,5.53,6.97,53.5,18.45,2,15.3,9.3,7.5,5.4
-107,5.11,7.86,73.5,21.02,1,16.5,10.4,8.4,6.5
-108,5.13,5.09,57.4,3.561,1,13.4,10.8,7.6,6.1
-109,7.76,2.94,108,3.511,2,16.1,9.1,8.3,5.4
-110,6.4,5.14,58.2,24.95,2,15.6,9.9,8.7,6.4
-111,5.01,3.19,37.6,20.3,2,15.3,9.3,8,5.2
-112,6.65,5.68,58.7,24.93,2,14.4,9.6,7.1,5.4
-113,5,4.42,97.2,17.59,3,15.1,10.3,8.7,6.1
-114,4.47,5.13,111,7.746,2,15.2,9.3,6.7,5.1
-115,5.31,4.25,83.2,12.38,1,16.2,10.5,9.6,6.9
-116,7.9,8.77,30.9,7.856,4,15.4,11.3,9.4,7.3
-117,5.79,2.47,81.5,19.66,4,14.8,11,8,6.8
-118,7.26,0.847,79,4.359,3,15.3,11.2,9.5,7.4
-119,5.95,3.62,67.9,18.37,2,13.9,9,6.9,5
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-122,6.22,9.1,81.9,27.22,1,13.8,9,6.3,4.5
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-425,7.06,10.1,37.9,14.54,2,15.3,8.8,6.6,5.2
-426,4.42,1.35,45.8,18.02,2,15,12,8.6,7.5
-427,8.7,9.67,67.3,14.65,4,13.7,8.3,6.5,4
-428,4.52,0.779,79.8,3.804,2,15.7,11.8,9.6,8
-429,5.63,13.7,32.8,0.004718,2,15.2,10.3,7.7,6.1
-430,7.21,4.41,65.1,16.77,0,13.6,9.4,6.7,5.2
-431,5.13,7.32,66.5,34.11,1,14.2,9.9,7.4,5.8
-432,5.4,3.42,41.2,2.903,2,15,9.8,7.5,5.4
-433,6.05,2.41,67.7,12.55,2,15.3,11.1,8,6.7
-434,4.55,12.8,68.5,12.36,1,15.3,10.2,8.3,6.3
-435,4.05,4.39,75.1,13.05,1,15.1,7.2,5.8,3.2
-436,6.5,7.17,38.3,39.97,4,14.9,8.6,7.2,5
-437,6.61,0.961,74,20.32,3,16.8,10.7,8.7,7.1
-438,5.07,0.187,79.9,0.5563,3,15.4,11.1,8.6,7.1
-439,7.32,5.35,78.5,24.35,3,15.1,9.7,8.2,5.9
-440,4.84,1.65,115,11.53,3,14,11.2,8.1,6.7
-441,4.46,2.84,18.2,16.57,2,15.7,9.9,8.4,6.1
-442,6.66,3.81,45.9,17.67,3,16.3,10.5,8.9,7.1
-443,4.72,4.52,52.4,1.91,1,16.3,11.2,9.6,7.3
-444,5.61,4.11,46.5,9.352,2,14.3,7.3,6.2,3.4
-445,5.67,7.97,100,12.42,2,15.8,10.9,8.5,6.9
-446,5.26,3.07,42.9,8.822,2,14.8,10.2,7.7,6.1
-447,3.83,7.04,69.1,3.129,1,14.1,8.5,6.6,4.2
-448,5.91,4.02,69.3,26.92,1,14.9,9.8,8.6,6.1
-449,3.9,6.84,57.4,19.42,-0,15,10.3,7.8,5.8
-450,5.69,2.41,62,0.2754,2,14.3,10.3,7.6,6.1
-451,5.3,4.97,59,5.976,2,16,9.2,7.5,5.7
-452,6.04,7.91,69.3,9.451,1,16.8,11.1,9,7.3
-453,6.76,4.46,46.2,13.05,1,15.6,9.5,8.2,6.1
-454,6.85,4.84,61.1,0.7701,3,15.2,10.7,8.8,6.8
-455,4.99,3.64,76.2,3.862,2,13.3,9.5,6.6,4.9
-456,6.23,8.2,79,7.955,2,13.5,8.1,5.1,3.5
-457,4.99,4.2,79.2,25.17,2,15.4,10.1,8.2,6.2
-458,5.16,1.23,111,26.01,3,14.6,9.7,7.6,5.8
-459,4.71,8.3,43.8,14.43,1,15.6,10.1,8.3,6.4
-460,4.34,10.3,116,11.7,2,15.3,9.8,8.4,5.7
-461,7.16,8.95,86.6,6.412,1,16.8,9.1,9.2,5.8
-462,7.46,2.96,89.2,19.3,1,15.1,9.4,8,5.4
-463,5.29,5.38,95.4,36.57,1,13.9,8.9,6.1,4.1
-464,5.3,14.5,89.1,10.79,4,14.3,12.2,9.7,8
-465,5.84,4.06,73.7,22.45,1,15.5,9.9,7.7,6.1
-466,4.68,5.25,62.3,15.92,2,14.3,9.6,7.6,5.5
-467,7.42,4.77,75,5.38,1,14.7,11.2,8.8,6.8
-468,6.1,5.73,71.8,1.206,1,16.9,9.4,9.1,5.8
-469,4,3.9,57.7,5.454,2,15.2,11.4,9.3,7.4
-470,7.03,4.14,64.9,10.49,-0,12.2,10.5,6.6,5.6
-471,5.75,2.66,52.9,21.76,2,14.8,9.5,7,5.6
-472,6.28,1.6,64.3,6.402,2,13.8,8.9,7.1,4.6
-473,2.69,7.35,68.2,6.243,2,16.6,9.9,8.9,6.3
-474,6.36,3.76,85.4,12.72,1,15.6,8.8,7.7,5.4
-475,4.57,7.96,96.2,25.46,2,15.5,10.2,8.3,6.1
-476,6.1,0.448,94.5,4.999,1,15.9,9.3,8.2,5.8
-477,4.72,4.86,70.3,12.26,2,15.4,9.3,8.2,5.4
-478,6.41,3.8,86.9,4.855,1,15.4,12.6,9.5,8.3
-479,6.02,4.62,12,7.232,2,14.7,10.2,8.2,6.2
-480,3.64,2.32,94.9,3.936,2,14.2,10.6,7.4,6.3
diff --git a/modules/module_4/data/nonvascular_plants.csv b/modules/module_4/data/nonvascular_plants.csv
deleted file mode 100644
index ac91d4b..0000000
--- a/modules/module_4/data/nonvascular_plants.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-site,plant_type,percent_plant_cover,penguin_density_m2
-1,moss,47.51238089656832,1.8920162820463555
-2,moss,39.493225419227414,1.1822269930946046
-3,moss,39.27307347564329,1.8068843661402367
-4,moss,40.90552830595114,1.632128300083696
-5,moss,45.3763094719382,0.8427337315672636
-6,moss,36.665887002727374,0.6130658243183681
-7,moss,50.737990668778544,1.451594081962368
-8,moss,32.34401566645143,2.496136664748217
-9,moss,45.70518477028222,1.6753254334432557
-10,moss,40.30909134234935,2.217478578793667
-11,moss,33.3278228445144,1.6320917666372377
-12,moss,30.54433940479093,0.8530049498682747
-13,moss,38.6135274569426,1.4761404236858606
-14,moss,35.30420366666612,1.926537288507742
-15,moss,38.94061344844721,2.2838505265300837
-16,moss,38.302786644606975,1.402269724806728
-17,moss,46.05195335874146,2.168257660888063
-18,moss,34.69280107052829,1.6196513977312996
-19,moss,43.09511368955402,0.929005098694066
-20,moss,42.629719718385225,1.613723561843306
-21,moss,42.16967018822584,1.172587780786623
-22,moss,40.610766883562555,1.1654592490174025
-23,moss,38.83801529425033,1.7407741513252066
-24,moss,43.50324562941342,1.0248718469711604
-25,moss,38.87386042787737,1.6213073378675853
-26,moss,42.47909759002484,1.541576634035584
-27,moss,45.07697105043477,1.960459894741079
-28,moss,48.057117242740816,1.7267844692803056
-29,moss,42.47711835926137,2.0051133493763524
-30,moss,44.19270780722768,2.02178188719075
-31,moss,34.52143142271312,2.3904114265984084
-32,moss,36.33381466052387,1.7805356948015678
-33,moss,44.68790625653601,1.3762256311419652
-34,moss,56.53684399545847,0.828500094890096
-35,moss,55.09503448763771,0.6735996302528218
-36,moss,68.83279425313432,2.098896587324311
-37,moss,56.949561735672944,1.2080817019814587
-38,moss,64.12146361794422,0.9270099967733508
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-41,moss,60.78308943168722,1.6800998776767335
-42,moss,57.82443538540807,1.5847736233432237
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-96,moss,75.39774557498814,2.716784656544415
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-111,liverwort,65.78714530878943,1.6807095934332421
-112,liverwort,57.687506048222474,1.9020120834372032
-113,liverwort,58.30451479157698,2.2987069440703847
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-115,liverwort,69.36512933926419,1.8947497663403516
-116,liverwort,58.49776949437314,2.054552628627439
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-118,liverwort,56.424616338496,2.0686231869770575
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-120,liverwort,64.76030068218,2.137266509928338
-121,liverwort,66.45869330402368,1.3053086552063364
-122,liverwort,58.03006206622357,2.2750708981054943
-123,liverwort,49.59452501260989,1.6914564186329957
-124,liverwort,62.416853062939595,1.6342386441553602
-125,liverwort,63.5896647935591,1.961714973482301
-126,liverwort,57.87211312676452,1.4160675986548656
-127,liverwort,55.29149514460506,1.9150803634120352
-128,liverwort,60.000414878800576,2.0662259758708017
-129,liverwort,63.44411508861488,0.8888110827517355
-130,liverwort,57.16942792207881,0.8806299684498248
-131,liverwort,52.45652781937185,0.7407158404635024
-132,liverwort,54.29480912555916,1.640125351953729
-133,liverwort,63.66319989158639,0.7762287086972808
-134,liverwort,40.505897517896464,0.8439833612589739
-135,liverwort,35.52215153283349,1.3443920591341905
-136,liverwort,48.34237889722954,0.9613945967969892
-137,liverwort,40.20377706808545,1.8479969491461097
-138,liverwort,46.44097019197733,2.437886777892704
-139,liverwort,46.73308653198154,1.1037702815673716
-140,liverwort,38.69305499306208,2.012803495811509
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-146,liverwort,50.16673755914735,0.6778634071991123
-147,liverwort,31.48378395261451,1.4266615597789103
-148,liverwort,38.158979990331474,2.3071078484601832
-149,liverwort,43.44105022535951,1.9391252078076235
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-152,liverwort,53.58173984866888,1.69727888487561
-153,liverwort,49.210874053333725,2.070234245559208
-154,liverwort,41.96202935797004,2.0986663682888436
-155,liverwort,37.31748830847056,2.5624896773392223
-156,liverwort,39.71089352242835,2.431259761291354
-157,liverwort,38.26028565688,2.2663447608572462
-158,liverwort,43.85418729055418,2.89590679148635
-159,liverwort,33.55728759489731,3.2157580200626317
-160,liverwort,44.67740908667714,1.79744900570102
-161,liverwort,39.47178596060075,2.738189699389173
-162,liverwort,38.13472466302208,3.5140569227079297
-163,liverwort,40.08075290662555,2.6202574591770524
-164,liverwort,36.56683027981641,2.4344363426032576
-165,liverwort,37.42103866287423,2.860723947897878
-166,liverwort,31.180948948738234,2.8119011923126447
-167,liverwort,42.91147741808771,1.926368577160686
-168,liverwort,16.57983616124714,2.6737469746050553
-169,liverwort,19.046586434721164,2.7061641590520655
-170,liverwort,18.67858993528079,2.4464211618428493
-171,liverwort,18.93404974120244,2.5022840216398325
-172,liverwort,15.452711175835436,2.10704331405171
-173,liverwort,16.83419079572427,3.0910492142310755
-174,liverwort,29.655595135321104,2.2445183119355736
-175,liverwort,16.484329045798077,2.141814722224022
-176,liverwort,24.66373770542535,2.564808232862584
-177,liverwort,19.681485911044287,2.3343264297370476
-178,liverwort,15.27737688677938,3.2728528942294264
-179,liverwort,19.509760683540495,2.0732612072220564
-180,liverwort,26.623975594509716,2.6798974150802177
-181,liverwort,13.927506941446222,2.8228215562402452
-182,liverwort,19.870369994328797,3.1596332430404237
-183,liverwort,17.49410232298165,2.519502133681826
-184,liverwort,27.09273269264923,2.965360630014186
-185,liverwort,21.70032562460021,2.303885601440571
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-188,liverwort,14.904949631279896,1.4756526136773715
-189,liverwort,22.507314022527883,2.6057469378884774
-190,liverwort,19.69543284874168,3.175106548538922
-191,liverwort,21.06625435784417,1.0622485085426296
-192,liverwort,23.580469149359093,2.368633061314534
-193,liverwort,19.83435435316054,3.2217402956337833
-194,liverwort,23.94665583473453,2.519306841671175
-195,liverwort,22.43285962405052,2.3781716118806107
-196,liverwort,24.609390236504893,2.564726531815741
-197,liverwort,19.05466085852354,2.6802244506272386
-198,liverwort,31.641066061361308,2.121569091202681
-199,liverwort,20.64737154316816,1.9846588891621335
-200,liverwort,11.436926999462491,1.9931879917184547
diff --git a/modules/module_4/data/penguin_chicks.csv b/modules/module_4/data/penguin_chicks.csv
deleted file mode 100644
index 14f7884..0000000
--- a/modules/module_4/data/penguin_chicks.csv
+++ /dev/null
@@ -1,3201 +0,0 @@
-chick_ID,site,avg_tussocks,dist_open_water
-1,Cape Royds,7.528183309964453,8.801327286942312
-15,Cape Royds,6.672933856928177,9.953285417412474
-17,Cape Royds,6.135715012187574,12.528954272742657
-21,Cape Royds,6.495235447899651,9.64433604911505
-54,Cape Royds,6.627822613825244,9.192124090675213
-80,Cape Royds,6.8117103739605,9.362385707119296
-100,Cape Royds,5.987593950313221,10.632144607274277
-148,Cape Royds,7.015462815274699,9.440532756203464
-168,Cape Royds,6.264628447053283,10.43003495339251
-178,Cape Royds,7.102896542019172,9.709301997838654
-197,Cape Royds,7.020702797220468,5.899817575391453
-206,Cape Royds,7.698339564577963,10.113831630940178
-207,Cape Royds,6.849488768952798,9.434559464483723
-210,Cape Royds,7.022362147564759,11.190977951751005
-214,Cape Royds,6.520502814484619,10.858217056972597
-241,Cape Royds,6.748895788044802,8.828897723787227
-254,Cape Royds,6.588815372927261,9.62501632821496
-291,Cape Royds,6.500891378296073,10.181922045737515
-352,Cape Royds,7.355849694591265,10.728013209497108
-369,Cape Royds,6.876509624541635,11.042249731012049
-375,Cape Royds,6.960399361276368,11.248451855232549
-387,Cape Royds,7.346689130580119,9.54265638898744
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-1899,Cape Royds,6.991804164298511,9.046423655308569
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-1937,Cape Royds,6.396962416272157,9.686174011821642
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-11,Erebus East,10.23389849657238,126.04304933298228
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-55,Ferrar Coast,1.0422728206173288,501.13209852771814
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-4,Marble Point East,0.5240891756055814,197.8269688692435
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-1484,Marble Point North,7.348535839508558,205.53093252439217
-1491,Marble Point North,7.6316030089026015,205.64111483769412
-1509,Marble Point North,8.343935524875596,205.45893794010777
-1521,Marble Point North,8.107326364081231,204.34368573547374
-1525,Marble Point North,8.075949207446355,204.64567274911408
-1536,Marble Point North,8.897264268331888,206.3097332446814
-1562,Marble Point North,7.583205966438562,205.1427144143074
-1582,Marble Point North,7.7434747043453855,204.3754680848213
-1586,Marble Point North,7.868599003161548,203.6909058712275
-1611,Marble Point North,8.712724664894846,204.7913322768146
-1638,Marble Point North,7.907886584431623,204.15822188078226
-1642,Marble Point North,8.040726526157455,205.9269421994895
-1689,Marble Point North,6.954713009373865,205.98855403461945
-1692,Marble Point North,8.302360743189315,204.35622670077692
-1759,Marble Point North,8.165385180320584,204.57449542407528
-1774,Marble Point North,7.97335826682256,203.64330474624123
-1814,Marble Point North,8.896109172870043,205.6475644163655
-1849,Marble Point North,8.380526276292985,204.78525898725252
-1884,Marble Point North,8.182136306007584,205.05791861810442
-1921,Marble Point North,8.853128045542043,204.8730777785521
-1925,Marble Point North,7.5880706176807795,205.0937254623631
-1945,Marble Point North,7.723245918851616,203.99307071232894
-1974,Marble Point North,8.792311269674776,206.076202796779
-1997,Marble Point North,7.6503372037054636,203.37154624223533
-2019,Marble Point North,8.112448403459146,204.81296473100917
-2038,Marble Point North,8.164255798766359,204.94912844375474
-2047,Marble Point North,6.882973327923474,206.28862813084208
-2066,Marble Point North,7.724952773960782,204.85644662976682
-2077,Marble Point North,8.217355756703068,205.9001937801563
-2088,Marble Point North,8.069013023098762,206.04105236619844
-2155,Marble Point North,8.157641296148324,206.14218118193781
-2179,Marble Point North,8.206785845796519,206.07406092458396
-2190,Marble Point North,7.427319423893844,205.87526920463222
-2195,Marble Point North,8.707507428682645,205.3595499908155
-2199,Marble Point North,8.443094153613979,203.41309954345303
-2209,Marble Point North,8.852118237995976,204.23916815062887
-2241,Marble Point North,7.516782209346789,205.40535543993332
-2247,Marble Point North,7.973742151235048,204.3342450951571
-2252,Marble Point North,8.340976633149532,206.87885999567584
-2253,Marble Point North,7.67488229288902,203.73970333731796
-2270,Marble Point North,8.591844455577581,204.8729075564607
-2271,Marble Point North,7.056824236102335,204.27747764137837
-2283,Marble Point North,7.670294543113417,205.2250773386518
-2292,Marble Point North,8.198596672133633,204.7025558609247
-2314,Marble Point North,7.575057138977192,206.2342385599042
-2324,Marble Point North,7.972527715402937,205.81857480229246
-2325,Marble Point North,7.5209848107849675,205.38133839636785
-2347,Marble Point North,8.267335423132433,204.61161359061452
-2361,Marble Point North,7.030148549143967,203.57075456830555
-2363,Marble Point North,8.250880336031159,204.6882014949577
-2369,Marble Point North,8.146917942347411,204.72955773037563
-2507,Marble Point North,7.999947253290948,205.0063159908657
-2511,Marble Point North,8.166220732408846,205.32250453696193
-2533,Marble Point North,7.104617296995112,206.25760484412942
-2541,Marble Point North,8.68327316841746,206.7721965145948
-2549,Marble Point North,8.132916512093523,205.17174069997432
-2560,Marble Point North,8.102539833816875,204.6630534139761
-2564,Marble Point North,8.195224107137813,205.23921195828765
-2617,Marble Point North,8.756706277298184,204.44936849613254
-2674,Marble Point North,7.910742080295721,203.58274271093006
-2691,Marble Point North,8.848858619007231,204.92389667219143
-2709,Marble Point North,7.566927468895568,204.63093422104066
-2720,Marble Point North,8.162230145525092,204.71677539261614
-2725,Marble Point North,8.16338837034439,206.35109913857852
-2759,Marble Point North,7.739631667629651,204.9635544837962
-2771,Marble Point North,7.381988140359775,203.85710849939656
-2783,Marble Point North,8.36688314003694,206.10167361185432
-2819,Marble Point North,8.248913826466525,205.13970587165997
-2850,Marble Point North,8.171439424717198,205.31151186302517
-2904,Marble Point North,8.499291093453728,206.87803409232737
-2906,Marble Point North,8.429379277885216,203.35632877565791
-2939,Marble Point North,8.097252457516797,204.7817117237938
-2982,Marble Point North,8.024881077121039,204.21427434712876
-3030,Marble Point North,8.590719486615326,205.86335819913623
-3060,Marble Point North,7.9385735463350855,204.12294719919913
-3064,Marble Point North,8.443790970030161,204.1902388081761
-3099,Marble Point North,8.010864841534824,204.91807552706695
-3106,Marble Point North,7.86047096224994,207.17110978890076
-3118,Marble Point North,8.048903539907856,201.8525609664573
-3151,Marble Point North,7.407429975941742,205.25192199934128
-3185,Marble Point North,7.253817502975468,204.7654601116239
-3194,Marble Point North,7.902059887965607,205.1536410790244
-3,Marble Point South,20.03953607086445,9.272772178658919
-6,Marble Point South,21.15924364365875,9.447840579832803
-28,Marble Point South,21.776395772837304,7.406229075799576
-42,Marble Point South,20.993503403235756,7.796386100356472
-97,Marble Point South,20.646532667072478,8.661819288643516
-101,Marble Point South,20.948048410118574,6.723163453801593
-136,Marble Point South,21.132149668613124,7.972915874491299
-146,Marble Point South,21.193239782277093,9.625295230981614
-159,Marble Point South,20.386874416942177,8.209440020905024
-160,Marble Point South,21.338725037270134,7.633084660397337
-173,Marble Point South,20.65217426058582,7.41528141348472
-212,Marble Point South,20.74240832483779,6.221975368514423
-215,Marble Point South,20.75917189205142,6.109851265853205
-222,Marble Point South,21.51905026128764,8.413316484535201
-247,Marble Point South,21.38165033905432,7.583429560330588
-249,Marble Point South,21.944272344240172,8.258244378315645
-251,Marble Point South,21.405414125027214,9.986734143482442
-255,Marble Point South,20.697910933403715,4.82706806319379
-262,Marble Point South,21.41797729889165,8.578355811473521
-289,Marble Point South,19.99028334767637,8.993305052917611
-330,Marble Point South,20.58971934404147,7.40486508182183
-396,Marble Point South,21.390769731164934,8.724316530012098
-399,Marble Point South,20.909907383943892,9.186903950968187
-430,Marble Point South,21.165344311818046,8.196813390936102
-440,Marble Point South,21.24796898116226,8.524752186912115
-456,Marble Point South,20.129459795851602,8.404803363492505
-553,Marble Point South,20.777337491499487,7.7648833501040135
-575,Marble Point South,20.91820187708419,6.994317021360546
-576,Marble Point South,20.639941859071772,7.185676104976567
-581,Marble Point South,21.360708214629014,8.573447300119923
-584,Marble Point South,21.720430227026142,7.672566046323459
-604,Marble Point South,21.634813663798628,7.240267178561746
-607,Marble Point South,21.608726872264757,7.529648842414145
-613,Marble Point South,21.185507680652886,10.204265754838604
-642,Marble Point South,21.073394546022943,7.567831516537749
-660,Marble Point South,20.532896679835787,5.621472949511544
-669,Marble Point South,20.773757294632095,7.9668579608476415
-687,Marble Point South,21.28758021101639,8.692779339789944
-689,Marble Point South,20.439388462041812,6.267355599711283
-698,Marble Point South,20.376910353773372,7.990801719937142
-722,Marble Point South,21.15751401297351,8.809944684065549
-851,Marble Point South,20.852460133425737,8.113092232151196
-858,Marble Point South,20.832178208724507,6.9798434493071095
-864,Marble Point South,20.472781162484303,6.881427346635808
-866,Marble Point South,20.67228053890665,8.805551595471927
-877,Marble Point South,20.882334833790708,7.686384738172411
-885,Marble Point South,20.667911383156735,8.295532908781903
-923,Marble Point South,20.92795437768776,8.222401991801771
-956,Marble Point South,20.81919334089494,8.35006780260509
-962,Marble Point South,21.31936174858551,9.085347743303593
-968,Marble Point South,19.952179094921206,11.094787347602797
-979,Marble Point South,20.045476242990308,7.763027571114788
-995,Marble Point South,21.325108793502178,9.127924357299143
-996,Marble Point South,20.27731849172848,8.19834114573895
-1020,Marble Point South,20.814180668952556,8.376091742365364
-1063,Marble Point South,21.405963097007156,7.808037532721541
-1074,Marble Point South,21.16068914951782,8.103933285615971
-1100,Marble Point South,21.218163291479694,9.34020075177598
-1136,Marble Point South,22.055227436081807,8.25114041757708
-1143,Marble Point South,21.009821816318283,9.03434318387826
-1169,Marble Point South,20.586845945806417,7.062810284368043
-1179,Marble Point South,21.03790348695638,7.977969685416529
-1183,Marble Point South,19.72278390935612,8.626118372220962
-1191,Marble Point South,20.973490453686985,7.57106366999683
-1195,Marble Point South,20.439325590012086,8.552114017740498
-1220,Marble Point South,20.795425241685695,8.992033627315145
-1223,Marble Point South,21.743088918748715,6.201015170465984
-1236,Marble Point South,21.661274886811686,6.183558961338356
-1242,Marble Point South,21.49076828384657,7.104432286162945
-1263,Marble Point South,20.77688770440803,8.417383782270761
-1277,Marble Point South,20.751658046446167,8.82439907115383
-1282,Marble Point South,20.589824698573818,9.692800288903731
-1284,Marble Point South,21.13173174577564,7.755886135253716
-1308,Marble Point South,21.639423501393104,9.642596649536914
-1309,Marble Point South,20.992815446096337,6.9983866792083695
-1312,Marble Point South,22.22108090805477,7.650789006964384
-1314,Marble Point South,21.41833180369356,7.92684513715253
-1338,Marble Point South,20.79384737620467,8.122013789800373
-1353,Marble Point South,21.490122525167276,8.058394179238148
-1354,Marble Point South,20.703606279539652,7.160620021087975
-1385,Marble Point South,20.601406935235552,8.13081266788023
-1417,Marble Point South,20.364745575775746,8.981850379636702
-1430,Marble Point South,20.9664399175355,8.906689482168886
-1435,Marble Point South,21.765435168460204,9.406741294179977
-1438,Marble Point South,20.175298701877235,8.571290042257555
-1448,Marble Point South,21.400197352796756,7.218771835688612
-1478,Marble Point South,21.302649281742596,8.22805290408068
-1480,Marble Point South,21.492302769066224,7.1312091938553
-1523,Marble Point South,20.86357290421486,7.9515412593309
-1551,Marble Point South,20.72545465406398,7.813077493799694
-1578,Marble Point South,20.835914868288324,7.155797706040663
-1583,Marble Point South,21.632676579622498,8.048799909472974
-1605,Marble Point South,21.532376885917074,6.852848985994162
-1609,Marble Point South,21.168930699188365,9.151182631357996
-1623,Marble Point South,21.095820030306317,5.718038072900278
-1640,Marble Point South,21.021901393000505,7.136543383124217
-1658,Marble Point South,20.962570766185955,8.814013067563883
-1668,Marble Point South,20.870268664491125,7.945500217159368
-1676,Marble Point South,21.02900409692821,8.63857039718002
-1709,Marble Point South,21.270418067304977,8.280276343373963
-1710,Marble Point South,20.538525736886925,8.880047000038509
-1720,Marble Point South,20.631292840082573,6.97967107986156
-1735,Marble Point South,20.61921953335108,8.126411886401089
-1736,Marble Point South,20.493563914869462,7.78442920850996
-1737,Marble Point South,21.031468650492375,9.287406049245138
-1750,Marble Point South,21.404678054954527,7.08591634974962
-1778,Marble Point South,21.339302202148748,7.7729778542825265
-1783,Marble Point South,21.258278778027,7.456208598964241
-1790,Marble Point South,21.716044271103303,7.599235912168881
-1811,Marble Point South,20.961945501425557,5.830634598015149
-1818,Marble Point South,21.062929676554894,7.504121040119805
-1820,Marble Point South,21.06936097639108,8.152714511545115
-1835,Marble Point South,20.813328373462294,7.6914666298546495
-1844,Marble Point South,21.891463240695664,7.5335440487657674
-1852,Marble Point South,21.295689456998108,8.285226784705793
-1875,Marble Point South,20.394386554752593,7.28970431008418
-1882,Marble Point South,20.79229304501846,9.921449867837115
-1886,Marble Point South,20.334390909925883,7.320679558198679
-1905,Marble Point South,20.952091806936217,8.304776662649978
-1907,Marble Point South,20.81129278577239,7.042316551679125
-1919,Marble Point South,21.892472722438438,8.237158339643182
-1927,Marble Point South,21.337323650271514,8.033116729704677
-1930,Marble Point South,20.69442702398602,6.756300784874565
-1940,Marble Point South,20.267956784610455,7.1484851405175025
-1957,Marble Point South,20.674352974031763,7.4427634769388344
-1969,Marble Point South,20.44255181303624,7.423155482665227
-2007,Marble Point South,21.028263187813682,7.425804573131485
-2037,Marble Point South,21.36442644894658,9.341291147728422
-2041,Marble Point South,21.353403469503263,8.363261094638913
-2044,Marble Point South,20.423604848260258,6.007201632596521
-2065,Marble Point South,20.42469707535744,8.096397933080175
-2074,Marble Point South,21.55101432045783,7.561287230667382
-2095,Marble Point South,20.45901057092808,10.252640895094839
-2124,Marble Point South,21.507639718415973,7.032066346091441
-2143,Marble Point South,20.12164462098077,7.234386115827524
-2165,Marble Point South,20.81191588084136,7.946282652862503
-2193,Marble Point South,20.625545739270592,8.580303605269519
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-2227,Marble Point South,20.974620124136784,7.068063946896753
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-2288,Marble Point South,20.45027788052846,6.930046822251209
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-2309,Marble Point South,21.29702347425259,7.552090622580899
-2330,Marble Point South,21.7667797503357,9.30792401724063
-2336,Marble Point South,19.926987258890325,6.725602124137646
-2352,Marble Point South,20.8574072170744,7.9567038639102305
-2376,Marble Point South,21.24514914981411,8.13601721459954
-2377,Marble Point South,21.10196699368569,8.190257435916186
-2387,Marble Point South,20.74161962317987,7.782259704797393
-2394,Marble Point South,21.202937890140056,9.10223370193519
-2423,Marble Point South,21.158594644058752,8.22846405138318
-2437,Marble Point South,21.37675977206142,8.17729623505209
-2443,Marble Point South,20.8683275087036,8.344343828459099
-2447,Marble Point South,20.751569172447514,9.882361690047807
-2464,Marble Point South,21.48640925907913,6.769623750387693
-2474,Marble Point South,21.481692814547102,8.019723294258398
-2492,Marble Point South,20.99334297124037,9.359807389100224
-2502,Marble Point South,20.9265543558244,10.146269035074862
-2528,Marble Point South,21.929021858967342,8.071366511042994
-2567,Marble Point South,22.024553004132773,8.14229089605774
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-2591,Marble Point South,21.33667038945095,10.432722248908991
-2640,Marble Point South,20.7472763142371,8.566078674259051
-2665,Marble Point South,21.512178166163434,9.308781337020845
-2666,Marble Point South,20.538928557380935,5.842995813834845
-2673,Marble Point South,21.342141792228798,8.687413024734141
-2678,Marble Point South,20.899000476438335,7.897671857357674
-2777,Marble Point South,21.06859293003224,7.928254169706678
-2780,Marble Point South,20.745453964819742,7.9235058881721985
-2782,Marble Point South,21.095053421813887,9.413769614292878
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-2815,Marble Point South,20.218199493094986,7.689543056648086
-2853,Marble Point South,21.017745959078002,8.94871223829601
-2854,Marble Point South,21.829936324917565,8.650136167360989
-2899,Marble Point South,21.25666948235094,10.744658451947835
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-2909,Marble Point South,20.989649163698815,9.753013978710625
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-2949,Marble Point South,20.540038963453476,6.444859735889135
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-3139,Marble Point South,20.090422155185436,8.733505927511812
-5,Marble Point West,8.778175297562335,44.53444480916007
-13,Marble Point West,9.4240390157789,44.81715415371291
-34,Marble Point West,7.5590458693609515,42.990623705967295
-154,Marble Point West,9.019232893406654,44.17130337695391
-187,Marble Point West,9.58628905435095,45.30441379914872
-218,Marble Point West,9.075431004838634,44.632151472207056
-245,Marble Point West,9.803542509222172,45.05309016653908
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-292,Marble Point West,9.095970158927605,43.643046274988954
-341,Marble Point West,9.101021828557537,43.351094991174996
-402,Marble Point West,9.081759316070979,42.93764829316863
-432,Marble Point West,8.489919241727717,44.69117246699706
-435,Marble Point West,9.483028449141857,42.21037355883543
-457,Marble Point West,9.079278932632585,42.852417789790344
-487,Marble Point West,8.860347324509766,44.02416532174416
-502,Marble Point West,9.399977306781395,44.4331263686854
-508,Marble Point West,8.998285115417987,44.99327080947563
-517,Marble Point West,8.951985778406542,42.99127360972958
-537,Marble Point West,9.288562933838469,43.07764501614907
-540,Marble Point West,9.537429537530388,42.86058246207449
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-9,McMurdo East,17.835328943175913,37.97386086717183
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-1977,McMurdo East,17.923078412547945,36.64805108469088
-1981,McMurdo East,17.390535269041408,35.49033164752212
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-2016,McMurdo East,18.400613919388768,35.75178447914179
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-3045,McMurdo East,17.81182118725492,38.179975724361945
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-3187,McMurdo East,17.67860034653763,38.13932126914431
-24,McMurdo North,9.663878000962754,201.2836807816876
-63,McMurdo North,9.825463247433428,199.5057708460451
-64,McMurdo North,9.527963093022151,201.09313997320749
-66,McMurdo North,9.872318192135662,198.90958140260364
-76,McMurdo North,10.257185696925605,202.17019537053446
-96,McMurdo North,10.146685592280908,200.42653569328502
-109,McMurdo North,10.257792556377053,198.55378747587977
-118,McMurdo North,10.2045889610543,201.51576077568794
-143,McMurdo North,9.741385174988288,199.8947080095396
-144,McMurdo North,10.180614629397262,198.73853749181447
-153,McMurdo North,9.414507758863184,200.1734094086397
-163,McMurdo North,10.06192982928503,200.16421412459385
-175,McMurdo North,9.742531170836232,199.89259983113965
-196,McMurdo North,10.121321370968092,201.74226442782654
-257,McMurdo North,10.35149554107914,198.51622364053586
-259,McMurdo North,8.668220218019195,200.78987726299727
-269,McMurdo North,9.930806914571777,198.2015856683975
-283,McMurdo North,9.648732657810466,200.33680629508603
-294,McMurdo North,9.706053671541103,200.67891899117984
-371,McMurdo North,8.960122435712375,200.04125194515439
-389,McMurdo North,9.36437329122072,200.69101510304716
-393,McMurdo North,10.616865613080323,200.76406524143587
-394,McMurdo North,9.049110803614703,201.47110674656415
-411,McMurdo North,9.704069385441937,197.66823519611333
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-445,McMurdo North,9.856380208569927,200.18721437889187
-454,McMurdo North,8.989130210778725,199.82561426679504
-488,McMurdo North,10.05307194765475,199.11257155629286
-533,McMurdo North,10.920279402546086,199.57604104468246
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-557,McMurdo North,9.368857874508565,200.32102360782824
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-681,McMurdo North,9.711359763208755,200.51471391261393
-696,McMurdo North,10.058959152637723,198.2015874989925
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-757,McMurdo North,9.92221163427247,201.34123540855444
-768,McMurdo North,10.023478574871124,200.13365770813834
-771,McMurdo North,9.86716268970881,199.82829788249694
-784,McMurdo North,9.266937966713042,199.06444979915247
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-795,McMurdo North,9.501992554178774,200.04783004092425
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-839,McMurdo North,9.970322397384175,202.85612594701635
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-921,McMurdo North,9.456557509276466,201.79425489403616
-955,McMurdo North,9.387060431587475,200.11840019510547
-976,McMurdo North,10.124186077688913,199.99873227240008
-982,McMurdo North,9.858890924895674,201.7019254576967
-985,McMurdo North,9.969783947049992,200.078378159557
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-1035,McMurdo North,10.13192041722476,200.83704261302918
-1051,McMurdo North,9.780174349951823,199.13157590626048
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-1094,McMurdo North,10.305832429652854,198.73372279686583
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-1111,McMurdo North,9.68261127181099,199.73327770174552
-1116,McMurdo North,10.377904304681469,199.9026937762515
-1123,McMurdo North,10.858502113707708,200.18402102047025
-1144,McMurdo North,10.008371072662369,200.28568189211305
-1228,McMurdo North,9.414080380225466,200.01365884975982
-1241,McMurdo North,9.731076251025913,200.33760537422393
-1244,McMurdo North,10.614306784900469,198.55115740290145
-1247,McMurdo North,9.279685371204373,199.1346898401749
-1265,McMurdo North,9.820016140926073,200.53819178085766
-1278,McMurdo North,9.75833603044667,200.21632594229152
-1285,McMurdo North,10.164912059023202,199.434520044781
-1302,McMurdo North,9.97893703841961,200.46984698980094
-1321,McMurdo North,10.196124509473371,199.66901752666422
-1335,McMurdo North,10.386999481138753,200.40646581338677
-1409,McMurdo North,10.450507314544412,200.1045018036386
-1423,McMurdo North,9.810986811627108,199.66466577909617
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-1477,McMurdo North,10.165059155356346,198.38365772306122
-1619,McMurdo North,10.52772519106785,201.466549666166
-1666,McMurdo North,9.561019425935735,202.01526959602103
-1672,McMurdo North,9.65499046867747,200.18644718386892
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-1731,McMurdo North,9.891699360059546,199.5015485132859
-1757,McMurdo North,10.17064807432659,199.36636907918358
-1761,McMurdo North,10.685717417023737,200.530951193792
-1775,McMurdo North,9.464641907313316,200.06881317044122
-1789,McMurdo North,9.827582605748542,200.10149201768832
-1808,McMurdo North,9.351664376910302,200.98634801813384
-1826,McMurdo North,9.380844271925891,200.82795760772848
-1831,McMurdo North,9.31576107212527,199.5798406502291
-1873,McMurdo North,10.053480969999153,199.7849673570785
-1894,McMurdo North,9.920852301266962,198.42528968837823
-1914,McMurdo North,10.681597547607012,199.55573948261713
-1943,McMurdo North,9.635655078674022,199.10254332153536
-1982,McMurdo North,9.365647492377137,198.82639861748785
-2002,McMurdo North,9.759100170875804,201.85981225773284
-2027,McMurdo North,10.677773301060807,200.7432471992581
-2055,McMurdo North,10.4780799830855,200.4182011524153
-2059,McMurdo North,8.52841914802926,198.9553727299996
-2099,McMurdo North,10.974855785662383,200.0525531139084
-2139,McMurdo North,10.185449893578651,199.4866219374388
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-2164,McMurdo North,10.287016575778555,198.1978997473642
-2180,McMurdo North,10.043283223438978,198.72430724448535
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-2321,McMurdo North,9.78699217172899,200.22940637972005
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-954,McMurdo West,10.804077244190813,89.89828036314617
-1023,McMurdo West,10.788002871641202,89.14949611860072
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-1095,McMurdo West,10.868296667591519,89.54562659871705
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-1118,McMurdo West,10.619064142854546,90.00997535255073
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-1146,McMurdo West,10.414599605893173,88.97549589690853
-1154,McMurdo West,10.775278318252216,88.9946563557479
-1163,McMurdo West,11.325271890288645,90.61312741514656
-1172,McMurdo West,10.926137820582912,90.22622324822343
-1175,McMurdo West,11.36701756759098,89.31246028778337
-1196,McMurdo West,11.327445135399897,87.77178485372308
-1249,McMurdo West,10.847506522340751,90.06797935386787
-1254,McMurdo West,10.785500135680488,91.14272953615662
-1356,McMurdo West,10.711704966416992,89.95095415587012
-1384,McMurdo West,11.542521194396134,91.68033505959643
-1411,McMurdo West,10.892643877099777,90.54521696035991
-1445,McMurdo West,11.714126792932738,90.1684257269488
-1450,McMurdo West,10.194622040666669,89.51659271474534
-1479,McMurdo West,11.100515956953222,89.18031509886278
-1503,McMurdo West,11.01976691850967,88.88397334965767
-1504,McMurdo West,11.377551846936996,90.43713238370619
-1507,McMurdo West,11.90913155502475,90.50121926044316
-1510,McMurdo West,11.69192414095576,89.35769608343854
-1513,McMurdo West,10.908965972803374,88.9309423656514
-1553,McMurdo West,10.884681960849338,91.23552537421979
-1612,McMurdo West,11.315583399078054,89.06542520047753
-1656,McMurdo West,11.29351833116838,90.03169004876818
-1677,McMurdo West,11.27143051239069,90.43998143863847
-1723,McMurdo West,10.517880294859031,87.87755300182361
-1741,McMurdo West,11.093527799093179,91.52223725772939
-1752,McMurdo West,11.88605821920393,89.60933501053137
-1791,McMurdo West,11.453713514690845,89.63051275697327
-1800,McMurdo West,10.993106491065658,89.97675644064701
-1804,McMurdo West,11.417600595541147,89.82721899451542
-1821,McMurdo West,11.384097426697892,91.8217678498502
-1860,McMurdo West,10.820217646811301,87.84610036446136
-1910,McMurdo West,10.535113106523863,92.2685909676054
-1936,McMurdo West,11.734075467704207,90.12065483861348
-1953,McMurdo West,11.083469133731741,89.72325318112422
-1968,McMurdo West,11.104487058671426,90.51010124630263
-2022,McMurdo West,10.84676927290619,90.59032792436645
-2051,McMurdo West,10.223200925724111,89.30308795096883
-2061,McMurdo West,10.906496472254888,89.86213353466566
-2068,McMurdo West,10.59479585513298,89.13018971883541
-2100,McMurdo West,12.126228215313827,89.14926712481574
-2131,McMurdo West,11.007492423101702,90.0485198987588
-2166,McMurdo West,11.34780819968871,91.80515418502628
-2186,McMurdo West,12.012884763140011,89.85171716411394
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-2278,McMurdo West,11.557724785047865,88.60484157166756
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-2338,McMurdo West,10.374909091643742,87.90439666259604
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-2356,McMurdo West,10.72733135225417,90.38647259387962
-2358,McMurdo West,10.751954178492444,90.48781990423
-2395,McMurdo West,10.959761508066988,90.32671274664582
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-2459,McMurdo West,10.979672324448703,89.52382341706033
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-2634,McMurdo West,10.886480332264641,88.01387857032626
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-3160,McMurdo West,11.539922009731445,88.49624918621193
-3170,McMurdo West,11.536732871391562,89.3520359871822
-3197,McMurdo West,11.13458610885204,90.59369271310138
-19,Ross Island North,15.635509516317244,87.3098332371029
-20,Ross Island North,16.13893668321215,87.58789670586262
-25,Ross Island North,15.864561881930875,89.23771718167374
-45,Ross Island North,16.366056065896107,86.66553826312365
-52,Ross Island North,16.355613291998225,88.6300641165889
-65,Ross Island North,16.00566261743446,86.78637383664265
-82,Ross Island North,17.401911363864436,88.49478858502982
-85,Ross Island North,15.914940589859725,87.73901431607577
-116,Ross Island North,16.85345262826262,88.24175910636059
-119,Ross Island North,15.624097249903722,88.12832747567782
-135,Ross Island North,15.665210090722548,89.50467782693318
-190,Ross Island North,15.9947498009107,86.89666104179958
-200,Ross Island North,16.001970715507827,88.1075646779469
-216,Ross Island North,15.642043475874267,86.50106425402676
-317,Ross Island North,15.721681819406337,88.6238239652212
-326,Ross Island North,15.782550626684188,86.21912913237269
-346,Ross Island North,16.09851047557888,89.10328473953354
-381,Ross Island North,16.233905317184536,90.28255356076379
-391,Ross Island North,16.124614836067828,87.92276050275616
-400,Ross Island North,15.858021464315106,86.65601015032664
-421,Ross Island North,16.684172514522935,87.99446782072403
-447,Ross Island North,16.108814660378357,88.85588835808794
-455,Ross Island North,15.350133445541315,87.83866902905531
-465,Ross Island North,16.291408619355213,88.50980394142546
-529,Ross Island North,16.311194916511813,87.8718894710439
-558,Ross Island North,15.567533409701754,87.57357619806076
-562,Ross Island North,15.893233715498877,88.3634168841296
-565,Ross Island North,15.729768739112407,88.41229384991675
-570,Ross Island North,16.957084439143685,89.49858824342714
-634,Ross Island North,15.229732381220412,88.86354652480554
-658,Ross Island North,14.91920619167334,88.45841460889031
-663,Ross Island North,15.202158308919387,88.32311877095532
-679,Ross Island North,15.107856433967873,87.77520664962204
-714,Ross Island North,15.598736763913351,87.75999144051883
-725,Ross Island North,15.710435616578588,87.08080751836926
-737,Ross Island North,16.061669385528564,85.92124900281641
-745,Ross Island North,16.143446133495633,88.17187816823498
-760,Ross Island North,15.451104116695758,88.48759524687799
-765,Ross Island North,15.988956991127603,88.78421329496386
-803,Ross Island North,15.996460976370035,87.35581344669922
-838,Ross Island North,17.038895854966253,88.96320600645717
-873,Ross Island North,15.409058767088478,88.52174723893864
-879,Ross Island North,16.135000188439587,88.94640751799112
-928,Ross Island North,16.939705007585097,88.43338764538463
-942,Ross Island North,15.562352130094125,89.03348321050491
-965,Ross Island North,15.662247340119082,87.92044127623623
-972,Ross Island North,15.775041432334405,87.69604016421077
-1059,Ross Island North,15.74074315281153,88.46426327896091
-1066,Ross Island North,16.523803694642748,88.1368488964946
-1069,Ross Island North,15.670031143652533,87.49172525132965
-1112,Ross Island North,15.725527328591847,90.53345937916764
-1113,Ross Island North,15.638826690779467,89.21288754053906
-1119,Ross Island North,16.309506618180095,88.02594954858819
-1132,Ross Island North,16.371908997166894,88.97132802636472
-1174,Ross Island North,16.441868423629955,87.43629303592513
-1199,Ross Island North,16.05196002107544,89.17699989772281
-1214,Ross Island North,15.535352963608926,88.03887767151564
-1219,Ross Island North,16.088538320643618,90.14520274690798
-1246,Ross Island North,16.684901305708944,87.00065815745221
-1256,Ross Island North,15.544839146952112,88.60470432307791
-1290,Ross Island North,16.344656290173827,88.94067399727652
-1301,Ross Island North,15.660949805468025,87.29381899373527
-1357,Ross Island North,15.171564948794195,87.77787185929252
-1358,Ross Island North,14.947217985536831,87.09279756673862
-1366,Ross Island North,15.308832922835158,88.14376669072152
-1374,Ross Island North,16.038242725301373,86.51657698882161
-1382,Ross Island North,16.79637977854615,88.726109015464
-1500,Ross Island North,14.814512332083,87.63244587980111
-1602,Ross Island North,16.376815664033856,88.30764168610212
-1659,Ross Island North,15.98616250472489,87.98270391430489
-1671,Ross Island North,15.616989235134241,87.99521606410374
-1698,Ross Island North,16.97361973457475,86.03040591946115
-1700,Ross Island North,16.502038153372705,88.01672595326656
-1708,Ross Island North,16.149808166264283,88.17459262359161
-1719,Ross Island North,16.000362340737702,87.91396273423416
-1724,Ross Island North,16.30382547447071,87.41617741987511
-1765,Ross Island North,16.387386341754443,87.57627523300134
-1784,Ross Island North,16.334140854650077,87.7744456233427
-1785,Ross Island North,17.06714578555601,87.31112468881558
-1805,Ross Island North,16.528663394404933,86.634109271109
-1823,Ross Island North,16.86008661517746,88.183517740229
-1833,Ross Island North,14.639694347762024,86.81785399255875
-1867,Ross Island North,15.023965927485259,88.1797393999879
-1877,Ross Island North,15.957468145754413,88.6139446572131
-1898,Ross Island North,16.31915565096318,88.3413117547719
-1901,Ross Island North,15.971510499625118,88.03211018538931
-1950,Ross Island North,16.44960516431863,89.63289944252523
-1960,Ross Island North,15.45120087231783,88.48304895111895
-1988,Ross Island North,16.397246749564886,86.86220817072225
-2010,Ross Island North,16.426055529150105,89.35284093088434
-2043,Ross Island North,15.601600926731926,91.21210204404038
-2050,Ross Island North,16.225403944049212,89.08138264764891
-2070,Ross Island North,16.28702036351384,90.1512315211325
-2103,Ross Island North,16.385465460346385,88.12918849217093
-2157,Ross Island North,16.17972549753576,88.7891662382611
-2191,Ross Island North,15.510278130637866,86.81391285012161
-2221,Ross Island North,16.44121647547136,87.37176701354716
-2248,Ross Island North,15.80491264371482,87.89081497459588
-2276,Ross Island North,15.681847526829761,87.38840713157356
-2297,Ross Island North,16.316340061085402,86.72069462469146
-2396,Ross Island North,15.85263236253664,88.10045982299864
-2408,Ross Island North,16.292463418044097,88.1854847607574
-2410,Ross Island North,16.067994986864583,88.35416745496542
-2412,Ross Island North,16.401182785655532,87.24813564008421
-2428,Ross Island North,15.209526027983344,87.48773771849933
-2498,Ross Island North,15.830375223681763,87.59362538314343
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-2553,Ross Island North,15.197431764917864,89.20325333082657
-2559,Ross Island North,16.541679651053048,87.97744758908263
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-2569,Ross Island North,16.280050421572813,87.38721567812435
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-2655,Ross Island North,15.533048583398605,86.55399000144045
-2664,Ross Island North,16.6250769025292,88.41679034463108
-2675,Ross Island North,16.725782260334796,89.12937836050428
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-2703,Ross Island North,15.299923497686535,87.52266783700874
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-2750,Ross Island North,15.881006108680792,87.40409262366775
-2756,Ross Island North,16.348014826865093,90.06421274952795
-2773,Ross Island North,17.147771383739503,87.02580497153124
-2788,Ross Island North,15.281852390518637,88.71395924945377
-2830,Ross Island North,15.644629331125884,88.8378597276883
-2847,Ross Island North,15.895304149350167,87.69631359599394
-2859,Ross Island North,16.667250307107658,87.76708359278035
-2888,Ross Island North,15.435062530608247,88.30593958441935
-2891,Ross Island North,16.307535822720716,87.48910666384009
-2902,Ross Island North,15.867331915726192,86.9452715629146
-2941,Ross Island North,16.95986225652999,88.43716786587818
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-1980,Scott Base East,4.802581764594244,16.881333780733815
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-2015,Scott Base East,4.576251012982295,15.978302867387026
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-40,Scott Base North,16.741903631689617,201.07849133166738
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-1903,Scott Base North,15.372421100254615,199.4571473047762
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-2004,Scott Base North,15.35161574753129,202.4179427917407
-2009,Scott Base North,15.048798232255264,200.90235841743637
-2020,Scott Base North,16.051121284935057,202.28452213993256
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-1227,Taylor Valley Entrance,10.900727556606064,22.526083457057652
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-1288,Taylor Valley Entrance,10.022035409314759,24.116669986336564
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-1347,Taylor Valley Entrance,11.406085578629263,19.957060792374964
-1376,Taylor Valley Entrance,10.531463857348248,23.098622854966475
-1379,Taylor Valley Entrance,11.292518329780865,22.809273802765418
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-1636,Taylor Valley Entrance,11.253021422281645,23.366944269336035
-1647,Taylor Valley Entrance,11.225473842954388,24.460399206461936
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-1684,Taylor Valley Entrance,11.402052133124474,23.178825056568197
-1685,Taylor Valley Entrance,10.891666935050043,22.005286245500397
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-1728,Taylor Valley Entrance,11.096763875384468,22.400078391549695
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-1773,Taylor Valley Entrance,10.02908409564983,22.181029218241463
-1776,Taylor Valley Entrance,10.617330310584396,24.153439984908093
-1786,Taylor Valley Entrance,10.52732277701903,24.00577225114592
-1815,Taylor Valley Entrance,11.099292683587132,23.152398439054735
-1816,Taylor Valley Entrance,11.117925249377107,23.383965870138624
-1819,Taylor Valley Entrance,10.650268151230147,23.24850131589957
-1843,Taylor Valley Entrance,10.856393006120358,21.2128285757799
-1858,Taylor Valley Entrance,11.518088832033714,23.14998797011927
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-2021,Taylor Valley Entrance,10.850511264096436,21.826142905212535
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diff --git a/modules/module_4/module4_1.qmd b/modules/module_4/module4_1.qmd
index 5f98a01..a065693 100644
--- a/modules/module_4/module4_1.qmd
+++ b/modules/module_4/module4_1.qmd
@@ -1,305 +1,199 @@
 ---
-title: "4.1: Roads and Regressions"
+title: '4.1: Population Growth'
+author: "Jessie Golding and Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
-## The Scenario
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, fig.align = "center")
+```
 
-Last module, we tried to figure out which bays we should prioritize for fishing based on the following data sets:
+# Introduction to Population Growth
 
--   Radio collar data
--   Fish catch data
--   Leopard seal abundance data
+Let's use descriptive statistics and data visualization to better understand population dynamics.
 
-Based on our results from Assignment 2, it looks like Sulzberger Bay and Hope Bay are our best bets. Now, we need to make these bays accessible over land to transport the fish we've caught to our home base.
+### Exponential Growth
 
-**We want to build a road while minimizing our impact on the delicate antarctic ecosystem.**
+A population that shows [exponential growth]{.underline} has a growth rate that depends the average number of births (`b`) and deaths (`d`) per individual.
 
-Our first order of business is to make sure that we avoid areas with that are well-suited for [Antarctica hair grass](https://oceanwide-expeditions.com/blog/the-plants-of-antarctic) (*Deschamsia antarctica*), one of only two flowering species of plants on the continent.
+We call rates that are averages per individual in a population (rather than a total) *per capita* growth rates; *per capita* translates from Latin to "by head," meaning for individuals.
 
-![](assets/hairgrass_indiv.jpeg)
+Combined, birth and death rates can be described as the [intrinsic rate of increase]{.underline} (`r`). We calculate `r` by subtracting the number of individuals that are removed from the population (deaths) from the number of individuals that are joining the population (births): $r = b - d$.
 
-We want to know what environmental conditions are associated with hair grass. This way, we can avoid areas where these conditions are met and not destroy precious habitat for the hair grass.
+We can calculate by how many individuals the population will change in a given time period  (`N`) by multiplying the starting population size (`Ni`) by the intrinsic rate of increase (`r`) using this equation:
 
-It would take far too long to survey every bit of land between our base and our fishing spots, so we are going to build a **model** based on some samples of where hair grass is found to help us predict where else it might be.
+![](assets/clipboard-1439389577.png){width="90" fig-align="center"}
 
-### What is a model?
+### Logistic Growth
 
-A model is a way for us to take complex systems and break them down into small, more understandable bits. We can use models to help us understand the relationship between different variables. We can then use those model to make predictions based on those relationships.
+Populations that show logistic growth start out in the same way as populations with exponential growth–driven by *per capita* birth and death rates.
 
-## Data
+After a while, though, limited resources also start to come into play. When calculating the change in the size of the population (`N`), we also need to take into account the maximum number of individuals the population can sustainably support, called the [carrying capacity]{.underline}, `K`.
 
-![](assets/hairgrass_landscape.jpeg)
+We use the following equation to calculate the population size:
 
-Knowing that we would soon be building roads, we asked our botanists to collect data for us on key components of the hairgrass' environment. Since it would take too long to sample everywhere hairgrass grows, they collected data from a sample of the hairgrass population.
+![](assets/clipboard-3908560513.png){width="188" fig-align="center"}
 
-Our botanists collected data for the following variables:
+### Visualizing Growth Models
 
--   ***soil pH*****:** most plants prefer mildly acidic to neutral environments
--   ***nitrogen (N) content*****:** as percentage per 100 mL soil sample; important for plant growth and tissue building
--   ***phosphorous (P) content*****:** as percentage per 100 mL soil sample; important for plant growth and tissue building
--   ***percent rock*****:** how rocky the location is; rocks in soil impact water drainage and temperature
--   ***max windspeed (knots per hour)*****:** extreme wind can pose a challenge to plants of all types
--   ***average summer temperature (C)*****:** temperature in the growing season
--   ***penguin density*****:** the number of penguins per 5 m\^2 within 100 m of the sample quadrant for hair grass; penguin poop increases nitrogen content in the system
--   ***hairgrass density*****:** number of individual clumps (tussocks) of hairgrass per 1 m\^2
+As a reminder, this is what each type of population growth model looks like:
 
-This data is based on this article: [Parnikoza, et al. 2007](https://doi.org/10.1016/j.polar.2007.10.002)
+![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
 
-## Summarize and Visualize
+# What Population Factors Matter?
 
-As we see above, there are many environmental conditions that may be associated with hair grass density.
+We still need to figure out what fish population we will target for Antarctica.
 
-For this lesson, we are going to focus on two: *nitrogen (N) content* and *soil pH*.
+Briefly, let's consider what we think might be important for sustainability.
 
-### Set-Up
+1.  Large population
+2.  Stable population (i.e., not declining)
 
-As usual, we start with loading our packages and our data.
+## Fish Data
 
-```{r message=FALSE, warning=FALSE}
-# Load library
-library(tidyverse)
+Let's look at fish data from around our station in Antarctica over the past 20 years to get a better feel for how different population factors impact fish populations. These fish moved into the bay by our station when we established it, so we know the populations started at around the same time and had the same starting populations.
 
-# Load data
-hairgrass <- read_csv("data/hairgrass_data.csv")
-```
+These aren't necessarily our targets for fishing, particularly since it would be difficult and potentially disruptive to fish in the bay. Right now our goal is just to learn so we know what to look for when we are making our decision to conduct sustainable fishing.
 
-Let's take a look at the data in the data set. What does each row represent?
+![Adapted from Figure 2 from Zheng et al. (2022)](assets/fish_data_fish.png){fig-align="center"}
 
-```{r}
-# View first few rows
-head(hairgrass)
+As always, first we load the `tidyverse` and read in the data.
 
-# View last few rows
-tail(hairgrass)
+```{r}
+library(tidyverse)
+fish_pops <- read_csv("data/fish_pops.csv")
 ```
 
-### Nitrogen Content
-
-Let's start by investigating any relationship between hair grass density and nitrogen content.
+### Largest Population
 
-First, we should spend a little time thinking about our variables. Spend a few minutes in small groups discussing the answer to the questions below.
-
--   Which columns are we interested in right now? **N_content, hairgrass_density**
--   Which one is the independent variable and which is the dependent variable?
-    -   independent: **N_content**
-    -   dependent: **hairgrass_density_m2**
--   Are the variables categorical or continuous? **Both are continuous**
--   Which type of data visualization should we use? **Scatterplot**
-
-Next, calculate the mean (`mean()`) and standard deviation (`sd()`) for the nitrogen content.
+Which population grows the biggest? Let's start by calculating some summary statistics of the data.
 
 ```{r}
-# Mean and standard deviation of nitrogen content
-hairgrass %>% 
-  summarise(mean_N = mean(n_content),
-            sd_N = sd(n_content))
+max_pop <- fish_pops %>%
+  group_by(population)%>%
+  summarise(max_N = max(N))
+max_pop
 ```
 
-Now that we have summarized the data, let's take a look at the data visually.
+It looks like Population B grows the largest by far. Let's learn more about Population B and filter out the data so we can look at it separately.
 
 ```{r}
-# Scatterplot
-ggplot(hairgrass, aes(n_content, hairgrass_density_m2)) +
-  geom_point() +
-  labs(x = "N Content",
-       y = "Hairgrass Density (per m^2)") +
-  theme_bw()
+popB <- fish_pops %>%
+  filter(population == "B")
+popB
 ```
 
-Do you see a pattern? What type of relationship do you see?
-
-How do we analyze this type of relationship statistically?
+**What type of growth is the population exhibiting?**  
+*Answer: Exponential.*
 
-## Statistical Analysis
+**Why is there no carrying capacity in this population data set? What does this mean about the resources Population B needs?**  
+*Answer: Exponential models assume unlimited resources, so no carrying capacity (K) is included.*
 
-We are going to use two (related) statistical methods to understand the relationship between two continuous (numeric) variables:
+**What do you notice about Population A that is different than Population B?**  
+*Answer: The intrinsic growth rate (r) for Population A is negative.*
 
--   Correlation coefficient ($r$) and R-squared ($R^2$)
--   Linear regression
+**What has happened to this population?**  
+*Answer: Population A crashes and reaches zero.*
 
-### Line of Best Fit
+**Between Population A and Population B, which is a better target population for fishing? Why?**  
+*Answer: Population B, because r > 0 and it grows quickly.*
 
-To understand correlation, R-squared, and linear regression, we first need to talk about something we call the *line of best fit*.
+### Exponential Growth Populations
 
-The line of best fit aims to minimize the distance between each observation (points) and the line. The distances between each observation and any line are called *residuals* (the dotted gray lines). The line of best fit is the line that has the smallest *residuals*.
+It looks like populations exhibiting exponential growth might be good targets for our fishing operations because they can become very large. Let's look at the data we have on exponential growth populations a little closer.
 
-![](assets/lines.png) ![](assets/residuals.jpg)
-
-We can add a line of best fit to a ggplot by using the `geom_smooth()` function. We need to specify that the method we want should produce a straight line ("linear model").
+Let's filter out the exponential growth models to examine them.
 
 ```{r}
-# Scatterplot
-ggplot(hairgrass, aes(n_content, hairgrass_density_m2)) +
-  geom_point() +
-  geom_smooth(method = "lm") +
-  labs(x = "N Content",
-       y = "Hairgrass Density (per m^2)") +
-  theme_bw()
+exp_pops <-fish_pops %>%
+  filter(growth_type == "exponential")
+exp_pops
 ```
 
-### Correlation Coefficient
-
-The correlation coefficient, `r`, is a measurement of the strength of the relationship between two continuous variables.
-
-The correlation coefficient is a number between -1 and 1 that looks at the relationship between two numeric variables. If the value is negative, there is a negative relationship between the two variable; if the value is positive, there is a positive relationship.
-
-If all the points fall exactly on the line of best fit, r = 1 or -1. If there is no relationship between the variables, `r` is 0 (or something very close to it).
-
-![](assets/coefficient_correlation.jpg)
-
-The greater the magnitude (size) of the correlation coefficient, the stronger the relationship between the two variables.
-
-![](assets/correlation.jpg)
-
-It is also important to recognize that the correlation coefficient has nothing to do with the slope of the line (we use linear regression to assess that!).
-
-![](assets/steepness.jpeg)
-
-Based on the hairgrass plot we did above, do you expect the correlation coefficient to be positive or negative? For this class, we'll say that any `r` value with 0.1 of 0 means there is no relationship.
-
-To calculate the correlation coefficient, we need to go back to base R and indicate which columns we are referencing with the `$` operator. We use the `cor()` function.
+It's hard to see what's happening with only the data frame. Let's look at the exponential growth populations on the same plot:
 
 ```{r}
-# Find the correlation coefficient
-r <- cor(x = hairgrass$n_content, y = hairgrass$hairgrass_density_m2)
-
-# View result
-r
+ggplot(exp_pops, aes(x = year, y = N, color = population)) +
+  geom_point() +
+  geom_line()+
+  labs(x = "Time", y = "Abundance", color = "Population") +
+  theme_light()
 ```
 
-### R-squared ($R^2$)
-
-The R-squared ($R^2$) value is a representation of how much variation is explained by the line of best fit.
-
-When we have a dependent variable and an independent variable, the R-squared value tells us how much of the variation in the *dependent* variable is explained by the *independent* variable.
-
-Sometimes we don't have obvious dependent and independent variables, but we can still use similar language ($R^2$ tells us how much of the variation in the data is explained).
-
-To calculate R-squared, we square the correlation coefficient value we calculated above. It is always positive because we are squaring it `r` value; that means that R-squared values range from 0 to 1. The closer to 1, the more variation is explained.
+It looks like Population B is very large, but Population A stays small. It's hard to see what is happening on this plot because the scales are so different, so let's look at what is happening with Population A by filtering out the data just for that population.
 
 ```{r}
-# What is r^2?
-r^2
+popA <-fish_pops %>%
+  filter(population == "A")
+popA
 ```
 
-How would we interpret this `r^2` value?
-
-### Linear Regression Analysis
-
-A regression analysis approximates the relationship between a dependent variable and one or more independent variables. It evaluates the strength of that relationship, ultimately giving us a p-value.
-
-Since we are using linear regressions in this course, the regression model will take the form of a line: `y = mx + b`
-
--   `y` = dependent variable (y-axis)
--   `x` = independent variable (x-axis)
--   `m` = slope of the line is the slope
--   `b` = y-intercept
-
-Using our variables, what would our linear regression model look like (we don't know `m` or `b` yet...)?
-
-### Hypothesis Testing
-
-What is the null hypothesis? What is the alternative hypothesis?
-
-**Null Hypothesis** ($H_{0}$): There is *no* relationship between hair grass density and nitrogen content.
-
-**Alternative Hypothesis** ($H_{A}$): There *is* a relationship between hair grass density and nitrogen content.
-
-What do you think this means for the slopes?
-
-### Regression Analysis
-
-Thankfully, R can calculate the slope (`m`) and y-intercept (`b`) of the line of best fit for us.
+What do you notice about Population A that is different than Population B? Intrinsic growth rate negative
 
-Let's find the equation for our line of best fit, our test statistic, and our p-value. To do this, we use a function called `lm()`: this stands for "linear model."
-
-Like with ANOVA, we will then want to use the `summary()` function to get out the values we need.
+The intrinsic rate of increase (r) for Population A is negative. Let's see what that means for Population A and plot it by itself:
 
 ```{r}
-# Linear model of hairgrass density v. nitrogen content
-nitrogen <- lm(hairgrass_density_m2 ~ n_content, hairgrass)
-
-# Summary of results
-summary(nitrogen)
+ggplot(popA, aes(x = year, y = N)) +
+  geom_point() +
+  geom_line()+
+  labs(x = "Time", y = "Abundance") +
+  theme_light()
 ```
 
-As with our other statistical tests --- t-tests and ANOVAs --- the results give us some important values:
-
--   `b` (y-intercept): part of our line of best fit equation
-    -   The "intercept estimate", in this case 1.385, is our y-intercept in our line of best fit
--   `m` (slope): also part of our line of best fit equation
-    -   This is the estimate for our independent variable (N_content in this case)
-    -   In this model, m = 0.659
--   *F-statistic*: this is our test statistic
--   *p-value*: calculated from our regression model, used to determine significance (0.05 cut-off, as usual)
-    -   There are multiple p-values here. Focus either on the p-value for the independent variable (N_content) or the overall p-value displayed in the last line of the results summary (p \< 2.2e-16)
--   *R-squared*: this is our R-squared value that we calculated earlier
-    -   You can report either the "multiple" or the "adjusted"
-    -   The "multiple" will typically match the one we calculate with code
+What has happened to this population? Crashed
 
-This means our equation for the line of best fit is: y = 0.659x + 1.385 and there is a statistically significant relationship between nitrogen content and hairgrass density.
+Even with "exponential growth", the population has crashed (gone to 0), because *r* is negative. It took approximately 7 years for this to happen.
 
-## Soil pH
+Between Population A and Population B, which do you think is a better target population for fishing? Population B, `r` is positive
 
-Let's do the same series of steps to determine how soil pH impacts hair grass densities. Work on this in your small groups, and we will go over it in about 10 minutes.
+**Logistic growth populations**
 
-Start with summarizing the data: mean and standard deviation.
+Ok, so we can't rely only on the type of growth as a way to make our fishing decisions. But, we're still curious about our populations that are limited by resources (i.e., exhibiting logistic growth).
 
 ```{r}
-# Mean and standard deviation of soil pH
-hairgrass %>% 
-  summarize(mean_pH = mean(soil_pH),
-            sd_pH = sd(soil_pH))
+log_pops <- fish_pops %>%
+  filter(growth_type == "logistic")
+log_pops
 ```
 
-Visualize the data. Remember to add in the line of best fit using `ggplot2`. Also add labels and a theme for practice.
+Notice in this case we have carrying capacity data. Let's use some data visualizations to help us understand what that means.
+
+Lets look at the two logistic growth populations on the same plot:
 
 ```{r}
-# Scatterplot
-ggplot(hairgrass, aes(soil_pH, hairgrass_density_m2)) +
+ggplot(log_pops, aes(x = year, y = N, color = population))+
   geom_point() +
-  geom_smooth(method = "lm") +
-  labs(x = "Soil pH",
-       y = "Hairgrass Density per m^2") + 
-  theme_bw()
+  geom_line()+
+  labs(x = "Time", y = "Abundance", color = "Population")+
+  theme_light()
 ```
 
-Calculate the correlation coefficient (`r`)? What does this tell us?
+Which population gets the largest? Population 4
 
-```{r}
-# Correlation coefficient
-r <- cor(x = hairgrass$soil_pH, y = hairgrass$hairgrass_density_m2)
-```
+### Finding the Carrying Capacity
 
-How much variation does soil pH explain in the hair grass density data?
+When we have a logistic growth model, we often will want to find the carrying capacity of the population
 
-```{r}
-# What is r^2?
-r^2
-```
+What values does each population level off at?
 
-Write out the model for our question about soil pH (without values)?
+-   Population C: 20
 
-```{r}
-# hairgrass density  = m(soil_pH) + b
-```
+-   Population D: 100
 
-Run the regression model and write out the equation for the line of best fit.
+These are the carrying capacities. If you look back at the data, you will see Population C has a carrying capacity of 20 individuals, whereas Population D has a carrying capacity of 100 individuals. Which population do you think might be the best candidate to fish between these two? Population D is more abundant and has a higher carrying capacity.
 
-```{r}
-# Linear model of hairgrass density v. soil pH
-soil_pH <- lm(hairgrass_density_m2 ~ soil_pH, data = hairgrass)
-summary(soil_pH)
-```
+**A review of what we have learned**
 
-Equation for line of best fit: y = 0.005x + 7.65
+The following factors are important to know about a population before fishing:
 
-Interpret the results of the regression (our p-value cutoff is still = 0.05). What do we conclude about the relationship between soil pH and hair grass density? Why?
+1.  Exponential growth can result in bigger populations than logistic growth
 
-Because p \> 0.05 for the regression model, we fail to reject the null hypothesis and conclude that there is no relationship between soil pH and hairgrass density.
+2.  Intrinsic rate of increase (`r`) should not be negative
 
-## Data-driven Decision Making
+3.  A higher carrying capacity means the population can take advantage of more resources to grow larger, which may indicate that the population is a better candidate for fishing
 
-The reason we are using regression analysis is to inform where we should (or should not) build our road so we don't harm the sensitive hair grass or take away their prime habitat.
+## References
 
-What do results above for nitrogen content and soil pH mean for the road we are building?
+Zhang, M., Liu, S., Bo, J., Zheng, R., Hong, F., Gao, F., Miao, X., Li, H., & Fang, C. (2022). First evidence of microplastic contamination in Antarctic fish (Actinopterygii, Perciformes),*Water*, *14*(19).
\ No newline at end of file
diff --git a/modules/module_4/module4_2.qmd b/modules/module_4/module4_2.qmd
index 8606f62..9826190 100644
--- a/modules/module_4/module4_2.qmd
+++ b/modules/module_4/module4_2.qmd
@@ -1,206 +1,184 @@
 ---
-title: "4.2: Multiple Regression"
+title: '4.2: Sustainable Fishing'
+author: "Jessie Golding and Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
-Sometimes (often!), we have multiple independent variables that we think might be influencing our dependent variable. In order to take into account more than one variable, we can perform what is called a "multiple regression."
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
 
-Multiple regressions can become complicated beasts, but we are going to focus on adding in just one additional variable and how it can impact our interpretations.
+# Sustainable Fishing
 
-Let's use some penguins nesting site data to demonstrate.
+## Applying Principles of Population Growth
 
-## Set-Up
+We have decided that the Antarctic toothfish (*Dissostichus mawsoni*) may be a good candidate for our fishing.
 
-As usual, we want to load our packages and our data before we begin.
+![](assets/antarctic_toothfish.jpg){fig-align="center"}
 
-```{r message=FALSE, warning=FALSE}
-# Load library
-library(tidyverse)
+The Antarctic toothfish is fairly widely distributed around Antarctica. The hexagons below show where the Antarctic toothfish occurs. Gray represents known distribution and red represents locations where genetic samples were taken for a study on their population (Maschette et al. 2023).
 
-# Load data
-site_data <- read_csv("data/site_changes.csv") %>% 
-  mutate(year = as.factor(year)) # don't worry about this line too much
+![](assets/antarctic_toothfish_distribution.jpg){width="75%" fig-align="center"}
 
-# We just want the year column treated as a category, not a number
-```
+However, given concerns about maintaining the fishery (an important ecosystem service), we want to make sure we do not overfish the species but rather fish sustainably.
+
+Let's keep exploring our fish populations to help us understand what we should be focusing on when making decisions about how many fish we can sustainably harvest from any given population.
 
-We want to understand what factors influence penguin nesting --- we wouldn't want to build our road through important penguin nesting territories!
+## Set-Up
 
-Let's quickly take a look at the data we have.
+As usual, we start with loading our packages and our data.
 
 ```{r}
-# View first few rows
-head(site_data)
+library(tidyverse)
 
-# View last few rows
-tail(site_data)
+fish <- read_csv("data/fish_pops.csv")
 ```
 
-Okay, it looks like we have data for 100 different sites collected in two different years. We have the number of penguin nests, which is the variable that we are interested in. We also have a few other variables that might be influencing the number of nests at each site: the number of tussocks of grass, the stone size, the distance to water, and the year that data were collected.
+Let's take a look at the data and remind ourselves what the data look like.
 
-Let's start with looking at how the number of grass tussocks affects nest numbers.
+```{r}
+head(fish)
+```
 
-## Regression Model (1 Independent Variable)
+## Population Growth Models
 
-Let's start by plotting our tussocks data and run a model.
+Let's remind ourselves about exponential growth and logistic growth.
 
-```{r}
-# Scatterplot
-ggplot(site_data, aes(x = tussocks, y = num_nests)) +
-  geom_point() +
-  geom_smooth(method = "lm") +
-  labs(x = "Tussocks", y = "Number of Nests") +
-  theme_classic()
-```
+**Expotential growth** takes births and deaths (in the form of the intrinsic growth rate, `r`) into account:
 
-This model is showing us a positive relationship between the number of tussocks and number of nests. Let's run our linear regression model.
+![](assets/clipboard-4205079346.png){width="83" fig-align="center"}
 
-```{r}
-# Linear model of number of nests v. number of tussocks
-tussocks_model <- lm(num_nests ~ tussocks, data = site_data)
+**Logistic growth** includes the intrinsic growth rate and another important factor—resources. The resources determine the maximum number of individuals a population can have, which we call the carrying capacity (`K`):
 
-# View results
-summary(tussocks_model)
-```
+![](assets/clipboard-3779797644.png){width="130" fig-align="center"}
 
-Our model is telling us that the relationship between the number of nests and number of tussocks is highly significant.
+This is what each type of population growth model looks like:
 
-## Running a Multiple Regression
+![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
 
-What if we incorporate the year the data were collected into our model? Adding an additional independent variable to our model can change how we interpret the results.
+### Which Population to Fish?
 
-First, let's plot a multiple scatterplot. Do we see the same patterns?
+We've previously discussed what factors we want to focus on when we are deciding which population to fish. Ideally, we want to target both a large population and a stable population.
 
-```{r}
-# Scatterplot + year
-ggplot(site_data, aes(x = tussocks, y = num_nests, color = year)) +
-  geom_point() +
-  geom_smooth(method = "lm") +
-  labs(x = "Tussocks", y = "Number of Nests") +
-  theme_classic()
-```
+At first glance, a population that is growing exponentially seems ideal—and it is, in some ways.
 
-Huh, that looks a little bit different...we no longer seem to have much of a positive relationship. Instead, both lines are looking pretty flat! Let's see what happens when we add year into our regression model.
+But in reality, all populations will *eventually* hit some capacity on resources. Populations that are currently demonstrating exponential growth either haven't yet hit their carrying capacity or have dramatically overshot their carrying capacity, which can lead to a huge crash or a smaller crash with boom and bust cycles until it evens out around the carrying capacity.
 
-By adding the year variable into the model, we are also asking if the number of nests changes by year. Does it change our significance?
+![](assets/pop_crash.jpeg){width="75%" fig-align="center"}
 
-The way we add an independent variable is with a `+` in the equation.
+So, in order for us to feel confident that our population will be large for a long time, we probably want to choose the safer option, which is choosing a population that is showing logistic growth.
 
-```{r}
-# Linear model of number of nests v. number of tussocks and the year
-tussocks_model <- lm(num_nests ~ tussocks + year, data = site_data)
+Let's select the logistic growth population with the largest value, Population D, for our next analyses.
 
-# View results
-summary(tussocks_model)
+```{r}
+log_pop <- fish %>% 
+  filter(population == "D")
 ```
 
-We have a bunch of estimates now! Don't worry, I won't be asking you to put them into an equation :) We are only focusing on significance values.
-
--   `tussocks`: the p-value for the tussocks row (p = 0.7) shows whether the number of tussocks is a significant driver of nest numbers (it isn't!)
--   `year2011`: this p-value is significant (p = 4.69e-05) --- year does seem to impact how many nests there are
-    -   (you don't need to know this, but if you are curious...): the reason this says `year2011` and not just `year` is because year is acting as a category, and we can get different coefficient estimates for year. The first option in the categorical variable (1971, in this case) is incorporated into the current intercept estimate, so `year2011` indicates the difference from the original.
+## Estimating Carrying Capacity
 
-### Interactions
+In our journey to determine how many fish we can sustainably harvest (i.e., remove from the population without causing the population to go extinct), we first need to determine the carrying capacity.
 
-Sometimes, we might expect that there will be in interactive effect between the two independent variables we are including in our model. This means that the combinations of independent variables impact the dependent variable in different ways.
+While the dataset contains carrying capacity estimates, those are only visual estimates. We are going to use code (and R will use a lot of math behind the scenes) to calculate a more accurate estimate from the data.
 
-We can typically spot interactive effects in a plot when the slopes of the regression lines differ from each other a lot.
+To do so, we are actually going to use a new package, `drc`.
 
-Let's talk through the visual demonstration below to hopefully get a better understanding of how interaction effects work. These plots are showing different possible relationships between two independent variables: how many cups of coffee someone has had and whether they drank them in the morning or the afternoon (categorical variable), and how those might impact the dependent variable, how long it takes to complete a task.
+This package will let us fit a logistic model and provide an estimate of the asymptote of the curve, or the value where the curve levels off and not longer increases. This is our estimate of `K`, the carrying capacity of the population.
 
--   The top two panels show examples of no interactive effect. It looks like there is an effect of time of day; in each, drinking coffee in the morning makes tasks take longer. However, the *slopes* between time of day are the same. In the first panel (top left), there also looks to be an impact from the number of cups of coffee.
--   The bottom four panels demonstrate possible variations of *interactive* effects. Not only does time of day and/or number of cups of coffee impact how long it takes to complete a task, but those independent variable affect how long it takes to complete a task *in different ways*.
+```{r message=FALSE}
+# load the drc package; text when you load it is fine (as long as it doesn't say error)
+library(drc)
+```
 
-![](assets/coffee_interaction.jpg)
+Now, we are going to use a function from the `drc` package called `drm()` to fit our logistic growth model.
 
-In our example above, an interactive effect between tussocks and year would mean that we think the number of tussocks impacts the number of nests *in different ways* between the two categories (year). Since the slopes between the years don't look very different, we aren't really expecting there to be a significant interaction, but let's check anyway.
+It works very similarly to the inferential statistics models from Module 3 except instead of finding a straight line of best fit, this line of best fit is allowed to curve like the logistic growth model.
 
-Let's run a model that includes the interaction term in our model. To do this, we will use an asterisk (`*`) instead of a plus sign.
+What kind of growth do we think we have based on this plot? Logistic
 
 ```{r}
-# Linear model with interaction
-tussocks_model <- lm(data = site_data, num_nests ~ tussocks * year)
-
-# View results
-summary(tussocks_model)
+# Fit the model to the data
+# 'N ~ year' means we're predicting N (population) as a function of 'year'
+# fct = LL.4() specifies to fit a logistic growth model
+model <- drm(N ~ year, data = log_pop, fct = LL.4())
 ```
 
-We can see that we still have estimates for both tussocks and year alone. However, we now also have something new: our interaction term, `tussocks:year2011`.
-
-It turns out that adding in the year variable has changed the significance in our model, and the interaction term is also not significant.
-
--   The positive relationship we originally observed is no longer present when we account for the year.
--   There is no significant interaction, meaning that penguins respond to the number of tussocks the same way in each year.
--   Interestingly (and maybe frustratingly), the significant effect of year that we saw before is also gone.
-    -   This is a confusing bit of statistics, and I won't go into depth here about why this happens. Similar things sometimes happen when we have a significant overall ANOVA but no significant pairwise comparisons.
-    -   What is happening behind the scenes has to do with how the information for each variable is being partitioned. Adding new variables (including an interaction) decreases the likelihood of any individual variable being significant.
-    -   I will never ask you to explain this! Only to correctly interpret the significance values.
-
-Let's explore another example to get a better understanding.
+This time, unlike with the linear model, we are interested not in a p-value but in the estimate of a specific value from the model--the carrying capacity.
 
-## Practicing with Stone Size
+To get all of the estimated values, we will use a function called `coef`, which is short for "coefficient," to give us the estimates from the `model` object.
 
-Let's investigate how stone size influences nest numbers. Gentoo penguins build their nests out of stones (they actually court their penguin partner with stones, too)!
+```{r}
+estimates <- coef(model)
+estimates
+```
 
-![](assets/gentoo-penguin-cornell-lab2.jpg)
+In this case, we happen to be interested in the estimate they are calling `d:(Intercept)`; this is what we are calling `K`, the carrying capacity. Let's save that value.
 
-Start by plotting the relationship between stone size and the number of nests.
+The simplest way to choose one value from the vector `estimates` is to use square brackets for subsetting, something we haven't done since Module 1! We want the 3rd value.
 
 ```{r}
-# Scatterplot
-ggplot(site_data, aes(x = stone_size, y = num_nests)) +
-  geom_point() +
-  geom_smooth(method = "lm")
+K <- estimates[3]
+K
 ```
 
-What does the regression model?
+Our estimate for the carrying capacity is 104.5 fish, which is close to the carrying capacity value in the dataset (100).
 
-```{r}
-# Linear model of number of nests v. stone size
-stone_size_mod = lm(num_nests ~ stone_size, data = site_data)
+We can remove the label (`d:(Intercept)`) if we want to by using the `unname()` function. Now our object `K` is only the number for the estimate of the carrying capacity.
 
-# View results
-summary(stone_size_mod)
+```{r}
+K <- unname(K)
+K
 ```
 
-How to we interpret this model?
+### Plotting the Carrying Capacity
 
-Next, let's add year in the mix. First, plot the relationship between the number of nests and stone size by year.
+Let's now add this estimated carrying capacity value to our plot of the data.
+
+First, let's plot the population data alone.
 
 ```{r}
-# Scatterplot + year
-ggplot(site_data, aes(x = stone_size, y = num_nests, color = year)) +
+ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs. population)
   geom_point() +
-  geom_smooth(method = "lm")
+  geom_line() +
+  labs(x = "Year", y = "Population") +
+  theme_bw()
 ```
 
-This plot looks pretty different from the one with tussocks. First, let's see what happens when we add the year variable *without* the interactive term. To do that, we use a plus sign (`+`), not an asterisk (`*`).
+To add the a line representing the carrying capacity, we can use `geom_hline()` to add a horizontal line where the y-intercept is equal to the carrying capacity.
 
-```{r}
-# Linear model of number of nests v. stone size and the year
-stone_size_mod = lm(num_nests ~ stone_size + year, data = site_data)
+This is very similar to how we used `geom_vline()` to add a vertical line to histograms to represent the mean value.
 
-# View results
-summary(stone_size_mod)
+```{r}
+ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs. population)
+  geom_point() +
+  geom_line() +
+  # use geom_hline to plot a horizontal line where the y-intercept is equal to K
+  geom_hline(aes(yintercept = K), color = "red") + # outside of aes() sets color
+  labs(x = "Year", y = "Population") +
+  theme_bw()
 ```
 
-How do we interpret this multiple regression model?
+## Maximum Sustainable Yield
 
-From the ggplot above, we can see that the slopes for the two different years are quite different: one is pretty flat while the other is definitely positive. Different slopes often indicate that an *interaction* might be at work. Let's find out. Run another multiple regression model, this time adding in the interactive effect (`*`).
+Once we have an estimate of the carrying capacity for a population, we can calculate something called the *maximum sustainable yield*, or MSY.
 
-```{r}
-# Linear model with interaction
-stone_size_mod = lm(num_nests ~ stone_size * year, data = site_data)
+The MSY is the number of individuals that we can remove from the population and keep the population stable (or increasing). If we remove too many individuals (above MSY), the population will not be able to reproduce fast enough to replace the harvested individuals, causing a decline in the population—in this case, from over-fishing.
 
-# View results
-summary(stone_size_mod)
-```
+![](assets/logistic_growth_msy.png){width="49%"} ![](assets/msy.png){width="49%"}
+
+While the concept behind how we get to the equation for MSY may be a bit complex, thankfully, the equation itself is quite straightforward:
 
-Wow, a bunch of significant variables! What does that mean, exactly?
+![](assets/clipboard-959845017.png){fig-align="center"}
 
-Not only are stone size and year significant, but so is the interaction between stone size and year. What does this mean? It means that penguins in different years seems to respond different to stone size. In 1971, the number of nests increases with stone size, but in 2011, the number of nests doesn't seem impacted by stone size. Therefore, there is an *interaction* between stone size and year.
+Let's calculate the maximum sustainable yield for Population D.
 
-### Resources
+```{r}
+MSY <- K/2
+MSY
+```
 
-Still feeling a bit confused about multiple regression and interactions? I recommend checking out [this website](https://stattrek.com/multiple-regression/interaction?tutorial=reg). You can skip all the equation stuff at the beginning. Below that, though, I think they do a nice job of laying out what interactions are.
+This means that we can take about 52 fish from this population annually and keep the population stable!
 
-Also, if you are interested in how we select which model is the best model, [this article](https://www.statology.org/aic-in-r/) is a decent place to start.
+If we take fewer than 52 fish, the population will increase (until the carrying capacity, that is). If we take more than 52 fish, however, we are over-fishing the population, meaning the population will not be able to reproduce enough to maintain the population and will eventually collapse.
\ No newline at end of file
diff --git a/modules/module_4/module4_3.qmd b/modules/module_4/module4_3.qmd
index b71f58c..1b7e8ec 100644
--- a/modules/module_4/module4_3.qmd
+++ b/modules/module_4/module4_3.qmd
@@ -1,229 +1,260 @@
 ---
-title: "4.3: Using Functions to Automate Tasks"
+title: '4.3: Comparing Populations'
+author: "Jessie Golding and Ellen Bledsoe"
+format:
+  html:
+    toc: true
 ---
 
-## Introduction
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
+```
+
+# Comparing Populations
 
-In this lesson, we'll learn how to write functions in R. Functions are essential for automating tasks and making your code more efficient and reusable. Let's get started!
+Throughout this module, we have learned how populations demonstrating exponential growth and logistic growth differ from one another, both conceptually and visually.
 
-### What is a function?
+While we have fit models to some of these populations (specifically, logistic growth models), we have done so in order to estimate the carrying capacity of the population rather than to use inferential statistics to determine if the populations are statistically significantly different from one another.
 
-We've been using functions quite a lot in the course, but let's remind ourselves *exactly* what a function actually is.
+In today's lesson, we are going to learn how to (a) add those logistic growth models to our data visualizations and (b) compare populations statistically.
 
-A function is a block of code designed to perform a specific task. A function is **executed** when it is **called.** This means that the block of code is run every time you use the function!
+## Set Up
 
-A function takes specific **arguments** as input, processes them, and **returns** the output.
+As always, we need to start by loading the packages we are going to use along with our data.
 
-![](assets/r-function-syntax.png)
+First, let's load our packages. We are going to be using both the `tidyverse` and `drc` packages in this lesson, so we need to load both of them.
 
-We've used a lot of built-in functions already, like the `mean()` function.
+```{r}
+library(tidyverse)
+library(drc)
+```
 
-Now, we are going to write our own, user-defined functions!
+Next, we want to bring in our dataset.
 
-## Writing Our First Function
+```{r}
+pops <- read_csv("data/comparing_populations.csv")
+```
 
-Following the syntax from the image above, let's write a function called `add_numbers` that takes **two numbers as arguments and returns their sum.**
+We can use the `head()` function to take a look at the data. What data do we have?
 
 ```{r}
-# An addition function
-add_numbers <- function(x, y) {
-  z <- x + y
-  return(z)
-}
+head(pops)
 ```
 
-Notice that we didn't put any actual numbers in the function! Instead, we used generic (undefined) arguments (e.g., `x` and `y`) to represent the values that we will eventually supply to the function when we execute it.
+After inspecting our dataset, it looks as though we have more time-series data of population abundances. It isn't immediately clear how many populations there are, but perhaps the populations are named after their locations ("NE" could stand for "Northeast").
+
+## Visually
 
-Using our undefined arguments, we set up a statement (or series of statements) for the function to perform: add `x` and `y` together and save that value as an object called `z`.
+Let's first start our investigation of these populations by plotting the data points as a scatterplot.
 
-We use the `return()` function to indicate that `z` is the value we want the `add_numbers` function to give back to us when it is executed.
+As we have done throughout this module, we will want the variable representing time to be on the x-axis and the population abundance (N) on the y-axis.
 
-Let's test our function with numbers, now!
+We will also want the data points to be distinguished by which population they are from, so we need to specify the `color` argument within the `aes()` function.
 
 ```{r}
-# Use our new function
-add_numbers(5, 5)
+ggplot(pops, aes(year, abund, color = population)) +
+  geom_point() + 
+  labs(x = "Year",
+       y = "Abundance",
+       color = "Fish Population") +
+  theme_bw()
+```
 
-# Store our result
-sum <- add_numbers(10, 10)
+### Interpretation
 
-# View our result
-sum
-```
+Based on the plot, what do you notice about these populations?
 
-#### Let's Practice
+-   Are they more likely exhibiting exponential or logistic growth? Why?
+-   How do the populations appear to differ from one another?
 
-Now try to write a function called `multiply_numbers` that takes **three** numbers as arguments and returns the **product.**
+Given your answers to the questions above, what might be a good next step?
 
-Make sure to test your function, and remember a function should have a name, a list of arguments, and return something.
+## Numerically
 
-```{r}
-# Multiplication function
-multiply_numbers <- function(x, y, z) {
-  a <- x * y * z
-  return(z)
-}
-
-# Test function
-multiply_numbers(1, 3, 3)
-```
+When comparing different categories or groups (in this case, comparing different populations), we typically compare them both visually and numerically before conducting statistical tests.
 
-### Exercise 1: Simple Function
+Since we have already made visual comparisons, let's compare the populations numerically.
 
-**Objective:** Write a function called `inspect` that takes a data frame as an argument and prints the head and tail of the data frame.
+Typically, we have compared groups with measures of central tendency and measures of variability. Do you think that is what we should do here? Why or why not?
 
-Setup:
+### Separate Populations
 
-```{r message = FALSE}
-# Load in tidyverse first!
-library(tidyverse)
-```
+Unlike our typical procedure, in order to fit logistic models to each population, our simplest way forward will be to create one data frame for each population.
+
+| NOTE: in case you are wondering, yes, there are definitely ways to fit a logistic model to each population without separating them! However, we would have to introduce quite a few new concepts to do so efficiently, so we are going to stick with the tried-and-true process of running the models individually.
+
+We can use the `filter` function to select each population, one at a time.
 
 ```{r}
-# Load the dataframe into your environment by reading the hairgrass_data.csv file
-hairgrass <- read_csv('data/hairgrass_data.csv')
+pop_NE <- pops |> 
+  filter(population == "NE")
+
+pop_SE <- pops |> 
+  filter(population == "SE")
+
+pop_SW <- pops |> 
+  filter(population == "SW")
 ```
 
-Before we attempt to write this function, you'll need to know about the `print()` function.
+### Finding the Carrying Capacity
 
-In R, if you have multiple expressions (in our case, head and tail) and you want to see the output of each, you need to explicitly print them using the `print()` function. Otherwise, R will just show the last expression it ran.
+Now that we have data frames for each of the populations, we can fit the logistic model to each population and determine the estimate of the carrying capacity for each.
+
+As we run the code to fit the model to each population, we might receive some warnings: "Warning :NaNs produced". Don't worry--that's ok! Keep going!
 
 ```{r}
-# Demonstration of the print function
-print(mean(hairgrass$soil_pH))
+# NE population
+model_NE <- drm(abund ~ year, data = pop_NE, fct = LL.4())
+model_NE
+
+# if you want to be fancy and save the carrying capacity as an object
+# this code combines several steps from the last lesson into one step
+K_NE <- unname(coef(model_NE)[3])
+K_NE
 ```
 
-Now we can start to write our function! Write a function that prints the head and tail of the dataframe. If you're up for an extra challenge, have the function print out the first 10 and last 10 rows (instead of 6 and 6).
+Now we can do the same for the SE and SW populations, too.
 
 ```{r}
-# Write your function here
-# Remember to use print!
-
-load_and_inspect <- function(dataframe) {
-  
-  # Print first 10 rows
-  print(head(dataframe, 10))
-  
-  # Print last 10 rows
-  print(tail(dataframe, 10))
-  
-}
-```
+# SE population
+model_SE <- drm(abund ~ year, data = pop_SE, fct = LL.4())
+model_SE
 
-Test your function with the hairgrass data frame.
+K_SE <- unname(coef(model_SE)[3])
+K_SE
 
-```{r}
-# Test our function on hairgrass data
-load_and_inspect(hairgrass)
+# SW population
+model_SW <- drm(abund ~ year, data = pop_SW, fct = LL.4())
+model_SW
+
+K_SW <- unname(coef(model_SW)[3])
+K_SW
 ```
 
-### Exercise 2: Linear Regression Function
+Let's record the carrying capacity estimates for each population here so we can refer back to them later, if needed.
+
+| Population | Carrying Capacity  |
+|:----------:|:------------------:|
+|     NE     | `r round(K_NE, 1)` |
+|     SE     | `r round(K_SE, 1)` |
+|     SW     | `r round(K_SW, 1)` |
+
+As we saw in the plot, the estimated carrying capacity for the SW population is below that of the NE population.
 
-**Objective**: Develop a function that performs linear regression between two columns of a data frame and returns the model summary.
+What was much more challenging to see clearly in the plot, however, is that the estimated carrying capacity of the SE population is in between the other two carrying capacities, albeit closer to the NE population.
 
-Some helpful hints:
+### Plotting the Logistic Model Curves
 
--   Have 2 arguments, one for each column
--   In the `lm()` function, you'll want to run it by specifying the dataframe and column (like `dataframe$column`) rather than just using the name of the column and setting the `data = dataframe` argument. It will look similar to how we run the code to get a correlation coefficient.
+We can add the logistic growth models that we fit to each population into our ggplot to more easily compare the curves.
+
+To do so requires a few steps.
+
+1.  Use the `predict()` function to get the values for the "logistic line of best fit." Remember, these will not match up perfectly with our *actual, observed* values; these are the y-axis or `abundance` values for the best fitting logistic model at each observed x-axis value, or year.
+
+2.  Use the `mutate()` function to create a new column in each data frame with these logistic *model* values.
+
+3.  Combine all three dataframes back together into one dataframe using the `bind_rows()` function.
+
+4.  Plot the actual data points using `geom_point()` and plot the logistic model data points using the `geom_line()` function.
+
+#### Add the Logistic Model Points to the Dataframes
+
+First, let's see what output the `predict()` function gives us for the NE population.
 
 ```{r}
-# Write your function here!
-do_regression <- function(independent, dependent) {
-  
-  # Linear model
-  lm_model <- lm(dependent ~ independent)
-  
-  # Print result
-  print(summary(lm_model))
-  
-}
+predict(model_NE)
 ```
 
-Test the function with two columns from the hairgrass data set.
+If we compare these numbers to the numbers in the `abund` column of `pop_NE`, we notice that they are not the same. The difference between these values from the model of best fit and the actual, observed values are called *residuals*, just as they are with lines of best fit (from linear regressions).
+
+![](assets/residuals.jpg){width="50%" fig-align="center"}
+
+Let's use the `mutate` function to add these model values to each dataframe.
 
 ```{r}
-# Test function on hairgrass data
-do_regression(hairgrass$soil_pH, hairgrass$hairgrass_density_m2)
-```
+pop_NE <- pop_NE |> 
+  mutate(logistic_model = predict(model_NE))
+pop_NE
+
+pop_SE <- pop_SE |> 
+  mutate(logistic_model = predict(model_SE))
 
-### Exercise 3: Plotting Function
+pop_SW <- pop_SW |> 
+  mutate(logistic_model = predict(model_SW))
+```
 
-**Objective:** Create a function to plot a scatter plot between two variables and add a regression line.
+#### Combine Dataframes
 
-For complicated reasons we won't get into, we need to do a few important things in this function to make it work:
+We can now bring all three individual data frames back together into one data frame using a function called `bind_rows()`. You can think of `bind_rows` as gluing the data frames together, one after another.
 
--   When you write the `ggplot` code, you'll need to use `aes_string()` in place of our usual `aes()` function.
--   When you test your function with the hairgrass data, you'll need to put the names of the two columns in quotation marks (e.g., "penguin_density_m2" instead of penguin_density_m2).
--   Don't worry about adding labels to your plot, but do add a theme.
+The arguments in the `bind_rows()` function are all of the data frames we want glued together.
 
 ```{r}
-# Write your function here!
-plot_data <- function(dataframe, x, y) {
-  
-  # Make plot 
-  plot <- ggplot(dataframe, aes_string(x = x, y = y)) +
-    geom_point() +
-    geom_smooth(method = "lm") +
-    theme_classic()
-  
-  # Return plot
-  return(plot)
-  
-}
+pops_models <- bind_rows(pop_NE, pop_SE, pop_SW)
+pops_models
 ```
 
-Test the function with two columns from the hairgrass data set.
+The resulting data frame again has 78 observations, as did the original `pops` data frame.
+
+#### Add Logistic Models to the Plot
+
+We can now add the logistic models to our plot using the `geom_line()` function. Specifically, the values from the `logistic_model` column will be the y-axis values for the lines we are adding; the x-axis values are the same as the overall plot: `year`.
 
 ```{r}
-# Test function on harigrass data
-plot_data(hairgrass, "soil_pH", "p_content")
+ggplot(pops_models, aes(x = year, y = abund, color = population)) +
+  geom_point() +
+  geom_line(aes(y = logistic_model)) +
+  labs(x = "Year", y = "Abundance", color = "Fish Population") +
+  theme_bw()
 ```
 
-### Bonus:
+It is now much easier to visually distinguish the differences between the growth curves and the carrying capacities between the three populations!
 
-**Objective:** Create a function, `plot_data_with_r2`, that modifies the scatter plot to include the R\^2 value from the linear regression.
+## Statistically
 
-First, outside of the function, calculate the R\^2 value for your chosen variables. Then, modify your plotting function to add an `r_squared` argument.
+We still want to analyze our three populations using inferential statistics to determine if there are significant differences between the populations.
 
-Use the `annotate()` to add the R\^2 value to the plot. You can learn more about `annotate()` [here!](https://ggplot2.tidyverse.org/reference/annotate.html)
+First, we need to decide what type of statistical test makes sense with the data we have. This is *not* a straight-forward question with a straight-forward answer. How we determine which test to use ultimately depends on the question we are asking and, therefore, the hypothesis we are testing.
 
-You might also want to learn about the `paste()` function [here](https://www.educative.io/answers/what-is-the-paste-function-in-r) to specify what the value you are putting on the plot represents. It might look something like `R squared = 0.4`.
+For the sake of the class (and in your assignment, too), let's assume the question we want to ask is whether the average population sizes significantly differ between the populations.
 
-\*\*Remember to use `aes_string()` instead of `aes()`!
+What type of statistical test should we use to answer that question?
 
-```{r}
-# Write your function here!
+What would the null and alternative hypotheses be for that statistical test?
 
-# Calculate R-squared outside the function
-r <- cor(x = hairgrass$soil_pH, y = hairgrass$p_content)
+### Filtering the Data
 
-# Round the value because otherwise it is extremely long!
-r_squared <- round(r^2, 6) 
+To most accurately compare the populations, we likely want to restrict the comparison to when the populations (or the models of best fit, at least) are fairly leveled off.
 
-# Make our function
-plot_with_r2 <- function(dataframe, x, y, r_squared) {
-  
-  # Create the plot with points and regression line
-  plot <- ggplot(dataframe, aes_string(x = x, y = y)) + 
-    geom_point() +
-    geom_smooth(method = "lm") +
-    annotate("text", x = 8, y = 20, label = paste("R^2 =", r_squared)) +
-    theme_classic()
+(Again, this is more of an estimation than an exact science, though there are more precise--and complicated--tools that could be used.)
 
-  # Return plot
-  return(plot)
-  
-}
+A rough estimation would be that the models mostly level off around year 15; let's filter our data to include only abundance from year 15 and later.
 
+```{r}
+pops_15_25 <- pops_models |> 
+  filter(year >= 15)
+pops_15_25
 ```
 
-Test your function here:
+### Running the Statistical Analysis
+
+Now that we have filtered our data, we can conduct our statistical test.
 
 ```{r}
-# Test function on hairgrass data
-plot_with_r2(hairgrass, "soil_pH", "p_content", r_squared)
+model_aov <- aov(abund ~ population, data = pops_15_25)
+summary(model_aov)
 ```
 
-### Great Work!
+This p-value tells us that the overall model is significant (we reject the null hypothesis), but it doesn't tell us which populations are significantly different from one another. To determine which populations differ from each other, we need to run *post hoc* pairwise-comparisons.
+
+```{r}
+TukeyHSD(model_aov)
+```
+
+How do we interpret these results? What are our conclusions?
+
+## Data-Driven Decision Making
+
+Our ultimate goal in this module has been to determine which population of fish is best for fishing sustainably. We are prioritizing large, stable populations.
 
-Remember, we will only ask you to make a function like the example at the beginning :)
+Given those requirements and what our numeric, visual, and statistical tests have told us, which of these populations would be the best for us to harvest? Why?
\ No newline at end of file
diff --git a/scripts/pdf-button.js b/scripts/pdf-button.js
new file mode 100644
index 0000000..f052bc1
--- /dev/null
+++ b/scripts/pdf-button.js
@@ -0,0 +1,19 @@
+document.addEventListener("DOMContentLoaded", () => {
+  const tocSidebar = document.querySelector("#quarto-margin-sidebar");
+
+  if (!tocSidebar) return;
+
+  if (document.querySelector(".toc-pdf-button")) return;
+
+  const btn = document.createElement("a");
+  btn.href = "#";
+  btn.textContent = "PDF";
+  btn.className = "toc-pdf-button";
+
+  btn.addEventListener("click", (e) => {
+    e.preventDefault();
+    window.print();
+  });
+
+  tocSidebar.appendChild(btn);
+});
\ No newline at end of file
diff --git a/styles.scss b/styles.scss
index 1380b4d..90a0f30 100644
--- a/styles.scss
+++ b/styles.scss
@@ -112,3 +112,21 @@ a:hover {
     font-size: 14px;
   }
 }
+
+/* PDF button in the sidebar */
+.toc-pdf-button {
+  display: block;
+  margin-top: 1rem;
+  padding: 0.5rem 0.75rem;
+  border-radius: 8px;
+  text-align: center;
+  font-family: "Righteous";
+  background: $magenta;
+  color: $blanched-almond !important;
+  text-decoration: none;
+}
+
+.toc-pdf-button:hover {
+  background: darken($magenta, 10%);
+  text-decoration: none;
+}
\ No newline at end of file

From 2265aabd0699af425f9b2314af2fc0b07654a43a Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Fri, 6 Mar 2026 17:39:34 -0700
Subject: [PATCH 09/15] lesson rearrangement

---
 _quarto.yml                                   |  13 +-
 images/bdsc-logo-hex.png                      | Bin 127349 -> 18225 bytes
 modules/module_1/module1_1.qmd                |   9 +-
 modules/module_1/module1_2.qmd                |   2 +-
 modules/module_2/module2_2.qmd                |   4 +-
 modules/module_3/module3_1.qmd                |   2 +-
 modules/module_3/module3_4.qmd                | 307 ----------------
 modules/module_3/module3_5.qmd                | 181 ----------
 modules/module_4/assets/binds.png             | Bin 0 -> 683596 bytes
 .../assets/hairgrass_indiv.jpeg               | Bin
 .../assets/hairgrass_landscape.jpeg           | Bin
 modules/module_4/assets/joins.png             | Bin 0 -> 262301 bytes
 .../assets/nearest_neighbor.png               | Bin
 modules/module_4/data/all_collar_data.csv     | 151 ++++++++
 modules/module_4/data/collar_data.csv         | 101 ++++++
 modules/module_4/data/hairgrass_data.csv      | 241 +++++++++++++
 modules/module_4/data/new_collars.csv         |  51 +++
 .../module_4/data/old_collars_new_data.csv    | 101 ++++++
 modules/module_4/data/site_changes.csv        | 201 +++++++++++
 modules/module_4/module4_1.qmd                | 206 ++++-------
 modules/module_4/module4_2.qmd                | 212 ++++++-----
 modules/module_4/module4_3.qmd                | 341 ++++++++++--------
 .../module3_6.qmd => module_4/module4_4.qmd}  |   3 +-
 .../assets/antarctic_toothfish.jpg            | Bin
 .../antarctic_toothfish_distribution.jpg      | Bin
 .../assets/clipboard-1439389577.png           | Bin
 .../assets/clipboard-3779797644.png           | Bin
 .../assets/clipboard-3908560513.png           | Bin
 .../assets/clipboard-4205079346.png           | Bin
 .../assets/clipboard-959845017.png            | Bin
 .../assets/exp_and_logisitc.png               | Bin
 .../assets/fish_data_fish.png                 | Bin
 .../assets/logistic_growth_msy.png            | Bin
 modules/{module_4 => module_5}/assets/msy.png | Bin
 .../assets/pop_crash.jpeg                     | Bin
 .../assets/residuals.jpg                      | Bin
 .../data/comparing_populations.csv            |   0
 .../{module_4 => module_5}/data/fish_pops.csv |   0
 modules/module_5/module5_1.qmd                | 199 ++++++++++
 modules/module_5/module5_2.qmd                | 184 ++++++++++
 modules/module_5/module5_3.qmd                | 260 +++++++++++++
 structure.qmd                                 |  36 +-
 42 files changed, 1910 insertions(+), 895 deletions(-)
 delete mode 100644 modules/module_3/module3_4.qmd
 delete mode 100644 modules/module_3/module3_5.qmd
 create mode 100644 modules/module_4/assets/binds.png
 rename modules/{module_3 => module_4}/assets/hairgrass_indiv.jpeg (100%)
 rename modules/{module_3 => module_4}/assets/hairgrass_landscape.jpeg (100%)
 create mode 100644 modules/module_4/assets/joins.png
 rename modules/{module_3 => module_4}/assets/nearest_neighbor.png (100%)
 create mode 100644 modules/module_4/data/all_collar_data.csv
 create mode 100644 modules/module_4/data/collar_data.csv
 create mode 100644 modules/module_4/data/hairgrass_data.csv
 create mode 100644 modules/module_4/data/new_collars.csv
 create mode 100644 modules/module_4/data/old_collars_new_data.csv
 create mode 100644 modules/module_4/data/site_changes.csv
 rename modules/{module_3/module3_6.qmd => module_4/module4_4.qmd} (99%)
 rename modules/{module_4 => module_5}/assets/antarctic_toothfish.jpg (100%)
 rename modules/{module_4 => module_5}/assets/antarctic_toothfish_distribution.jpg (100%)
 rename modules/{module_4 => module_5}/assets/clipboard-1439389577.png (100%)
 rename modules/{module_4 => module_5}/assets/clipboard-3779797644.png (100%)
 rename modules/{module_4 => module_5}/assets/clipboard-3908560513.png (100%)
 rename modules/{module_4 => module_5}/assets/clipboard-4205079346.png (100%)
 rename modules/{module_4 => module_5}/assets/clipboard-959845017.png (100%)
 rename modules/{module_4 => module_5}/assets/exp_and_logisitc.png (100%)
 rename modules/{module_4 => module_5}/assets/fish_data_fish.png (100%)
 rename modules/{module_4 => module_5}/assets/logistic_growth_msy.png (100%)
 rename modules/{module_4 => module_5}/assets/msy.png (100%)
 rename modules/{module_4 => module_5}/assets/pop_crash.jpeg (100%)
 rename modules/{module_4 => module_5}/assets/residuals.jpg (100%)
 rename modules/{module_4 => module_5}/data/comparing_populations.csv (100%)
 rename modules/{module_4 => module_5}/data/fish_pops.csv (100%)
 create mode 100644 modules/module_5/module5_1.qmd
 create mode 100644 modules/module_5/module5_2.qmd
 create mode 100644 modules/module_5/module5_3.qmd

diff --git a/_quarto.yml b/_quarto.yml
index cbe9a01..e0f76de 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -2,6 +2,9 @@ project:
   type: website
   output-dir: docs
 
+execute:
+  freeze: auto
+
 website:
   title: "Data In The Wild"
   search: false
@@ -45,15 +48,19 @@ website:
           - modules/module_3/module3_1.qmd
           - modules/module_3/module3_2.qmd
           - modules/module_3/module3_3.qmd
-          - modules/module_3/module3_4.qmd
-          - modules/module_3/module3_5.qmd
-          - modules/module_3/module3_6.qmd
 
       - section: "Module 4"
         contents:
           - modules/module_4/module4_1.qmd
           - modules/module_4/module4_2.qmd
           - modules/module_4/module4_3.qmd
+          - modules/module_4/module4_4.qmd
+
+      - section: "Module 5"
+        contents:
+          - modules/module_5/module5_1.qmd
+          - modules/module_5/module5_2.qmd
+          - modules/module_5/module5_3.qmd
 
       - text: "---"
 
diff --git a/images/bdsc-logo-hex.png b/images/bdsc-logo-hex.png
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diff --git a/modules/module_1/module1_1.qmd b/modules/module_1/module1_1.qmd
index 0ff8048..13ce184 100644
--- a/modules/module_1/module1_1.qmd
+++ b/modules/module_1/module1_1.qmd
@@ -21,8 +21,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ## Check-in (5 minutes)
 
-Let's all sign-up for [Posit Cloud!](https://posit.cloud/)
-We use Posit Cloud as our coding environment so everyone has access to R and RStudio thorugh a web browser.
+Let's all sign-up for [Posit Cloud!](https://posit.cloud/) We use Posit Cloud as our coding environment so everyone has access to R and RStudio thorugh a web browser.
 
 ## Rstudio Mini-tour
 
@@ -37,7 +36,7 @@ Perform a live-coding mini-tour of RStudio.
 
 You can do basic math in the Console (the bottom left part of the screen). The Console only understands R code, but it can also be treated as a calculator! Hence, we can type in some numbers and mathematical symbols.
 
-Try multiplying 5 and 3 in the Console (hint: * means multiply). Hit `Enter` to run that line of code.
+Try multiplying 5 and 3 in the Console (hint: \* means multiply). Hit `Enter` to run that line of code.
 
 ### Rmarkdown and Code Chunks
 
@@ -46,7 +45,7 @@ Rmarkdown (`.Rmd`) is a file format that lets us incorporate text and code into
 -   For writing text, you can type as you normally would.
 -   Code chunks are a bit different:
     -   Near the top right of your screen you can toggle between viewing this document in "Source" or "Visual" mode.
-    -   If you view this document under "Source," you will see that all code chunks are sandwiched between ```{r} and ```.
+    -   If you view this document under "Source," you will see that all code chunks are sandwiched between ` ```{r} ` and ` ``` `.
     -   Under "Visual," you can type R code in the lines under `{r}`.
     -   To include text in chunks, you will need to put a `#` in front. R will not read anything in the line after `#` as code.
 
@@ -60,7 +59,7 @@ To run a chunk of code, click the green arrow on the top right corner of the chu
 
 You can also run one or a few lines of code at a time by having your cursor on the line or highlighting multiple lines and hitting `Ctrl` + `Enter` (or `Cmd` + `Enter` on a Mac).
 
-A quick shortcut for adding a code chunk is `Ctrl + Alt + i` (`Cmd + Opt + i` on a Mac). Alternatively, you can go to Code > Insert Chunk.
+A quick shortcut for adding a code chunk is `Ctrl + Alt + i` (`Cmd + Opt + i` on a Mac). Alternatively, you can go to Code \> Insert Chunk.
 
 ### Some Code Chunk Practice
 
diff --git a/modules/module_1/module1_2.qmd b/modules/module_1/module1_2.qmd
index dfcd3ed..1da6a63 100644
--- a/modules/module_1/module1_2.qmd
+++ b/modules/module_1/module1_2.qmd
@@ -218,7 +218,7 @@ Because data frames are two-dimensional, we can subset the data in a data frame
 
 R *always* takes information for the row first, then the column.
 
-Think of it as: data[row, column]
+Think of it as: data\[row, column\]
 
 Just like with vectors, we can subset data frames by index or by condition using square brackets.
 
diff --git a/modules/module_2/module2_2.qmd b/modules/module_2/module2_2.qmd
index 2c9e32f..1949529 100644
--- a/modules/module_2/module2_2.qmd
+++ b/modules/module_2/module2_2.qmd
@@ -5,10 +5,12 @@ format:
   html:
     toc: true
 ---
+
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
-# Plotting with `ggplot2`
+
+# Intro to `ggplot2`
 
 So far, we've used the base R plotting syntax. While quick plots in base R can still be really useful ways to do preliminary data exploration and visualization, we often want plots that go beyond the basics without too much additional effort. This is where `ggplot2` comes in and really shines!
 
diff --git a/modules/module_3/module3_1.qmd b/modules/module_3/module3_1.qmd
index b7d717e..f9d0019 100644
--- a/modules/module_3/module3_1.qmd
+++ b/modules/module_3/module3_1.qmd
@@ -16,7 +16,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 Given the issues with our farmed fish, we will need to supplement our diets with some fish that are wild-caught for a while. These fish are caught by members of Team Antarctica, but there is quite a bit of risk involved.
 
-![](assets/seal2.jpeg){width="50%"} 
+![](assets/seal2.jpeg){width="50%"}
 
 One of the main hazards to these fishing teams are [leopard seals](https://en.wikipedia.org/wiki/Leopard_seal). They're apex predators in this ecosystem and are quite large, averaging 2.4--3.5 m (7.9--11.5 ft) in length and 200-600 kilograms (440-1,320 lb) in weight. Due to their large size and predatory nature, they can cause serious injury, especially when large schools of fish are involved ([a researcher was killed by a leopard seal in Antarctica in 2003](https://www.theguardian.com/uk/2003/jul/24/science.highereducation)).
 
diff --git a/modules/module_3/module3_4.qmd b/modules/module_3/module3_4.qmd
deleted file mode 100644
index c151fa1..0000000
--- a/modules/module_3/module3_4.qmd
+++ /dev/null
@@ -1,307 +0,0 @@
----
-title: "3.4: Roads and Regressions"
-author: "Mila Pruiett and Ellen Bledsoe"
-format:
-  html:
-    toc: true
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-# Roads and Regressions
-
-## The Scenario
-
-In this module, we have tried to figure out which bays we should prioritize for fishing based on the following data sets:
-
--   radio collar data
--   fish catch data
--   leopard seal abundance data
-
-Based on our results from Assignment 2, we made a decision about which two bays look like our best options. Now, we need to make these bays accessible over land to transport the fish we've caught to our home base.
-
-**Our Goal: build a road while minimizing our impact on the delicate antarctic ecosystem.**
-
-Our first order of business is to make sure that we avoid areas with that are well-suited for [Antarctica hair grass](https://oceanwide-expeditions.com/blog/the-plants-of-antarctic) (*Deschamsia antarctica*), one of only two flowering species of plants on the continent.
-
-![](assets/hairgrass_indiv.jpeg){width="50%"}
-
-We want to know what environmental conditions are associated with hair grass. This way, we can avoid areas where these conditions are met and not destroy precious habitat for the hair grass.
-
-It would take far too long to survey every bit of land between our base and our fishing spots, so we are going to build a **model** based on some samples of where hair grass is found to help us predict where else it might be.
-
-### What is a model?
-
-A model is a way for us to take complex systems and break them down into small, more understandable bits. We can use models to help us understand the relationship between different variables. We can then use those models to make predictions based on those relationships.
-
-## Data
-
-![](assets/hairgrass_landscape.jpeg){width="50%"}
-
-Knowing that we would soon be building roads, we asked our botanists to collect data for us on key components of the hairgrass' environment. Since it would take too long to sample everywhere hairgrass grows, they collected data from a sample of the hairgrass population.
-
-Our botanists collected data for the following variables:
-
--   *hairgrass density*: number of individual clumps (tussocks) of hairgrass (per 25 meters squared)
--   *soil pH*: most plants prefer mildly acidic to neutral environments
--   *nitrogen (N) content*: as parts per million (ppm); important for plant growth and tissue building
--   *phosphorous (P) content*: as parts per million (ppm); important for plant growth and tissue building
--   *percent rock*: how rocky the location is; rocks in soil impact water drainage and temperature
--   *max windspeed (knots per hour)*: extreme wind can pose a challenge to plants of all types
--   *average summer temperature (C)*: temperature in the growing season
--   *penguin density*: the number of penguins per the sample quadrant penguin poop increases nitrogen and phosphorus content in the system but penguins may trample the plants
-
-This data is based on this article: [Parnikoza, et al. 2007](https://doi.org/10.1016/j.polar.2007.10.002)
-
-## Summarize and Visualize
-
-As we see above, there are many environmental conditions that may be associated with hair grass density.
-
-For this lesson, we are going to focus on two: *nitrogen (N) content* and *soil pH*.
-
-### Set-Up
-
-As usual, we start with loading our packages and our data.
-
-```{r message=FALSE}
-library(tidyverse)
-hairgrass_data <- read_csv("data/hairgrass_data.csv")
-```
-
-Let's take a look at the data in the data set. What does each row represent?
-
-```{r}
-head(hairgrass_data)
-tail(hairgrass_data)
-```
-
-### Nitrogen Content
-
-Let's start by investigating any relationship between hair grass density and nitrogen content.
-
-First, we should spend a little time thinking about our variables. Spend a few minutes in small groups discussing the answer to the questions below.
-
--   Which columns are we interested in right now? soil_nitrogen, hairgrass_density
--   Which one is the independent variable and which is the dependent variable?
-    -   independent: soil_nitrogen
-    -   dependent: hairgrass_density
--   Are the variables categorical or continuous? both are continuous
--   Which type of data visualization should we use? scatterplot
-
-Next, calculate the mean (`mean()`) and standard deviation (`sd()`) for the nitrogen content.
-
-```{r}
-hairgrass_data %>% 
-  summarise(mean_N = mean(soil_nitrogen),
-            sd_N = sd(soil_nitrogen))
-```
-
-Now that we have summarized the data, let's take a look at the data visually.
-
-```{r}
-ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
-  geom_point() +
-  labs(x = "N Content",
-       y = "Hairgrass Density (per 25 m^2)") +
-  theme_bw()
-```
-
-Do you see a pattern? What type of relationship do you see?
-
-How do we analyze this type of relationship statistically?
-
-## Statistical Analysis
-
-We are going to use two (related) statistical methods to understand the relationship between two continuous (numeric) variables:
-
--   correlation coefficient ($r$) and R-squared ($R^2$)
--   linear regression
-
-### Line of Best Fit
-
-To understand correlation, R-squared, and linear regression, we first need to talk about something we call the *line of best fit*.
-
-The line of best fit aims to minimize the distance between each observation (points) and the line. The distances between each observation and any line are called *residuals* (the dotted gray lines). The line of best fit is the line that has the smallest *residuals*.
-
-![](assets/lines.png){width="50%"} ![](assets/residuals.jpg){width="50%"}
-
-We can add a line of best fit to a ggplot by using the `geom_smooth()` function. We need to specify that the method we want should produce a straight line ("linear model").
-
-```{r}
-ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
-  geom_point() +
-  geom_smooth(method = "lm", se = FALSE) +
-  labs(x = "N Content",
-       y = "Hairgrass Density (per m^2)") +
-  theme_bw()
-
-# method = "lm" is required!
-# se = FALSE is optional
-```
-
-### Correlation Coefficient
-
-The correlation coefficient, `r`, is a measurement of the strength of the relationship between two continuous variables.
-
-The correlation coefficient is a number between -1 and 1 that looks at the relationship between two numeric variables. If the value is negative, there is a negative relationship between the two variable; if the value is positive, there is a positive relationship.
-
-If all the points fall exactly on the line of best fit, r = 1 or -1. If there is no relationship between the variables, `r` is 0 (or something very close to it).
-
-![](assets/coefficient_correlation.jpg){width="50%"}
-
-The greater the magnitude (size) of the correlation coefficient, the stronger the relationship between the two variables.
-
-![](assets/correlation.jpg){width="50%"}
-
-It is also important to recognize that the correlation coefficient has nothing to do with the slope of the line (we use linear regression to assess that!)
-
-![](assets/steepness.jpeg){width="50%"}
-
-Based on the hairgrass plot we did above, do you expect the correlation coefficient to be positive or negative? For this class, we'll say that any `r` value with 0.1 of 0 means there is no relationship.
-
-To calculate the correlation coefficient, we need to go back to base R and indicate which columns we are referencing with the `$` operator. We use the `cor()` function.
-
-```{r}
-r_nitrogen <- cor(x = hairgrass_data$soil_nitrogen, y = hairgrass_data$hairgrass_density)
-r_nitrogen
-```
-
-### R-squared ($R^2$)
-
-The R-squared ($R^2$) value is a representation of how much variation is explained by the line of best fit.
-
-When we have a dependent variable and an independent variable, the R-squared value tells us how much of the variation in the *dependent* variable is explained by the *independent* variable.
-
-Sometimes we don't have obvious dependent and independent variables, but we can still use similar language ($R^2$ tells us how much of the variation in the data is explained).
-
-To calculate R-squared, we square the correlation coefficient value we calculated above. It is always positive because we are squaring it `r` value; that means that R-squared values range from 0 to 1. The closer to 1, the more variation is explained.
-
-```{r}
-r_nitrogen^2
-```
-
-How would we interpret this `r^2` value?
-
-One way to think of the `r^2` value that might be helpful is to convert it from a proportion to a percentage—what *percentage* of the variation in the dependent variable is explained by the independent variable?
-
-To convert the proportion to a percentage, we can multiply the value by 100.
-
-```{r}
-r_nitrogen^2 * 100
-```
-
-### Linear Regression Analysis
-
-A regression analysis approximates the relationship between a dependent variable and one or more independent variables. It evaluates the strength of that relationship, ultimately giving us a p-value.
-
-Since we are using linear regressions in this course, the regression model will take the form of a straight line. The generic equation for a straight line is `y = mx + b`.
-
--   `y` = dependent variable (y-axis)
--   `x` = independent variable (x-axis)
--   `m` = slope of the line is the slope
--   `b` = y-intercept
-
-When we have a line with the specific slope and y-intercept, this equation lets us calculate the expected y-axis value for any given point on the x-axis.
-
-Using our variables, what would our linear regression model look like (we don't know `m` or `b` yet...)?
-
-### Hypothesis Testing
-
-What is the null hypothesis? What is the alternative hypothesis?
-
-**Null Hypothesis** ($H_{0}$): There is no relationship between hair grass density and nitrogen content.
-
-**Alternative Hypothesis** ($H_{A}$): There is a relationship between hair grass density and nitrogen content.
-
-What do you think this means for the slopes?
-
-### Regression Analysis
-
-Thankfully, R can calculate the slope (`m`) and y-intercept (`b`) of the line of best fit for us.
-
-Let's find the equation for our line of best fit, our test statistic, and our p-value. To do this, we use a function called `lm()`: this stands for "linear model."
-
-Like with ANOVA, we will then want to use the `summary()` function to get out the values we need.
-
-```{r}
-nitrogen_model <- lm(hairgrass_density ~ soil_nitrogen, data = hairgrass_data)
-summary(nitrogen_model)
-```
-
-As with our other statistical tests---t-tests and ANOVAs---the results give us some important values:
-
--   `b` (y-intercept): part of our line of best fit equation
-    -   The "intercept estimate", in this case 84.24, is our y-intercept in our line of best fit
--   `m` (slope): also part of our line of best fit equation
-    -   this is the estimate for our independent variable (soil_nitrogen in this case)
-    -   in this model, m = 1.8
--   *F-statistic*: this is our test statistic
--   *p-value*: calculated from our regression model, used to determine significance (0.05 cut-off, as usual)
-    -   There are multiple p-values here. Focus either on the p-value for the independent variable (soil_nitrogen) or the overall p-value displayed in the last line of the results summary (p \< 2.2e-16)
--   *R-squared*: this is our R-squared value that we calculated earlier
-    -   You can report either the "multiple" or the "adjusted"
-    -   The "multiple" will typically match the one we calculate with code
-
-This means our equation for the line of best fit is: y = 1.8x + 84.24 and there is a statistically significant relationship between nitrogen content and hairgrass density.
-
-## Soil pH
-
-Let's do the same series of steps to determine how soil pH impacts hair grass densities. Work on this in your small groups, and we will go over it in about 10 minutes.
-
-Start with summarizing the data: mean and standard deviation.
-
-```{r}
-hairgrass_data %>% 
-  summarize(mean_pH = mean(soil_ph),
-            sd_pH = sd(soil_ph))
-```
-
-Visualize the data. Remember to add in the line of best fit using `ggplot2`. Also add labels and a theme for practice.
-
-```{r}
-ggplot(hairgrass_data, aes(soil_ph, hairgrass_density)) +
-  geom_point() +
-  geom_smooth(method = "lm", se = FALSE) +
-  labs(x = "Soil pH",
-       y = "Hairgrass Density per m^2") + 
-  theme_bw()
-```
-
-Calculate the correlation coefficient (`r`)? What does this tell us?
-
-```{r}
-r_ph <- cor(x = hairgrass_data$soil_ph, y = hairgrass_data$hairgrass_density)
-```
-
-How much variation does soil pH explain in the hair grass density data?
-
-```{r}
-r_ph^2
-```
-
-Write out the model for our question about soil pH (without values)?
-
-```{r}
-# hairgrass density  = m(soil_ph) + b
-```
-
-Run the regression model and write out the equation for the line of best fit.
-
-```{r}
-ph_model <- lm(hairgrass_density ~ soil_ph, data = hairgrass_data)
-summary(ph_model)
-```
-
-Equation for line of best fit: y = -4.648x + 150.24
-
-Interpret the results of the regression (our p-value cutoff is still = 0.05). What do we conclude about the relationship between soil pH and hair grass density? Why?
-
-Because p \< 0.05 for the regression model, we reject the null hypothesis and conclude that there is a relationship between soil pH and hairgrass density.
-
-## Data-driven Decision Making
-
-The reason we are using regression analysis is to inform where we should (or should not) build our road so we don't harm the sensitive hair grass or take away their prime habitat.
-
-What do results above for nitrogen content and soil pH mean for the road we are building?
\ No newline at end of file
diff --git a/modules/module_3/module3_5.qmd b/modules/module_3/module3_5.qmd
deleted file mode 100644
index bc96f54..0000000
--- a/modules/module_3/module3_5.qmd
+++ /dev/null
@@ -1,181 +0,0 @@
----
-title: "3.5: K-Nearest Neighbor"
-author: "Ellen Bledsoe"
-format:
-  html:
-    toc: true
----
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-# K-Nearest Neighbors
-
-## Machine Learning
-
-Now, we're going to work through an example of machine learning, which is used in a variety of ways in data science, across industries and disciplines.
-
-Machine learning is a subfield of artificial intelligence (AI). Machine learning uses a data-driven approach; a model is typically trained on data with known outcomes, then the model uses that previous knowledge to predict the outcomes of new data.
-
-Remember, we have 50 unlabeled collars, and we want to be able to predict who made them so we can increase the safety of our fishers. We need to throw out any collars that we think were made by Budget Collars LLC, and only incorporate new collars from our unlabeled batch that we think were made by Collarium Inc.
-
-Given the data we have from the collars we know were created by Budget Collars LLC and Collarium Inc, our goal is to train a model on collars with known makers to then apply to our unknown collars.
-
-## KNN in R
-
-K-nearest neighbors (KNN) is a classification algorithm (e.g., it attempts to predict labels or "classes"). It uses the classes of the nearest neighbors to a new data point (the number of neighbors = k) to predict the class of the new data point.
-
-![](assets/nearest_neighbor.png)
-
-You don't need to know the inner-workings of the model or even the code below; all you need to know is the overall concept of how K-nearest neighbors works and what the results mean.
-
-### How does KNN work?
-
-Let's start with watch [a video](https://www.youtube.com/watch?v=MDniRwXizWo&ab_channel=AugmentedStartups) that does a nice job explaining the basics of KNN.
-
-What are your takeaways?
-
-### Visualizing
-
-Creating plots that visualize KNN is pretty complicated, so we won't be doing that as a class. However, you can find a nice interactive data visualization [here](http://vision.stanford.edu/teaching/cs231n-demos/knn/).
-
-Play around with the data visualization. Can you figure out what it is telling you?
-
-## Classifying Collars
-
-Now that we have an idea of how KNN works, let's get started with our dataset.
-
-Here's our overall trajectory: 1) Load our collars data 2) Split the data into collars where we know the maker and collars where we don't 3) Split the collars with known makers into 2 data frames: one for training our model and one for testing the model out on data where we already know the answers 4) Apply the best model to our collars with unknown makers to get predictions for which maker made the collar
-
-First, we need our data!
-
-### Data Cleaning and Prepping
-
-```{r message=FALSE}
-library(tidyverse)
-full_collars <- read_csv("data/all_collar_data.csv")
-```
-
-Next, we need to scale our data.
-
-What does this mean? It means we are transforming the data so that they are all in the same scale. Because KNN uses distances between points to determine neighbors, we need to have our data measured on the same scale.
-
-Before scaling, battery life (\~100) and signal distance (\~4000) were working in very different ranges, with signal distance being much bigger. Once we scale them, they will both have means around 0 and similar standard deviations.
-
-```{r}
-# standardize the data and remove unnecessary data
-
-# start with the full data set
-knn_data <- full_collars %>%
-  # mutate the battery and signal columns to be scaled
-  mutate(battery_scaled = scale(battery_life),
-         signal_scaled = scale(signal_distance)) %>%
-  # remove columns we aren't using anymore
-  dplyr::select(-battery_life, -signal_distance, -fail:-weight) %>% 
-  # make the maker column a factor (categorical)
-  mutate(maker = as.factor(maker))
-
-```
-
-Let's summarize and plot the scaled data to see what it looks like. Let's focus on battery life.
-
-```{r}
-knn_data %>% 
-  summarise(mean_bat = mean(battery_scaled),
-            sd_bat = sd(battery_scaled))
-
-ggplot(knn_data, aes(battery_scaled, fill = maker)) +
-  geom_histogram()
-```
-
-Right now, we have 3 collar categories because we haven't separated the unknown collars. Let's do that.
-
-### Known and Unknown Labels
-
-Before we continue, we need to make separate data frames to work with, one with collars with known makers and one with collars with unknown labels
-
-```{r}
-# we also need to remove the collar_id labels
-collars_to_label <- knn_data %>%
-  filter(is.na(maker))
-
-# we need to save the collar ids before removing them
-unknown_collar_ids <- collars_to_label %>% dplyr::select(collar_id)
-collars_to_label <- collars_to_label %>% dplyr::select(-maker, -collar_id)
-
-labeled_collars <- knn_data %>%
-  filter(!is.na(maker)) %>% 
-  select(-collar_id)
-
-```
-
-### Training and Testing Data
-
-First, we need to train and test our model. We will do that with the data with known collars makers.
-
-Note: this is where the code starts getting a bit complicated. It's ok if you aren't entirely clear on what everything is doing.
-
-```{r}
-# randomly choose 30 out of the 100 collars to be our test data
-# we will train the model with the other 70 collars, then test how well that model does using the 30 collars we held back
-# because we know the actual makers of those 30 collars, we will know if our model made any mistakes
-
-# since randomly choosing, we will use set.seed() to make sure everyone is getting the same values
-set.seed(411)
-
-# randomly select 30 row positions (70%) to pull out
-rows_to_pull <- sample(1:100, 30)
-rows_to_pull
-
-training <- labeled_collars[-rows_to_pull, ] # remove test data from labeled collars
-testing <- labeled_collars[rows_to_pull, ] # pull out the 30 rows for testing
-
-```
-
-### Training and Testing KNN
-
-Now that we have training and testing data, we can perform the first phase of KNN.
-
-We want to know how many nearest neighbors we should use and how well our model works on the testing data. We will use the `caret` package to run these analyses.
-
-```{r}
-#loading package to do knn
-library(caret)
-
-# set seed again
-# training the model and finding the best k value
-set.seed(411)
-knn_training_model <- train(maker ~ ., data = training, method = "knn")
-knn_training_model # best k value is 5
-```
-
-Ok, we have determined that our best k value is 5. Now we can use our training model on the testing data to see if it makes any mistakes. We use the `predict` function from `caret` to do this.
-
-```{r}
-# making predictions with the test data
-knn_predict_test <- predict(knn_training_model, testing)
-confusionMatrix(knn_predict_test, testing$maker) # no mistakes!
-```
-
-Great, our model didn't make any mistakes! It is probably ready to apply to our collars of unknown origin.
-
-### Predicting the Unknown Collar Makers
-
-We will apply the same model but now to the new data.
-
-```{r}
-# making predictions for the collars without makers
-knn_predict <- predict(knn_training_model, collars_to_label)
-knn_predict
-table(knn_predict)
-```
-
-We have predictions! Our last step is to recombine our data so that the collars now have their new labels.
-
-```{r}
-# let's bind the columns together
-new_labels <- bind_cols(unknown_collar_ids, knn_predict)
-new_labels
-```
-
-Typically, you would want to replace the columns in the entire dataframe, but we will end here for today :) Our goal of labeling our unlabeled collars is complete! We will deploy any that are now labeled as Collarium LLC.
\ No newline at end of file
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diff --git a/modules/module_3/assets/nearest_neighbor.png b/modules/module_4/assets/nearest_neighbor.png
similarity index 100%
rename from modules/module_3/assets/nearest_neighbor.png
rename to modules/module_4/assets/nearest_neighbor.png
diff --git a/modules/module_4/data/all_collar_data.csv b/modules/module_4/data/all_collar_data.csv
new file mode 100644
index 0000000..30ceb07
--- /dev/null
+++ b/modules/module_4/data/all_collar_data.csv
@@ -0,0 +1,151 @@
+collar_id,maker,battery_life,signal_distance,fail,antenna_length,weight
+1,Collarium Inc.,141.05317479220514,4170.846997991083,0,7.441049683137828,21.89808786308
+2,Collarium Inc.,121.0921670618851,4134.00476195513,0,5.386726294327846,21.75072596096861
+3,Collarium Inc.,126.02642597016708,4277.101582564608,1,4.815551113154395,24.792463667918703
+4,Collarium Inc.,126.71447667102986,4197.7790340318425,0,5.273131943436047,18.1951866073506
+5,Collarium Inc.,141.010259627129,4172.9933032964045,1,7.651056213392951,20.838808845414633
+6,Collarium Inc.,104.8181081848806,4174.955506348206,0,4.946670393629738,22.950883366536996
+7,Collarium Inc.,110.36767499322768,4214.844899700962,0,5.312841213309257,23.9933914484654
+8,Collarium Inc.,108.44976782802068,4232.043773903803,0,7.26723112189867,21.2539791548867
+9,Collarium Inc.,128.65375430026796,4250.968481375221,0,6.058328892133725,21.90424198164509
+10,Collarium Inc.,123.78974933579651,4227.509088844665,0,6.221260002342018,21.379847341874523
+11,Collarium Inc.,130.69636669144532,4140.801672423008,0,5.347334445824152,22.934506640117785
+12,Collarium Inc.,122.45971577499228,4202.96937709263,0,6.173193837954627,21.245182956929376
+13,Collarium Inc.,139.95367944438198,4228.707065452046,0,4.050965095274371,22.08513825010298
+14,Collarium Inc.,109.00448727134876,4240.017723780288,0,5.52784777791954,26.03631966529095
+15,Collarium Inc.,128.9792302123998,4258.390131896045,0,8.958491784604764,21.26236134320608
+16,Collarium Inc.,109.9860974176209,4140.470264987905,0,6.323247400076654,24.391722265923935
+17,Collarium Inc.,128.58048438855693,4172.080009759122,0,5.490508075786347,22.646044497606393
+18,Collarium Inc.,114.524643998697,4159.468625046546,0,6.02365470898383,22.675308963695485
+19,Collarium Inc.,119.33775283599896,4137.360050012676,0,4.939141745086154,23.091259151679964
+20,Collarium Inc.,124.79141115476968,4199.802636172046,0,5.574026608149863,21.316803150335737
+21,Collarium Inc.,95.20226817713876,4229.178740403667,0,3.9983270063097143,23.17617561803206
+22,Collarium Inc.,104.42222920058752,4220.389992240861,0,5.363242155541145,20.436595284549483
+23,Collarium Inc.,118.96204742122008,4206.399635518712,0,5.668403208517654,22.589836721355574
+24,Collarium Inc.,119.56779844761525,4237.807141353698,0,6.076902930692613,22.38205167639776
+25,Collarium Inc.,114.87052341293769,4219.2030188193685,0,5.169740870729678,24.40550058822832
+26,Collarium Inc.,126.07594734922974,4199.211503043213,0,5.9344569903815225,20.978210952798772
+27,Collarium Inc.,125.62089717163886,4210.671295273121,0,6.181066760776162,20.869737165163816
+28,Collarium Inc.,147.39737024065985,4126.979677155231,0,4.490272197824118,23.3712536941156
+29,Collarium Inc.,116.42275571231708,4216.83393590613,0,6.701961833218941,21.264586455005407
+30,Collarium Inc.,124.49584187476988,4151.225921304575,0,6.742607307174054,20.209665864861975
+31,Collarium Inc.,115.47468886179242,4193.844406615272,0,4.515043890776944,21.989829729868426
+32,Collarium Inc.,124.6727259273278,4205.0526460226465,1,6.31676285483352,22.309453828569943
+33,Collarium Inc.,131.65415290623406,4180.446114964672,0,6.743159767514537,24.15190046335083
+34,Collarium Inc.,106.98221926931882,4159.781856153344,0,6.674399127519353,20.372132552762533
+35,Collarium Inc.,132.04243353128928,4249.888815821739,0,4.630844472828251,21.755858319831077
+36,Collarium Inc.,120.11392036899142,4195.741537744812,0,5.442299279577893,23.961244108593675
+37,Collarium Inc.,144.6224697070443,4150.462177704308,0,6.683962782908566,23.05431298113524
+38,Collarium Inc.,111.95498336803226,4201.781828046767,0,6.699130912539179,22.33179346810699
+39,Collarium Inc.,132.60974165238346,4189.8112737515685,0,6.688635417331819,22.225473409770625
+40,Collarium Inc.,128.12578696356852,4151.7847345263135,0,4.13884719280234,21.46014771416562
+41,Collarium Inc.,113.36202130208385,4224.662765133372,0,6.7483693684086,23.392772691398527
+42,Collarium Inc.,130.93150724549724,4194.210430743653,0,4.541337621116111,22.94827375564323
+43,Collarium Inc.,108.46940067407634,4179.310274441157,1,4.856352733230578,20.90314916436393
+44,Collarium Inc.,106.4116467588606,4176.103739002826,0,7.65431330295257,23.064130805245263
+45,Collarium Inc.,127.59877983174124,4193.254285098876,0,7.265109159494871,21.10619766170342
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+47,Collarium Inc.,100.62009131848474,4199.552892842718,0,7.857584720638968,20.383422485185484
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diff --git a/modules/module_4/data/collar_data.csv b/modules/module_4/data/collar_data.csv
new file mode 100644
index 0000000..4c0a03c
--- /dev/null
+++ b/modules/module_4/data/collar_data.csv
@@ -0,0 +1,101 @@
+collar_id,maker,battery_life,signal_distance,fail
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diff --git a/modules/module_4/data/hairgrass_data.csv b/modules/module_4/data/hairgrass_data.csv
new file mode 100644
index 0000000..1b65745
--- /dev/null
+++ b/modules/module_4/data/hairgrass_data.csv
@@ -0,0 +1,241 @@
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diff --git a/modules/module_4/data/new_collars.csv b/modules/module_4/data/new_collars.csv
new file mode 100644
index 0000000..2e35e1b
--- /dev/null
+++ b/modules/module_4/data/new_collars.csv
@@ -0,0 +1,51 @@
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diff --git a/modules/module_4/data/old_collars_new_data.csv b/modules/module_4/data/old_collars_new_data.csv
new file mode 100644
index 0000000..330a978
--- /dev/null
+++ b/modules/module_4/data/old_collars_new_data.csv
@@ -0,0 +1,101 @@
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diff --git a/modules/module_4/data/site_changes.csv b/modules/module_4/data/site_changes.csv
new file mode 100644
index 0000000..bce3ad3
--- /dev/null
+++ b/modules/module_4/data/site_changes.csv
@@ -0,0 +1,201 @@
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diff --git a/modules/module_4/module4_1.qmd b/modules/module_4/module4_1.qmd
index a065693..d5c43be 100644
--- a/modules/module_4/module4_1.qmd
+++ b/modules/module_4/module4_1.qmd
@@ -1,199 +1,151 @@
 ---
-title: '4.1: Population Growth'
-author: "Jessie Golding and Ellen Bledsoe"
+title: "4.1: Combining Datasets (Joins & Binds)"
+author: "Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
 
 ```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, fig.align = "center")
+knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# Introduction to Population Growth
+# The Context
 
-Let's use descriptive statistics and data visualization to better understand population dynamics.
+After the series of incidents where a number of the collars made by Budget Collars LLC seem to be failing, our team decided to try and replace as many of them as possible. Besides, their battery life is far inferior.
 
-### Exponential Growth
+We're placing as many collars on as many seals as we can, and we are starting to run short on collars.
 
-A population that shows [exponential growth]{.underline} has a growth rate that depends the average number of births (`b`) and deaths (`d`) per individual.
+One of our intrepid data science team members found an old box of collars and some data on all of the collars (it was a real challenge getting this data off of an old floppy drive, but she managed).
 
-We call rates that are averages per individual in a population (rather than a total) *per capita* growth rates; *per capita* translates from Latin to "by head," meaning for individuals.
+## Our Tasks
 
-Combined, birth and death rates can be described as the [intrinsic rate of increase]{.underline} (`r`). We calculate `r` by subtracting the number of individuals that are removed from the population (deaths) from the number of individuals that are joining the population (births): $r = b - d$.
+We're going to spend the next lessons tackling two tasks:
 
-We can calculate by how many individuals the population will change in a given time period  (`N`) by multiplying the starting population size (`Ni`) by the intrinsic rate of increase (`r`) using this equation:
+**1) First, we'll work to join data sets together.**
 
-![](assets/clipboard-1439389577.png){width="90" fig-align="center"}
+Our main goal is to create a single data set that includes the data we have previously looked at for collars, the data on the new collars, as well as the additional data on the old collars.
 
-### Logistic Growth
+This is a little tricky, as the collar IDs need to be matched up with their counterparts across datasets, and new collars need be added. Unfortunately, the new collars have IDs and some additional data, but we don't know the maker of the new collars.
 
-Populations that show logistic growth start out in the same way as populations with exponential growth–driven by *per capita* birth and death rates.
+**2) Second, we will try to identify the maker of mystery collars.**
 
-After a while, though, limited resources also start to come into play. When calculating the change in the size of the population (`N`), we also need to take into account the maximum number of individuals the population can sustainably support, called the [carrying capacity]{.underline}, `K`.
+In order to do this, we will skim the surface of machine learning. We can use a common classification algorithm, K-Nearnest Neighbors (KNN), to predict which maker made which of our unidentified collars. Don't worry, we won't go into *too* much detail here, but I want to you to at least have a general idea of how it works.
 
-We use the following equation to calculate the population size:
+## Combining Data (Joins and Binds)
 
-![](assets/clipboard-3908560513.png){width="188" fig-align="center"}
+Often times, we have a lot of data for one project that are related but storing all of the data in one file would add unnecessary redundancy (e.g., data in certain rows would need to be repeated too often). Other times, data has been collected separately and needs to be combined before analysis.
 
-### Visualizing Growth Models
+Being able to join together data from related tables is a key skill in data science, and for working with larger data structures (databases with their own languages, like SQL).
 
-As a reminder, this is what each type of population growth model looks like:
+### The Data
 
-![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
+Let's load in the `tidyverse` and the data we're working.
 
-# What Population Factors Matter?
-
-We still need to figure out what fish population we will target for Antarctica.
-
-Briefly, let's consider what we think might be important for sustainability.
-
-1.  Large population
-2.  Stable population (i.e., not declining)
-
-## Fish Data
+```{r message=FALSE}
+# load the tidyverse
+library(tidyverse)
 
-Let's look at fish data from around our station in Antarctica over the past 20 years to get a better feel for how different population factors impact fish populations. These fish moved into the bay by our station when we established it, so we know the populations started at around the same time and had the same starting populations.
+#loading in the data
+collars <- read_csv("data/collar_data.csv")
+new_collars <- read_csv("data/new_collars.csv")
+old_collars_new_data <- read_csv("data/old_collars_new_data.csv")
 
-These aren't necessarily our targets for fishing, particularly since it would be difficult and potentially disruptive to fish in the bay. Right now our goal is just to learn so we know what to look for when we are making our decision to conduct sustainable fishing.
+# Tell me about these data, how are we going to join them?
+```
 
-![Adapted from Figure 2 from Zheng et al. (2022)](assets/fish_data_fish.png){fig-align="center"}
+First, let's explore our data. We want to focus on 2 things here:
 
-As always, first we load the `tidyverse` and read in the data.
+(1) the columns: which ones match columns in other datasets
+(2) collar identity: which datasets have matching collars or new collars
 
 ```{r}
-library(tidyverse)
-fish_pops <- read_csv("data/fish_pops.csv")
+head(collars)
+head(new_collars)
+head(old_collars_new_data)
 ```
 
-### Largest Population
+### Diagramming
 
-Which population grows the biggest? Let's start by calculating some summary statistics of the data.
+In small groups, talk through the process of combining these three datasets. Think about the following:
 
-```{r}
-max_pop <- fish_pops %>%
-  group_by(population)%>%
-  summarise(max_N = max(N))
-max_pop
-```
+-   which columns match and which ones don't?
+-   which rows match and which ones don't?
+-   does the order in which we combine datasets matter?
 
-It looks like Population B grows the largest by far. Let's learn more about Population B and filter out the data so we can look at it separately.
+Draw out a diagram that represents how this process might go.
 
-```{r}
-popB <- fish_pops %>%
-  filter(population == "B")
-popB
-```
+## Joins vs. Binds
 
-**What type of growth is the population exhibiting?**  
-*Answer: Exponential.*
+Now that we've decided on a process for how to combine our data, let's figure out which functions we are going to use to accomplish this task.
 
-**Why is there no carrying capacity in this population data set? What does this mean about the resources Population B needs?**  
-*Answer: Exponential models assume unlimited resources, so no carrying capacity (K) is included.*
+We have 2 main methods of combining datasets, and they work in different ways.
 
-**What do you notice about Population A that is different than Population B?**  
-*Answer: The intrinsic growth rate (r) for Population A is negative.*
+### Joins
 
-**What has happened to this population?**  
-*Answer: Population A crashes and reaches zero.*
+Joins are arguably the more complicated of the two types of ways to combine data, but they are, therefore, the more flexible and useful.
 
-**Between Population A and Population B, which is a better target population for fishing? Why?**  
-*Answer: Population B, because r > 0 and it grows quickly.*
+The magic of comes comes because they match up columns of data based on unique identifiers in each row of data.
 
-### Exponential Growth Populations
+In the following diagram, the two example data frames have the column `x1` in common, and each of the values in `x1` are unique (no repeats in the same data frame). When combining the datasets, all of the columns are added, and their rows are matched up to their respective values in the `x1` column.
 
-It looks like populations exhibiting exponential growth might be good targets for our fishing operations because they can become very large. Let's look at the data we have on exponential growth populations a little closer.
+This can happen a couple ways, depending on which data frame is the reference and how much data you want to retain.
 
-Let's filter out the exponential growth models to examine them.
+![](assets/joins.png){width="50%"}
 
-```{r}
-exp_pops <-fish_pops %>%
-  filter(growth_type == "exponential")
-exp_pops
-```
+### Binds
 
-It's hard to see what's happening with only the data frame. Let's look at the exponential growth populations on the same plot:
+The other way we can combine data sets in through binds. Binds act similarly to gluing datasets together.
 
-```{r}
-ggplot(exp_pops, aes(x = year, y = N, color = population)) +
-  geom_point() +
-  geom_line()+
-  labs(x = "Time", y = "Abundance", color = "Population") +
-  theme_light()
-```
+They don't match up data based on unique identifiers; instead they match up data by column name (`bind_rows`) or row position (`bind_cols`)
 
-It looks like Population B is very large, but Population A stays small. It's hard to see what is happening on this plot because the scales are so different, so let's look at what is happening with Population A by filtering out the data just for that population.
+![](assets/binds.png){width="50%"}
 
-```{r}
-popA <-fish_pops %>%
-  filter(population == "A")
-popA
-```
+How should we go about combining our three datasets? Come up with a plan that you think will work.
+
+## Task 1: Combine Our Data
 
-What do you notice about Population A that is different than Population B? Intrinsic growth rate negative
+### Step 1
 
-The intrinsic rate of increase (r) for Population A is negative. Let's see what that means for Population A and plot it by itself:
+Our first step in to merge the old collar data with the new data about those old collars.
 
 ```{r}
-ggplot(popA, aes(x = year, y = N)) +
-  geom_point() +
-  geom_line()+
-  labs(x = "Time", y = "Abundance") +
-  theme_light()
-```
+# use a left join
+# full join would accomplish the same thing in this case because we don't have any missing rows
+old_collars_combined <- collars %>% 
+  left_join(old_collars_new_data, by = c("collar_id", "maker"))
 
-What has happened to this population? Crashed
+```
 
-Even with "exponential growth", the population has crashed (gone to 0), because *r* is negative. It took approximately 7 years for this to happen.
+If we take a look our new data frame, we should hopefully see that the values for `antenna_length` and `weight` have been matched up with their respective collar id.
 
-Between Population A and Population B, which do you think is a better target population for fishing? Population B, `r` is positive
+Could we have used another tactic to combine these two datasets? What would the pros and cons be?
 
-**Logistic growth populations**
+### Step 2
 
-Ok, so we can't rely only on the type of growth as a way to make our fishing decisions. But, we're still curious about our populations that are limited by resources (i.e., exhibiting logistic growth).
+Now we need to add the new collars to our dataset. What is our best method for combining?
 
 ```{r}
-log_pops <- fish_pops %>%
-  filter(growth_type == "logistic")
-log_pops
+full <- bind_rows(old_collars_combined, new_collars)
 ```
 
-Notice in this case we have carrying capacity data. Let's use some data visualizations to help us understand what that means.
-
-Lets look at the two logistic growth populations on the same plot:
+Let's take a look at our new data frame! Have we correctly accomplished our first task?
 
 ```{r}
-ggplot(log_pops, aes(x = year, y = N, color = population))+
-  geom_point() +
-  geom_line()+
-  labs(x = "Time", y = "Abundance", color = "Population")+
-  theme_light()
+head(full)
+tail(full)
 ```
 
-Which population gets the largest? Population 4
-
-### Finding the Carrying Capacity
-
-When we have a logistic growth model, we often will want to find the carrying capacity of the population
-
-What values does each population level off at?
+### Saving our New Data
 
--   Population C: 20
+Now that we've accomplished our first task, we are going to want to use this combined dataset to accomplish our next task, which is using a classification algorithm to help us predict which maker made the mystery collars.
 
--   Population D: 100
+To save our data as a .csv file that we can use in another analysis, we are going to use a function that exports the dataset (`write_csv`) instead of importing it (`read_csv`).
 
-These are the carrying capacities. If you look back at the data, you will see Population C has a carrying capacity of 20 individuals, whereas Population D has a carrying capacity of 100 individuals. Which population do you think might be the best candidate to fish between these two? Population D is more abundant and has a higher carrying capacity.
+The `write_csv` function requires the name of the dataframe to export as the first argument and the name of the file we want to create as the second argument.
 
-**A review of what we have learned**
-
-The following factors are important to know about a population before fishing:
-
-1.  Exponential growth can result in bigger populations than logistic growth
-
-2.  Intrinsic rate of increase (`r`) should not be negative
-
-3.  A higher carrying capacity means the population can take advantage of more resources to grow larger, which may indicate that the population is a better candidate for fishing
-
-## References
+```{r}
+write_csv(full, "data/all_collar_data.csv")
+```
 
-Zhang, M., Liu, S., Bo, J., Zheng, R., Hong, F., Gao, F., Miao, X., Li, H., & Fang, C. (2022). First evidence of microplastic contamination in Antarctic fish (Actinopterygii, Perciformes),*Water*, *14*(19).
\ No newline at end of file
+If we look over in our Files tab, you should see your new .csv file!
\ No newline at end of file
diff --git a/modules/module_4/module4_2.qmd b/modules/module_4/module4_2.qmd
index 9826190..bf006e5 100644
--- a/modules/module_4/module4_2.qmd
+++ b/modules/module_4/module4_2.qmd
@@ -1,6 +1,6 @@
 ---
-title: '4.2: Sustainable Fishing'
-author: "Jessie Golding and Ellen Bledsoe"
+title: "4.2: K-Nearest Neighbor"
+author: "Ellen Bledsoe"
 format:
   html:
     toc: true
@@ -10,175 +10,173 @@ format:
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# Sustainable Fishing
+# K-Nearest Neighbors
 
-## Applying Principles of Population Growth
+## Machine Learning
 
-We have decided that the Antarctic toothfish (*Dissostichus mawsoni*) may be a good candidate for our fishing.
+Now, we're going to work through an example of machine learning, which is used in a variety of ways in data science, across industries and disciplines.
 
-![](assets/antarctic_toothfish.jpg){fig-align="center"}
+Machine learning is a subfield of artificial intelligence (AI). Machine learning uses a data-driven approach; a model is typically trained on data with known outcomes, then the model uses that previous knowledge to predict the outcomes of new data.
 
-The Antarctic toothfish is fairly widely distributed around Antarctica. The hexagons below show where the Antarctic toothfish occurs. Gray represents known distribution and red represents locations where genetic samples were taken for a study on their population (Maschette et al. 2023).
+Remember, we have 50 unlabeled collars, and we want to be able to predict who made them so we can increase the safety of our fishers. We need to throw out any collars that we think were made by Budget Collars LLC, and only incorporate new collars from our unlabeled batch that we think were made by Collarium Inc.
 
-![](assets/antarctic_toothfish_distribution.jpg){width="75%" fig-align="center"}
+Given the data we have from the collars we know were created by Budget Collars LLC and Collarium Inc, our goal is to train a model on collars with known makers to then apply to our unknown collars.
 
-However, given concerns about maintaining the fishery (an important ecosystem service), we want to make sure we do not overfish the species but rather fish sustainably.
+## KNN in R
 
-Let's keep exploring our fish populations to help us understand what we should be focusing on when making decisions about how many fish we can sustainably harvest from any given population.
+K-nearest neighbors (KNN) is a classification algorithm (e.g., it attempts to predict labels or "classes"). It uses the classes of the nearest neighbors to a new data point (the number of neighbors = k) to predict the class of the new data point.
 
-## Set-Up
+![](assets/nearest_neighbor.png)
 
-As usual, we start with loading our packages and our data.
+You don't need to know the inner-workings of the model or even the code below; all you need to know is the overall concept of how K-nearest neighbors works and what the results mean.
 
-```{r}
-library(tidyverse)
-
-fish <- read_csv("data/fish_pops.csv")
-```
-
-Let's take a look at the data and remind ourselves what the data look like.
-
-```{r}
-head(fish)
-```
+### How does KNN work?
 
-## Population Growth Models
+Let's start with watch [a video](https://www.youtube.com/watch?v=MDniRwXizWo&ab_channel=AugmentedStartups) that does a nice job explaining the basics of KNN.
 
-Let's remind ourselves about exponential growth and logistic growth.
+What are your takeaways?
 
-**Expotential growth** takes births and deaths (in the form of the intrinsic growth rate, `r`) into account:
+### Visualizing
 
-![](assets/clipboard-4205079346.png){width="83" fig-align="center"}
+Creating plots that visualize KNN is pretty complicated, so we won't be doing that as a class. However, you can find a nice interactive data visualization [here](http://vision.stanford.edu/teaching/cs231n-demos/knn/).
 
-**Logistic growth** includes the intrinsic growth rate and another important factor—resources. The resources determine the maximum number of individuals a population can have, which we call the carrying capacity (`K`):
+Play around with the data visualization. Can you figure out what it is telling you?
 
-![](assets/clipboard-3779797644.png){width="130" fig-align="center"}
+## Classifying Collars
 
-This is what each type of population growth model looks like:
+Now that we have an idea of how KNN works, let's get started with our dataset.
 
-![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
+Here's our overall trajectory: 1) Load our collars data 2) Split the data into collars where we know the maker and collars where we don't 3) Split the collars with known makers into 2 data frames: one for training our model and one for testing the model out on data where we already know the answers 4) Apply the best model to our collars with unknown makers to get predictions for which maker made the collar
 
-### Which Population to Fish?
+First, we need our data!
 
-We've previously discussed what factors we want to focus on when we are deciding which population to fish. Ideally, we want to target both a large population and a stable population.
+### Data Cleaning and Prepping
 
-At first glance, a population that is growing exponentially seems ideal—and it is, in some ways.
-
-But in reality, all populations will *eventually* hit some capacity on resources. Populations that are currently demonstrating exponential growth either haven't yet hit their carrying capacity or have dramatically overshot their carrying capacity, which can lead to a huge crash or a smaller crash with boom and bust cycles until it evens out around the carrying capacity.
+```{r message=FALSE}
+library(tidyverse)
+full_collars <- read_csv("data/all_collar_data.csv")
+```
 
-![](assets/pop_crash.jpeg){width="75%" fig-align="center"}
+Next, we need to scale our data.
 
-So, in order for us to feel confident that our population will be large for a long time, we probably want to choose the safer option, which is choosing a population that is showing logistic growth.
+What does this mean? It means we are transforming the data so that they are all in the same scale. Because KNN uses distances between points to determine neighbors, we need to have our data measured on the same scale.
 
-Let's select the logistic growth population with the largest value, Population D, for our next analyses.
+Before scaling, battery life (\~100) and signal distance (\~4000) were working in very different ranges, with signal distance being much bigger. Once we scale them, they will both have means around 0 and similar standard deviations.
 
 ```{r}
-log_pop <- fish %>% 
-  filter(population == "D")
-```
-
-## Estimating Carrying Capacity
-
-In our journey to determine how many fish we can sustainably harvest (i.e., remove from the population without causing the population to go extinct), we first need to determine the carrying capacity.
+# standardize the data and remove unnecessary data
+
+# start with the full data set
+knn_data <- full_collars %>%
+  # mutate the battery and signal columns to be scaled
+  mutate(battery_scaled = scale(battery_life),
+         signal_scaled = scale(signal_distance)) %>%
+  # remove columns we aren't using anymore
+  dplyr::select(-battery_life, -signal_distance, -fail:-weight) %>% 
+  # make the maker column a factor (categorical)
+  mutate(maker = as.factor(maker))
 
-While the dataset contains carrying capacity estimates, those are only visual estimates. We are going to use code (and R will use a lot of math behind the scenes) to calculate a more accurate estimate from the data.
+```
 
-To do so, we are actually going to use a new package, `drc`.
+Let's summarize and plot the scaled data to see what it looks like. Let's focus on battery life.
 
-This package will let us fit a logistic model and provide an estimate of the asymptote of the curve, or the value where the curve levels off and not longer increases. This is our estimate of `K`, the carrying capacity of the population.
+```{r}
+knn_data %>% 
+  summarise(mean_bat = mean(battery_scaled),
+            sd_bat = sd(battery_scaled))
 
-```{r message=FALSE}
-# load the drc package; text when you load it is fine (as long as it doesn't say error)
-library(drc)
+ggplot(knn_data, aes(battery_scaled, fill = maker)) +
+  geom_histogram()
 ```
 
-Now, we are going to use a function from the `drc` package called `drm()` to fit our logistic growth model.
+Right now, we have 3 collar categories because we haven't separated the unknown collars. Let's do that.
 
-It works very similarly to the inferential statistics models from Module 3 except instead of finding a straight line of best fit, this line of best fit is allowed to curve like the logistic growth model.
+### Known and Unknown Labels
 
-What kind of growth do we think we have based on this plot? Logistic
+Before we continue, we need to make separate data frames to work with, one with collars with known makers and one with collars with unknown labels
 
 ```{r}
-# Fit the model to the data
-# 'N ~ year' means we're predicting N (population) as a function of 'year'
-# fct = LL.4() specifies to fit a logistic growth model
-model <- drm(N ~ year, data = log_pop, fct = LL.4())
-```
+# we also need to remove the collar_id labels
+collars_to_label <- knn_data %>%
+  filter(is.na(maker))
 
-This time, unlike with the linear model, we are interested not in a p-value but in the estimate of a specific value from the model--the carrying capacity.
+# we need to save the collar ids before removing them
+unknown_collar_ids <- collars_to_label %>% dplyr::select(collar_id)
+collars_to_label <- collars_to_label %>% dplyr::select(-maker, -collar_id)
 
-To get all of the estimated values, we will use a function called `coef`, which is short for "coefficient," to give us the estimates from the `model` object.
+labeled_collars <- knn_data %>%
+  filter(!is.na(maker)) %>% 
+  select(-collar_id)
 
-```{r}
-estimates <- coef(model)
-estimates
 ```
 
-In this case, we happen to be interested in the estimate they are calling `d:(Intercept)`; this is what we are calling `K`, the carrying capacity. Let's save that value.
+### Training and Testing Data
+
+First, we need to train and test our model. We will do that with the data with known collars makers.
 
-The simplest way to choose one value from the vector `estimates` is to use square brackets for subsetting, something we haven't done since Module 1! We want the 3rd value.
+Note: this is where the code starts getting a bit complicated. It's ok if you aren't entirely clear on what everything is doing.
 
 ```{r}
-K <- estimates[3]
-K
-```
+# randomly choose 30 out of the 100 collars to be our test data
+# we will train the model with the other 70 collars, then test how well that model does using the 30 collars we held back
+# because we know the actual makers of those 30 collars, we will know if our model made any mistakes
 
-Our estimate for the carrying capacity is 104.5 fish, which is close to the carrying capacity value in the dataset (100).
+# since randomly choosing, we will use set.seed() to make sure everyone is getting the same values
+set.seed(411)
 
-We can remove the label (`d:(Intercept)`) if we want to by using the `unname()` function. Now our object `K` is only the number for the estimate of the carrying capacity.
+# randomly select 30 row positions (70%) to pull out
+rows_to_pull <- sample(1:100, 30)
+rows_to_pull
+
+training <- labeled_collars[-rows_to_pull, ] # remove test data from labeled collars
+testing <- labeled_collars[rows_to_pull, ] # pull out the 30 rows for testing
 
-```{r}
-K <- unname(K)
-K
 ```
 
-### Plotting the Carrying Capacity
+### Training and Testing KNN
 
-Let's now add this estimated carrying capacity value to our plot of the data.
+Now that we have training and testing data, we can perform the first phase of KNN.
 
-First, let's plot the population data alone.
+We want to know how many nearest neighbors we should use and how well our model works on the testing data. We will use the `caret` package to run these analyses.
 
 ```{r}
-ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs. population)
-  geom_point() +
-  geom_line() +
-  labs(x = "Year", y = "Population") +
-  theme_bw()
+#loading package to do knn
+library(caret)
+
+# set seed again
+# training the model and finding the best k value
+set.seed(411)
+knn_training_model <- train(maker ~ ., data = training, method = "knn")
+knn_training_model # best k value is 5
 ```
 
-To add the a line representing the carrying capacity, we can use `geom_hline()` to add a horizontal line where the y-intercept is equal to the carrying capacity.
-
-This is very similar to how we used `geom_vline()` to add a vertical line to histograms to represent the mean value.
+Ok, we have determined that our best k value is 5. Now we can use our training model on the testing data to see if it makes any mistakes. We use the `predict` function from `caret` to do this.
 
 ```{r}
-ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs. population)
-  geom_point() +
-  geom_line() +
-  # use geom_hline to plot a horizontal line where the y-intercept is equal to K
-  geom_hline(aes(yintercept = K), color = "red") + # outside of aes() sets color
-  labs(x = "Year", y = "Population") +
-  theme_bw()
+# making predictions with the test data
+knn_predict_test <- predict(knn_training_model, testing)
+confusionMatrix(knn_predict_test, testing$maker) # no mistakes!
 ```
 
-## Maximum Sustainable Yield
-
-Once we have an estimate of the carrying capacity for a population, we can calculate something called the *maximum sustainable yield*, or MSY.
-
-The MSY is the number of individuals that we can remove from the population and keep the population stable (or increasing). If we remove too many individuals (above MSY), the population will not be able to reproduce fast enough to replace the harvested individuals, causing a decline in the population—in this case, from over-fishing.
+Great, our model didn't make any mistakes! It is probably ready to apply to our collars of unknown origin.
 
-![](assets/logistic_growth_msy.png){width="49%"} ![](assets/msy.png){width="49%"}
+### Predicting the Unknown Collar Makers
 
-While the concept behind how we get to the equation for MSY may be a bit complex, thankfully, the equation itself is quite straightforward:
+We will apply the same model but now to the new data.
 
-![](assets/clipboard-959845017.png){fig-align="center"}
+```{r}
+# making predictions for the collars without makers
+knn_predict <- predict(knn_training_model, collars_to_label)
+knn_predict
+table(knn_predict)
+```
 
-Let's calculate the maximum sustainable yield for Population D.
+We have predictions! Our last step is to recombine our data so that the collars now have their new labels.
 
 ```{r}
-MSY <- K/2
-MSY
+# let's bind the columns together
+new_labels <- bind_cols(unknown_collar_ids, knn_predict)
+new_labels
 ```
 
-This means that we can take about 52 fish from this population annually and keep the population stable!
-
-If we take fewer than 52 fish, the population will increase (until the carrying capacity, that is). If we take more than 52 fish, however, we are over-fishing the population, meaning the population will not be able to reproduce enough to maintain the population and will eventually collapse.
\ No newline at end of file
+Typically, you would want to replace the columns in the entire dataframe, but we will end here for today :) Our goal of labeling our unlabeled collars is complete! We will deploy any that are now labeled as Collarium LLC.
\ No newline at end of file
diff --git a/modules/module_4/module4_3.qmd b/modules/module_4/module4_3.qmd
index 1b7e8ec..b33016b 100644
--- a/modules/module_4/module4_3.qmd
+++ b/modules/module_4/module4_3.qmd
@@ -1,260 +1,307 @@
 ---
-title: '4.3: Comparing Populations'
-author: "Jessie Golding and Ellen Bledsoe"
+title: "4.3: Roads and Regressions"
+author: "Mila Pruiett and Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
 
 ```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
+knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# Comparing Populations
+# Roads and Regressions
 
-Throughout this module, we have learned how populations demonstrating exponential growth and logistic growth differ from one another, both conceptually and visually.
+## The Scenario
 
-While we have fit models to some of these populations (specifically, logistic growth models), we have done so in order to estimate the carrying capacity of the population rather than to use inferential statistics to determine if the populations are statistically significantly different from one another.
+In this module, we have tried to figure out which bays we should prioritize for fishing based on the following data sets:
 
-In today's lesson, we are going to learn how to (a) add those logistic growth models to our data visualizations and (b) compare populations statistically.
+-   radio collar data
+-   fish catch data
+-   leopard seal abundance data
 
-## Set Up
+Based on our results from Assignment 2, we made a decision about which two bays look like our best options. Now, we need to make these bays accessible over land to transport the fish we've caught to our home base.
 
-As always, we need to start by loading the packages we are going to use along with our data.
+**Our Goal: build a road while minimizing our impact on the delicate antarctic ecosystem.**
 
-First, let's load our packages. We are going to be using both the `tidyverse` and `drc` packages in this lesson, so we need to load both of them.
+Our first order of business is to make sure that we avoid areas with that are well-suited for [Antarctica hair grass](https://oceanwide-expeditions.com/blog/the-plants-of-antarctic) (*Deschamsia antarctica*), one of only two flowering species of plants on the continent.
 
-```{r}
-library(tidyverse)
-library(drc)
-```
+![](assets/hairgrass_indiv.jpeg){width="50%"}
 
-Next, we want to bring in our dataset.
+We want to know what environmental conditions are associated with hair grass. This way, we can avoid areas where these conditions are met and not destroy precious habitat for the hair grass.
 
-```{r}
-pops <- read_csv("data/comparing_populations.csv")
-```
+It would take far too long to survey every bit of land between our base and our fishing spots, so we are going to build a **model** based on some samples of where hair grass is found to help us predict where else it might be.
 
-We can use the `head()` function to take a look at the data. What data do we have?
+### What is a model?
 
-```{r}
-head(pops)
-```
+A model is a way for us to take complex systems and break them down into small, more understandable bits. We can use models to help us understand the relationship between different variables. We can then use those models to make predictions based on those relationships.
 
-After inspecting our dataset, it looks as though we have more time-series data of population abundances. It isn't immediately clear how many populations there are, but perhaps the populations are named after their locations ("NE" could stand for "Northeast").
+## Data
 
-## Visually
+![](assets/hairgrass_landscape.jpeg){width="50%"}
 
-Let's first start our investigation of these populations by plotting the data points as a scatterplot.
+Knowing that we would soon be building roads, we asked our botanists to collect data for us on key components of the hairgrass' environment. Since it would take too long to sample everywhere hairgrass grows, they collected data from a sample of the hairgrass population.
 
-As we have done throughout this module, we will want the variable representing time to be on the x-axis and the population abundance (N) on the y-axis.
+Our botanists collected data for the following variables:
 
-We will also want the data points to be distinguished by which population they are from, so we need to specify the `color` argument within the `aes()` function.
+-   *hairgrass density*: number of individual clumps (tussocks) of hairgrass (per 25 meters squared)
+-   *soil pH*: most plants prefer mildly acidic to neutral environments
+-   *nitrogen (N) content*: as parts per million (ppm); important for plant growth and tissue building
+-   *phosphorous (P) content*: as parts per million (ppm); important for plant growth and tissue building
+-   *percent rock*: how rocky the location is; rocks in soil impact water drainage and temperature
+-   *max windspeed (knots per hour)*: extreme wind can pose a challenge to plants of all types
+-   *average summer temperature (C)*: temperature in the growing season
+-   *penguin density*: the number of penguins per the sample quadrant penguin poop increases nitrogen and phosphorus content in the system but penguins may trample the plants
 
-```{r}
-ggplot(pops, aes(year, abund, color = population)) +
-  geom_point() + 
-  labs(x = "Year",
-       y = "Abundance",
-       color = "Fish Population") +
-  theme_bw()
-```
+This data is based on this article: [Parnikoza, et al. 2007](https://doi.org/10.1016/j.polar.2007.10.002)
 
-### Interpretation
+## Summarize and Visualize
 
-Based on the plot, what do you notice about these populations?
+As we see above, there are many environmental conditions that may be associated with hair grass density.
 
--   Are they more likely exhibiting exponential or logistic growth? Why?
--   How do the populations appear to differ from one another?
+For this lesson, we are going to focus on two: *nitrogen (N) content* and *soil pH*.
 
-Given your answers to the questions above, what might be a good next step?
+### Set-Up
 
-## Numerically
+As usual, we start with loading our packages and our data.
 
-When comparing different categories or groups (in this case, comparing different populations), we typically compare them both visually and numerically before conducting statistical tests.
+```{r message=FALSE}
+library(tidyverse)
+hairgrass_data <- read_csv("data/hairgrass_data.csv")
+```
+
+Let's take a look at the data in the data set. What does each row represent?
 
-Since we have already made visual comparisons, let's compare the populations numerically.
+```{r}
+head(hairgrass_data)
+tail(hairgrass_data)
+```
 
-Typically, we have compared groups with measures of central tendency and measures of variability. Do you think that is what we should do here? Why or why not?
+### Nitrogen Content
 
-### Separate Populations
+Let's start by investigating any relationship between hair grass density and nitrogen content.
 
-Unlike our typical procedure, in order to fit logistic models to each population, our simplest way forward will be to create one data frame for each population.
+First, we should spend a little time thinking about our variables. Spend a few minutes in small groups discussing the answer to the questions below.
 
-| NOTE: in case you are wondering, yes, there are definitely ways to fit a logistic model to each population without separating them! However, we would have to introduce quite a few new concepts to do so efficiently, so we are going to stick with the tried-and-true process of running the models individually.
+-   Which columns are we interested in right now? soil_nitrogen, hairgrass_density
+-   Which one is the independent variable and which is the dependent variable?
+    -   independent: soil_nitrogen
+    -   dependent: hairgrass_density
+-   Are the variables categorical or continuous? both are continuous
+-   Which type of data visualization should we use? scatterplot
 
-We can use the `filter` function to select each population, one at a time.
+Next, calculate the mean (`mean()`) and standard deviation (`sd()`) for the nitrogen content.
 
 ```{r}
-pop_NE <- pops |> 
-  filter(population == "NE")
+hairgrass_data %>% 
+  summarise(mean_N = mean(soil_nitrogen),
+            sd_N = sd(soil_nitrogen))
+```
 
-pop_SE <- pops |> 
-  filter(population == "SE")
+Now that we have summarized the data, let's take a look at the data visually.
 
-pop_SW <- pops |> 
-  filter(population == "SW")
+```{r}
+ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
+  geom_point() +
+  labs(x = "N Content",
+       y = "Hairgrass Density (per 25 m^2)") +
+  theme_bw()
 ```
 
-### Finding the Carrying Capacity
+Do you see a pattern? What type of relationship do you see?
 
-Now that we have data frames for each of the populations, we can fit the logistic model to each population and determine the estimate of the carrying capacity for each.
+How do we analyze this type of relationship statistically?
 
-As we run the code to fit the model to each population, we might receive some warnings: "Warning :NaNs produced". Don't worry--that's ok! Keep going!
+## Statistical Analysis
 
-```{r}
-# NE population
-model_NE <- drm(abund ~ year, data = pop_NE, fct = LL.4())
-model_NE
-
-# if you want to be fancy and save the carrying capacity as an object
-# this code combines several steps from the last lesson into one step
-K_NE <- unname(coef(model_NE)[3])
-K_NE
-```
+We are going to use two (related) statistical methods to understand the relationship between two continuous (numeric) variables:
 
-Now we can do the same for the SE and SW populations, too.
+-   correlation coefficient ($r$) and R-squared ($R^2$)
+-   linear regression
 
-```{r}
-# SE population
-model_SE <- drm(abund ~ year, data = pop_SE, fct = LL.4())
-model_SE
+### Line of Best Fit
 
-K_SE <- unname(coef(model_SE)[3])
-K_SE
+To understand correlation, R-squared, and linear regression, we first need to talk about something we call the *line of best fit*.
 
-# SW population
-model_SW <- drm(abund ~ year, data = pop_SW, fct = LL.4())
-model_SW
+The line of best fit aims to minimize the distance between each observation (points) and the line. The distances between each observation and any line are called *residuals* (the dotted gray lines). The line of best fit is the line that has the smallest *residuals*.
 
-K_SW <- unname(coef(model_SW)[3])
-K_SW
-```
+![](assets/lines.png){width="50%"} ![](assets/residuals.jpg){width="50%"}
 
-Let's record the carrying capacity estimates for each population here so we can refer back to them later, if needed.
+We can add a line of best fit to a ggplot by using the `geom_smooth()` function. We need to specify that the method we want should produce a straight line ("linear model").
 
-| Population | Carrying Capacity  |
-|:----------:|:------------------:|
-|     NE     | `r round(K_NE, 1)` |
-|     SE     | `r round(K_SE, 1)` |
-|     SW     | `r round(K_SW, 1)` |
+```{r}
+ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE) +
+  labs(x = "N Content",
+       y = "Hairgrass Density (per m^2)") +
+  theme_bw()
+
+# method = "lm" is required!
+# se = FALSE is optional
+```
 
-As we saw in the plot, the estimated carrying capacity for the SW population is below that of the NE population.
+### Correlation Coefficient
 
-What was much more challenging to see clearly in the plot, however, is that the estimated carrying capacity of the SE population is in between the other two carrying capacities, albeit closer to the NE population.
+The correlation coefficient, `r`, is a measurement of the strength of the relationship between two continuous variables.
 
-### Plotting the Logistic Model Curves
+The correlation coefficient is a number between -1 and 1 that looks at the relationship between two numeric variables. If the value is negative, there is a negative relationship between the two variable; if the value is positive, there is a positive relationship.
 
-We can add the logistic growth models that we fit to each population into our ggplot to more easily compare the curves.
+If all the points fall exactly on the line of best fit, r = 1 or -1. If there is no relationship between the variables, `r` is 0 (or something very close to it).
 
-To do so requires a few steps.
+![](assets/coefficient_correlation.jpg){width="50%"}
 
-1.  Use the `predict()` function to get the values for the "logistic line of best fit." Remember, these will not match up perfectly with our *actual, observed* values; these are the y-axis or `abundance` values for the best fitting logistic model at each observed x-axis value, or year.
+The greater the magnitude (size) of the correlation coefficient, the stronger the relationship between the two variables.
 
-2.  Use the `mutate()` function to create a new column in each data frame with these logistic *model* values.
+![](assets/correlation.jpg){width="50%"}
 
-3.  Combine all three dataframes back together into one dataframe using the `bind_rows()` function.
+It is also important to recognize that the correlation coefficient has nothing to do with the slope of the line (we use linear regression to assess that!)
 
-4.  Plot the actual data points using `geom_point()` and plot the logistic model data points using the `geom_line()` function.
+![](assets/steepness.jpeg){width="50%"}
 
-#### Add the Logistic Model Points to the Dataframes
+Based on the hairgrass plot we did above, do you expect the correlation coefficient to be positive or negative? For this class, we'll say that any `r` value with 0.1 of 0 means there is no relationship.
 
-First, let's see what output the `predict()` function gives us for the NE population.
+To calculate the correlation coefficient, we need to go back to base R and indicate which columns we are referencing with the `$` operator. We use the `cor()` function.
 
 ```{r}
-predict(model_NE)
+r_nitrogen <- cor(x = hairgrass_data$soil_nitrogen, y = hairgrass_data$hairgrass_density)
+r_nitrogen
 ```
 
-If we compare these numbers to the numbers in the `abund` column of `pop_NE`, we notice that they are not the same. The difference between these values from the model of best fit and the actual, observed values are called *residuals*, just as they are with lines of best fit (from linear regressions).
+### R-squared ($R^2$)
 
-![](assets/residuals.jpg){width="50%" fig-align="center"}
+The R-squared ($R^2$) value is a representation of how much variation is explained by the line of best fit.
 
-Let's use the `mutate` function to add these model values to each dataframe.
+When we have a dependent variable and an independent variable, the R-squared value tells us how much of the variation in the *dependent* variable is explained by the *independent* variable.
 
-```{r}
-pop_NE <- pop_NE |> 
-  mutate(logistic_model = predict(model_NE))
-pop_NE
+Sometimes we don't have obvious dependent and independent variables, but we can still use similar language ($R^2$ tells us how much of the variation in the data is explained).
 
-pop_SE <- pop_SE |> 
-  mutate(logistic_model = predict(model_SE))
+To calculate R-squared, we square the correlation coefficient value we calculated above. It is always positive because we are squaring it `r` value; that means that R-squared values range from 0 to 1. The closer to 1, the more variation is explained.
 
-pop_SW <- pop_SW |> 
-  mutate(logistic_model = predict(model_SW))
+```{r}
+r_nitrogen^2
 ```
 
-#### Combine Dataframes
+How would we interpret this `r^2` value?
 
-We can now bring all three individual data frames back together into one data frame using a function called `bind_rows()`. You can think of `bind_rows` as gluing the data frames together, one after another.
+One way to think of the `r^2` value that might be helpful is to convert it from a proportion to a percentage—what *percentage* of the variation in the dependent variable is explained by the independent variable?
 
-The arguments in the `bind_rows()` function are all of the data frames we want glued together.
+To convert the proportion to a percentage, we can multiply the value by 100.
 
 ```{r}
-pops_models <- bind_rows(pop_NE, pop_SE, pop_SW)
-pops_models
+r_nitrogen^2 * 100
 ```
 
-The resulting data frame again has 78 observations, as did the original `pops` data frame.
+### Linear Regression Analysis
+
+A regression analysis approximates the relationship between a dependent variable and one or more independent variables. It evaluates the strength of that relationship, ultimately giving us a p-value.
+
+Since we are using linear regressions in this course, the regression model will take the form of a straight line. The generic equation for a straight line is `y = mx + b`.
+
+-   `y` = dependent variable (y-axis)
+-   `x` = independent variable (x-axis)
+-   `m` = slope of the line is the slope
+-   `b` = y-intercept
+
+When we have a line with the specific slope and y-intercept, this equation lets us calculate the expected y-axis value for any given point on the x-axis.
+
+Using our variables, what would our linear regression model look like (we don't know `m` or `b` yet...)?
+
+### Hypothesis Testing
+
+What is the null hypothesis? What is the alternative hypothesis?
+
+**Null Hypothesis** ($H_{0}$): There is no relationship between hair grass density and nitrogen content.
+
+**Alternative Hypothesis** ($H_{A}$): There is a relationship between hair grass density and nitrogen content.
+
+What do you think this means for the slopes?
+
+### Regression Analysis
+
+Thankfully, R can calculate the slope (`m`) and y-intercept (`b`) of the line of best fit for us.
 
-#### Add Logistic Models to the Plot
+Let's find the equation for our line of best fit, our test statistic, and our p-value. To do this, we use a function called `lm()`: this stands for "linear model."
 
-We can now add the logistic models to our plot using the `geom_line()` function. Specifically, the values from the `logistic_model` column will be the y-axis values for the lines we are adding; the x-axis values are the same as the overall plot: `year`.
+Like with ANOVA, we will then want to use the `summary()` function to get out the values we need.
 
 ```{r}
-ggplot(pops_models, aes(x = year, y = abund, color = population)) +
-  geom_point() +
-  geom_line(aes(y = logistic_model)) +
-  labs(x = "Year", y = "Abundance", color = "Fish Population") +
-  theme_bw()
+nitrogen_model <- lm(hairgrass_density ~ soil_nitrogen, data = hairgrass_data)
+summary(nitrogen_model)
 ```
 
-It is now much easier to visually distinguish the differences between the growth curves and the carrying capacities between the three populations!
+As with our other statistical tests---t-tests and ANOVAs---the results give us some important values:
 
-## Statistically
+-   `b` (y-intercept): part of our line of best fit equation
+    -   The "intercept estimate", in this case 84.24, is our y-intercept in our line of best fit
+-   `m` (slope): also part of our line of best fit equation
+    -   this is the estimate for our independent variable (soil_nitrogen in this case)
+    -   in this model, m = 1.8
+-   *F-statistic*: this is our test statistic
+-   *p-value*: calculated from our regression model, used to determine significance (0.05 cut-off, as usual)
+    -   There are multiple p-values here. Focus either on the p-value for the independent variable (soil_nitrogen) or the overall p-value displayed in the last line of the results summary (p \< 2.2e-16)
+-   *R-squared*: this is our R-squared value that we calculated earlier
+    -   You can report either the "multiple" or the "adjusted"
+    -   The "multiple" will typically match the one we calculate with code
 
-We still want to analyze our three populations using inferential statistics to determine if there are significant differences between the populations.
+This means our equation for the line of best fit is: y = 1.8x + 84.24 and there is a statistically significant relationship between nitrogen content and hairgrass density.
 
-First, we need to decide what type of statistical test makes sense with the data we have. This is *not* a straight-forward question with a straight-forward answer. How we determine which test to use ultimately depends on the question we are asking and, therefore, the hypothesis we are testing.
+## Soil pH
 
-For the sake of the class (and in your assignment, too), let's assume the question we want to ask is whether the average population sizes significantly differ between the populations.
+Let's do the same series of steps to determine how soil pH impacts hair grass densities. Work on this in your small groups, and we will go over it in about 10 minutes.
 
-What type of statistical test should we use to answer that question?
+Start with summarizing the data: mean and standard deviation.
 
-What would the null and alternative hypotheses be for that statistical test?
-
-### Filtering the Data
+```{r}
+hairgrass_data %>% 
+  summarize(mean_pH = mean(soil_ph),
+            sd_pH = sd(soil_ph))
+```
 
-To most accurately compare the populations, we likely want to restrict the comparison to when the populations (or the models of best fit, at least) are fairly leveled off.
+Visualize the data. Remember to add in the line of best fit using `ggplot2`. Also add labels and a theme for practice.
 
-(Again, this is more of an estimation than an exact science, though there are more precise--and complicated--tools that could be used.)
+```{r}
+ggplot(hairgrass_data, aes(soil_ph, hairgrass_density)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE) +
+  labs(x = "Soil pH",
+       y = "Hairgrass Density per m^2") + 
+  theme_bw()
+```
 
-A rough estimation would be that the models mostly level off around year 15; let's filter our data to include only abundance from year 15 and later.
+Calculate the correlation coefficient (`r`)? What does this tell us?
 
 ```{r}
-pops_15_25 <- pops_models |> 
-  filter(year >= 15)
-pops_15_25
+r_ph <- cor(x = hairgrass_data$soil_ph, y = hairgrass_data$hairgrass_density)
 ```
 
-### Running the Statistical Analysis
+How much variation does soil pH explain in the hair grass density data?
 
-Now that we have filtered our data, we can conduct our statistical test.
+```{r}
+r_ph^2
+```
+
+Write out the model for our question about soil pH (without values)?
 
 ```{r}
-model_aov <- aov(abund ~ population, data = pops_15_25)
-summary(model_aov)
+# hairgrass density  = m(soil_ph) + b
 ```
 
-This p-value tells us that the overall model is significant (we reject the null hypothesis), but it doesn't tell us which populations are significantly different from one another. To determine which populations differ from each other, we need to run *post hoc* pairwise-comparisons.
+Run the regression model and write out the equation for the line of best fit.
 
 ```{r}
-TukeyHSD(model_aov)
+ph_model <- lm(hairgrass_density ~ soil_ph, data = hairgrass_data)
+summary(ph_model)
 ```
 
-How do we interpret these results? What are our conclusions?
+Equation for line of best fit: y = -4.648x + 150.24
+
+Interpret the results of the regression (our p-value cutoff is still = 0.05). What do we conclude about the relationship between soil pH and hair grass density? Why?
+
+Because p \< 0.05 for the regression model, we reject the null hypothesis and conclude that there is a relationship between soil pH and hairgrass density.
 
-## Data-Driven Decision Making
+## Data-driven Decision Making
 
-Our ultimate goal in this module has been to determine which population of fish is best for fishing sustainably. We are prioritizing large, stable populations.
+The reason we are using regression analysis is to inform where we should (or should not) build our road so we don't harm the sensitive hair grass or take away their prime habitat.
 
-Given those requirements and what our numeric, visual, and statistical tests have told us, which of these populations would be the best for us to harvest? Why?
\ No newline at end of file
+What do results above for nitrogen content and soil pH mean for the road we are building?
\ No newline at end of file
diff --git a/modules/module_3/module3_6.qmd b/modules/module_4/module4_4.qmd
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@@ -1,10 +1,11 @@
 ---
-title: "3.6: Multiple Regression"
+title: "4.4: Multiple Regression"
 author: "Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
+
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
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diff --git a/modules/module_5/module5_1.qmd b/modules/module_5/module5_1.qmd
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@@ -0,0 +1,199 @@
+---
+title: '5.1: Population Growth'
+author: "Jessie Golding and Ellen Bledsoe"
+format:
+  html:
+    toc: true
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, fig.align = "center")
+```
+
+# Introduction to Population Growth
+
+Let's use descriptive statistics and data visualization to better understand population dynamics.
+
+### Exponential Growth
+
+A population that shows [exponential growth]{.underline} has a growth rate that depends the average number of births (`b`) and deaths (`d`) per individual.
+
+We call rates that are averages per individual in a population (rather than a total) *per capita* growth rates; *per capita* translates from Latin to "by head," meaning for individuals.
+
+Combined, birth and death rates can be described as the [intrinsic rate of increase]{.underline} (`r`). We calculate `r` by subtracting the number of individuals that are removed from the population (deaths) from the number of individuals that are joining the population (births): $r = b - d$.
+
+We can calculate by how many individuals the population will change in a given time period (`N`) by multiplying the starting population size (`Ni`) by the intrinsic rate of increase (`r`) using this equation:
+
+![](assets/clipboard-1439389577.png){width="90" fig-align="center"}
+
+### Logistic Growth
+
+Populations that show logistic growth start out in the same way as populations with exponential growth–driven by *per capita* birth and death rates.
+
+After a while, though, limited resources also start to come into play. When calculating the change in the size of the population (`N`), we also need to take into account the maximum number of individuals the population can sustainably support, called the [carrying capacity]{.underline}, `K`.
+
+We use the following equation to calculate the population size:
+
+![](assets/clipboard-3908560513.png){width="188" fig-align="center"}
+
+### Visualizing Growth Models
+
+As a reminder, this is what each type of population growth model looks like:
+
+![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
+
+# What Population Factors Matter?
+
+We still need to figure out what fish population we will target for Antarctica.
+
+Briefly, let's consider what we think might be important for sustainability.
+
+1.  Large population
+2.  Stable population (i.e., not declining)
+
+## Fish Data
+
+Let's look at fish data from around our station in Antarctica over the past 20 years to get a better feel for how different population factors impact fish populations. These fish moved into the bay by our station when we established it, so we know the populations started at around the same time and had the same starting populations.
+
+These aren't necessarily our targets for fishing, particularly since it would be difficult and potentially disruptive to fish in the bay. Right now our goal is just to learn so we know what to look for when we are making our decision to conduct sustainable fishing.
+
+![Adapted from Figure 2 from Zheng et al. (2022)](assets/fish_data_fish.png){fig-align="center"}
+
+As always, first we load the `tidyverse` and read in the data.
+
+```{r}
+library(tidyverse)
+fish_pops <- read_csv("data/fish_pops.csv")
+```
+
+### Largest Population
+
+Which population grows the biggest? Let's start by calculating some summary statistics of the data.
+
+```{r}
+max_pop <- fish_pops %>%
+  group_by(population)%>%
+  summarise(max_N = max(N))
+max_pop
+```
+
+It looks like Population B grows the largest by far. Let's learn more about Population B and filter out the data so we can look at it separately.
+
+```{r}
+popB <- fish_pops %>%
+  filter(population == "B")
+popB
+```
+
+**What type of growth is the population exhibiting?**\
+*Answer: Exponential.*
+
+**Why is there no carrying capacity in this population data set? What does this mean about the resources Population B needs?**\
+*Answer: Exponential models assume unlimited resources, so no carrying capacity (K) is included.*
+
+**What do you notice about Population A that is different than Population B?**\
+*Answer: The intrinsic growth rate (r) for Population A is negative.*
+
+**What has happened to this population?**\
+*Answer: Population A crashes and reaches zero.*
+
+**Between Population A and Population B, which is a better target population for fishing? Why?**\
+*Answer: Population B, because r \> 0 and it grows quickly.*
+
+### Exponential Growth Populations
+
+It looks like populations exhibiting exponential growth might be good targets for our fishing operations because they can become very large. Let's look at the data we have on exponential growth populations a little closer.
+
+Let's filter out the exponential growth models to examine them.
+
+```{r}
+exp_pops <-fish_pops %>%
+  filter(growth_type == "exponential")
+exp_pops
+```
+
+It's hard to see what's happening with only the data frame. Let's look at the exponential growth populations on the same plot:
+
+```{r}
+ggplot(exp_pops, aes(x = year, y = N, color = population)) +
+  geom_point() +
+  geom_line()+
+  labs(x = "Time", y = "Abundance", color = "Population") +
+  theme_light()
+```
+
+It looks like Population B is very large, but Population A stays small. It's hard to see what is happening on this plot because the scales are so different, so let's look at what is happening with Population A by filtering out the data just for that population.
+
+```{r}
+popA <-fish_pops %>%
+  filter(population == "A")
+popA
+```
+
+What do you notice about Population A that is different than Population B? Intrinsic growth rate negative
+
+The intrinsic rate of increase (r) for Population A is negative. Let's see what that means for Population A and plot it by itself:
+
+```{r}
+ggplot(popA, aes(x = year, y = N)) +
+  geom_point() +
+  geom_line()+
+  labs(x = "Time", y = "Abundance") +
+  theme_light()
+```
+
+What has happened to this population? Crashed
+
+Even with "exponential growth", the population has crashed (gone to 0), because *r* is negative. It took approximately 7 years for this to happen.
+
+Between Population A and Population B, which do you think is a better target population for fishing? Population B, `r` is positive
+
+**Logistic growth populations**
+
+Ok, so we can't rely only on the type of growth as a way to make our fishing decisions. But, we're still curious about our populations that are limited by resources (i.e., exhibiting logistic growth).
+
+```{r}
+log_pops <- fish_pops %>%
+  filter(growth_type == "logistic")
+log_pops
+```
+
+Notice in this case we have carrying capacity data. Let's use some data visualizations to help us understand what that means.
+
+Lets look at the two logistic growth populations on the same plot:
+
+```{r}
+ggplot(log_pops, aes(x = year, y = N, color = population))+
+  geom_point() +
+  geom_line()+
+  labs(x = "Time", y = "Abundance", color = "Population")+
+  theme_light()
+```
+
+Which population gets the largest? Population 4
+
+### Finding the Carrying Capacity
+
+When we have a logistic growth model, we often will want to find the carrying capacity of the population
+
+What values does each population level off at?
+
+-   Population C: 20
+
+-   Population D: 100
+
+These are the carrying capacities. If you look back at the data, you will see Population C has a carrying capacity of 20 individuals, whereas Population D has a carrying capacity of 100 individuals. Which population do you think might be the best candidate to fish between these two? Population D is more abundant and has a higher carrying capacity.
+
+**A review of what we have learned**
+
+The following factors are important to know about a population before fishing:
+
+1.  Exponential growth can result in bigger populations than logistic growth
+
+2.  Intrinsic rate of increase (`r`) should not be negative
+
+3.  A higher carrying capacity means the population can take advantage of more resources to grow larger, which may indicate that the population is a better candidate for fishing
+
+## References
+
+Zhang, M., Liu, S., Bo, J., Zheng, R., Hong, F., Gao, F., Miao, X., Li, H., & Fang, C. (2022). First evidence of microplastic contamination in Antarctic fish (Actinopterygii, Perciformes),*Water*, *14*(19).
\ No newline at end of file
diff --git a/modules/module_5/module5_2.qmd b/modules/module_5/module5_2.qmd
new file mode 100644
index 0000000..6754c2e
--- /dev/null
+++ b/modules/module_5/module5_2.qmd
@@ -0,0 +1,184 @@
+---
+title: '5.1: Sustainable Fishing'
+author: "Jessie Golding and Ellen Bledsoe"
+format:
+  html:
+    toc: true
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Sustainable Fishing
+
+## Applying Principles of Population Growth
+
+We have decided that the Antarctic toothfish (*Dissostichus mawsoni*) may be a good candidate for our fishing.
+
+![](assets/antarctic_toothfish.jpg){fig-align="center"}
+
+The Antarctic toothfish is fairly widely distributed around Antarctica. The hexagons below show where the Antarctic toothfish occurs. Gray represents known distribution and red represents locations where genetic samples were taken for a study on their population (Maschette et al. 2023).
+
+![](assets/antarctic_toothfish_distribution.jpg){width="75%" fig-align="center"}
+
+However, given concerns about maintaining the fishery (an important ecosystem service), we want to make sure we do not overfish the species but rather fish sustainably.
+
+Let's keep exploring our fish populations to help us understand what we should be focusing on when making decisions about how many fish we can sustainably harvest from any given population.
+
+## Set-Up
+
+As usual, we start with loading our packages and our data.
+
+```{r}
+library(tidyverse)
+
+fish <- read_csv("data/fish_pops.csv")
+```
+
+Let's take a look at the data and remind ourselves what the data look like.
+
+```{r}
+head(fish)
+```
+
+## Population Growth Models
+
+Let's remind ourselves about exponential growth and logistic growth.
+
+**Expotential growth** takes births and deaths (in the form of the intrinsic growth rate, `r`) into account:
+
+![](assets/clipboard-4205079346.png){width="83" fig-align="center"}
+
+**Logistic growth** includes the intrinsic growth rate and another important factor—resources. The resources determine the maximum number of individuals a population can have, which we call the carrying capacity (`K`):
+
+![](assets/clipboard-3779797644.png){width="130" fig-align="center"}
+
+This is what each type of population growth model looks like:
+
+![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
+
+### Which Population to Fish?
+
+We've previously discussed what factors we want to focus on when we are deciding which population to fish. Ideally, we want to target both a large population and a stable population.
+
+At first glance, a population that is growing exponentially seems ideal—and it is, in some ways.
+
+But in reality, all populations will *eventually* hit some capacity on resources. Populations that are currently demonstrating exponential growth either haven't yet hit their carrying capacity or have dramatically overshot their carrying capacity, which can lead to a huge crash or a smaller crash with boom and bust cycles until it evens out around the carrying capacity.
+
+![](assets/pop_crash.jpeg){width="75%" fig-align="center"}
+
+So, in order for us to feel confident that our population will be large for a long time, we probably want to choose the safer option, which is choosing a population that is showing logistic growth.
+
+Let's select the logistic growth population with the largest value, Population D, for our next analyses.
+
+```{r}
+log_pop <- fish %>% 
+  filter(population == "D")
+```
+
+## Estimating Carrying Capacity
+
+In our journey to determine how many fish we can sustainably harvest (i.e., remove from the population without causing the population to go extinct), we first need to determine the carrying capacity.
+
+While the dataset contains carrying capacity estimates, those are only visual estimates. We are going to use code (and R will use a lot of math behind the scenes) to calculate a more accurate estimate from the data.
+
+To do so, we are actually going to use a new package, `drc`.
+
+This package will let us fit a logistic model and provide an estimate of the asymptote of the curve, or the value where the curve levels off and not longer increases. This is our estimate of `K`, the carrying capacity of the population.
+
+```{r message=FALSE}
+# load the drc package; text when you load it is fine (as long as it doesn't say error)
+library(drc)
+```
+
+Now, we are going to use a function from the `drc` package called `drm()` to fit our logistic growth model.
+
+It works very similarly to the inferential statistics models from Module 3 except instead of finding a straight line of best fit, this line of best fit is allowed to curve like the logistic growth model.
+
+What kind of growth do we think we have based on this plot? Logistic
+
+```{r}
+# Fit the model to the data
+# 'N ~ year' means we're predicting N (population) as a function of 'year'
+# fct = LL.4() specifies to fit a logistic growth model
+model <- drm(N ~ year, data = log_pop, fct = LL.4())
+```
+
+This time, unlike with the linear model, we are interested not in a p-value but in the estimate of a specific value from the model--the carrying capacity.
+
+To get all of the estimated values, we will use a function called `coef`, which is short for "coefficient," to give us the estimates from the `model` object.
+
+```{r}
+estimates <- coef(model)
+estimates
+```
+
+In this case, we happen to be interested in the estimate they are calling `d:(Intercept)`; this is what we are calling `K`, the carrying capacity. Let's save that value.
+
+The simplest way to choose one value from the vector `estimates` is to use square brackets for subsetting, something we haven't done since Module 1! We want the 3rd value.
+
+```{r}
+K <- estimates[3]
+K
+```
+
+Our estimate for the carrying capacity is 104.5 fish, which is close to the carrying capacity value in the dataset (100).
+
+We can remove the label (`d:(Intercept)`) if we want to by using the `unname()` function. Now our object `K` is only the number for the estimate of the carrying capacity.
+
+```{r}
+K <- unname(K)
+K
+```
+
+### Plotting the Carrying Capacity
+
+Let's now add this estimated carrying capacity value to our plot of the data.
+
+First, let's plot the population data alone.
+
+```{r}
+ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs. population)
+  geom_point() +
+  geom_line() +
+  labs(x = "Year", y = "Population") +
+  theme_bw()
+```
+
+To add the a line representing the carrying capacity, we can use `geom_hline()` to add a horizontal line where the y-intercept is equal to the carrying capacity.
+
+This is very similar to how we used `geom_vline()` to add a vertical line to histograms to represent the mean value.
+
+```{r}
+ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs. population)
+  geom_point() +
+  geom_line() +
+  # use geom_hline to plot a horizontal line where the y-intercept is equal to K
+  geom_hline(aes(yintercept = K), color = "red") + # outside of aes() sets color
+  labs(x = "Year", y = "Population") +
+  theme_bw()
+```
+
+## Maximum Sustainable Yield
+
+Once we have an estimate of the carrying capacity for a population, we can calculate something called the *maximum sustainable yield*, or MSY.
+
+The MSY is the number of individuals that we can remove from the population and keep the population stable (or increasing). If we remove too many individuals (above MSY), the population will not be able to reproduce fast enough to replace the harvested individuals, causing a decline in the population—in this case, from over-fishing.
+
+![](assets/logistic_growth_msy.png){width="49%"} ![](assets/msy.png){width="49%"}
+
+While the concept behind how we get to the equation for MSY may be a bit complex, thankfully, the equation itself is quite straightforward:
+
+![](assets/clipboard-959845017.png){fig-align="center"}
+
+Let's calculate the maximum sustainable yield for Population D.
+
+```{r}
+MSY <- K/2
+MSY
+```
+
+This means that we can take about 52 fish from this population annually and keep the population stable!
+
+If we take fewer than 52 fish, the population will increase (until the carrying capacity, that is). If we take more than 52 fish, however, we are over-fishing the population, meaning the population will not be able to reproduce enough to maintain the population and will eventually collapse.
\ No newline at end of file
diff --git a/modules/module_5/module5_3.qmd b/modules/module_5/module5_3.qmd
new file mode 100644
index 0000000..415a054
--- /dev/null
+++ b/modules/module_5/module5_3.qmd
@@ -0,0 +1,260 @@
+---
+title: '5.3: Comparing Populations'
+author: "Jessie Golding and Ellen Bledsoe"
+format:
+  html:
+    toc: true
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
+```
+
+# Comparing Populations
+
+Throughout this module, we have learned how populations demonstrating exponential growth and logistic growth differ from one another, both conceptually and visually.
+
+While we have fit models to some of these populations (specifically, logistic growth models), we have done so in order to estimate the carrying capacity of the population rather than to use inferential statistics to determine if the populations are statistically significantly different from one another.
+
+In today's lesson, we are going to learn how to (a) add those logistic growth models to our data visualizations and (b) compare populations statistically.
+
+## Set Up
+
+As always, we need to start by loading the packages we are going to use along with our data.
+
+First, let's load our packages. We are going to be using both the `tidyverse` and `drc` packages in this lesson, so we need to load both of them.
+
+```{r}
+library(tidyverse)
+library(drc)
+```
+
+Next, we want to bring in our dataset.
+
+```{r}
+pops <- read_csv("data/comparing_populations.csv")
+```
+
+We can use the `head()` function to take a look at the data. What data do we have?
+
+```{r}
+head(pops)
+```
+
+After inspecting our dataset, it looks as though we have more time-series data of population abundances. It isn't immediately clear how many populations there are, but perhaps the populations are named after their locations ("NE" could stand for "Northeast").
+
+## Visually
+
+Let's first start our investigation of these populations by plotting the data points as a scatterplot.
+
+As we have done throughout this module, we will want the variable representing time to be on the x-axis and the population abundance (N) on the y-axis.
+
+We will also want the data points to be distinguished by which population they are from, so we need to specify the `color` argument within the `aes()` function.
+
+```{r}
+ggplot(pops, aes(year, abund, color = population)) +
+  geom_point() + 
+  labs(x = "Year",
+       y = "Abundance",
+       color = "Fish Population") +
+  theme_bw()
+```
+
+### Interpretation
+
+Based on the plot, what do you notice about these populations?
+
+-   Are they more likely exhibiting exponential or logistic growth? Why?
+-   How do the populations appear to differ from one another?
+
+Given your answers to the questions above, what might be a good next step?
+
+## Numerically
+
+When comparing different categories or groups (in this case, comparing different populations), we typically compare them both visually and numerically before conducting statistical tests.
+
+Since we have already made visual comparisons, let's compare the populations numerically.
+
+Typically, we have compared groups with measures of central tendency and measures of variability. Do you think that is what we should do here? Why or why not?
+
+### Separate Populations
+
+Unlike our typical procedure, in order to fit logistic models to each population, our simplest way forward will be to create one data frame for each population.
+
+| NOTE: in case you are wondering, yes, there are definitely ways to fit a logistic model to each population without separating them! However, we would have to introduce quite a few new concepts to do so efficiently, so we are going to stick with the tried-and-true process of running the models individually.
+
+We can use the `filter` function to select each population, one at a time.
+
+```{r}
+pop_NE <- pops |> 
+  filter(population == "NE")
+
+pop_SE <- pops |> 
+  filter(population == "SE")
+
+pop_SW <- pops |> 
+  filter(population == "SW")
+```
+
+### Finding the Carrying Capacity
+
+Now that we have data frames for each of the populations, we can fit the logistic model to each population and determine the estimate of the carrying capacity for each.
+
+As we run the code to fit the model to each population, we might receive some warnings: "Warning :NaNs produced". Don't worry--that's ok! Keep going!
+
+```{r}
+# NE population
+model_NE <- drm(abund ~ year, data = pop_NE, fct = LL.4())
+model_NE
+
+# if you want to be fancy and save the carrying capacity as an object
+# this code combines several steps from the last lesson into one step
+K_NE <- unname(coef(model_NE)[3])
+K_NE
+```
+
+Now we can do the same for the SE and SW populations, too.
+
+```{r}
+# SE population
+model_SE <- drm(abund ~ year, data = pop_SE, fct = LL.4())
+model_SE
+
+K_SE <- unname(coef(model_SE)[3])
+K_SE
+
+# SW population
+model_SW <- drm(abund ~ year, data = pop_SW, fct = LL.4())
+model_SW
+
+K_SW <- unname(coef(model_SW)[3])
+K_SW
+```
+
+Let's record the carrying capacity estimates for each population here so we can refer back to them later, if needed.
+
+| Population | Carrying Capacity  |
+|:----------:|:------------------:|
+|     NE     | `r round(K_NE, 1)` |
+|     SE     | `r round(K_SE, 1)` |
+|     SW     | `r round(K_SW, 1)` |
+
+As we saw in the plot, the estimated carrying capacity for the SW population is below that of the NE population.
+
+What was much more challenging to see clearly in the plot, however, is that the estimated carrying capacity of the SE population is in between the other two carrying capacities, albeit closer to the NE population.
+
+### Plotting the Logistic Model Curves
+
+We can add the logistic growth models that we fit to each population into our ggplot to more easily compare the curves.
+
+To do so requires a few steps.
+
+1.  Use the `predict()` function to get the values for the "logistic line of best fit." Remember, these will not match up perfectly with our *actual, observed* values; these are the y-axis or `abundance` values for the best fitting logistic model at each observed x-axis value, or year.
+
+2.  Use the `mutate()` function to create a new column in each data frame with these logistic *model* values.
+
+3.  Combine all three dataframes back together into one dataframe using the `bind_rows()` function.
+
+4.  Plot the actual data points using `geom_point()` and plot the logistic model data points using the `geom_line()` function.
+
+#### Add the Logistic Model Points to the Dataframes
+
+First, let's see what output the `predict()` function gives us for the NE population.
+
+```{r}
+predict(model_NE)
+```
+
+If we compare these numbers to the numbers in the `abund` column of `pop_NE`, we notice that they are not the same. The difference between these values from the model of best fit and the actual, observed values are called *residuals*, just as they are with lines of best fit (from linear regressions).
+
+![](assets/residuals.jpg){width="50%" fig-align="center"}
+
+Let's use the `mutate` function to add these model values to each dataframe.
+
+```{r}
+pop_NE <- pop_NE |> 
+  mutate(logistic_model = predict(model_NE))
+pop_NE
+
+pop_SE <- pop_SE |> 
+  mutate(logistic_model = predict(model_SE))
+
+pop_SW <- pop_SW |> 
+  mutate(logistic_model = predict(model_SW))
+```
+
+#### Combine Dataframes
+
+We can now bring all three individual data frames back together into one data frame using a function called `bind_rows()`. You can think of `bind_rows` as gluing the data frames together, one after another.
+
+The arguments in the `bind_rows()` function are all of the data frames we want glued together.
+
+```{r}
+pops_models <- bind_rows(pop_NE, pop_SE, pop_SW)
+pops_models
+```
+
+The resulting data frame again has 78 observations, as did the original `pops` data frame.
+
+#### Add Logistic Models to the Plot
+
+We can now add the logistic models to our plot using the `geom_line()` function. Specifically, the values from the `logistic_model` column will be the y-axis values for the lines we are adding; the x-axis values are the same as the overall plot: `year`.
+
+```{r}
+ggplot(pops_models, aes(x = year, y = abund, color = population)) +
+  geom_point() +
+  geom_line(aes(y = logistic_model)) +
+  labs(x = "Year", y = "Abundance", color = "Fish Population") +
+  theme_bw()
+```
+
+It is now much easier to visually distinguish the differences between the growth curves and the carrying capacities between the three populations!
+
+## Statistically
+
+We still want to analyze our three populations using inferential statistics to determine if there are significant differences between the populations.
+
+First, we need to decide what type of statistical test makes sense with the data we have. This is *not* a straight-forward question with a straight-forward answer. How we determine which test to use ultimately depends on the question we are asking and, therefore, the hypothesis we are testing.
+
+For the sake of the class (and in your assignment, too), let's assume the question we want to ask is whether the average population sizes significantly differ between the populations.
+
+What type of statistical test should we use to answer that question?
+
+What would the null and alternative hypotheses be for that statistical test?
+
+### Filtering the Data
+
+To most accurately compare the populations, we likely want to restrict the comparison to when the populations (or the models of best fit, at least) are fairly leveled off.
+
+(Again, this is more of an estimation than an exact science, though there are more precise--and complicated--tools that could be used.)
+
+A rough estimation would be that the models mostly level off around year 15; let's filter our data to include only abundance from year 15 and later.
+
+```{r}
+pops_15_25 <- pops_models |> 
+  filter(year >= 15)
+pops_15_25
+```
+
+### Running the Statistical Analysis
+
+Now that we have filtered our data, we can conduct our statistical test.
+
+```{r}
+model_aov <- aov(abund ~ population, data = pops_15_25)
+summary(model_aov)
+```
+
+This p-value tells us that the overall model is significant (we reject the null hypothesis), but it doesn't tell us which populations are significantly different from one another. To determine which populations differ from each other, we need to run *post hoc* pairwise-comparisons.
+
+```{r}
+TukeyHSD(model_aov)
+```
+
+How do we interpret these results? What are our conclusions?
+
+## Data-Driven Decision Making
+
+Our ultimate goal in this module has been to determine which population of fish is best for fishing sustainably. We are prioritizing large, stable populations.
+
+Given those requirements and what our numeric, visual, and statistical tests have told us, which of these populations would be the best for us to harvest? Why?
\ No newline at end of file
diff --git a/structure.qmd b/structure.qmd
index 823f0b4..7a87230 100644
--- a/structure.qmd
+++ b/structure.qmd
@@ -9,7 +9,7 @@ format:
 
 This page serves as an inventory of the material associated with each subsection of this site. These are also available in the Data in the Wild Github repository, which can be accessed [here](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA.git).
 
-***Note*****:** The instructor versions of lessons are also available as PDFs, and they can be downloaded from the righthand table of contents within the respective module subsection (e.g. Click on 1.1: Introduction to RStudio in the left sidebar \> navigate your attention to the table of contents on the right side of the screen \> the link to download the PDF is at the bottom).
+***Note*****:** The instructor versions of lessons are also available as PDFs, and they can be downloaded within the respective module subsection (e.g. Click on 1.1: Introduction to RStudio in the left sidebar to open the lesson page -\> PDF button on the right).
 
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | Module        | Title                                                       | Lecture / Code-along (.Rmd)                                                                                                                                            | Data sets (.csv)                                                                                                            | Assignments (.Rmd)                                                                                                                                                  |
@@ -32,7 +32,7 @@ This page serves as an inventory of the material associated with each subsection
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment3.Rmd) |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.2           | Writing Functions                                           | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions.Rmd)                        |                                                                                                                             |                                                                                                                                                                     |
+| 2.2           | Introduction to `ggplot2`                                   | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions.Rmd)                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions_Instructor.Rmd)          |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
@@ -44,7 +44,7 @@ This page serves as an inventory of the material associated with each subsection
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_4_geoms_Instructor.Rmd)              |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.5           | Module 2 Wrap-Up                                            | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp.Rmd)                           |                                                                                                                             |                                                                                                                                                                     |
+| 2.5           | Module 2 Wrap-Up: What is Causing the Food Poisoning?       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp.Rmd)                           |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp_Instructor.pdf)             |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
@@ -54,33 +54,41 @@ This page serves as an inventory of the material associated with each subsection
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment3.Rmd) |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.2           | T-Tests                                                     | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests.Rmd)                          |                                                                                                                             |                                                                                                                                                                     |
+| 3.2           | Comparing Two Means                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests.Rmd)                          |                                                                                                                             |                                                                                                                                                                     |
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 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests_Instructor.Rmd)            |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.3           | ANOVA                                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA.Rmd)                            |                                                                                                                             |                                                                                                                                                                     |
+| 3.3           | Comparing Multiple Means                                    | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA.Rmd)                            |                                                                                                                             |                                                                                                                                                                     |
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 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA_Instructor.Rmd)              |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.4           | Combining Datasets                                          | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_4_Combining_Datasets.Rmd)               |                                                                                                                             |                                                                                                                                                                     |
+| 4.1           | Combining Datasets (Joins & Binds)                          | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M3_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment1.Rmd) |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_4_Combining_Datasets_Instructor.Rmd) |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment2.Rmd) |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment3.Rmd) |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 4.2           | K-Nearest Neighbors                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.5           | K-Nearest Neighbors                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              |                                                                                                                             |                                                                                                                                                                     |
+| 4.3           | Roads and Regressions                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              |                                                                                                                             |                                                                                                                                                                     |
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 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 4.1           | Roads and Regression                                        | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression.Rmd)               | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M4_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment1.Rmd) |
+| 4.4           | Multiple Regression                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression.Rmd)               |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
+|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression_Instructor.Rmd) |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 5.1           | Introduction to Population Growth                           | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression.Rmd)               | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M4_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment1.Rmd) |
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 |               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression_Instructor.Rmd) |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment2.Rmd) |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 |               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment3.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Final Project](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/final_project/FinalAssignment_Sp2024.Rmd)    |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 4.2           | Multiple Regression                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression.Rmd)               |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression_Instructor.Rmd) |                                                                                                                             |                                                                                                                                                                     |
+| 5.2           | Sustainable Fishing                                         |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
++---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| 5.3           | Comparing Populations                                       |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | Final Project |                                                             |                                                                                                                                                                        | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/final_proj_data.zip) | [Assignment Guidelines](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/final_project/FinalAssignment.pdf)              |
 +---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
\ No newline at end of file

From af991ca46679b56bcc3aac0d9c8319a91b9321c6 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Wed, 18 Mar 2026 11:26:31 -0700
Subject: [PATCH 10/15] lesson edits

---
 _quarto.yml                                   |     20 +-
 index.qmd                                     |     45 +-
 modules/final_project/FinalAssignment.pdf     |    Bin 1124876 -> 0 bytes
 modules/final_project/FinalAssignment.qmd     |    143 -
 modules/final_project/FinalProject.qmd        |    343 +
 modules/module_1/assets/dplyr_pipe.png        |    Bin 0 -> 33358 bytes
 modules/module_1/assignments/a1/m1a1.qmd      |    175 +
 .../module_1/assignments/a1/m1a1_expected.pdf |    Bin 0 -> 105215 bytes
 .../assignments/a1/team_antarctica.csv        |     35 +
 .../a2/aggregated_station_data.csv            | 139161 +++++++++++++++
 modules/module_1/assignments/a2/m1a2.qmd      |    123 +
 modules/module_1/module1_1.qmd                |      7 +-
 modules/module_1/module1_2.aux                |     37 +
 modules/module_1/module1_2.log                |   1055 +
 modules/module_1/module1_2.qmd                |      5 +-
 modules/module_1/module1_2.tex                |    969 +
 modules/module_1/module1_3.qmd                |     19 +-
 modules/module_1/module1_home.qmd             |     27 +
 modules/module_2/module2_1.qmd                |      4 +-
 modules/module_2/module2_2.qmd                |      3 +-
 modules/module_2/module2_3.qmd                |      3 +-
 modules/module_2/module2_4.qmd                |     19 +-
 modules/module_2/module2_5.qmd                |     10 +-
 modules/module_3/module3_1.qmd                |      5 +-
 modules/module_3/module3_2.qmd                |      8 +-
 modules/module_3/module3_3.qmd                |      3 +-
 modules/module_4/module4_1.qmd                |      5 +-
 modules/module_4/module4_2.qmd                |      3 +-
 modules/module_4/module4_3.qmd                |      3 +-
 modules/module_4/module4_4.qmd                |      3 +-
 modules/module_5/module5_1.qmd                |     46 +-
 modules/module_5/module5_2.qmd                |     13 +-
 modules/module_5/module5_3.qmd                |     13 +-
 structure.qmd                                 |     94 -
 34 files changed, 142061 insertions(+), 338 deletions(-)
 delete mode 100644 modules/final_project/FinalAssignment.pdf
 delete mode 100644 modules/final_project/FinalAssignment.qmd
 create mode 100644 modules/final_project/FinalProject.qmd
 create mode 100644 modules/module_1/assets/dplyr_pipe.png
 create mode 100644 modules/module_1/assignments/a1/m1a1.qmd
 create mode 100644 modules/module_1/assignments/a1/m1a1_expected.pdf
 create mode 100644 modules/module_1/assignments/a1/team_antarctica.csv
 create mode 100644 modules/module_1/assignments/a2/aggregated_station_data.csv
 create mode 100644 modules/module_1/assignments/a2/m1a2.qmd
 create mode 100644 modules/module_1/module1_2.aux
 create mode 100644 modules/module_1/module1_2.log
 create mode 100644 modules/module_1/module1_2.tex
 create mode 100644 modules/module_1/module1_home.qmd
 delete mode 100644 structure.qmd

diff --git a/_quarto.yml b/_quarto.yml
index e0f76de..eada1bc 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -20,20 +20,25 @@ website:
     collapse-level: 1
 
     contents:
-      - section: "Introduction"
-        contents:
-          - index.qmd
-          - structure.qmd
-          - text: "Contact Us"
-            href: https://github.com/BiodiversityDataScienceCorp
+      - text: "Home"
+        href: index.qmd
+      
+      - text: "Contact Us"
+        href: https://github.com/BiodiversityDataScienceCorp
 
       - text: "---"
 
       - section: "Module 1"
         contents:
+          - text: "Overview"
+            href: modules/module_1/module1_home.qmd
           - modules/module_1/module1_1.qmd
           - modules/module_1/module1_2.qmd
           - modules/module_1/module1_3.qmd
+          - text: "Assignment 1"
+            href: modules/module_1/assignments/a1/m1a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_1/assignments/a2/m1a2.qmd
 
       - section: "Module 2"
         contents:
@@ -62,6 +67,9 @@ website:
           - modules/module_5/module5_2.qmd
           - modules/module_5/module5_3.qmd
 
+      - text: "Final Project"
+        href: modules/final_project/FinalProject.qmd
+
       - text: "---"
 
       - section: "Resources"
diff --git a/index.qmd b/index.qmd
index a7615b6..f78d759 100644
--- a/index.qmd
+++ b/index.qmd
@@ -8,40 +8,45 @@ format:
 
 ![](images/penguins.jpg)
 
-*Data in the Wild* introduces students to data science through the R programming language, applied to real ecological and conservation challenges. The course is built around a narrative, a mission to establish a sustainable colony in Antarctica, in which each module presents a new crisis for students to investigate using data. No prior programming experience is required.
+*Data in the Wild* introduces students to data science through the R programming language and a series of ecological problems set in Antarctica. Across the course, students use data to investigate food safety, wildlife tracking, habitat relationships, and sustainable fisheries management. Along the way, they learn skills in data wrangling, visualization, statistical analysis, modeling, and interpretation.
 
-The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and is taught at the University of Arizona as part of the general education curriculum.
+The course is designed for students in the life sciences and assumes no prior programming experience. Lessons begin with the basics of coding in R and gradually build toward more advanced tools such as hypothesis testing, machine learning, regression, and population modeling.
 
-## **The Narrative**
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and has been taught at the University of Arizona as part of the general education curriculum.
+
+## Learning Objectives
+
+By the end of the course, students will be able to:
+
+1.  Import, organize, summarize, and work with ecological datasets in R.
+2.  Create and interpret appropriate data visualizations using `ggplot2`.
+3.  Apply statistical tools such as t-tests, ANOVA, and regression to ecological questions.
+4.  Combine datasets and use basic classification approaches to support inference and prediction.
+5.  Interpret population growth models, carrying capacity, and sustainable yield in an ecological management context.
+6.  Communicate quantitative findings clearly using code, figures, and written interpretation.
+
+## The Narrative
 
 ### **Module 1: Mission Antarctica!**
 
-The team has arrived in Antarctica and must set up the tools needed for all future data work. Students learn the basics of R and RStudio in Posit Cloud: navigating the interface, writing and running code, creating objects, working with vectors and data types, and importing datasets. They practice summarizing data frames and computing simple descriptive statistics. By the end of the module, students can use R to explore ecological data and are prepared for data visualization and deeper analysis in later modules.
+The team has arrived in Antarctica and needs to establish the technical foundation for all future data work. Students begin by learning how to use R and RStudio in Posit Cloud, including how to run code, create objects, work with vectors, understand data classes, subset data, and use functions. They are also introduced to the `tidyverse`, where they begin learning how to select, filter, summarize, group, and mutate data. By the end of the module, students are comfortable navigating their coding environment and working with simple ecological datasets.
 
 ### **Module 2: Good Food Gone Bad**
 
-A wave of food poisoning spreads through the team, and the colony must identify the source. Students use descriptive statistics and data visualization to explore dietary and aquaculture data and investigate whether plants or fish are responsible. They create and interpret histograms, scatterplots, and multi-group comparisons using both base R and ggplot2, learning how visual cues reveal patterns in single variables and relationships between variables. By the end of the module, students can summarize numeric data, choose appropriate plot types, compare groups visually, and use evidence from distributions and densities to identify which fish species and environmental conditions are driving the sickness.
+A wave of food poisoning spreads through the colony, and students must determine what is making people sick. They use descriptive statistics and data visualization to investigate dietary and aquaculture data, beginning with basic plots in base R and then building toward more flexible visualizations in `ggplot2`. Students practice interpreting histograms, scatterplots, boxplots, and multiple-group comparisons, and they begin connecting patterns in the data to biological explanations. This module focuses on using visualization and summary statistics to explore evidence and support decision-making.
 
 ### **Module 3: Follow That Seal**
 
-Leopard seal activity is disrupting fishing operations, and unreliable radio collars are making it difficult to track seal behavior safely. Students begin by cleaning multiple datasets on collar battery life and signal distance, then use t-tests and ANOVA to compare performance across manufacturers. They apply K-Nearest Neighbors to classify unlabeled collars and determine which supplier is producing faulty equipment. The module introduces regression: first simple linear regression to model relationships between ecological variables, and then multiple regression and interaction terms. By the end of the module, students can test group differences, fit and interpret linear and multiple regression models, and use a simple machine-learning method to support ecological decision-making.
-
-### **Module 4: March of the Penguins**
+Fishing operations are becoming dangerous because leopard seal activity is hard to track, and some radio collars appear to be failing. Students analyze collar data to compare manufacturers using t-tests and ANOVA, then use post-hoc comparisons to determine which groups differ from one another. They also learn how to interpret significance, p-values, and hypothesis tests in the context of a real ecological problem. This module builds students’ confidence with inferential statistics and group comparisons.
 
-To build a sustainable fishery, the team must understand how local Antarctic fish populations grow and respond to harvesting. Students begin by exploring exponential and logistic population growth, interpreting the intrinsic rate of increase and carrying capacity. They visualize population trajectories, compare species, and identify which populations are stable or declining. Students then fit logistic growth models using the `drc` package to estimate carrying capacity more accurately, and use those estimates to calculate the maximum sustainable yield. Then, they compare multiple fish populations by adding logistic model curves to their plots and testing differences statistically with ANOVA and post-hoc comparisons. By the end of the module, students can model population dynamics, estimate sustainable harvest levels, and use data-driven evidence to recommend which fish population is safest to fish.
+### **Module 4: Roads and Regressions**
 
-## **Learning Objectives**
-
-By the end of the course, students will be able to:
+As the colony expands, new infrastructure and equipment decisions depend on bringing multiple datasets together and modeling ecological relationships. Students learn how to combine datasets with joins and binds, then use K-Nearest Neighbors (KNN) to classify unlabeled seal collars. They also work with linear regression and multiple regression to investigate relationships between environmental variables and ecological responses, including habitat suitability and penguin nesting patterns. By the end of the module, students can integrate multiple sources of data, fit regression models, and interpret both main effects and interactions.
 
-1. Work with ecological data in R, including importing, cleaning, summarizing, and organizing datasets.
-2. Visualize patterns and relationships using appropriate plots.
-3. Apply statistical methods including t-tests, ANOVA, and regression to evaluate ecological questions.
-4. Fit and interpret ecological models, including logistic growth and carrying capacity, to support management decisions.
-5. Communicate data-driven conclusions clearly.
+### **Module 5: Sustainable Fishing**
 
-## **For Instructors**
+The final module highlights how the core themes of the course can be brought together and shaped by a particular disciplinary perspective. In this version, Module 5 was developed and taught by Dr. Ellen Bledsoe, whose training as an ecologist informed its focus on population growth, carrying capacity, and sustainable fisheries management in Antarctica.
 
-Course materials, including student and instructor versions of all lessons, datasets, and assignments, are openly available and designed to be adaptable for other institutions and programming languages. R is used throughout, but the course structure is transferable to Python or other environments.
+Students explore exponential and logistic population growth, interpret intrinsic rate of increase and carrying capacity, estimate carrying capacity from logistic models, calculate maximum sustainable yield, and compare fish populations visually and statistically to determine which populations are the best candidates for harvest. The module draws together many of the skills developed earlier in the course, including visualization, statistical analysis, modeling, and ecological interpretation.
 
-All materials are available on the [course site](structure.qmd) and in the [GitHub repository](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA).
\ No newline at end of file
+At the same time, Module 5 is intended as an example of how Data in the Wild can be specialized. The course can be taught with or without this module, and the broader structure can be adapted to reflect other disciplines, research questions, or instructor expertise while still building from the same data science foundation.
\ No newline at end of file
diff --git a/modules/final_project/FinalAssignment.pdf b/modules/final_project/FinalAssignment.pdf
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diff --git a/modules/final_project/FinalAssignment.qmd b/modules/final_project/FinalAssignment.qmd
deleted file mode 100644
index d9c99ac..0000000
--- a/modules/final_project/FinalAssignment.qmd
+++ /dev/null
@@ -1,143 +0,0 @@
----
-title: "Final Assignment"
-author: "Ellen Bledsoe"
-format:
-  html:
-    toc: true
----
-
-```{r}
-#| include: false
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-# Final Details
-
-### Purpose
-
-The goal of this final assignment is to assess your ability to integrate the many skills you have learned over the semester: filtering and summarizing data, creating new columns, choosing the appropriate data visualizations, and performing and interpreting the appropriate statistical tests.
-
-### Task
-
-Write R code which produces the correct data, summaries, plots and analyses. Correctly interpret the results of these plots and analyses.
-
-### Criteria for Success
-
--   Code chunks run without errors
--   Code produces the correct result
-    -   Code that produces the correct answer will receive **full** credit
-    -   Code attempts with logical direction will receive **partial** credit
--   Appropriate plot types are used to visualize the data
--   Appropriate statistical tests are used to analyze the data
--   Written answers address the questions in sufficient detail
-
-### Due Date
-
-Dec 11 at midnight MST
-
----
-
-For your final this semester, I am presenting you with 3 problem sets, totaling up to 60 points.
-
-I'm expecting you to be able to filter and summarize the data in the ways you need, choose the appropriate visualization, choose the appropriate analysis, and correctly interpret the analysis for the question I've asked you.
-
-It is important to note that I will not be giving you an answer key for this final project since a major part of your grade for the assignment is being able to choose the appropriate visualizations and analyses for the question and the data.
-
-We are going to use the `palmerpenguins` R package and data set, which we've used many times before! You can learn more about it [here](https://allisonhorst.github.io/palmerpenguins/). This is a real data set from a Long-Term Ecological Research (LTER) site in Antarctica.
-
-## Set-Up
-
-#### Be sure to run both of these code chunks before you begin!
-
-I've gone ahead and included the code to load the two packages you will need to successfully complete this project: the `tidyverse` and `palmerpenguins`. Be sure to run this code chunk!
-
-```{r}
-#| message: false
-library(tidyverse)
-library(palmerpenguins)
-```
-
-#### Important!
-
-I've also included one more code chunk below. Be sure to run this code chunk, as well!
-
-```{r}
-penguins <- penguins %>% drop_na()
-```
-
----
-
-## Structure & Guidelines
-
-Like the practice version, this final is structured as 3 different problem sets. For each problem set, I am presenting you with an initial question to guide your thinking and analysis.
-
-### Data
-
-Assume that nothing carries over between problem sets.
-
-Each problem set is stand-alone, meaning that you should always start with the `penguins` data frame at the beginning of each problem set.
-
-### Interpreting Statistical Results
-
-When I ask you to interpret statistical results, you should roughly follow these guidelines.
-
--   the cut-off for our p-values is always 0.05 ($\alpha = 0.05$)
--   report the p-value that we are focused on in that question
--   if there are multiple p-values of interest, report all of them
--   state whether the p-value indicates a significant difference/relationship
--   if applicable, state whether we should or should not reject the null hypothesis
-
-### Plotting
-
-All plots should be made using `ggplot2`.
-
-Your options for plot types to choose from are:
-
--   multiple histogram plots  
--   multiple density plots  
--   box-and-whisker plot  
--   scatter plot  
-
-**Note: All plots should have modified axis labels and legend labels.**
-
----
-
-# Let's Begin!
-
-(Have you run the setup code chunks?)
-
----
-
-## Problem Set 1 (30 points total)
-
-### Part 1 (20 points)
-
-#### Question: Is there a significant relationship between bill length and bill depth for penguins on Biscoe Island?
-
-```{r}
-
-```
-
-(Continue with empty chunks exactly as in your original file)
-
----
-
-## Problem Set 2 (15 points)
-
-```{r}
-
-```
-
----
-
-## Problem Set 3 (15 points)
-
-```{r}
-
-```
-
----
-
-## The End!
-
-Great work, and thanks for a wonderful semester!
\ No newline at end of file
diff --git a/modules/final_project/FinalProject.qmd b/modules/final_project/FinalProject.qmd
new file mode 100644
index 0000000..34de5d4
--- /dev/null
+++ b/modules/final_project/FinalProject.qmd
@@ -0,0 +1,343 @@
+---
+title: "Final Project"
+author: "Ellen Bledsoe"
+format:
+  html:
+    toc: true
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+### Purpose
+
+The goal of this final project is to assess your ability to integrate the many skills you have learned over the semester: filtering and summarizing data, choosing the appropriate data visualizations, and performing and interpreting the appropriate statistical tests.
+
+### Task
+
+Write R code which produces the correct data, summaries, plots and analyses. Correctly interpret the results of these plots and analyses.
+
+### Criteria for Success
+
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive **full** credit
+    -   Code attempts with logical direction will receive **partial** credit
+-   Appropriate plot types are used to visualize the data
+-   Appropriate statistical tests are used to analyze the data
+-   Written answers address the questions in sufficient detail
+
+------------------------------------------------------------------------
+
+## Structure & Guidelines
+
+### Interpreting Statistical Results
+
+When I ask you to interpret statistical results, you should roughly follow these guidelines:
+
+-   the cut-off for our p-values is always 0.05 ($\alpha = 0.05$)
+-   report the p-value that we are focused on in that question
+-   if there are multiple p-values of interest, report all of them
+-   state whether the p-value indicates a significant difference/relationship
+-   if applicable, state whether we should or should not reject the null hypothesis
+
+### Plotting
+
+All plots should be made using `ggplot2`.
+
+Your options for plot types to choose from are:
+
+-   histogram
+-   [multiple]{.underline} histogram plot
+    -   use transparency (`alpha`)
+    -   use `position = "identity"` with multiple groups to see the full distributions
+-   box-and-whisker plot
+    -   add points on top of the box plot to show the distribution of the points
+-   scatter plot
+    -   add the linear model to every scatter plot
+
+| **NOTE! All plots should have modified axis labels and legend labels.**
+| In many cases, this might mean capitalizing the axis label and/or legend label. In other cases, you might want to put units in parentheses after the words (e.g., Body Mass (g)).
+
+### Generative AI Guidelines
+
+While I do not recommend using generative AI to complete this assignment (throughout the semester, I've seen it cause more problems than not...), you are *technically* allowed to use it on the final.
+
+However, you *must* do so according to the guidelines I've set out for all other assignments this semester.
+
+| **CAUTION!** If you do use generative AI without following the course guidelines, you will receive a 0 for this final and you will not have the option to resubmit. Proceed wisely.
+
+You can find clear examples of these guidelines on D2L in *Syllabus \> Assignment Reminders*.
+
+------------------------------------------------------------------------
+
+# Let's Begin!
+
+Have you *actually* read through the "Structure & Guidelines" section above?
+
+If so, proceed! If not, scroll back up...
+
+## Set-Up (5 points)
+
+1.  First, we need to load the `tidyverse` package. (2 points)
+
+```{r}
+# load the tidyverse
+library(tidyverse)
+```
+
+2.  As a reminder, there are 4 datasets that you can use to complete this final assignment:
+
+    -   krill data from KrillBase -\> `krill.csv`
+    -   fur seal pups on Maiviken Island -\> `fur_seals.csv`
+    -   Gentoo penguins from Bird Island -\> `gentoos_and_temps.csv`
+    -   Chinstrap penguins from Signy Island -\> `chinstraps_on_signy.csv`
+
+Which dataset have you chosen to work with for this final assignment? (1 point)
+
+*Answer*:
+
+3.  Bring your chosen data into R to work with using the `read_csv` function. Remember to save it as an object. (2 points)
+
+```{r}
+
+```
+
+## Understanding the Data (12 points)
+
+4.  Use a function (e.g., `head()` or `str()`) to look at your data. (3 points)
+
+```{r}
+
+```
+
+5.  In 1-2 sentences, explain what the output from Question 4 is telling you about your dataset. The more detail, the better. (3 points)
+
+*Answer*:
+
+### Data Exploration
+
+Now, spend some time really getting to know your dataset. Information about the datasets can be found below, both in terms of how you should treat each column (continuous vs. categorical) as well as at the links to the datasets below (click on the names of the datasets).
+
+You might notice that the data at the links has many more columns than the datasets you have been given or some slightly different data. Focus only on the columns that are in the .csv file provided to you for this assignment.
+
+#### [Krill](https://www.bas.ac.uk/project/krillbase/#data)
+
+-   You will want to use the `Krill_per_1m2_log` column as your dependent variable. This is a log-transformed version of the `Krill_per_1m2` column. You don't need to worry about *why* we transformed the data, but this column should be a bit easier to work with.
+-   The `Date`, `Year`, and `Month` columns should all be treated as *continuous* variables.
+-   While the `Station` column can *technically* be considered a categorical variable, please use the `Day_Night` column as your categorical variable for this assignment and for your research question.
+
+#### [Fur Seals](https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=bc6d802a-fce5-495d-b5e1-bb26f824847c)
+
+-   If you choose to use the `Date` or `Year` column, treat them as *continuous* variables.
+-   On the other hand, if you use the `Month` column, treat it as a *categorical* variable.
+
+#### [Gentoo Penguins](https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=1fd5bc8a-96b5-4f53-aa15-33e5330e7705)
+
+-   If you are using the `Year` column, treat it as a *continuous* variable.
+-   The dataset has a column for the previous year's December temperature. Because counts of chicks took place in January or February (and nests were counted in October/November), the number of chicks is most likely to be related to the temperatures directly after chicks hatched (December of the previous year).
+
+#### [Chinstrap Penguins](https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=d784af69-c92b-444c-a12e-ee0361802cb1)
+
+-   I do not recommend using the `MONTH` or `DAY` column.
+-   If you use the `YEAR` column, treat it as a *continuous* variable.
+
+### Back to Assignment Questions
+
+6.  Based on your own general knowledge, the information at the links above, and the additional information I've provided, list out the columns (only those in the .csv file) in your chosen dataset and give a brief description (just a phrase is fine) of what the data in each columns represents. (3 points)
+
+*Answer*:
+
+7.  For each of the columns in your dataset, identify whether the data points they contain are categorical or continuous/numeric. Be sure your answers align with the information I've provided for you above. (3 points)
+
+    Many of your answers to subsequent questions will depend heavily on whether variables are categorical or continuous.
+
+    Type out the name of the column and then the descriptor. For example:
+
+    -   island_name: categorical
+    -   island_size_km: continuous
+
+*Answer*:
+
+## Building a Research Question (6 points)
+
+In order to write a successful research question, we need to identify our dependent variable and the independent variable that we think affects the dependent variable.
+
+8.  First, determine which variable is the *dependent* variable in your dataset (there is usually only one, but there might be two). It must be continuous/numeric. (2 points)
+
+*Answer*:
+
+9.  Next, choose 1 independent variable from your dataset (each dataset has more than one from which to choose). (2 points)
+
+    This should be a variable that you believe might affect the values of the dependent variable. It can be either categorical or continuous/numeric.
+
+*Answer*:
+
+10. Now, bring your dependent and independent variables together to form a research question based on the structure presented in class in Module 2 (also see Module 2 Assignment 3). (2 points)
+
+*Answer*:
+
+## Summarizing Our Data (6 points)
+
+Let's get to know our data a little better.
+
+11. Summarize your data by calculating at least 1 measure of central tendency and 1 measure of variability for your your *dependent* variable using the `tidyverse` functions (you might need `na.rm = TRUE`!). (4 points)
+
+Pay close attention here! How you summarize will differ based on whether your *independent* variable is categorical or continuous/numeric (2 points):
+
+-   If your research question has a *categorical* independent variable, find the measure of central tendency and the measure of variability for the dependent variable in *each* individual category of the independent variable.
+-   If your research question has a *continuous/numeric (numeric)* independent variable, find a measure of central tendency and the measure of variability for the independent variable in addition to the dependent variable
+
+```{r}
+
+
+```
+
+## Data Visualization (18 points)
+
+To continue our data exploration, we are going to plot our data.
+
+At least ONE of the plots should include both your dependent and independent variable. Think critically about which plot that should be based on your variable types.
+
+Also, at least one plot will need to use a variable that is NOT in your research question.
+
+12. Produce a histogram plot (just one, not multiple on the same plot) of the dependent variable. Write 1-2 sentences describing what information you can gather from plot. (3 points)
+
+```{r warning=FALSE}
+
+```
+
+*Answer:*
+
+13. Create a *multiple histogram* plot of your dependent variable and one other variable (this may or may not be your independent variable). (3 points)
+
+    Make sure we can see each histogram plot (make sure one isn't blocking another by modifying the transparency and position).
+
+```{r warning=FALSE}
+
+```
+
+14. Create a *box-and-whisker* plot which includes your dependent variable. Changing the color of the boxes is optional. Make sure to add points to the plot. (3 points)
+
+    *Hint: if your plot has `NA` values that you want to remove, you can remove them from the dataset with the `filter` function and the argument `!is.na(column_name)`.*
+
+```{r warning=FALSE}
+
+```
+
+15. Create a *scatter plot* that includes your dependent variable. Write 1-2 sentences describing what information you can gather from plot. (3 points)
+
+    *Hint: if your plot has `NA` values that you want to remove, you can remove them from the dataset with the `filter` function and the argument `!is.na(column_name)`.*
+
+```{r warning=FALSE}
+
+```
+
+16. Identify which plot(s) from Questions 15-17 match up with your research question. How you know it matches up correctly (based on categorical vs. continuous/numeric variables)? (3 point)
+
+*Answer*:
+
+17. In 2-3 sentences, interpret the plot(s) that you identified in the previous question. What does it tell you about your chosen research question? (3 points)
+
+*Answer*:
+
+## Running Your Statistical Model (15 points)
+
+In this course, we have covered 3 different statistical analyses: t-tests, ANOVAs, and linear regression.
+
+It is now time to decide how to apply these statistical analyses to your own research question!
+
+18. Based on everything you know about your independent and dependent variables and the requirements for each of the statistical analyses we covered in the course, determine which statistical analysis you should perform to answer your question. In 1-2 sentence, explain why. (3 points)
+
+*Answer*:
+
+19. Perform the appropriate statistical analysis below. If necessary, add an additional line of code to print out the results. (3 points)
+
+    *Hint: if your dataset has `NA` values that you need to remove, you can remove them from the dataset with the `filter` function and the argument `!is.na(column_name)`.*
+
+```{r}
+
+```
+
+20. If necessary, run a pairwise comparison. If a pairwise comparison is NOT necessary for your statistical analysis, explain why not. (3 points)
+
+*Answer*:
+
+```{r}
+# space for code to run a pairwise comparison if needed
+```
+
+21. Interpret the results of your statistical analysis (including the pairwise comparison, if relevant). In your answer, include the p-value and if the result of your analysis is significant. (3 points)
+
+*Answer*:
+
+22. What do these results mean in the context of your research question? (3 points)
+
+*Answer*:
+
+## Expanding Our Horizons (6 points)
+
+23. Spend some time thinking about the outcome of your research question. Is the outcome what you were expecting or not? Why or why not? What additional information would you like to have to add context to your results? Your answer should be 4-5 sentences. (3 points)
+
+*Answer:*
+
+24. Think about your research subjects in the context of ecosystem services. What type of ecosystem services might the study of your research question fall under? (You might have to get a little creative here). Explain your rationale. (3 points)
+
+*Answer*:
+
+## Literature Search (6 points)
+
+When we ask a research question, we always want to find out what other people have already found out about that question to give us an idea of what outcome we might expect.
+
+In Module 4 Assignment 1, you were asked to find at least 3 articles that related to your research question.
+
+The articles you find don't have to be *exactly* related but should be relevant, as you will be using them at the end of this assignment to help interpret your statistical output.
+
+25. Write the citation for your first article. Then, in *your own words*, write a 1-2 sentence summary of what the article is about, based on the title and the abstract. (2 points)
+
+*Article 1 Citation*:
+
+*Article 1 Description*:
+
+26. Do the same as above for the second article. (2 points)
+
+*Article 2 Citation*:
+
+*Article 2 Description*:
+
+27. Do the same as above for the last article. (2 points)
+
+*Article 3 Citation*:
+
+*Article 3 Description*:
+
+## Communicating Your Results (6 points)
+
+When researchers have come to some type of conclusion about a research question they have asked, we usually write up our findings in a scientific article to share our results with the scientific community and beyond.
+
+Just like the articles you found in the Literature Search section, most articles have an abstract that gives any potential reader some information about the topic of the research paper and the conclusions the researchers have drawn.
+
+Now that you have completed the process of coming up with a research question and finding some results, we want to communicate these results through writing an abstract. Feel free to look at your scientific articles for inspiration.
+
+28. Your abstract should be structured in the following manner, with at least one sentence for each topic (6 sentence minimum):
+
+-   An opening sentence about the general topic of your question and/or dataset
+-   General information about your dataset
+-   The research question your have chosen to ask
+-   What your data summarization *and* visualization shows you
+-   What statistical analysis you performed and its outcome
+-   Your final interpretation of your research question: does your independent variable impact your dependent variable and, if so, in what way?
+
+Be sure you include information from at least 2 of your 3 articles from your literature search somewhere in your abstract! Use in-text citations (e.g., Golding & Anderson, 2024; Bledsoe et al., 2024) to show where you do so.
+
+*Abstract*:
+
+------------------------------------------------------------------------
+
+## The End!
+
+Take a look back at everything you just did! You've come a *very* long way since the beginning of the semester and should be quite proud of that.
+
+Great work, and thanks for a great semester!
\ No newline at end of file
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diff --git a/modules/module_1/assignments/a1/m1a1.qmd b/modules/module_1/assignments/a1/m1a1.qmd
new file mode 100644
index 0000000..492ce00
--- /dev/null
+++ b/modules/module_1/assignments/a1/m1a1.qmd
@@ -0,0 +1,175 @@
+---
+title: "Module 1 Assignment 1"
+format:
+  html:
+    toc: true
+---
+
+## Downloads
+
+The files for this assignment are available below:
+
+-   [Download the assignment (.qmd or .rmd?)]()
+-   [Download the dataset (.csv)](team_antarctica.csv)
+-   [Download the expected outputs (.pdf)](m1a1_expected.pdf)
+
+## Using the Expected Outputs
+
+The expected outputs document is provided as an additional resource for students as they work through the assignment. It is intended to help students check that their code is producing the expected results.
+
+This document is **not** an answer key. It does not provide the code needed to generate those results. Instead, it allows students to verify their work while still completing the assignment independently.
+
+## For Instructors
+
+Assignment answer keys are available to instructors, but they are not included in the public course materials.
+
+If you are an instructor and would like access to the answer keys, please open an issue in the GitHub repository\[link here\]. After verification, instructors can be added to the private repository that contains the assignment keys and grading references.
+
+# Assignment Description
+
+## Getting to Know Team Antarctica!
+
+### Purpose
+
+The goal of this assignment is to get comfortable using R to look at 1- and 2-dimensional data sets using base R.
+
+### Task
+
+Write R code to successfully answer each question below.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code chunks run without errors
+-   Code will be assessed as follows:
+    -   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit
+-   Written answers address the questions in sufficient detail
+
+### Assignment Questions
+
+Each question is worth 2 points unless otherwise specified.
+
+### Vectors
+
+Let's start working with vectors, or 1-dimensional data, first.
+
+Run this chunk of code to create a vector of data to use. It represents the number of penguins counted at different sites.
+
+```{r}
+# vector with counts of penguins
+counts <- c(2, 9, 4, 3, 6, 7, 1, 0, 3)
+```
+
+1.  Write a line of code that returns the 2nd value in the `counts` vector.
+
+```{r}
+# your code here
+```
+
+2.  Subset the `counts` vector using a condition (not index) that returns values from the vector that are greater than 5.
+
+```{r}
+# your code here
+```
+
+3.  Calculate the average number of penguins that were counted. (Hint: the `mean()` function will calculate the average.)
+
+```{r}
+# your code here
+```
+
+### Data Frames
+
+Now that we've practiced with vectors, let's move on to 2-dimensional data.
+
+Remember those questions about our trip to Antarctica from the survey you completed the first week of classes? It's time to play around with that data!
+
+The following code chunk will read the data into RStudio. Be sure to run it before trying to answer the questions! A bunch of stuff will pop up, but it should work just fine.
+
+Once you run the code, you will see a data frame called `team_data` show up in the environment.
+
+```{r}
+team_data <- read.csv("team_antarctica.csv")
+```
+
+4.  Take a look at the data and answer the following questions (0.5 points each):
+
+    a.  What are the dimensions of the data (how many rows and how many columns)?
+    b.  Name a column that has *categorical* data.
+    c.  Name a column that has *numeric* data.
+    d.  Name a column that has data that could be treated as either *categorical* or *numeric*.
+
+*Answer*:
+
+5.  Take a look at the data frame using the `head()` function. Typically, `head()` provides the first 6 rows of data.
+
+    Modify one of the arguments in `head()` so that the line of code prints the first 10 rows. (If you aren't sure how to do that, remember how you can look for help about functions!)
+
+```{r}
+# your code here
+```
+
+6.  Using what you know about subsetting data frames, write a line of code that returns the parka color for Unique ID 4. (Hint: count the columns!)
+
+```{r}
+# your code here
+```
+
+When we have a large data set like this, it is often helpful to summarize the data in some way. The next few questions will help use get a better understanding of the content of the data set.
+
+7.  On average, how did people rate their ability to live in a remote location?
+
+```{r}
+# your code here
+```
+
+8.  What are the minimum and maximum distances that would be traveled by a team member to get to Antarctica? Use the `min()` and `max()` functions.
+
+```{r}
+
+```
+
+9.  Subset using a condition to return the rows of data only for people who rated their fishing skills as a 5. Retain all of the columns in those rows. (Hint: numbers do not need quotation marks around them).
+
+```{r}
+# your code here
+```
+
+10. Calculate the average cold tolerance of people who want blue parkas. Use these steps to think through how to answer this question.
+
+    a.  First, think about how to create a data frame with only people who want blue parkas
+
+    b.  Next, think about how you select the column with the cold tolerance data
+
+    c.  Finally, think about how to calculate the average
+
+```{r}
+# your code here
+```
+
+### Bonus (up to 2 points)!
+
+What animal should be on our team flag?
+
+-   First, create a vector called `mascot` that has only the values from the `team_flag` column.
+-   Next, use the `table()` function on the `mascot`. Think through what this output likely means.
+-   For full points, make sure you type out which animal should be our mascot below; providing only the code will result in partial credit.
+
+*Answer:*
+
+```{r}
+# your code here
+```
+
+## Turning in Your Assignment
+
+Follow these steps to successfully turn in your assignment on D2L.
+
+1.  Click the `Knit` button up near the top of this document. This should produce a PDF file that shows up in the `Files` panel on the bottom-right of your screen and/or pops up as a new window.
+2.  You might get a window that pops up that says to "Try Again." If you do, close the window and check whether there is a PDF in the Files tab--there usually is.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files tab.
+4.  Click on the blue gear near the top of the `Files` panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click the Download button. The PDF file of your assignment is now in your "Downloads" folder on your device.
+6.  Head over to D2L and navigate to Module 1. Submit the PDF file that you just downloaded.
\ No newline at end of file
diff --git a/modules/module_1/assignments/a1/m1a1_expected.pdf b/modules/module_1/assignments/a1/m1a1_expected.pdf
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diff --git a/modules/module_1/assignments/a1/team_antarctica.csv b/modules/module_1/assignments/a1/team_antarctica.csv
new file mode 100644
index 0000000..07a2b5c
--- /dev/null
+++ b/modules/module_1/assignments/a1/team_antarctica.csv
@@ -0,0 +1,35 @@
+team,cooking_skill,fishing_skill,swimming,cold_tolerance,working_with_animals,remote_location,parka_color,team_flag,distance_mi
+University of Arizona,5,1,Yes,3,TRUE,1,Black,Leopard Seal,8800.00
+University of Arizona,1,2,No,4,TRUE,3,Black,Orca,8275.00
+University of Arizona,4,3,Yes,4,TRUE,4,Black,Leopard Seal,8791.00
+University of Arizona,3,1,Yes,4,TRUE,4,Blue,Leopard Seal,8218.69
+University of Arizona,1,1,Yes,1,TRUE,3,Black,Leopard Seal,8000.00
+University of Arizona,3,1,Yes,4,TRUE,2,White,Emperor Penguin,9000.00
+University of Arizona,5,3,Yes,3,TRUE,2,Blue,Orca,9000.00
+University of Arizona,1,1,Yes,3,TRUE,3,Orange,Emperor Penguin,8223.00
+University of Arizona,5,3,Yes,4,TRUE,3,Black,Orca,8243.00
+University of Arizona,3,1,Yes,4,TRUE,3,Orange,Emperor Penguin,10000.00
+University of Arizona,3,1,I can dog paddle,2,TRUE,3,Orange,Emperor Penguin,10000.00
+University of Arizona,4,3,Yes,3,TRUE,5,Orange,Leopard Seal,8757.00
+University of Arizona,3,3,Yes,4,TRUE,2,Blue,Emperor Penguin,8774.00
+University of Arizona,4,2,Yes,2,TRUE,3,Red,Leopard Seal,9000.00
+University of Arizona,3,3,Yes,4,TRUE,4,White,Orca,10000.00
+University of Arizona,3,5,Yes,5,TRUE,4,Red,Leopard Seal,10000.00
+University of Arizona,3,2,Yes,3,TRUE,1,Black,Leopard Seal,9000.00
+University of Arizona,4,5,Yes,5,TRUE,4,White,Leopard Seal,10000.00
+University of Arizona,4,3,Yes,2,TRUE,3,White,Emperor Penguin,8356.00
+University of Arizona,3,1,Yes,4,TRUE,3,Black,Emperor Penguin,8444.30
+University of Arizona,3,3,Yes,2,TRUE,4,Blue,Leopard Seal,8529.00
+University of Arizona,4,2,Yes,2,TRUE,4,Orange,Leopard Seal,8356.00
+University of Arizona,3,1,Yes,4,TRUE,5,Black,Leopard Seal,9000.00
+University of Arizona,3,1,Yes,4,TRUE,5,Blue,Leopard Seal,8279.00
+University of Arizona,5,3,Yes,4,TRUE,5,Orange,Sea Spider,9000.00
+University of Arizona,2,1,Yes,2,TRUE,2,Black,Emperor Penguin,8872.00
+University of Arizona,3,1,I can dog paddle,3,TRUE,4,Black,Emperor Penguin,9000.00
+University of Arizona,4,1,Yes,3,TRUE,4,Black,Emperor Penguin,8791.00
+University of Arizona,4,3,Yes,3,TRUE,5,Blue,Leopard Seal,9143.00
+University of Arizona,3,1,Yes,4,TRUE,3,Blue,Orca,8270.00
+University of Arizona,4,1,Yes,3,TRUE,3,Black,Emperor Penguin,11000.00
+University of Arizona,4,3,Yes,3,TRUE,3,Black,Emperor Penguin,11000.00
+University of Arizona,3,2,Yes,2,TRUE,4,Blue,Leopard Seal,9000.00
+University of Arizona,4,2,Yes,3,TRUE,3,Orange,Sea Spider,8355.00
\ No newline at end of file
diff --git a/modules/module_1/assignments/a2/aggregated_station_data.csv b/modules/module_1/assignments/a2/aggregated_station_data.csv
new file mode 100644
index 0000000..0cf3366
--- /dev/null
+++ b/modules/module_1/assignments/a2/aggregated_station_data.csv
@@ -0,0 +1,139161 @@
+year,day,month,running_day,hour,temp,pressure,wind_speed,wind_direction,humidity,delta_t,station_id
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diff --git a/modules/module_1/assignments/a2/m1a2.qmd b/modules/module_1/assignments/a2/m1a2.qmd
new file mode 100644
index 0000000..75fd5ea
--- /dev/null
+++ b/modules/module_1/assignments/a2/m1a2.qmd
@@ -0,0 +1,123 @@
+---
+title: "Module 1 Assignment 2: Getting to Know Your Antarctic Home"
+author: "Ellen Bledsoe"     # <- your name here!
+date: "`r Sys.Date()`"      # <- uses the current date when rendered
+output: pdf_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Description
+
+### Purpose
+
+The goal of this assignment is to get comfortable using the `tidyverse` with 2-dimensional data sets (data frames).
+
+### Task
+
+Write R code using the `tidyverse` to successfully answer each question below.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit
+-   Written answers address the questions in sufficient detail
+
+# Assignment Questions
+
+For this assignment, you'll be working with another real-world data set--a collection of data from climate stations scattered across Antarctica.
+
+1.  In your own words, describe what the `tidyverse` is. Your answer should be between 1-3 sentences.
+
+    *Answer:*
+
+2.  Load in the `tidyverse` package.
+
+```{r}
+# your code here
+```
+
+3.  Load in the data file (called `aggregated_station_data.csv`) using the `read_csv()` function. Save the data as an object called `weather`.
+
+```{r}
+# Note: use the read_csv function, not the read.csv function!
+# your code here
+```
+
+4.  Take a look at the data in whichever way you would like (looking in the environment, viewing the data, typing a function into the console below, etc.). How many rows and columns are in the data? Type your answers below:
+
+    rows:\
+    columns:
+
+```{r}
+# using code is optional for Question 4
+```
+
+5.  Write some code to choose rows that only include temperatures which are above freezing (AKA greater than 0 because the temperature is currently in Celsius)
+
+```{r}
+# your code here
+```
+
+6.  Write some code to choose *only* columns with the following information: the year, day, month, temperature, and the station ID. Save these columns as a new object called `station_temp`.
+
+```{r}
+# your code here
+```
+
+7.  Using the data frame you created in Q6 above (`station_temp`), add a new column to that data frame that converts the temperature column (currently in Celsius) to Fahrenheit. Call the new column `tempF`.
+
+    *Hint: we did this in class--use that same equation!*
+
+```{r}
+
+```
+
+8.  In your own words (either bullet points or sentence form is fine), explain two benefits of using the pipe (`%>%`).
+
+    *Answer:*
+
+9.  Using the *original* data frame (`weather`), find the minimum (`min()`) temperature recorded for each month (in Celsius, the original column).
+
+    *Hint: think about months first (split) and then temperature (apply). You will also want to remove all the `NA` values.*
+
+```{r}
+# your code here
+```
+
+10. Again, using the *original* data frame, produce a data frame with the average temperature in the month of January for each station.
+
+    Some hints:
+
+    -   take note of how months are represented in the data
+
+    -   think about using the pipe, how we choose which rows we want, and how we split-apply-combine
+
+    -   remember to remove the `NA` values!
+
+```{r}
+# your code here
+```
+
+## Bonus! (up to 2 points)
+
+Write code to determine how many unique stations are in the `weather` data set. (Hint: look up the help files for the `distinct()` and the `count()` functions).
+
+```{r}
+# your code here
+```
+
+# Turning in Your Assignment
+
+Follow these steps to successfully turn in your assignment on D2L.
+
+1.  Click the `Knit` button up near the top of this document. This should produce a PDF file that shows up in the `Files` panel on the bottom-right of your screen.
+2.  Click the empty box to the left of the PDF file.
+3.  Click on the blue gear near the top of the `Files` panel and choose Export.
+4.  Put your last name at the front of the file name when prompted, then click the Download button. The PDF file of your assignment is now in your "Downloads" folder on your device.
+5.  Head over to D2L and navigate to Module 1. Submit the PDF file that you just downloaded.
\ No newline at end of file
diff --git a/modules/module_1/module1_1.qmd b/modules/module_1/module1_1.qmd
index 13ce184..b25daf2 100644
--- a/modules/module_1/module1_1.qmd
+++ b/modules/module_1/module1_1.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
@@ -21,7 +22,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ## Check-in (5 minutes)
 
-Let's all sign-up for [Posit Cloud!](https://posit.cloud/) We use Posit Cloud as our coding environment so everyone has access to R and RStudio thorugh a web browser.
+Let's all sign-up for [Posit Cloud!](https://posit.cloud/) We use Posit Cloud as our coding environment so everyone has access to R and RStudio through a web browser.
 
 ## Rstudio Mini-tour
 
@@ -45,7 +46,7 @@ Rmarkdown (`.Rmd`) is a file format that lets us incorporate text and code into
 -   For writing text, you can type as you normally would.
 -   Code chunks are a bit different:
     -   Near the top right of your screen you can toggle between viewing this document in "Source" or "Visual" mode.
-    -   If you view this document under "Source," you will see that all code chunks are sandwiched between ` ```{r} ` and ` ``` `.
+    -   If you view this document under "Source," you will see that all code chunks are sandwiched between ```` ```{r} ```` and ```` ``` ````.
     -   Under "Visual," you can type R code in the lines under `{r}`.
     -   To include text in chunks, you will need to put a `#` in front. R will not read anything in the line after `#` as code.
 
diff --git a/modules/module_1/module1_2.aux b/modules/module_1/module1_2.aux
new file mode 100644
index 0000000..498500d
--- /dev/null
+++ b/modules/module_1/module1_2.aux
@@ -0,0 +1,37 @@
+\relax 
+\providecommand*\new@tpo@label[2]{}
+\providecommand\hyper@newdestlabel[2]{}
+\providecommand\HyField@AuxAddToFields[1]{}
+\providecommand\HyField@AuxAddToCoFields[2]{}
+\providecommand\BKM@entry[2]{}
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+\@writefile{toc}{\contentsline {section}{\nonumberline Introduction to Coding}{1}{section*.1}\protected@file@percent }
+\newlabel{introduction-to-coding}{{}{1}{Introduction to Coding}{section*.1}{}}
+\@writefile{toc}{\contentsline {subsection}{\nonumberline Learning Outcomes}{1}{subsection*.2}\protected@file@percent }
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+\newlabel{assigning-objects}{{}{1}{Assigning Objects}{subsection*.3}{}}
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+\@writefile{toc}{\contentsline {subsection}{\nonumberline 1-Dimensional Data: Vectors}{2}{subsection*.4}\protected@file@percent }
+\newlabel{dimensional-data-vectors}{{}{2}{1-Dimensional Data: Vectors}{subsection*.4}{}}
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+\newlabel{data-classes}{{}{2}{Data classes}{subsection*.5}{}}
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+\@writefile{toc}{\contentsline {subsection}{\nonumberline Subsetting Vectors}{3}{subsection*.6}\protected@file@percent }
+\newlabel{subsetting-vectors}{{}{3}{Subsetting Vectors}{subsection*.6}{}}
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+\@writefile{toc}{\contentsline {subsubsection}{\nonumberline Subsetting by Index}{4}{subsubsection*.7}\protected@file@percent }
+\newlabel{subsetting-by-index}{{}{4}{Subsetting by Index}{subsubsection*.7}{}}
+\@writefile{toc}{\contentsline {subsubsection}{\nonumberline Subsetting by Condition}{4}{subsubsection*.8}\protected@file@percent }
+\newlabel{subsetting-by-condition}{{}{4}{Subsetting by Condition}{subsubsection*.8}{}}
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+\@writefile{toc}{\contentsline {subsubsection}{\nonumberline Small Group Challenge}{6}{subsubsection*.10}\protected@file@percent }
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diff --git a/modules/module_1/module1_2.log b/modules/module_1/module1_2.log
new file mode 100644
index 0000000..c758acc
--- /dev/null
+++ b/modules/module_1/module1_2.log
@@ -0,0 +1,1055 @@
+This is LuaHBTeX, Version 1.22.0 (TeX Live 2025)  (format=lualatex 2026.2.1)  23 FEB 2026 15:03
+ restricted system commands enabled.
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diff --git a/modules/module_1/module1_2.qmd b/modules/module_1/module1_2.qmd
index 1da6a63..a078461 100644
--- a/modules/module_1/module1_2.qmd
+++ b/modules/module_1/module1_2.qmd
@@ -1,12 +1,13 @@
 ---
 title: "1.2: Intro to Coding in R"
-authors: "Keaton Wilson, Ellen Bledsoe"
+authors: "Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_1/module1_2.tex b/modules/module_1/module1_2.tex
new file mode 100644
index 0000000..2412717
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+\newcommand{\RegionMarkerTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
+\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
+\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
+\newcommand{\StringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
+\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.07,0.07,0.07}{#1}}
+\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
+\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}
+
+\usepackage{longtable,booktabs,array}
+\usepackage{calc} % for calculating minipage widths
+% Correct order of tables after \paragraph or \subparagraph
+\usepackage{etoolbox}
+\makeatletter
+\patchcmd\longtable{\par}{\if@noskipsec\mbox{}\fi\par}{}{}
+\makeatother
+% Allow footnotes in longtable head/foot
+\IfFileExists{footnotehyper.sty}{\usepackage{footnotehyper}}{\usepackage{footnote}}
+\makesavenoteenv{longtable}
+\usepackage{graphicx}
+\makeatletter
+\newsavebox\pandoc@box
+\newcommand*\pandocbounded[1]{% scales image to fit in text height/width
+  \sbox\pandoc@box{#1}%
+  \Gscale@div\@tempa{\textheight}{\dimexpr\ht\pandoc@box+\dp\pandoc@box\relax}%
+  \Gscale@div\@tempb{\linewidth}{\wd\pandoc@box}%
+  \ifdim\@tempb\p@<\@tempa\p@\let\@tempa\@tempb\fi% select the smaller of both
+  \ifdim\@tempa\p@<\p@\scalebox{\@tempa}{\usebox\pandoc@box}%
+  \else\usebox{\pandoc@box}%
+  \fi%
+}
+% Set default figure placement to htbp
+\def\fps@figure{htbp}
+\makeatother
+
+\ifLuaTeX
+  \usepackage{luacolor}
+  \usepackage[soul]{lua-ul}
+\else
+  \usepackage{soul}
+\fi
+
+
+
+
+\setlength{\emergencystretch}{3em} % prevent overfull lines
+
+\providecommand{\tightlist}{%
+  \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+
+
+
+ 
+
+
+\KOMAoption{captions}{tableheading}
+\makeatletter
+\@ifpackageloaded{caption}{}{\usepackage{caption}}
+\AtBeginDocument{%
+\ifdefined\contentsname
+  \renewcommand*\contentsname{Table of contents}
+\else
+  \newcommand\contentsname{Table of contents}
+\fi
+\ifdefined\listfigurename
+  \renewcommand*\listfigurename{List of Figures}
+\else
+  \newcommand\listfigurename{List of Figures}
+\fi
+\ifdefined\listtablename
+  \renewcommand*\listtablename{List of Tables}
+\else
+  \newcommand\listtablename{List of Tables}
+\fi
+\ifdefined\figurename
+  \renewcommand*\figurename{Figure}
+\else
+  \newcommand\figurename{Figure}
+\fi
+\ifdefined\tablename
+  \renewcommand*\tablename{Table}
+\else
+  \newcommand\tablename{Table}
+\fi
+}
+\@ifpackageloaded{float}{}{\usepackage{float}}
+\floatstyle{ruled}
+\@ifundefined{c@chapter}{\newfloat{codelisting}{h}{lop}}{\newfloat{codelisting}{h}{lop}[chapter]}
+\floatname{codelisting}{Listing}
+\newcommand*\listoflistings{\listof{codelisting}{List of Listings}}
+\makeatother
+\makeatletter
+\makeatother
+\makeatletter
+\@ifpackageloaded{caption}{}{\usepackage{caption}}
+\@ifpackageloaded{subcaption}{}{\usepackage{subcaption}}
+\makeatother
+\usepackage{bookmark}
+\IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available
+\urlstyle{same}
+\hypersetup{
+  pdftitle={1.2: Intro to Coding in R},
+  pdfauthor={Ellen Bledsoe},
+  colorlinks=true,
+  linkcolor={blue},
+  filecolor={Maroon},
+  citecolor={Blue},
+  urlcolor={Blue},
+  pdfcreator={LaTeX via pandoc}}
+
+
+\title{1.2: Intro to Coding in R}
+\author{Ellen Bledsoe}
+\date{}
+\begin{document}
+\maketitle
+
+
+\section{Introduction to Coding}\label{introduction-to-coding}
+
+In this lesson, we will create objects, build vectors, subset data, use
+functions, and work with data frames.
+
+\subsection{Learning Outcomes}\label{learning-outcomes}
+
+\begin{itemize}
+\tightlist
+\item
+  Students will be able to define the following terms: object,
+  assignment, vector, function, data frame
+\item
+  Students will be able to run code line-by-line and as code chunks from
+  an Rmarkdown file.
+\item
+  Students will be able to write code assigning values to variables and
+  use these variables to perform various operations.
+\item
+  Students will be able to recall and explain how functions operate, and
+  the basic syntax around functions (arguments, auto-completion,
+  parentheses).
+\item
+  Students will be able to differentiate different data classes in R.
+\end{itemize}
+
+\subsection{Assigning Objects}\label{assigning-objects}
+
+An object is simply a name that stores a value in R so that we can reuse
+it later.
+
+Assignments are really key to almost everything we do in R. This is how
+we create permanence in R. Anything can be saved to an object, and we do
+this with the assignment operator, \texttt{\textless{}-}.
+
+The short-cut for \texttt{\textless{}-} is \texttt{Alt\ +\ -} (or
+\texttt{Option\ +\ -} on a Mac)
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# Assigning Objects}
+\NormalTok{height }\OtherTok{\textless{}{-}} \FloatTok{47.5}           
+\NormalTok{age }\OtherTok{\textless{}{-}} \DecValTok{122}
+
+\CommentTok{\# We can do math with objects}
+\NormalTok{height }\OtherTok{\textless{}{-}}\NormalTok{ height }\SpecialCharTok{*} \DecValTok{2}    \CommentTok{\# multiply}
+\CommentTok{\# Notice how we are overwriting the original value of height. R replaces the old value with the new one.}
+\NormalTok{age }\OtherTok{\textless{}{-}}\NormalTok{ age }\SpecialCharTok{{-}} \DecValTok{20}         \CommentTok{\# subtract}
+\NormalTok{height\_index }\OtherTok{\textless{}{-}}\NormalTok{ height}\SpecialCharTok{/}\NormalTok{age  }\CommentTok{\# divide}
+\NormalTok{height\_sq }\OtherTok{\textless{}{-}}\NormalTok{ height}\SpecialCharTok{\^{}}\DecValTok{2}       \CommentTok{\# raise to an exponent}
+
+\CommentTok{\# This is simplistic and you\textquotesingle{}ll rarely do it in real{-}world scenarios. }
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{1-Dimensional Data: Vectors}\label{dimensional-data-vectors}
+
+We can also assign more complex group of elements of the same type to a
+particular object. This is called a \textbf{vector}, a basic data
+structure in R.
+
+All elements in a vector must be the same type (all numbers, all text,
+etc.).
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{weight\_kg }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(}\DecValTok{3}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{9}\NormalTok{, }\DecValTok{7}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{6}\NormalTok{)}
+\NormalTok{weight\_kg}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 3 2 4 9 7 3 6
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{animals }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(}\StringTok{"cat"}\NormalTok{, }\StringTok{"rat"}\NormalTok{, }\StringTok{"bat"}\NormalTok{, }\StringTok{"rat"}\NormalTok{)}
+\NormalTok{animals}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "cat" "rat" "bat" "rat"
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# R does everything in vectors}
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{Data classes}\label{data-classes}
+
+There are a few main types of data in R, and they behave differently. We
+call these types of data ``classes.''
+
+\begin{itemize}
+\tightlist
+\item
+  numeric / double (numbers, decimals allowed)
+\item
+  integer (no decimals allowed)
+\item
+  character (letters or mixture)
+\item
+  logical (True or False; T or F)
+\item
+  factors (best used for data that need to be in a specific order;
+  levels indicate the order)
+\end{itemize}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# Examples of different data classes}
+\NormalTok{weight\_kg   }\CommentTok{\# numeric, integer, double}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 3 2 4 9 7 3 6
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{animals     }\CommentTok{\# character}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "cat" "rat" "bat" "rat"
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{animal\_size }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(}\StringTok{"medium"}\NormalTok{, }\StringTok{"large"}\NormalTok{, }\StringTok{"small"}\NormalTok{, }\StringTok{"medium"}\NormalTok{)}
+\NormalTok{animal\_size }\OtherTok{\textless{}{-}} \FunctionTok{factor}\NormalTok{(animal\_size, }\AttributeTok{levels =} \FunctionTok{c}\NormalTok{(}\StringTok{"small"}\NormalTok{, }\StringTok{"medium"}\NormalTok{, }\StringTok{"large"}\NormalTok{))}
+\NormalTok{animal\_size  }\CommentTok{\# factor, put in order }
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] medium large  small  medium
+Levels: small medium large
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{logic }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(T, F, F, T)  }\CommentTok{\# logical}
+\NormalTok{logic}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1]  TRUE FALSE FALSE  TRUE
+\end{verbatim}
+
+Vectors have to contain elements that are all of the same class. What
+happens if we put data of different classes into one vector?
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{vec }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{, }\FloatTok{1.000}\NormalTok{, }\StringTok{"1"}\NormalTok{)}
+\CommentTok{\# R converts everything to character because one element is text.}
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{Subsetting Vectors}\label{subsetting-vectors}
+
+Sometimes we want to keep specific values from a vector. This is called
+subsetting (taking a smaller set of the original).
+
+We can subset vectors in two different ways:
+
+\begin{itemize}
+\tightlist
+\item
+  by index (position)
+\item
+  by condition
+\end{itemize}
+
+Regardless of which type of subsetting we choose, we indicate that we
+want to subset by using square brackets: \texttt{{[}{]}}.
+
+\subsubsection{Subsetting by Index}\label{subsetting-by-index}
+
+When we subset by index, we are subsetting based on the position of an
+element in the vector.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# Use square brackets}
+\NormalTok{weight\_kg[}\DecValTok{2}\NormalTok{]    }\CommentTok{\# returns the 2nd element in the vector}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 2
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{weight\_kg[}\DecValTok{2}\SpecialCharTok{:}\DecValTok{4}\NormalTok{]  }\CommentTok{\# returns the 2nd, 3rd, and 4th elements in the vector}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 2 4 9
+\end{verbatim}
+
+\subsubsection{Subsetting by Condition}\label{subsetting-by-condition}
+
+Sometimes we don't know or don't want to list out all of the locations
+for the data we need. Instead, we might want to subset based on a
+quality of the data itself.
+
+To do this, we set a ``condition'' that must be met in order for the
+data to be returned.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# let\textquotesingle{}s start with a condition}
+\NormalTok{weight\_kg }\SpecialCharTok{\textgreater{}} \DecValTok{5}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# this returns a logical vector (true/false) that tells us which positions meet the condition.}
+
+\CommentTok{\# we now put that condition inside the square brackets}
+\NormalTok{weight\_kg[weight\_kg }\SpecialCharTok{\textgreater{}} \DecValTok{5}\NormalTok{]}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 9 7 6
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# we can also do this with characters}
+\NormalTok{animals }\SpecialCharTok{==} \StringTok{"cat"}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1]  TRUE FALSE FALSE FALSE
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{animals[animals }\SpecialCharTok{==} \StringTok{"cat"}\NormalTok{]}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "cat"
+\end{verbatim}
+
+\subsection{Functions}\label{functions}
+
+\ul{Functions} are pre-written bits of codes that perform specific tasks
+for us. Functions are always followed by parentheses.
+
+Anything you type into the parentheses are called \ul{arguments}.
+Arguments are pieces of information that we give to a function so it
+performs its task the way we want it to. To add more than one argument,
+you separate them with a comma.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\DocumentationTok{\#\# Functions}
+\NormalTok{weight\_kg\_mean }\OtherTok{\textless{}{-}} \FunctionTok{mean}\NormalTok{(weight\_kg)   }\CommentTok{\# average of the mass\_kg vector from above}
+\NormalTok{weight\_kg\_mean}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 4.857143
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# separate arguments with commas}
+\FunctionTok{round}\NormalTok{(weight\_kg\_mean)             }\CommentTok{\# rounding}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 5
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{round}\NormalTok{(weight\_kg\_mean, }\AttributeTok{digits =} \DecValTok{2}\NormalTok{) }\CommentTok{\# round to 2 digits past 0}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 4.86
+\end{verbatim}
+
+To get more information about a function, use the \texttt{help()}
+function or \texttt{?name\_of\_function}.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{help}\NormalTok{(round) }\CommentTok{\# or type ?help}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+starting httpd help server ... done
+\end{verbatim}
+
+We can use a function called \texttt{class()} to figure out the data
+type of a vector.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{class}\NormalTok{(weight\_kg)}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "numeric"
+\end{verbatim}
+
+\subsubsection{Small Group Challenge}\label{small-group-challenge}
+
+Let's practice! Write a few lines of code that do the following:
+
+\begin{itemize}
+\tightlist
+\item
+  create a vector with numbers from 6 to 1 (6, 5, 4, 3, 2, 1)
+\item
+  assign the vector to an object named \texttt{six\_to\_one}
+\item
+  subset \texttt{six\_to\_one} to include the last 3 numbers (should
+  include 3, 2, 1)
+\item
+  find the sum of the numbers (hint: use the \texttt{sum()} function)
+\end{itemize}
+
+Answer: 6
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{six\_to\_one }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(}\DecValTok{6}\NormalTok{, }\DecValTok{5}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{1}\NormalTok{)}
+\NormalTok{six\_to\_one}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 6 5 4 3 2 1
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# subsetting by index (position)}
+\NormalTok{last\_three }\OtherTok{\textless{}{-}}\NormalTok{ six\_to\_one[}\DecValTok{4}\SpecialCharTok{:}\DecValTok{6}\NormalTok{]}
+\NormalTok{last\_three}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 3 2 1
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# alternate: subsetting by condition}
+\NormalTok{last\_three }\OtherTok{\textless{}{-}}\NormalTok{ six\_to\_one[six\_to\_one }\SpecialCharTok{\textless{}} \DecValTok{4}\NormalTok{]}
+\NormalTok{last\_three}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 3 2 1
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{sum}\NormalTok{(last\_three)}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 6
+\end{verbatim}
+
+Already finished? See if you can condense your code down any further or
+turn around and help out a neighbor.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{six\_to\_one }\OtherTok{\textless{}{-}} \FunctionTok{seq}\NormalTok{(}\DecValTok{6}\NormalTok{, }\DecValTok{1}\NormalTok{)}
+\FunctionTok{sum}\NormalTok{(six\_to\_one[}\DecValTok{4}\SpecialCharTok{:}\DecValTok{6}\NormalTok{])}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 6
+\end{verbatim}
+
+\subsection{2-Dimensional Data: Data
+Frames}\label{dimensional-data-data-frames}
+
+Most of the data you will encounter is two-dimensional (i.e., it has
+columns and rows). Its structure resembles a spreadsheet. R is really
+good with these types of data. We call these 2D object \ul{data frames}.
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{rows} go side-to-side
+\item
+  \textbf{columns} go up-and-down
+\end{itemize}
+
+Columns typically represent variables (a factor, trait, or condition) we
+are interested in.
+
+Rows represent observations. Each row will be one set of observations.
+
+\pandocbounded{\includegraphics[keepaspectratio]{row_column.png}}
+
+Data frames are made up of multiple vectors. Each vector becomes a
+column.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# Create a simple data frame from scratch}
+\NormalTok{plants }\OtherTok{\textless{}{-}} \FunctionTok{data.frame}\NormalTok{(}\AttributeTok{height =} \FunctionTok{c}\NormalTok{(}\DecValTok{55}\NormalTok{, }\DecValTok{17}\NormalTok{, }\DecValTok{42}\NormalTok{, }\DecValTok{47}\NormalTok{, }\DecValTok{68}\NormalTok{, }\DecValTok{39}\NormalTok{, }\DecValTok{51}\NormalTok{, }\DecValTok{23}\NormalTok{),}
+                     \AttributeTok{nitrogen =} \FunctionTok{c}\NormalTok{(}\StringTok{"Y"}\NormalTok{, }\StringTok{"N"}\NormalTok{, }\StringTok{"N"}\NormalTok{, }\StringTok{"Y"}\NormalTok{, }\StringTok{"Y"}\NormalTok{, }\StringTok{"N"}\NormalTok{, }\StringTok{"Y"}\NormalTok{, }\StringTok{"N"}\NormalTok{))}
+
+\NormalTok{plants}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+  height nitrogen
+1     55        Y
+2     17        N
+3     42        N
+4     47        Y
+5     68        Y
+6     39        N
+7     51        Y
+8     23        N
+\end{verbatim}
+
+\subsection{Subsetting Data Frames}\label{subsetting-data-frames}
+
+Because data frames are two-dimensional, we can subset the data in a
+data frame by selecting specific columns, specific rows, or both!
+
+R \emph{always} takes information for the row first, then the column.
+
+Think of it as: data{[}row, column{]}
+
+Just like with vectors, we can subset data frames by index or by
+condition using square brackets.
+
+The pattern is \texttt{dataframe{[}rows,\ columns{]}}.
+
+\subsubsection{Subsetting by Index}\label{subsetting-by-index-1}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# Sub{-}setting data frames}
+\CommentTok{\# 2{-}dimensional, so you need to specify row and then column}
+\CommentTok{\# plants[3] \# doesn\textquotesingle{}t work}
+
+\CommentTok{\# row then column}
+\NormalTok{plants[}\DecValTok{4}\NormalTok{,}\DecValTok{1}\NormalTok{]}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 47
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{plants[,}\DecValTok{2}\NormalTok{]}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "Y" "N" "N" "Y" "Y" "N" "Y" "N"
+\end{verbatim}
+
+Another way to pull out a single column from a data frame is with the
+\texttt{\$} operator. This can really come in handy when you know the
+name of the column but not the position.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{plants}\SpecialCharTok{$}\NormalTok{height}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 55 17 42 47 68 39 51 23
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# The $ operator allows you to refer to a column by name instead of position.}
+\end{Highlighting}
+\end{Shaded}
+
+Regardless of how you specify the column, you can put that code inside
+of a function, such as the \texttt{mean()}.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{mean}\NormalTok{(plants}\SpecialCharTok{$}\NormalTok{height)}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 42.75
+\end{verbatim}
+
+\subsubsection{Subsetting by Condition}\label{subsetting-by-condition-1}
+
+This is a simple data set, but we can use it to ask a question.
+
+Example: Are the heights of plants treated with nitrogen different from
+those not treated?
+
+First, we will need to keep only the plants that were treated with
+nitrogen.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# filter rows based on values in the nitrogen column}
+\NormalTok{plants[plants}\SpecialCharTok{$}\NormalTok{nitrogen }\SpecialCharTok{==} \StringTok{"Y"}\NormalTok{, ]}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+  height nitrogen
+1     55        Y
+4     47        Y
+5     68        Y
+7     51        Y
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{\# notice how the comma is still required. leaving it blank after the comma means "keep all columns."}
+
+\CommentTok{\# calculate the mean}
+\FunctionTok{mean}\NormalTok{(plants[plants}\SpecialCharTok{$}\NormalTok{nitrogen }\SpecialCharTok{==} \StringTok{"Y"}\NormalTok{, }\DecValTok{1}\NormalTok{])}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 55.25
+\end{verbatim}
+
+We can create a new data frame by saving the subset data frame to a new
+object.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{plants\_no\_nitrogen }\OtherTok{\textless{}{-}}\NormalTok{ plants[plants}\SpecialCharTok{$}\NormalTok{nitrogen }\SpecialCharTok{==} \StringTok{"N"}\NormalTok{, ]}
+\end{Highlighting}
+\end{Shaded}
+
+\subsubsection{Small Group Challenge (5
+min)}\label{small-group-challenge-5-min}
+
+As a group, find the standard deviation (\texttt{sd()}) of the height of
+plants treated with nitrogen and those not treated with nitrogen. Which
+group has the larger standard deviation? Any ideas what that means?
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{sd}\NormalTok{(plants[plants}\SpecialCharTok{$}\NormalTok{nitrogen }\SpecialCharTok{==} \StringTok{"Y"}\NormalTok{, }\DecValTok{1}\NormalTok{])}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 9.105859
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{sd}\NormalTok{(plants[plants}\SpecialCharTok{$}\NormalTok{nitrogen }\SpecialCharTok{==} \StringTok{"N"}\NormalTok{, }\DecValTok{1}\NormalTok{])}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 12.14839
+\end{verbatim}
+
+\subsection{Helpful Functions}\label{helpful-functions}
+
+Below are some functions that I often find very helpful when working
+with vectors and data frames:
+
+\begin{itemize}
+\tightlist
+\item
+  \texttt{str()}: shows the structure of the object (e.g., rows and
+  columns)
+\item
+  \texttt{head()} and \texttt{tail()}: shows the first six and last six
+  rows, respectfully
+\item
+  \texttt{length()}: counts the number of elements in an object
+\item
+  \texttt{ncol()} and \texttt{nrow()}: counts the number of columns or
+  rows, respectfully
+\item
+  \texttt{names()}: shows the names of the columns in a data frame
+\item
+  \texttt{unique()}: shows one of each element in an object (removes
+  duplicate values)
+\end{itemize}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{str}\NormalTok{(plants) }\CommentTok{\# structure of the object}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+'data.frame':   8 obs. of  2 variables:
+ $ height  : num  55 17 42 47 68 39 51 23
+ $ nitrogen: chr  "Y" "N" "N" "Y" ...
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{head}\NormalTok{(plants) }\CommentTok{\# first 6 values or rows}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+  height nitrogen
+1     55        Y
+2     17        N
+3     42        N
+4     47        Y
+5     68        Y
+6     39        N
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{head}\NormalTok{(plants, }\AttributeTok{n =} \DecValTok{4}\NormalTok{) }\CommentTok{\# first n values or rows}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+  height nitrogen
+1     55        Y
+2     17        N
+3     42        N
+4     47        Y
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{tail}\NormalTok{(plants, }\AttributeTok{n =} \DecValTok{4}\NormalTok{) }\CommentTok{\# last n values or rows}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+  height nitrogen
+5     68        Y
+6     39        N
+7     51        Y
+8     23        N
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{length}\NormalTok{(plants)  }\CommentTok{\# for a dataframe, number of columns}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 2
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{length}\NormalTok{(plants}\SpecialCharTok{$}\NormalTok{height) }\CommentTok{\# for a column, number of rows}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 8
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{ncol}\NormalTok{(plants)  }\CommentTok{\# number of columns}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 2
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{nrow}\NormalTok{(plants)  }\CommentTok{\# number of rows}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] 8
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{names}\NormalTok{(plants) }\CommentTok{\# list of column or object names}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "height"   "nitrogen"
+\end{verbatim}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\FunctionTok{unique}\NormalTok{(plants}\SpecialCharTok{$}\NormalTok{nitrogen) }\CommentTok{\# one of each value present in the column, duplicates removed}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{verbatim}
+[1] "Y" "N"
+\end{verbatim}
+
+
+
+
+\end{document}
diff --git a/modules/module_1/module1_3.qmd b/modules/module_1/module1_3.qmd
index bc80ea3..2a88861 100644
--- a/modules/module_1/module1_3.qmd
+++ b/modules/module_1/module1_3.qmd
@@ -58,7 +58,6 @@ For future reference, though:
 -   we use the function `install.packages()` to download the package
 
 ```{r}
-#| label: install_tidyverse
 #| message: false
 # download and install the tidyverse package(s)
 # to run the line of code, remove the # in front of the line below and run this chunk
@@ -76,7 +75,6 @@ When you run this code, you'll see a message that says "Attaching packages" and
     -   If you're curious, though, it is telling us that there are some functions in the `tidyverse` that have the same names as functions that are automatically installed with R and that the `tidyverse` versions of those functions will be the ones that get used by default unless we specify otherwise.
 
 ```{r}
-#| label: load_tidyverse
 #| message: false
 # load the tidyverse (tell RStudio we want to use this package in this session)
 library(tidyverse)
@@ -87,7 +85,6 @@ library(tidyverse)
 To learn about the `tidyverse` syntax, we're going to use a real data set on climate change from Berkeley, CA, USA. It outlines temperatures in major cities across the world since 1750.
 
 ```{r}
-#| label: climate_data
 # read in the data file
 
 # `read_csv()` is part of the `tidyverse` and gives us nice options when reading in data
@@ -119,7 +116,6 @@ Let's use our first function, `select()`. Select allows us to pick out specific
 First, let's remind ourselves how we would accomplish this in base R.
 
 ```{r}
-#| label: select_baseR
 # column selection in base R
 # climate_df$dt
 climate_df[,1:2]
@@ -128,7 +124,6 @@ climate_df[,1:2]
 The `select()` function does the same thing but with more power (and, in my opinion, more easily). The first argument in the function is the data frame. Any following arguments are the columns we want to select.
 
 ```{r}
-#| label: select_tidy
 # first argument is the data frame, then the columns
 select(climate_df, dt)
 
@@ -155,7 +150,6 @@ It is important to remember that the computer interprets everything literally. W
 `filter()` allows you filter rows by certain conditions. The code below is how we would do this in base R.
 
 ```{r}
-#| label: filter_baseR
 # base R
 climate_df[climate_df$AverageTemperature > 25, ]
 ```
@@ -163,7 +157,6 @@ climate_df[climate_df$AverageTemperature > 25, ]
 The code above is, in my opinion, a bit unwieldy. Filter feels more intuitive. We still need the double equal signs, though!
 
 ```{r}
-#| label: filter_tidy
 # filter
 filter(climate_df, AverageTemperature > 25)
 
@@ -202,6 +195,8 @@ You can use the pipe operator to chain `tidyverse` functions together.
 
 You can think of the pipe as automatically sending the output from the first line into the next line as the input. The output of the previous line of code (to the "left" of the pipe) becomes the first argument in the function to the right of the pipe.
 
+![](assets/dplyr_pipe.png){fig-align="center" width="75%"}
+
 This is helpful for a lot of reasons, including:
 
 1.  removing the clutter of creating a lot of intermediate objects in your work space, which reduces the chance of errors caused by using the wrong input object
@@ -210,7 +205,6 @@ This is helpful for a lot of reasons, including:
 The shortcut for typing a pipe is `Ctrl` + `Shift` + `M` (or `Cmd` + `Shift` + `M` on a Mac)
 
 ```{r}
-#| label: pipes
 climate_df %>% 
   select(dt, City)
 
@@ -227,7 +221,6 @@ In small groups, use pipes to create a new data frame called `warm_nigeria` that
 -   only rows for the country Nigeria and temperatures that are greater than 30 degrees
 
 ```{r}
-#| label: nigeria30
 warm_nigeria <- climate_df %>% 
   select(AverageTemperature, City, Country) %>% 
   filter(Country == "Nigeria", AverageTemperature > 30)
@@ -237,14 +230,13 @@ warm_nigeria <- climate_df %>%
 
 Like we have talked about in previous classes, some of the best ways for us to understand our data is through what we call summary statistics such as the mean (average), standard deviation, minimums, maximums, etc. Fortunately, the `tidyverse` has a handy-dandy function to make this easy to do with data frames.
 
-The `summarize()` function creates a new data frame with columns and values we give it. 
+The `summarize()` function creates a new data frame with columns and values we give it.
 
 The first part of the argument in the `summarize` function (before the `=`) is the name of the new column we want to create. After the `=` is code that produces the values we want the new column to contain.
 
 | `summarize(new_column_name = summary_function(column_to_summarize))`
 
 ```{r}
-#| label: summarize
 # first attempt at mean and sd of average temperature
 climate_df %>% 
   summarise(mean_temp = mean(AverageTemperature),
@@ -260,7 +252,6 @@ It is important to note that if any of the values in the column that you are try
 Fortunately, `mean()` and `sd()` and some other functions have an argument to remove the missing values: `na.rm = TRUE`
 
 ```{r}
-#| label: summarize_na.rm
 climate_df %>% 
   summarise(mean_temp = mean(AverageTemperature, na.rm = TRUE),
             sd_temp = sd(AverageTemperature, na.rm = TRUE))
@@ -281,7 +272,6 @@ The `group_by()` function lets us do this. It is most often used in combination
 For example, we can use this method to calculate the mean temperatures of **each** country instead of the overall mean of the entire dataset. In order to do these, we create groups in the data based on the country.
 
 ```{r}
-#| label: group_by
 climate_df %>% 
   group_by(Country) %>% 
   summarise(mean_temp = mean(AverageTemperature, na.rm = TRUE),
@@ -293,7 +283,6 @@ climate_df %>%
 Practice using the combination of `group_by()` and `summarize()` to calculate the minimum (`min()`) and maximum (`max()`) average temperatures for each city. Save this data frame as `city_min_max`
 
 ```{r}
-#| label: group_by_city
 climate_df %>% 
   group_by(City) %>% 
   summarize(min_temp = min(AverageTemperature, na.rm = TRUE),
@@ -303,7 +292,6 @@ climate_df %>%
 Already accomplished this task? Try to figure out how you can keep the "Country" column in the final data frame. This is trickier than you might think!
 
 ```{r}
-#| label: group_by_city_country
 climate_df %>% 
   group_by(Country, City) %>% 
   summarize(min_temp = min(AverageTemperature, na.rm = TRUE),
@@ -323,7 +311,6 @@ Like the `summarize()` function, The first part of the argument in the `mutate`
 To help us out, here is the equation for converting: `Fahrenheit = Celsius * (9/5) + 32`
 
 ```{r}
-#| label: mutate
 # create a new column for temps in Fahrenheit
 climate_df %>% 
   select(dt, AverageTemperature) %>% 
diff --git a/modules/module_1/module1_home.qmd b/modules/module_1/module1_home.qmd
new file mode 100644
index 0000000..950533a
--- /dev/null
+++ b/modules/module_1/module1_home.qmd
@@ -0,0 +1,27 @@
+---
+title: "Module 1: Mission Antarctica!"
+format:
+  html:
+    toc: false
+---
+
+## Overview
+
+The team has arrived in Antarctica and needs to establish the technical foundation for all future data work. Students begin by learning how to use R and RStudio in Posit Cloud, including how to run code, create objects, work with vectors, understand data classes, subset data, and use functions. They are also introduced to the `tidyverse`, where they begin learning how to select, filter, summarize, group, and mutate data. By the end of the module, students are comfortable navigating their coding environment and working with simple ecological datasets.
+
+## Learning Objectives
+
+By the end of this module, students will be able to:
+
+-   describe the role of R and RStudio in a data science workflow
+-   use Posit Cloud to write, run, and organize code
+-   create objects and work with vectors in R
+-   distinguish among common data classes, including numeric, character, logical, factor, and data frame structures
+-   subset vectors and data frames using both index- and condition-based approaches
+-   use functions and interpret function arguments in R
+-   use core `tidyverse` tools such as `select()`, `filter()`, `mutate()`, `summarize()`, and `group_by()` to explore and organize data
+
+## Instructor Downloads
+
+-   [Download Module 1 source files](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA) on GitHub
+-   Add more details or downloads here...?
\ No newline at end of file
diff --git a/modules/module_2/module2_1.qmd b/modules/module_2/module2_1.qmd
index 903aabe..3ca9a4a 100644
--- a/modules/module_2/module2_1.qmd
+++ b/modules/module_2/module2_1.qmd
@@ -5,7 +5,9 @@ format:
   html:
     toc: true
 ---
-```{r setup, include=FALSE}
+
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_2/module2_2.qmd b/modules/module_2/module2_2.qmd
index 1949529..1ed7e75 100644
--- a/modules/module_2/module2_2.qmd
+++ b/modules/module_2/module2_2.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_2/module2_3.qmd b/modules/module_2/module2_3.qmd
index 8ffbd86..b485b39 100644
--- a/modules/module_2/module2_3.qmd
+++ b/modules/module_2/module2_3.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_2/module2_4.qmd b/modules/module_2/module2_4.qmd
index 4301c72..c38b575 100644
--- a/modules/module_2/module2_4.qmd
+++ b/modules/module_2/module2_4.qmd
@@ -5,8 +5,15 @@ format:
   html:
     toc: true
 ---
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(
+    echo = TRUE,
+    fig.width = 5,
+    fig.height = 4,
+    fig.aligh = "center"
+)
 ```
 
 # Plotting in `ggplot2`
@@ -41,9 +48,11 @@ They help us get a feel for the distribution of that data. To make histograms in
 ggplot(fish, aes(x = num_fish)) +
   geom_histogram(bins = 20)
 
-# we can change the number of bins (essentially, the number of columns) by modifying the bins argument in the geom_histogram function
-# we can also modify the colors, if we want
+# we can change the number of bins (essentially the number of columns) by modifying the bins argument in the geom_histogram() function.
+ggplot(fish, aes(x = num_fish)) +
+  geom_histogram(bins = 10)
 
+# we can also modify the colors!
 ggplot(fish, aes(x = num_fish)) +
   geom_histogram(bins = 20, fill = "lightblue", color = "black")
 ```
@@ -233,7 +242,7 @@ First, think about the variables we are plotting. How many are there? Are they c
 
 Based on those answers, determine which plot types you can produce for those variable types.
 
-Now, make your two plot! To each, add labels and a theme.
+Now, make your two plots! To each, add labels and a theme.
 
 ```{r}
 # boxplot
diff --git a/modules/module_2/module2_5.qmd b/modules/module_2/module2_5.qmd
index d339882..6f6decb 100644
--- a/modules/module_2/module2_5.qmd
+++ b/modules/module_2/module2_5.qmd
@@ -5,7 +5,9 @@ format:
   html:
     toc: true
 ---
-```{r setup, include=FALSE}
+
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
@@ -113,15 +115,15 @@ Are those columns numeric or categorical? What plot type have we talked about th
 In small groups, make plots using `density` column in the `sick_trout` data to try to figure out which environmental factor is causing problems in the trout. Treat your variables as numeric.
 
 ```{r}
-ggplot(sick_trout, aes(x = density, y = num_fish)) +
+ggplot(sick_trout, aes(x = num_fish, y = density)) +
   #geom_smooth(method = 'lm', se = FALSE) +
   geom_point() 
 
-ggplot(sick_trout, aes(x = density, y = avg_daily_temp)) +
+ggplot(sick_trout, aes(x = avg_daily_temp, y = density)) +
   #geom_smooth(method = 'lm', se = FALSE) +
   geom_point()
 
-ggplot(sick_trout, aes(density, day_length)) +
+ggplot(sick_trout, aes(day_length, density)) +
   #geom_smooth(method = 'lm', se = FALSE) +
   geom_point() 
 ```
diff --git a/modules/module_3/module3_1.qmd b/modules/module_3/module3_1.qmd
index f9d0019..4422ac0 100644
--- a/modules/module_3/module3_1.qmd
+++ b/modules/module_3/module3_1.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
@@ -114,7 +115,7 @@ We will use the `geom_vline()` function to add a vertical line onto our plot at
 
 Let's take a look at the help file for `geom_vline()` to see what arguments it takes. Scroll down to the "Aesthetics" section.
 
-```{r}
+```{r, eval=FALSE}
 ?geom_vline
 ```
 
diff --git a/modules/module_3/module3_2.qmd b/modules/module_3/module3_2.qmd
index bcfb63e..4429cc7 100644
--- a/modules/module_3/module3_2.qmd
+++ b/modules/module_3/module3_2.qmd
@@ -6,7 +6,12 @@ format:
     toc: true
 ---
 
-## Comparing Two Means
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Comparing Two Means
 
 When we are comparing data from two groups, we often want to compare the mean values of the different groups to see if there are differences. We can do this in a number of ways:
 
@@ -86,7 +91,6 @@ There is a set of statistical tools that can help us determine whether or not th
 Let's briefly remind ourselves of the logic here.
 
 1.  Our data are a *sample* of a larger *population* (think of the population as all of the collars ever produced by both companies).
-    -   Remember how we sampled our fish tank data for sick fish instead of getting data for every single tank? Same idea.
 2.  We're interested in the the difference in the means between the two groups.
     -   If they're exactly the same, the difference in means would be 0.
     -   If they're different, the difference between the means will be something either larger or smaller than 0.
diff --git a/modules/module_3/module3_3.qmd b/modules/module_3/module3_3.qmd
index 58c2b9f..4635ffa 100644
--- a/modules/module_3/module3_3.qmd
+++ b/modules/module_3/module3_3.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_4/module4_1.qmd b/modules/module_4/module4_1.qmd
index d5c43be..4063feb 100644
--- a/modules/module_4/module4_1.qmd
+++ b/modules/module_4/module4_1.qmd
@@ -6,11 +6,12 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# The Context
+## The Context
 
 After the series of incidents where a number of the collars made by Budget Collars LLC seem to be failing, our team decided to try and replace as many of them as possible. Besides, their battery life is far inferior.
 
diff --git a/modules/module_4/module4_2.qmd b/modules/module_4/module4_2.qmd
index bf006e5..7c25ad9 100644
--- a/modules/module_4/module4_2.qmd
+++ b/modules/module_4/module4_2.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_4/module4_3.qmd b/modules/module_4/module4_3.qmd
index b33016b..7c2b2df 100644
--- a/modules/module_4/module4_3.qmd
+++ b/modules/module_4/module4_3.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_4/module4_4.qmd b/modules/module_4/module4_4.qmd
index cd6db2a..50e8154 100644
--- a/modules/module_4/module4_4.qmd
+++ b/modules/module_4/module4_4.qmd
@@ -6,7 +6,8 @@ format:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
diff --git a/modules/module_5/module5_1.qmd b/modules/module_5/module5_1.qmd
index ca09f7d..26511da 100644
--- a/modules/module_5/module5_1.qmd
+++ b/modules/module_5/module5_1.qmd
@@ -1,13 +1,14 @@
 ---
 title: '5.1: Population Growth'
-author: "Jessie Golding and Ellen Bledsoe"
+authors: "Jessie Golding, Desiree Andersen, Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
 
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, fig.align = "center")
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
 ```
 
 # Introduction to Population Growth
@@ -16,7 +17,7 @@ Let's use descriptive statistics and data visualization to better understand pop
 
 ### Exponential Growth
 
-A population that shows [exponential growth]{.underline} has a growth rate that depends the average number of births (`b`) and deaths (`d`) per individual.
+A population that shows [exponential growth]{.underline} has a growth rate that depends on the average number of births (`b`) and deaths (`d`) per individual.
 
 We call rates that are averages per individual in a population (rather than a total) *per capita* growth rates; *per capita* translates from Latin to "by head," meaning for individuals.
 
@@ -91,15 +92,6 @@ popB
 **Why is there no carrying capacity in this population data set? What does this mean about the resources Population B needs?**\
 *Answer: Exponential models assume unlimited resources, so no carrying capacity (K) is included.*
 
-**What do you notice about Population A that is different than Population B?**\
-*Answer: The intrinsic growth rate (r) for Population A is negative.*
-
-**What has happened to this population?**\
-*Answer: Population A crashes and reaches zero.*
-
-**Between Population A and Population B, which is a better target population for fishing? Why?**\
-*Answer: Population B, because r \> 0 and it grows quickly.*
-
 ### Exponential Growth Populations
 
 It looks like populations exhibiting exponential growth might be good targets for our fishing operations because they can become very large. Let's look at the data we have on exponential growth populations a little closer.
@@ -130,7 +122,8 @@ popA <-fish_pops %>%
 popA
 ```
 
-What do you notice about Population A that is different than Population B? Intrinsic growth rate negative
+**What do you notice about Population A that is different than Population B?**\
+*Answer: The intrinsic growth rate (r) for Population A is negative.*
 
 The intrinsic rate of increase (r) for Population A is negative. Let's see what that means for Population A and plot it by itself:
 
@@ -142,11 +135,13 @@ ggplot(popA, aes(x = year, y = N)) +
   theme_light()
 ```
 
-What has happened to this population? Crashed
+**What has happened to this population?**\
+*Answer: Population A crashes and reaches zero.*
 
 Even with "exponential growth", the population has crashed (gone to 0), because *r* is negative. It took approximately 7 years for this to happen.
 
-Between Population A and Population B, which do you think is a better target population for fishing? Population B, `r` is positive
+**Between Population A and Population B, which is a better target population for fishing? Why?**\
+*Answer: Population B, because r \> 0 and it grows quickly.*
 
 **Logistic growth populations**
 
@@ -160,7 +155,7 @@ log_pops
 
 Notice in this case we have carrying capacity data. Let's use some data visualizations to help us understand what that means.
 
-Lets look at the two logistic growth populations on the same plot:
+Let's look at the two logistic growth populations on the same plot:
 
 ```{r}
 ggplot(log_pops, aes(x = year, y = N, color = population))+
@@ -170,19 +165,20 @@ ggplot(log_pops, aes(x = year, y = N, color = population))+
   theme_light()
 ```
 
-Which population gets the largest? Population 4
+**Which population gets the largest?**\
+*Answer: Population D*
 
 ### Finding the Carrying Capacity
 
-When we have a logistic growth model, we often will want to find the carrying capacity of the population
+When we have a logistic growth model, we often will want to find the carrying capacity of the population.
 
-What values does each population level off at?
+**What values does each population level off at?**
 
--   Population C: 20
+-   Population C: *20*
 
--   Population D: 100
+-   Population D: *100*
 
-These are the carrying capacities. If you look back at the data, you will see Population C has a carrying capacity of 20 individuals, whereas Population D has a carrying capacity of 100 individuals. Which population do you think might be the best candidate to fish between these two? Population D is more abundant and has a higher carrying capacity.
+These are the carrying capacities. If you look back at the data, you will see Population C has a carrying capacity of 20 individuals, whereas Population D has a carrying capacity of 100 individuals. **Which population do you think might be the best candidate to fish between these two?** *Answer: Population D is more abundant and has a higher carrying capacity.*
 
 **A review of what we have learned**
 
@@ -196,4 +192,6 @@ The following factors are important to know about a population before fishing:
 
 ## References
 
-Zhang, M., Liu, S., Bo, J., Zheng, R., Hong, F., Gao, F., Miao, X., Li, H., & Fang, C. (2022). First evidence of microplastic contamination in Antarctic fish (Actinopterygii, Perciformes),*Water*, *14*(19).
\ No newline at end of file
+Golding, J.; Andersen, D.; Bledsoe, E. (2024). [Data-Driven Decision-Making: Antarctic Fisheries](http://dx.doi.org/10.25334/YECE-7M02). [Teaching Ecology for All Undergraduate Audiences](https://qubeshub.org/groups/teespring2024), QUBES Educational Resources. [doi:10.25334/YECE-7M02](http://dx.doi.org/10.25334/YECE-7M02)
+
+Zhang, M., Liu, S., Bo, J., Zheng, R., Hong, F., Gao, F., Miao, X., Li, H., & Fang, C. (2022). First evidence of microplastic contamination in Antarctic fish (Actinopterygii, Perciformes), *Water*, *14*(19).
\ No newline at end of file
diff --git a/modules/module_5/module5_2.qmd b/modules/module_5/module5_2.qmd
index 6754c2e..d8003d5 100644
--- a/modules/module_5/module5_2.qmd
+++ b/modules/module_5/module5_2.qmd
@@ -1,12 +1,13 @@
 ---
-title: '5.1: Sustainable Fishing'
-author: "Jessie Golding and Ellen Bledsoe"
+title: '5.2: Sustainable Fishing'
+author: "Jessie Golding, Desiree Andersen, Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
@@ -181,4 +182,8 @@ MSY
 
 This means that we can take about 52 fish from this population annually and keep the population stable!
 
-If we take fewer than 52 fish, the population will increase (until the carrying capacity, that is). If we take more than 52 fish, however, we are over-fishing the population, meaning the population will not be able to reproduce enough to maintain the population and will eventually collapse.
\ No newline at end of file
+If we take fewer than 52 fish, the population will increase (until the carrying capacity, that is). If we take more than 52 fish, however, we are over-fishing the population, meaning the population will not be able to reproduce enough to maintain the population and will eventually collapse.
+
+## References
+
+Golding, J.; Andersen, D.; Bledsoe, E. (2024). [Data-Driven Decision-Making: Antarctic Fisheries](http://dx.doi.org/10.25334/YECE-7M02). [Teaching Ecology for All Undergraduate Audiences](https://qubeshub.org/groups/teespring2024), QUBES Educational Resources. [doi:10.25334/YECE-7M02](http://dx.doi.org/10.25334/YECE-7M02)
\ No newline at end of file
diff --git a/modules/module_5/module5_3.qmd b/modules/module_5/module5_3.qmd
index 415a054..3c3de21 100644
--- a/modules/module_5/module5_3.qmd
+++ b/modules/module_5/module5_3.qmd
@@ -1,13 +1,14 @@
 ---
 title: '5.3: Comparing Populations'
-author: "Jessie Golding and Ellen Bledsoe"
+author: "Jessie Golding, Desiree Andersen, Ellen Bledsoe"
 format:
   html:
     toc: true
 ---
 
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
 ```
 
 # Comparing Populations
@@ -257,4 +258,8 @@ How do we interpret these results? What are our conclusions?
 
 Our ultimate goal in this module has been to determine which population of fish is best for fishing sustainably. We are prioritizing large, stable populations.
 
-Given those requirements and what our numeric, visual, and statistical tests have told us, which of these populations would be the best for us to harvest? Why?
\ No newline at end of file
+Given those requirements and what our numeric, visual, and statistical tests have told us, which of these populations would be the best for us to harvest? Why?
+
+## References
+
+Golding, J.; Andersen, D.; Bledsoe, E. (2024). [Data-Driven Decision-Making: Antarctic Fisheries](http://dx.doi.org/10.25334/YECE-7M02). [Teaching Ecology for All Undergraduate Audiences](https://qubeshub.org/groups/teespring2024), QUBES Educational Resources. [doi:10.25334/YECE-7M02](http://dx.doi.org/10.25334/YECE-7M02)
\ No newline at end of file
diff --git a/structure.qmd b/structure.qmd
deleted file mode 100644
index 7a87230..0000000
--- a/structure.qmd
+++ /dev/null
@@ -1,94 +0,0 @@
----
-title: "Course Structure"
-format:
-  html:
-    toc: false
----
-
-## The Full Course Cheese Board
-
-This page serves as an inventory of the material associated with each subsection of this site. These are also available in the Data in the Wild Github repository, which can be accessed [here](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA.git).
-
-***Note*****:** The instructor versions of lessons are also available as PDFs, and they can be downloaded within the respective module subsection (e.g. Click on 1.1: Introduction to RStudio in the left sidebar to open the lesson page -\> PDF button on the right).
-
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Module        | Title                                                       | Lecture / Code-along (.Rmd)                                                                                                                                            | Data sets (.csv)                                                                                                            | Assignments (.Rmd)                                                                                                                                                  |
-+:=============:+:===========================================================:+:======================================================================================================================================================================:+:===========================================================================================================================:+:===================================================================================================================================================================:+
-| 1.1           | Introduction to RStudio                                     | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_1_IntrotoRStudio.Rmd)                   | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M1_data.zip)         | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_1/assignments/Module1_Assignment2.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_1_IntrotoRStudio_Instructor.Rmd)     |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_1/assignments/Module1_Assignment3.Rmd) |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 1.2           | Introduction to Coding                                      | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_2_IntrotoCoding.Rmd)                    |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_2_IntrotoCoding_Instructor.Rmd)      |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 1.3           | 2-dimensional Data and the `tidyverse`                      | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_3_tidyverse.Rmd)                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_1/lessons/Module1_3_tidyverse_Instructor.Rmd)          |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.1           | Introduction to Descriptive Statistics & Data Visualization | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_1_DescriptiveStats.Rmd)                 | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M2_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment1.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_1_DescriptiveStats_Instructor.Rmd)   |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment2.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_2/assignments/Module2_Assignment3.Rmd) |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.2           | Introduction to `ggplot2`                                   | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions.Rmd)                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_2_Functions_Instructor.Rmd)          |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.3           | Introduction to Data Visualization                          | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_3_DataViz.Rmd)                          |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_3_DataViz_Instructor.Rmd)            |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.4           | Plotting in `ggplot2`                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_4_geoms.Rmd)                            |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_4_geoms_Instructor.Rmd)              |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 2.5           | Module 2 Wrap-Up: What is Causing the Food Poisoning?       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp.Rmd)                           |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_2/lessons/Module2_5_WrapUp_Instructor.pdf)             |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.1           | Leopard Seals: Introducing a New Problem                    | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_1_LeopardSeals.Rmd)                     | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M3_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment1.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_1_LeopardSeals_Instructor.Rmd)       |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment2.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_3/assignments/Module3_Assignment3.Rmd) |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.2           | Comparing Two Means                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests.Rmd)                          |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_2_T-Tests_Instructor.Rmd)            |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 3.3           | Comparing Multiple Means                                    | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA.Rmd)                            |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_3_ANOVA_Instructor.Rmd)              |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 4.1           | Combining Datasets (Joins & Binds)                          | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M3_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment1.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment2.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment3.Rmd) |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 4.2           | K-Nearest Neighbors                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 4.3           | Roads and Regressions                                       | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN.Rmd)                              |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_3/lessons/Module3_5_KNN_Instructor.Rmd)                |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 4.4           | Multiple Regression                                         | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression.Rmd)               |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_2_MultipleRegression_Instructor.Rmd) |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 5.1           | Introduction to Population Growth                           | [Student](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression.Rmd)               | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/M4_data.zip)         | [Assignment 1](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment1.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             | [Instructor](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/module_4/lessons/Module4_1_RoadsandRegression_Instructor.Rmd) |                                                                                                                             | [Assignment 2](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment2.Rmd) |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-|               |                                                             |                                                                                                                                                                        |                                                                                                                             | [Assignment 3](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/refs/heads/main/modules/module_4/assignments/Module4_Assignment3.Rmd) |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 5.2           | Sustainable Fishing                                         |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| 5.3           | Comparing Populations                                       |                                                                                                                                                                        |                                                                                                                             |                                                                                                                                                                     |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Final Project |                                                             |                                                                                                                                                                        | [.zip](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/zipped_data/final_proj_data.zip) | [Assignment Guidelines](https://raw.githubusercontent.com/BiodiversityDataScienceCorp/DataInTheWild_UA/main/modules/final_project/FinalAssignment.pdf)              |
-+---------------+-------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
\ No newline at end of file

From 3f4096e6ffe41aadc04a307d3a64568e907e89fd Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Wed, 18 Mar 2026 12:23:47 -0700
Subject: [PATCH 11/15] adding for instructors page + updating resources page

---
 README.md                                | 24 +++----------
 _quarto.yml                              |  8 +++--
 instructors.qmd                          | 46 ++++++++++++++++++++++++
 modules/module_1/assignments/a1/m1a1.qmd | 22 ------------
 modules/module_1/module1_home.qmd        | 19 ++++++++--
 resources.qmd                            | 24 ++++++++++---
 6 files changed, 91 insertions(+), 52 deletions(-)
 create mode 100644 instructors.qmd

diff --git a/README.md b/README.md
index caf39e3..68f44b6 100644
--- a/README.md
+++ b/README.md
@@ -1,37 +1,21 @@
 # Data In The Wild
 
-An introductory data science course for the life sciences, developed as part of the NSF Harnessing the Data Revolution (HDR) Data Science Corps program in collaboration between the University of Arizona and Lewis & Clark College.
-
 ## About the Course
 
-Data in the Wild teaches students to apply data science skills to real-world problems in biodiversity and conservation. Using the R programming language, students work through four narrative-driven modules set in Antarctica, from their first steps in RStudio to building predictive models for penguin nesting habitat. The course is designed for students across all majors and fulfills general education requirements at the University of Arizona.
-
-The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe, and first taught in pilot form at UA in Spring 2020.
-
-## Course Modules
-
-**Module 1 - Mission Antarctica!** Introduction to data science, RStudio, RMarkdown, and the R programming language.
+Data in the Wild introduces students to data science through the R programming language and a series of ecological problems set in Antarctica. Across the course, students use data to investigate food safety, wildlife tracking, habitat relationships, and sustainable fisheries management. Along the way, they learn skills in data wrangling, visualization, statistical analysis, modeling, and interpretation.
 
-**Module 2 - Good Food Gone Bad** Data visualization and simulation to investigate a food poisoning outbreak among team members.
+The course is designed for students in the life sciences and assumes no prior programming experience. Lessons begin with the basics of coding in R and gradually build toward more advanced tools such as hypothesis testing, machine learning, regression, and population modeling.
 
-**Module 3 - Follow That Seal** Classification and machine learning to identify failing radio collars on leopard seals.
-
-**Module 4 - March of the Penguins** Regression and bootstrapping to model penguin nesting success and plan a new road route.
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and has been taught at the University of Arizona as part of the general education curriculum.
 
 ## Accessing Course Materials
 
 ### Website
 
-The course website contains all lesson pages, module narratives, and links to downloadable materials:
+The course website contains all modules, lessons, assignments, and their downloads:
 
 <https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/>
 
-### GitHub
-
-All source files are available in the course repository:
-
-<https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA>
-
 ## Building the Website Locally
 
 This site is built with [Quarto](https://quarto.org) and requires R and several packages. To preview locally:
diff --git a/_quarto.yml b/_quarto.yml
index eada1bc..3a234fd 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -26,6 +26,9 @@ website:
       - text: "Contact Us"
         href: https://github.com/BiodiversityDataScienceCorp
 
+      - text: "For Instructors"
+        href: instructors.qmd
+
       - text: "---"
 
       - section: "Module 1"
@@ -72,9 +75,8 @@ website:
 
       - text: "---"
 
-      - section: "Resources"
-        contents:
-          - resources.qmd
+      - text: "Resources"
+        href: resources.qmd
 
   page-footer:
     background: $dark-blue
diff --git a/instructors.qmd b/instructors.qmd
new file mode 100644
index 0000000..f3a0095
--- /dev/null
+++ b/instructors.qmd
@@ -0,0 +1,46 @@
+---
+title: "For Instructors"
+format:
+  html:
+    toc: false
+---
+
+This page is for instructors who are interested in teaching with, adapting, or learning more about *Data in the Wild*.
+
+The course was designed to make data science more accessible, applied, and meaningful for students in the life sciences. While the version shared here is rooted in ecological questions and a narrative set in Antarctica, the broader structure is flexible and can be adapted to other disciplines, datasets, and teaching contexts.
+
+## Context
+
+The course has been taught at the University of Arizona as part of the general education curriculum, and the materials shared here are openly available so that others can use and adapt them in ways that fit their own classrooms.
+
+## R, RStudio, and Posit Cloud
+
+This course uses the R programming language and was taught using Posit Cloud.
+
+A quick distinction:
+
+-   **R** is the programming language itself.
+-   **RStudio** is a coding environment used to write and run R code.
+-   **Posit Cloud** is a browser-based version of that environment, which lets students work online without needing to install software on their own computers.
+
+For this course, Posit Cloud was especially useful because it gave students a shared, consistent workspace and lowered the barrier to getting started. That was particularly helpful in an introductory course where many students were new to coding.
+
+That said, the materials can also be taught in local RStudio or adapted to other environments.
+
+## Source Files - Download on GitHub
+
+Lessons, assignments, datasets, and other course materials are available in the public GitHub repository.
+
+## Assignments
+
+### Using the Expected Outputs
+
+The expected outputs document is provided as an additional resource for students as they work through the assignment. It is intended to help students check that their code is producing the expected results.
+
+This document is **not** an answer key. It does not provide the code needed to generate those results. Instead, it gives students a way to verify their work while still doing the problem-solving themselves.
+
+### Answer Keys for Instructors
+
+Assignment answer keys are available to instructors, but they are not included in the public course materials.
+
+If you are an instructor and would like access to the answer keys, please open an issue in the GitHub repository \[add link here when repo is set up\]. This repository contains the assignment keys and grading references.
\ No newline at end of file
diff --git a/modules/module_1/assignments/a1/m1a1.qmd b/modules/module_1/assignments/a1/m1a1.qmd
index 492ce00..e2778b1 100644
--- a/modules/module_1/assignments/a1/m1a1.qmd
+++ b/modules/module_1/assignments/a1/m1a1.qmd
@@ -5,28 +5,6 @@ format:
     toc: true
 ---
 
-## Downloads
-
-The files for this assignment are available below:
-
--   [Download the assignment (.qmd or .rmd?)]()
--   [Download the dataset (.csv)](team_antarctica.csv)
--   [Download the expected outputs (.pdf)](m1a1_expected.pdf)
-
-## Using the Expected Outputs
-
-The expected outputs document is provided as an additional resource for students as they work through the assignment. It is intended to help students check that their code is producing the expected results.
-
-This document is **not** an answer key. It does not provide the code needed to generate those results. Instead, it allows students to verify their work while still completing the assignment independently.
-
-## For Instructors
-
-Assignment answer keys are available to instructors, but they are not included in the public course materials.
-
-If you are an instructor and would like access to the answer keys, please open an issue in the GitHub repository\[link here\]. After verification, instructors can be added to the private repository that contains the assignment keys and grading references.
-
-# Assignment Description
-
 ## Getting to Know Team Antarctica!
 
 ### Purpose
diff --git a/modules/module_1/module1_home.qmd b/modules/module_1/module1_home.qmd
index 950533a..631c01d 100644
--- a/modules/module_1/module1_home.qmd
+++ b/modules/module_1/module1_home.qmd
@@ -21,7 +21,20 @@ By the end of this module, students will be able to:
 -   use functions and interpret function arguments in R
 -   use core `tidyverse` tools such as `select()`, `filter()`, `mutate()`, `summarize()`, and `group_by()` to explore and organize data
 
-## Instructor Downloads
+## Downloads
 
--   [Download Module 1 source files](https://github.com/BiodiversityDataScienceCorp/DataInTheWild_UA) on GitHub
--   Add more details or downloads here...?
\ No newline at end of file
+The files below are available for instructors and students. Instructor versions include teaching notes or answer content where applicable, while student versions are designed for classroom use and independent work.
+
+| Item | Instructor QMD | Student QMD | PDF | Data |
+|------------|------------|------------|------------|------------|
+| 1.1: Introduction to R and RStudio | [Download]() | [Download]() | [PDF]() |  |
+| 1.2: Intro to Coding in R | [Download]() | [Download]() | [PDF]() |  |
+| 1.3: Introduction to the `tidyverse` | [Download]() | [Download]() | [PDF]() | [CSV]() |
+| Assignment 1: Getting to Know the Team | [Download]() | [Download]() | [PDF]() | [CSV]() |
+| Assignment 2: Getting to Know Your Antarctic Home | [Download]() | [Download]() | [PDF]() | [CSV]() |
+
+## Download All Files
+
+-   [Download all Module 1 files (.zip)]()
+
+**Links are not yet live! Working on this!**
\ No newline at end of file
diff --git a/resources.qmd b/resources.qmd
index f37649b..e695048 100644
--- a/resources.qmd
+++ b/resources.qmd
@@ -1,12 +1,28 @@
 ---
-title: "Learn More!"
+title: "Resources"
 format: 
   html: 
     toc: false
 ---
 
-There are many external resources if you're interested in learning more about the R language, and any programming language really. Here, we have compiled a list of some places you might turn to for additional nuggets of knowledge.
+There are many resources available if you would like to keep building your skills in R, data science, and data visualization beyond this course. Here, we have compiled a list of some places you might turn to for additional nuggets of knowledge.
 
--   Jeff Oliver (one of the people who helped develop this course) holds workshops for both students and instructors at the University of Arizona and beyond. Here is one of his lesson sequences for novices in R: [learn-r (jcoliver.github.io)](https://jcoliver.github.io/learn-r/)
+## Learn More!
 
--   If you enjoy the customization of visualizations as much as I do, here is a good resource: [R CHARTS \| A collection of charts and graphs made with the R programming language (r-charts.com)](https://r-charts.com/)
+- [learn-r by Jeff Oliver](https://jcoliver.github.io/learn-r/)  
+  A helpful introductory lesson sequence in R developed by Jeff Oliver, one of the contributors to this course.
+
+- [Hello Data Science](https://hellodata.science/)  
+  A beginner-friendly resource designed to introduce students to data science concepts and tools in a clear, accessible way.
+
+- [R for Data Science](https://r4ds.hadley.nz/)  
+  A widely used open-access textbook for learning R, data wrangling, visualization, and analysis.
+
+- [The Carpentries](https://carpentries.org/)  
+  A well-known community focused on teaching foundational coding and data skills for research. Their workshops and teaching materials are especially helpful for beginners.
+
+- [From Data to Viz](https://www.data-to-viz.com/)  
+  A guide for choosing the right kind of visualization for your data.
+
+- [R Charts](https://r-charts.com/)  
+  A useful reference for creating and customizing charts and graphs in R.
\ No newline at end of file

From 8bc53e77493059d1a4f44a0a806e2d39402482f5 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Thu, 9 Apr 2026 09:36:12 -0700
Subject: [PATCH 12/15] add module overview pages, update module descriptions

---
 _quarto.yml                       |  32 ++++++++++++++++++++++
 index.qmd                         |  12 ++++-----
 instructors.qmd                   |  10 +------
 modules/module_1/module1_home.qmd |  24 ++++++++---------
 modules/module_2/module2_home.qmd |  42 +++++++++++++++++++++++++++++
 modules/module_3/module3_home.qmd |  37 +++++++++++++++++++++++++
 modules/module_4/module4_home.qmd |  43 ++++++++++++++++++++++++++++++
 modules/module_5/module5_home.qmd |  43 ++++++++++++++++++++++++++++++
 zipped_data/M1_data.zip           | Bin 30800208 -> 0 bytes
 zipped_data/M2_data.zip           | Bin 160145 -> 0 bytes
 zipped_data/M3_data.zip           | Bin 343306 -> 0 bytes
 zipped_data/M4_data.zip           | Bin 228592 -> 0 bytes
 zipped_data/final_proj_data.zip   | Bin 973204 -> 0 bytes
 13 files changed, 214 insertions(+), 29 deletions(-)
 create mode 100644 modules/module_2/module2_home.qmd
 create mode 100644 modules/module_3/module3_home.qmd
 create mode 100644 modules/module_4/module4_home.qmd
 create mode 100644 modules/module_5/module5_home.qmd
 delete mode 100644 zipped_data/M1_data.zip
 delete mode 100644 zipped_data/M2_data.zip
 delete mode 100644 zipped_data/M3_data.zip
 delete mode 100644 zipped_data/M4_data.zip
 delete mode 100644 zipped_data/final_proj_data.zip

diff --git a/_quarto.yml b/_quarto.yml
index 3a234fd..51e5be9 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -45,30 +45,62 @@ website:
 
       - section: "Module 2"
         contents:
+          - text: "Overview"
+            href: modules/module_2/module2_home.qmd
           - modules/module_2/module2_1.qmd
           - modules/module_2/module2_2.qmd
           - modules/module_2/module2_3.qmd
           - modules/module_2/module2_4.qmd
           - modules/module_2/module2_5.qmd
+          - text: "Assignment 1"
+            href: modules/module_2/assignments/a1/m2a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_2/assignments/a2/m2a2.qmd
+          - text: "Assignment 3"
+            href: modules/module_2/assignments/a3/m2a3.qmd
 
       - section: "Module 3"
         contents:
+          - text: "Overview"
+            href: modules/module_3/module3_home.qmd
           - modules/module_3/module3_1.qmd
           - modules/module_3/module3_2.qmd
           - modules/module_3/module3_3.qmd
+          - text: "Assignment 1"
+            href: modules/module_3/assignments/a1/m3a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_3/assignments/a2/m3a2.qmd
 
       - section: "Module 4"
         contents:
+          - text: "Overview"
+            href: modules/module_4/module4_home.qmd
           - modules/module_4/module4_1.qmd
           - modules/module_4/module4_2.qmd
           - modules/module_4/module4_3.qmd
           - modules/module_4/module4_4.qmd
+          - text: "Assignment 1"
+            href: modules/module_4/assignments/a1/m4a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_4/assignments/a2/m4a2.qmd
+          - text: "Assignment 3"
+            href: modules/module_4/assignments/a3/m4a3.qmd
+          - text: "Assignment 4"
+            href: modules/module_4/assignments/a4/m4a4.qmd
 
       - section: "Module 5"
         contents:
+          - text: "Overview"
+            href: modules/module_5/module5_home.qmd
           - modules/module_5/module5_1.qmd
           - modules/module_5/module5_2.qmd
           - modules/module_5/module5_3.qmd
+          - text: "Assignment 1"
+            href: modules/module_5/assignments/a1/m5a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_5/assignments/a2/m5a2.qmd
+          - text: "Assignment 3"
+            href: modules/module_5/assignments/a3/m5a3.qmd
 
       - text: "Final Project"
         href: modules/final_project/FinalProject.qmd
diff --git a/index.qmd b/index.qmd
index f78d759..94aa4b9 100644
--- a/index.qmd
+++ b/index.qmd
@@ -33,20 +33,18 @@ The team has arrived in Antarctica and needs to establish the technical foundati
 
 ### **Module 2: Good Food Gone Bad**
 
-A wave of food poisoning spreads through the colony, and students must determine what is making people sick. They use descriptive statistics and data visualization to investigate dietary and aquaculture data, beginning with basic plots in base R and then building toward more flexible visualizations in `ggplot2`. Students practice interpreting histograms, scatterplots, boxplots, and multiple-group comparisons, and they begin connecting patterns in the data to biological explanations. This module focuses on using visualization and summary statistics to explore evidence and support decision-making.
+A wave of food poisoning spreads through the Antarctic base, and the team needs to figure out why. Students investigate a potential food contamination event using descriptive statistics and data visualization to track down the culprit. Along the way, they are introduced to histograms, scatter plots, and box plots in both base R and `ggplot2`, and they develop an eye for what makes a visualization effective or misleading. By the end of the module, students have solved the problem and begun exploring a dataset of their own for the course's final project.
 
 ### **Module 3: Follow That Seal**
 
-Fishing operations are becoming dangerous because leopard seal activity is hard to track, and some radio collars appear to be failing. Students analyze collar data to compare manufacturers using t-tests and ANOVA, then use post-hoc comparisons to determine which groups differ from one another. They also learn how to interpret significance, p-values, and hypothesis tests in the context of a real ecological problem. This module builds students’ confidence with inferential statistics and group comparisons.
+With the aquaponics system compromised, the team must turn to wild-caught fish to supplement their food supply, but fishing in Antarctic waters comes with risks. Leopard seals are apex predators in the area, and the radio collars used to track them have been failing unexpectedly. Students are tasked with figuring out which collar manufacturer is to blame by comparing means numerically, visually, and statistically - first using t-tests to compare two groups, then extending to ANOVA and post-hoc comparisons when more than two groups are involved. By the end of the module, students can determine whether differences between groups are statistically meaningful and apply these tools to new ecological contexts.
 
 ### **Module 4: Roads and Regressions**
 
-As the colony expands, new infrastructure and equipment decisions depend on bringing multiple datasets together and modeling ecological relationships. Students learn how to combine datasets with joins and binds, then use K-Nearest Neighbors (KNN) to classify unlabeled seal collars. They also work with linear regression and multiple regression to investigate relationships between environmental variables and ecological responses, including habitat suitability and penguin nesting patterns. By the end of the module, students can integrate multiple sources of data, fit regression models, and interpret both main effects and interactions.
+With fishing bays selected and collars in need of replacement, the team faces two new challenges: identifying the makers of a mystery batch of collars, and planning a road to the fishing sites that minimizes harm to Antarctica's fragile ecosystem - including avoiding habitat for Antarctic hair grass and penguin nesting sites. Students learn to combine datasets using joins and binds, get an introduction to machine learning through K-Nearest Neighbors classification, and dive into linear and multiple regression to model environmental relationships. By the end of the module, students can work with data from multiple sources, fit regression models, and interpret their results.
 
 ### **Module 5: Sustainable Fishing**
 
-The final module highlights how the core themes of the course can be brought together and shaped by a particular disciplinary perspective. In this version, Module 5 was developed and taught by Dr. Ellen Bledsoe, whose training as an ecologist informed its focus on population growth, carrying capacity, and sustainable fisheries management in Antarctica.
+With fishing bays selected and roads planned, the team must now figure out how many fish they can sustainably harvest without depleting the population. Students are introduced to population growth models - exponential and logistic - and use them to estimate carrying capacities and maximum sustainable yield for Antarctic toothfish populations. They then learn to fit logistic growth curves to real data and compare populations statistically. Along the way, students also step outside of R to practice finding and citing scientific literature and writing a scientific abstract in preparation for the final project.
 
-Students explore exponential and logistic population growth, interpret intrinsic rate of increase and carrying capacity, estimate carrying capacity from logistic models, calculate maximum sustainable yield, and compare fish populations visually and statistically to determine which populations are the best candidates for harvest. The module draws together many of the skills developed earlier in the course, including visualization, statistical analysis, modeling, and ecological interpretation.
-
-At the same time, Module 5 is intended as an example of how Data in the Wild can be specialized. The course can be taught with or without this module, and the broader structure can be adapted to reflect other disciplines, research questions, or instructor expertise while still building from the same data science foundation.
\ No newline at end of file
+> **Note:** Module 5 is an example of how *Data in the Wild* can be extended or adapted to reflect a specific disciplinary focus. This version was developed by Dr. Ellen Bledsoe and draws on their background in ecology. The course can be taught with or without this module, and instructors are encouraged to develop alternatives that reflect their own expertise or research context.
\ No newline at end of file
diff --git a/instructors.qmd b/instructors.qmd
index f3a0095..dc6624a 100644
--- a/instructors.qmd
+++ b/instructors.qmd
@@ -31,15 +31,7 @@ That said, the materials can also be taught in local RStudio or adapted to other
 
 Lessons, assignments, datasets, and other course materials are available in the public GitHub repository.
 
-## Assignments
-
-### Using the Expected Outputs
-
-The expected outputs document is provided as an additional resource for students as they work through the assignment. It is intended to help students check that their code is producing the expected results.
-
-This document is **not** an answer key. It does not provide the code needed to generate those results. Instead, it gives students a way to verify their work while still doing the problem-solving themselves.
-
-### Answer Keys for Instructors
+## Answer Keys for Instructors
 
 Assignment answer keys are available to instructors, but they are not included in the public course materials.
 
diff --git a/modules/module_1/module1_home.qmd b/modules/module_1/module1_home.qmd
index 631c01d..5f9c7bc 100644
--- a/modules/module_1/module1_home.qmd
+++ b/modules/module_1/module1_home.qmd
@@ -7,7 +7,7 @@ format:
 
 ## Overview
 
-The team has arrived in Antarctica and needs to establish the technical foundation for all future data work. Students begin by learning how to use R and RStudio in Posit Cloud, including how to run code, create objects, work with vectors, understand data classes, subset data, and use functions. They are also introduced to the `tidyverse`, where they begin learning how to select, filter, summarize, group, and mutate data. By the end of the module, students are comfortable navigating their coding environment and working with simple ecological datasets.
+The team has arrived in Antarctica and needs to establish the technical foundation for all future data work. Students begin by learning how to use R and RStudio in Posit Cloud, including how to run code, create objects, work with vectors, understand data classes, subset data, and use functions. They are also introduced to the tidyverse, where they begin learning how to select, filter, summarize, group, and mutate data. By the end of the module, students are comfortable navigating their coding environment and working with simple ecological datasets.
 
 ## Learning Objectives
 
@@ -21,20 +21,18 @@ By the end of this module, students will be able to:
 -   use functions and interpret function arguments in R
 -   use core `tidyverse` tools such as `select()`, `filter()`, `mutate()`, `summarize()`, and `group_by()` to explore and organize data
 
-## Downloads
+## Lessons & Assignments
 
-The files below are available for instructors and students. Instructor versions include teaching notes or answer content where applicable, while student versions are designed for classroom use and independent work.
+Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
 
-| Item | Instructor QMD | Student QMD | PDF | Data |
-|------------|------------|------------|------------|------------|
-| 1.1: Introduction to R and RStudio | [Download]() | [Download]() | [PDF]() |  |
-| 1.2: Intro to Coding in R | [Download]() | [Download]() | [PDF]() |  |
-| 1.3: Introduction to the `tidyverse` | [Download]() | [Download]() | [PDF]() | [CSV]() |
-| Assignment 1: Getting to Know the Team | [Download]() | [Download]() | [PDF]() | [CSV]() |
-| Assignment 2: Getting to Know Your Antarctic Home | [Download]() | [Download]() | [PDF]() | [CSV]() |
+-   [1.1: Introduction to R and RStudio](module1_1.qmd)
+-   [1.2: Intro to Coding in R](module1_2.qmd)
+-   [1.3: Introduction to the `tidyverse`](module1_3.qmd)
+-   [Assignment 1: Getting to Know the Team](assignments/a1/m1a1.qmd)
+-   [Assignment 2: Getting to Know Your Antarctic Home](assignments/a2/m1a2.qmd) Another assignment?
 
-## Download All Files
+## GitHub
 
--   [Download all Module 1 files (.zip)]()
+To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
 
-**Links are not yet live! Working on this!**
\ No newline at end of file
+**Links are not yet live!**
\ No newline at end of file
diff --git a/modules/module_2/module2_home.qmd b/modules/module_2/module2_home.qmd
new file mode 100644
index 0000000..f03a1e6
--- /dev/null
+++ b/modules/module_2/module2_home.qmd
@@ -0,0 +1,42 @@
+---
+title: "Module 2: Good Food Gone Bad!"
+format:
+  html:
+    toc: false
+---
+
+## Overview
+
+A wave of food poisoning spreads through the Antarctic base, and the team needs to figure out why. Students investigate a potential food contamination event using descriptive statistics and data visualization to track down the culprit. Along the way, they are introduced to histograms, scatter plots, and box plots in both base R and `ggplot2`, and they develop an eye for what makes a visualization effective or misleading. By the end of the module, students have solved the problem and begun exploring a dataset of their own for the course's final project.
+
+## Learning Objectives
+
+By the end of this module, students will be able to:
+
+-   distinguish between descriptive and inferential statistics and explain when each is appropriate
+-   calculate and interpret measures of central tendency and variation, including mean, median, and standard deviation
+-   create histograms and scatter plots using base R syntax
+-   build plots iteratively in `ggplot2` using `ggplot()`, `aes()`, geom functions, `labs()`, and `theme()` functions
+-   select an appropriate plot type based on the number and types of variables being visualized
+-   create histograms, multiple histograms, scatter plots, and box-and-whisker plots in `ggplot2`
+-   evaluate the effectiveness of a data visualization and identify common pitfalls
+-   write a research question with a clearly defined dependent and independent variable
+
+## Lessons & Assignments
+
+Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+
+-   [2.1: Good Food Gone Bad](module2_1.qmd)
+-   [2.2: Plotting with `ggplot2`](module2_2.qmd)
+-   [2.3: Data Visualization](module2_3.qmd)
+-   [2.4: Exploring `geom` Functions](module2_4.qmd)
+-   [2.5: Module 2 Wrap-Up](module2_5.qmd)
+-   Assignment 1: Scatter Plots
+-   Assignment 2: From Data to Plot
+-   Assignment 3: Final Project Kickoff
+
+## GitHub
+
+To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
+
+**Links are not yet live!**
\ No newline at end of file
diff --git a/modules/module_3/module3_home.qmd b/modules/module_3/module3_home.qmd
new file mode 100644
index 0000000..8b06a1d
--- /dev/null
+++ b/modules/module_3/module3_home.qmd
@@ -0,0 +1,37 @@
+---
+title: "Module 3: Follow That Seal"
+format:
+  html:
+    toc: false
+---
+
+## Overview
+
+With the aquaponics system compromised, the team must turn to wild-caught fish to supplement their food supply, but fishing in Antarctic waters comes with risks. Leopard seals are apex predators in the area, and the radio collars used to track them have been failing unexpectedly. Students are tasked with figuring out which collar manufacturer is to blame by comparing means numerically, visually, and statistically, first using t-tests to compare two groups, then extending to ANOVA and post-hoc comparisons when more than two groups are involved. By the end of the module, students can determine whether differences between groups are statistically meaningful and apply these tools to new ecological contexts.
+
+## Learning Objectives
+
+By the end of this module, students will be able to:
+
+-   explain the logic of hypothesis testing, including null and alternative hypotheses
+-   distinguish between independent and dependent variables and identify each in a research question
+-   determine when a t-test versus an ANOVA is the appropriate statistical test
+-   run a t-test using the `t.test()` function and interpret the output, including t-value, p-value, and confidence interval
+-   run a one-way ANOVA using the `aov()` and `summary()` functions and interpret the F-value and p-value
+-   run a Tukey's HSD post hoc test using `TukeyHSD()` and interpret pairwise comparisons
+
+## Lessons & Assignments
+
+Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+
+-   [3.1: Leopard Seals](module3_1.qmd)
+-   [3.2: T-Tests](module3_2.qmd)
+-   [3.3: Comparing (Multiple) Means](module3_3.qmd)
+-   Assignment 1: Comparing Two Means
+-   Assignment 2: Comparing Multiple Means
+
+## GitHub
+
+To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
+
+**Links are not yet live!**
\ No newline at end of file
diff --git a/modules/module_4/module4_home.qmd b/modules/module_4/module4_home.qmd
new file mode 100644
index 0000000..d400d62
--- /dev/null
+++ b/modules/module_4/module4_home.qmd
@@ -0,0 +1,43 @@
+---
+title: "Module 4: Roads and Regressions"
+format:
+  html:
+    toc: false
+---
+
+## Overview
+
+With fishing bays selected and collars in need of replacement, the team faces two new challenges: identifying the makers of a mystery batch of collars, and planning a road to the fishing sites that minimizes harm to Antarctica's fragile ecosystem - including avoiding habitat for Antarctic hair grass and penguin nesting sites. Students learn to combine datasets using joins and binds, get an introduction to machine learning through K-Nearest Neighbors classification, and dive into linear and multiple regression to model environmental relationships. By the end of the module, students can work with data from multiple sources, fit regression models, and interpret their results.
+
+## Learning Objectives
+
+By the end of this module, students will be able to:
+
+-   combine data frames using binding and joining operations in `tidyverse`
+-   explain the basic concept of K-Nearest Neighbors (KNN) as a classification algorithm
+-   describe what a model is and how it can be used to understand and predict relationships between variables
+-   run and interpret a simple linear regression using `lm()` and `summary()`
+-   interpret regression output including the slope, intercept, R-squared value, and p-value
+-   add a regression line to a `ggplot2` scatter plot using `geom_smooth()`
+-   explain when and why multiple regression is preferable to simple linear regression
+-   run and interpret a multiple regression with more than one independent variable
+-   select the appropriate statistical test for a given research question and dataset
+
+## Lessons & Assignments
+
+Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+
+-   [4.1: Combining Datasets (Joins & Binds)](module4_1.qmd)
+-   [4.2: K-Nearest Neighbor](module4_2.qmd)
+-   [4.3: Roads and Regressions](module4_3.qmd)
+-   [4.4: Multiple Regression](module4_4.qmd)
+-   Assignment 1: Linear Regression
+-   Assignment 2: More Regressions
+-   Assignment 3: Multiple Regressions
+-   Assignment 4: Final Project Practice
+
+## GitHub
+
+To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
+
+**Links are not yet live!**
\ No newline at end of file
diff --git a/modules/module_5/module5_home.qmd b/modules/module_5/module5_home.qmd
new file mode 100644
index 0000000..f035286
--- /dev/null
+++ b/modules/module_5/module5_home.qmd
@@ -0,0 +1,43 @@
+---
+title: "Module 5: Sustainable Fishing"
+format:
+  html:
+    toc: false
+---
+
+## Overview
+
+With fishing bays selected and roads planned, the team must now figure out how many fish they can sustainably harvest without depleting the population. Students are introduced to population growth models - exponential and logistic - and use them to estimate carrying capacities and maximum sustainable yield for Antarctic toothfish populations. They then learn to fit logistic growth curves to real data and compare populations statistically. Along the way, students also step outside of R to practice finding and citing scientific literature and writing a scientific abstract in preparation for the final project.
+
+> **Note:** Module 5 is an example of how *Data in the Wild* can be extended or adapted to reflect a specific disciplinary focus. This version was developed by Dr. Ellen Bledsoe and draws on her background in ecology. The course can be taught with or without this module, and instructors are encouraged to develop alternatives that reflect their own expertise or research context.
+
+## Learning Objectives
+
+By the end of this module, students will be able to:
+
+-   explain the difference between exponential and logistic population growth and identify each from a graph
+-   define carrying capacity (`K`) and intrinsic rate of increase (`r`) and use them in population growth calculations
+-   calculate maximum sustainable yield from population growth parameters
+-   fit a logistic growth model to population data using the `drc` package
+-   overlay a fitted logistic growth curve onto a `ggplot2` scatter plot
+-   compare multiple populations statistically using tools from previous modules
+-   find and evaluate scientific literature using AI-assisted search tools
+-   summarize scientific articles and integrate them into written work
+-   write a scientific abstract for a research question using supporting literature
+
+## Lessons & Assignments
+
+Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+
+-   [5.1: Population Growth](module5_1.qmd)
+-   [5.2: Sustainable Fishing](module5_2.qmd)
+-   [5.3: Comparing Populations](module5_3.qmd)
+-   Assignment 1: Scientific Literature & Abstract Writing *(writing assignment)*
+-   Assignment 2: Choosing a Bay to Fish
+-   Assignment 3: Comparing Populations
+
+## GitHub
+
+To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
+
+**Links are not yet live!**
\ No newline at end of file
diff --git a/zipped_data/M1_data.zip b/zipped_data/M1_data.zip
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From 5edee7fa15c1036db5bf532dac20380a63ee86a7 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Mon, 20 Apr 2026 15:05:42 -0700
Subject: [PATCH 13/15] created student/intsructor view for lessons, updated
 page for instructors, updated readme and index page w grant info

---
 README.md                      |   4 +-
 _quarto.yml                    |   1 +
 index.qmd                      |   2 +-
 instructors.qmd                |  95 ++++++++++++++---
 modules/module_1/module1_1.qmd |  42 ++++++--
 modules/module_1/module1_2.qmd |  32 ++++--
 modules/module_1/module1_3.qmd |  71 +++++++++++--
 modules/module_2/module2_1.qmd |  50 +++++++--
 scripts/toggle-view.js         |  76 ++++++++++++++
 styles.scss                    | 181 ++++++++++++++++++++++++++-------
 10 files changed, 464 insertions(+), 90 deletions(-)
 create mode 100644 scripts/toggle-view.js

diff --git a/README.md b/README.md
index 68f44b6..221f1db 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ Data in the Wild introduces students to data science through the R programming l
 
 The course is designed for students in the life sciences and assumes no prior programming experience. Lessons begin with the basics of coding in R and gradually build toward more advanced tools such as hypothesis testing, machine learning, regression, and population modeling.
 
-The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and has been taught at the University of Arizona as part of the general education curriculum.
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe, and has been taught at the University of Arizona as part of the general education curriculum.
 
 ## Accessing Course Materials
 
@@ -59,7 +59,7 @@ quarto preview
 
 ## Support
 
-This course was developed with support from the National Science Foundation under the Harnessing the Data Revolution (HDR) Data Science Corps (DSC) program. Materials are openly available and designed to be adaptable for use at other institutions.
+This course was developed with support from the National Science Foundation under the Harnessing the Data Revolution (HDR) Data Science Corps (DSC) program, and the NSF-funded project "Building Capacity in Data Science through Biodiversity, Conservation, and General Education" (Awards [2122967](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122967) and [2122991](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122991)). Materials are openly available and designed to be adaptable for use at other institutions.
 
 ------------------------------------------------------------------------
 
diff --git a/_quarto.yml b/_quarto.yml
index 51e5be9..6cb0f5a 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -130,5 +130,6 @@ format:
     include-in-header:
       text: |
         <script src="/scripts/pdf-button.js"></script>
+        <script src="/scripts/toggle-view.js"></script>
 
 editor: visual
diff --git a/index.qmd b/index.qmd
index 94aa4b9..b3e3052 100644
--- a/index.qmd
+++ b/index.qmd
@@ -12,7 +12,7 @@ format:
 
 The course is designed for students in the life sciences and assumes no prior programming experience. Lessons begin with the basics of coding in R and gradually build toward more advanced tools such as hypothesis testing, machine learning, regression, and population modeling.
 
-The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe with support from the NSF Harnessing the Data Revolution program, and has been taught at the University of Arizona as part of the general education curriculum.
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe, and has been taught at the University of Arizona as part of the general education curriculum. Funding comes from the NSF-funded project "Building Capacity in Data Science through Biodiversity, Conservation, and General Education" (Awards [2122967](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122967) and [2122991](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122991)).
 
 ## Learning Objectives
 
diff --git a/instructors.qmd b/instructors.qmd
index dc6624a..1731eef 100644
--- a/instructors.qmd
+++ b/instructors.qmd
@@ -2,37 +2,100 @@
 title: "For Instructors"
 format:
   html:
-    toc: false
+    toc: true
 ---
 
-This page is for instructors who are interested in teaching with, adapting, or learning more about *Data in the Wild*.
+# Instructor Resources
 
-The course was designed to make data science more accessible, applied, and meaningful for students in the life sciences. While the version shared here is rooted in ecological questions and a narrative set in Antarctica, the broader structure is flexible and can be adapted to other disciplines, datasets, and teaching contexts.
+This page is for instructors who are interested in teaching with, adapting, or learning more about *Data in the Wild*. It covers how to navigate the site as an instructor, how to set up Posit Cloud for your class, and how to access source files and answer keys.
 
-## Context
+## Using This Site as an Instructor
 
-The course has been taught at the University of Arizona as part of the general education curriculum, and the materials shared here are openly available so that others can use and adapt them in ways that fit their own classrooms.
+The lesson pages on this site have two modes: **Student View** and **Instructor View**. Student View is the default, coding challenge answers and instructor notes are hidden. Instructor View reveals both!
+
+Look for the **View: Student** | **Instructor** toggle in the upper-right sidebar on any lesson page (and this page!).
+
+::: {.instructor-only}
+## Instructor View reveals...
+
+-   **Answers** to all in-class coding challenges, shown after each prompt
+-   **Instructor notes** with suggested talking points, teaching tips
+
+You can also use the **Download PDF** button (also in the right sidebar) to print either version.
+:::
 
 ## R, RStudio, and Posit Cloud
 
-This course uses the R programming language and was taught using Posit Cloud.
+This course uses the R programming language and was designed to be taught using Posit Cloud.
 
-A quick distinction:
+A quick distinction for those new to the ecosystem:
 
 -   **R** is the programming language itself.
--   **RStudio** is a coding environment used to write and run R code.
--   **Posit Cloud** is a browser-based version of that environment, which lets students work online without needing to install software on their own computers.
+-   **RStudio** is the coding environment used to write and run R code.
+-   **Posit Cloud** is a browser-based version of RStudio that lets students work online without installing anything on their own computers.
+
+The materials can also be taught using a local RStudio installation or adapted to other environments, though some file path references in the lessons may need updating.
+
+### Setting Up Posit Cloud for Your Class
+
+**1. Create your instructor workspace**
+
+Sign in to [Posit Cloud](https://posit.cloud) and create a new workspace. This will be the shared space your students join.
+
+**2. Set up the base project**
+
+Inside your workspace, create a new project. Add the course modules from Github\[\].
+
+**3. Invite students to the workspace**
+
+In your workspace settings, go to **Members** and share the join link with your students. Students will need to create a free Posit Cloud account if they don't have one. Lesson 1.1 gives students time to do this!
+
+**4. Students copy the project**
+
+Once in the workspace, students can see your base project but cannot edit it directly. They click **Save a Permanent Copy** to create their own editable version in the workspace. All their work lives in their copy.
+
+::: callout-note
+The free tier of Posit Cloud has limits on compute hours per month. For a full-semester course with active coding, consider whether your institution qualifies for an academic plan, or check current pricing at [posit.cloud/plans](https://posit.cloud/plans).
+:::
+
+## Access Course Files
+
+> [\[GitHub repo\]](https://github.com/BiodiversityDataScienceCorp)
+
+Contains:
+
+-   All lesson `.qmd` files (instructor and student versions)
+-   All assignment `.qmd` files (student versions)
+-   All datasets (`.csv`) used in lessons and assignments
+-   Expected outputs (`.pdf`) for assignments that include them
+
+This is the repo to clone if you want to run or adapt the course. You are welcome to fork or adapt any of these materials (under a certain license??)
+
+## For Instructors: Assignment Keys & the Final Project
+
+The private repository contains everything in the course repo plus:
+
+-   Answer keys for all assignments
+-   Final Project assignment, rubric, and key
+
+To request access, open an issue on the public course repository and we will add you as a collaborator. We review requests manually and typically respond within a few business days.
+
+Link here \*\*
+
+::: callout-note
+You will need a free [GitHub account](https://github.com/join) to submit a request and access the private repository.
+:::
 
-For this course, Posit Cloud was especially useful because it gave students a shared, consistent workspace and lowered the barrier to getting started. That was particularly helpful in an introductory course where many students were new to coding.
+## Expected Outputs
 
-That said, the materials can also be taught in local RStudio or adapted to other environments.
+Some assignments include expected outputs: the tables, plots, or values a correct solution should produce. These are provided for students to reference while completing their assignments.
 
-## Source Files - Download on GitHub
+This was a deliberate design choice. Data science and coding problems often have more than one path to the correct answer. Showing students what a correct answer looks like without showing how to get there preserves that open-ended problem-solving while reducing unnecessary point loss.
 
-Lessons, assignments, datasets, and other course materials are available in the public GitHub repository.
+The actual graded work, the code that produces those outputs, is what appears in the answer keys in the private repository.
 
-## Answer Keys for Instructors
+**Website source**:
 
-Assignment answer keys are available to instructors, but they are not included in the public course materials.
+> Quarto site[\[DataInTheWild-Website placeholder\]](https://github.com/BiodiversityDataScienceCorp)
 
-If you are an instructor and would like access to the answer keys, please open an issue in the GitHub repository \[add link here when repo is set up\]. This repository contains the assignment keys and grading references.
\ No newline at end of file
+Contains `_quarto.yml`, `styles.scss`, scripts, images, and all non-lesson pages. Fork this if you want to adapt your own website for a course!
\ No newline at end of file
diff --git a/modules/module_1/module1_1.qmd b/modules/module_1/module1_1.qmd
index b25daf2..e980c5a 100644
--- a/modules/module_1/module1_1.qmd
+++ b/modules/module_1/module1_1.qmd
@@ -24,12 +24,19 @@ knitr::opts_chunk$set(echo = TRUE)
 
 Let's all sign-up for [Posit Cloud!](https://posit.cloud/) We use Posit Cloud as our coding environment so everyone has access to R and RStudio through a web browser.
 
-## Rstudio Mini-tour
+## RStudio Mini-tour
 
-Perform a live-coding mini-tour of RStudio.
+Let's start with a brief live demo of RStudio. As you follow along, pay attention to:
 
 1.  What do the four main panels allow you to do?
-2.  Console (runs immediately) versus a script/R Markdown file (allows you to save and organize your work.)
+2.  The difference between the **Console** (runs code immediately) versus a **script or Quarto file** (lets you save and organize your work).
+
+::: instructor-only
+**Instructor Note:** Perform a live-coding mini-tour using your own Posit Cloud project so students see a realistic setup. Cover:
+
+1.  Walk through each panel: script/editor (top-left), console (bottom-left), environment/history (top-right), files/plots/help (bottom-right).
+2.  Demonstrate running code in the Console vs. a chunk in a `.qmd` file, emphasizing that the Console doesn't save your work.
+:::
 
 ## Using R as a Calculator
 
@@ -39,16 +46,16 @@ You can do basic math in the Console (the bottom left part of the screen). The C
 
 Try multiplying 5 and 3 in the Console (hint: \* means multiply). Hit `Enter` to run that line of code.
 
-### Rmarkdown and Code Chunks
+### Quarto Documents and Code Chunks
 
-Rmarkdown (`.Rmd`) is a file format that lets us incorporate text and code into one document seamlessly. In fact, it is the file format for this document!
+Quarto (`.qmd`) is a file format that lets us incorporate text and code into one document seamlessly. In fact, it is the file format for this document!
 
 -   For writing text, you can type as you normally would.
 -   Code chunks are a bit different:
     -   Near the top right of your screen you can toggle between viewing this document in "Source" or "Visual" mode.
-    -   If you view this document under "Source," you will see that all code chunks are sandwiched between ```` ```{r} ```` and ```` ``` ````.
-    -   Under "Visual," you can type R code in the lines under `{r}`.
-    -   To include text in chunks, you will need to put a `#` in front. R will not read anything in the line after `#` as code.
+    -   In **Source** view, all code chunks are sandwiched between ```` ```{r} ```` and ```` ``` ````.
+    -   In **Visual** view, you type R code in the lines beneath `{r}`.
+    -   To add a comment (a note R won't run as code), put a `#` in front of the line.
 
 Code chunks look like this:
 
@@ -62,6 +69,10 @@ You can also run one or a few lines of code at a time by having your cursor on t
 
 A quick shortcut for adding a code chunk is `Ctrl + Alt + i` (`Cmd + Opt + i` on a Mac). Alternatively, you can go to Code \> Insert Chunk.
 
+::: instructor-only
+**Instructor Note:** Pause here and make sure everyone can toggle between Source and Visual mode and can run a chunk.
+:::
+
 ### Some Code Chunk Practice
 
 Let's work with an example code chunk.
@@ -78,7 +89,7 @@ Let's work with an example code chunk.
 3 * 5        # multiplication
 ```
 
-Notice that if you run code in the Console (below) rather than the code chunk in a `.Rmd` file, you don't need to add the `{r}` to tell it that you are typing R code. The Console only understands R code anyway, so we don't need to tell it what it is!
+Notice that if you run code in the Console rather than the code chunk in a `.qmd` file, you don't need to add the `{r}` to tell it that you are typing R code. The Console only understands R code anyway, so we don't need to tell it what it is!
 
 ### Math: Small Group Challenge
 
@@ -88,6 +99,17 @@ First, write and run the code in the console.
 
 Next, write and run the code in the code chunk below. What did you have to do differently?
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 2^3
-```
\ No newline at end of file
+```
+
+**Instructor Note:** The key difference students should notice is that in the Console you just type R code directly, while in a `.qmd` chunk you need the ```` ```{r} ```` wrapper.
+:::
\ No newline at end of file
diff --git a/modules/module_1/module1_2.qmd b/modules/module_1/module1_2.qmd
index a078461..f0d0505 100644
--- a/modules/module_1/module1_2.qmd
+++ b/modules/module_1/module1_2.qmd
@@ -16,7 +16,7 @@ knitr::opts_chunk$set(echo = TRUE)
 ## Learning Outcomes
 
 -   Students will be able to define the following terms: object, assignment, vector, function, data frame
--   Students will be able to run code line-by-line and as code chunks from an Rmarkdown file.
+-   Students will be able to run code line-by-line and as code chunks from a Quarto file.
 -   Students will be able to write code assigning values to variables and use these variables to perform various operations.
 -   Students will be able to recall and explain how functions operate, and the basic syntax around functions (arguments, auto-completion, parentheses).
 -   Students will be able to differentiate different data classes in R.
@@ -147,8 +147,9 @@ round(weight_kg_mean, digits = 2) # round to 2 digits past 0
 
 To get more information about a function, use the `help()` function or `?name_of_function`.
 
-```{r, eval=FALSE}
-help(round) # or type ?help
+```{r}
+#| eval: false
+help(round) # or type ?round
 ```
 
 We can use a function called `class()` to figure out the data type of a vector.
@@ -166,7 +167,13 @@ Let's practice! Write a few lines of code that do the following:
 -   subset `six_to_one` to include the last 3 numbers (should include 3, 2, 1)
 -   find the sum of the numbers (hint: use the `sum()` function)
 
-Answer: 6
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:** 6
 
 ```{r}
 six_to_one <- c(6, 5, 4, 3, 2, 1)
@@ -186,9 +193,9 @@ sum(last_three)
 Already finished? See if you can condense your code down any further or turn around and help out a neighbor.
 
 ```{r}
-six_to_one <- seq(6, 1)
 sum(six_to_one[4:6])
 ```
+:::
 
 ## 2-Dimensional Data: Data Frames
 
@@ -277,19 +284,30 @@ plants_no_nitrogen <- plants[plants$nitrogen == "N", ]
 
 As a group, find the standard deviation (`sd()`) of the height of plants treated with nitrogen and those not treated with nitrogen. Which group has the larger standard deviation? Any ideas what that means?
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 sd(plants[plants$nitrogen == "Y", 1])
 sd(plants[plants$nitrogen == "N", 1])
 ```
 
+**Instructor Note:** The **no-nitrogen group** has the larger standard deviation. Discuss: a larger SD means more variability in plant heights among untreated plants. Ask students why that might be? Without nitrogen, other factors like light or water availability may drive more variation in growth outcomes. This is a preview of the statistical thinking they will develop in later modules!
+:::
+
 ## Helpful Functions
 
 Below are some functions that I often find very helpful when working with vectors and data frames:
 
 -   `str()`: shows the structure of the object (e.g., rows and columns)
--   `head()` and `tail()`: shows the first six and last six rows, respectfully
+-   `head()` and `tail()`: shows the first six and last six rows, respectively
 -   `length()`: counts the number of elements in an object
--   `ncol()` and `nrow()`: counts the number of columns or rows, respectfully
+-   `ncol()` and `nrow()`: counts the number of columns or rows, respectively
 -   `names()`: shows the names of the columns in a data frame
 -   `unique()`: shows one of each element in an object (removes duplicate values)
 
diff --git a/modules/module_1/module1_3.qmd b/modules/module_1/module1_3.qmd
index 2a88861..c2f0dce 100644
--- a/modules/module_1/module1_3.qmd
+++ b/modules/module_1/module1_3.qmd
@@ -18,7 +18,7 @@ knitr::opts_chunk$set(echo = TRUE)
 -   Students will be able to load packages.
 -   Students will be able to use some functions of the tidyverse: select, filter, the pipe, mutate, summarize, and group by.
 -   Students will compare and contrast base R and tidyverse methodology for sub-setting data frames.
--   Students will able to use tidyverse functions to summarize real-world data.
+-   Students will be able to use tidyverse functions to summarize real-world data.
 
 ## The `tidyverse`: What is it?
 
@@ -88,7 +88,6 @@ To learn about the `tidyverse` syntax, we're going to use a real data set on cli
 # read in the data file
 
 # `read_csv()` is part of the `tidyverse` and gives us nice options when reading in data
-# your file path should only be read_csv("global_temps.csv")
 climate_df <- read_csv("data/global_temps.csv")
 
 # Let's take a look at the climate data. 
@@ -139,9 +138,18 @@ Note that we haven't put any column names in quotations. Using the `tidyverse`,
 
 Write a line of code to select the following data from the `climate_df`: average temperature, latitude and longitude
 
+```{r}
+# Write your code here
+
+```
+
+::: {.instructor-only}
+**Answer:**
+
 ```{r}
 select(climate_df, AverageTemperature, Latitude, Longitude)
 ```
+:::
 
 It is important to remember that the computer interprets everything literally. We need to tell the function the **exact** names of the columns. R will interpret `latitude` and `Latitude` as different things; it doesn't know that they are probably the same!
 
@@ -183,12 +191,22 @@ Work with the climate data we've been using this class period. Construct a small
 3.  Name this new data frame "cold_paris"
 
 ```{r}
-# not piped
+# Write your code here
+
+```
+
+::: {.instructor-only}
+**Answer:**
+
+```{r}
 slim <- select(climate_df, dt, AverageTemperature, City)
 filtered <- filter(slim, City == "Paris", AverageTemperature < 22)
 cold_paris <- filtered
 ```
 
+**Instructor Note:** Students haven't learned the pipe yet so the unpiped version is correct here. 
+:::
+
 ## The Pipe `%>%`
 
 You can use the pipe operator to chain `tidyverse` functions together.
@@ -220,12 +238,23 @@ In small groups, use pipes to create a new data frame called `warm_nigeria` that
 -   the columns AverageTemperature, City, Country
 -   only rows for the country Nigeria and temperatures that are greater than 30 degrees
 
+```{r}
+# Write your code here
+
+```
+
+::: {.instructor-only}
+**Answer:**
+
 ```{r}
 warm_nigeria <- climate_df %>% 
   select(AverageTemperature, City, Country) %>% 
   filter(Country == "Nigeria", AverageTemperature > 30)
 ```
 
+**Instructor Note:** A common mistake here is putting `filter()` before `select()` and then getting an error because the pipe tries to filter on `Country` after it has already been dropped. If students hit this, it's a great moment to learn about the order of operations in a pipe chain.
+:::
+
 ## Turning Data into Information with `summarize()`
 
 Like we have talked about in previous classes, some of the best ways for us to understand our data is through what we call summary statistics such as the mean (average), standard deviation, minimums, maximums, etc. Fortunately, the `tidyverse` has a handy-dandy function to make this easy to do with data frames.
@@ -269,7 +298,7 @@ One common way we analyze data is through something we call the "split, apply, c
 
 The `group_by()` function lets us do this. It is most often used in combination with `summarize()`.
 
-For example, we can use this method to calculate the mean temperatures of **each** country instead of the overall mean of the entire dataset. In order to do these, we create groups in the data based on the country.
+For example, we can use this method to calculate the mean temperatures of **each** country instead of the overall mean of the entire dataset. In order to do this, we create groups in the data based on the country.
 
 ```{r}
 climate_df %>% 
@@ -280,10 +309,18 @@ climate_df %>%
 
 ### Let's practice!
 
-Practice using the combination of `group_by()` and `summarize()` to calculate the minimum (`min()`) and maximum (`max()`) average temperatures for each city. Save this data frame as `city_min_max`
+Practice using the combination of `group_by()` and `summarize()` to calculate the minimum (`min()`) and maximum (`max()`) average temperatures for each city. Save this data frame as `city_min_max`.
 
 ```{r}
-climate_df %>% 
+# Write your code here
+
+```
+
+::: {.instructor-only}
+**Answer:**
+
+```{r}
+city_min_max <- climate_df %>% 
   group_by(City) %>% 
   summarize(min_temp = min(AverageTemperature, na.rm = TRUE),
             max_temp = max(AverageTemperature, na.rm = TRUE))
@@ -298,13 +335,16 @@ climate_df %>%
             max_temp = max(AverageTemperature, na.rm = TRUE))
 ```
 
+**Instructor Note:** The bonus challenge works because `group_by()` can take multiple columns. Students often try to add `select(Country)` somewhere in the pipe instead, which either drops columns they need or doesn't work as expected.
+:::
+
 ## Creating New Columns with `mutate()`
 
 Sometimes our data frame doesn't have our data points in exactly the format we want. For example, we might want our temperature data in Fahrenheit instead of Celsius.
 
-The `tidyverse` has a function called `mutate()` that lets us create a new column. Often, we want to apply a function to the entire column or perform some type of calculation, such as converting temp from F to C.
+The `tidyverse` has a function called `mutate()` that lets us create a new column. Often, we want to apply a function to the entire column or perform some type of calculation, such as converting temperature from Celsius to Fahrenheit.
 
-Like the `summarize()` function, The first part of the argument in the `mutate` function (before the `=`) is the name of the new column we want to create (or, sometime, the name of a column we want to overwrite). After the `=` is code producing the values we want the new column to contain.
+Like the `summarize()` function, the first part of the argument in the `mutate` function (before the `=`) is the name of the new column we want to create (or, sometimes, the name of a column we want to overwrite).
 
 | `mutate(new_column_name = code_producing_values_for_the_new_column)`
 
@@ -319,11 +359,22 @@ climate_df %>%
 
 ### Let's Practice
 
-Write some code that uses the `round()` function to create a new column of average temperatures (Celsius) with only point value after the decimal point.
+Write some code that uses the `round()` function to create a new column of average temperatures (Celsius) rounded to one decimal place.
+
+```{r}
+# Write your code here
+
+```
+
+::: {.instructor-only}
+**Answer:**
 
 ```{r}
 climate_df %>% 
   mutate(AverageTemperatureRound = round(AverageTemperature, digits = 1))
 ```
 
-If we set the "new" column name in the `mutate()` function as the same name of a column that already exists, the code with replace that column with the new values.
\ No newline at end of file
+**Instructor Note:** Students may try `round(AverageTemperature, 1)` without the `digits =` argument name, that also works.
+:::
+
+If we set the "new" column name in the `mutate()` function as the same name of a column that already exists, the code will replace that column with the new values.
\ No newline at end of file
diff --git a/modules/module_2/module2_1.qmd b/modules/module_2/module2_1.qmd
index 3ca9a4a..84a6ae5 100644
--- a/modules/module_2/module2_1.qmd
+++ b/modules/module_2/module2_1.qmd
@@ -13,9 +13,9 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Descriptive Statistics and Data Visualization
 
-## Student Learning Outcomes
+## Learning Outcomes
 
-1.  Students will be able to apply basic data science knowledge to find the cause of a real-world scenario--food poisoning!
+1.  Students will be able to apply basic data science knowledge to find the cause of a real-world scenario: food poisoning!
 2.  Students will be able to generate two types of plots using base R syntax to visualize a single continuous variable (histograms and distributions) and two continuous variables (scatter plots).
 3.  Students will be able to use visual-thinking skills to create visualizations that allow them to explore patterns in data, draw inferences, and create solutions.
 
@@ -65,9 +65,13 @@ Which variables are we particularly interested in? Are they continuous or catego
 
 How might we figure out what is causing the problem? Try to focus on potential solutions that involve the data we already have.
 
-Spend 5 minutes brainstorming in your groups how you might figure out whether plants or fish are the culprits? Be ready to report out.
+Spend 5 minutes brainstorming in your groups how you might figure out whether plants or fish are the culprits. Be ready to report out.
 
-Write a research question with a dependent variable and an independent variable (see the "Aquaponics & Research Questions" slide deck for more info).
+Write a research question with a dependent variable and an independent variable.
+
+::: instructor-only
+**Instructor Note:** Students should land on the idea of comparing doctor visits against fish consumption vs. plant consumption. Guide them toward thinking about the dependent variable (doctor visits / sickness) and independent variables (percent fish, percent plant). The goal of this discussion is to motivate the scatter plot they will build later in the lesson.
+:::
 
 ## Descriptive Statistics and Data Visualization
 
@@ -75,7 +79,7 @@ In order to begin addressing the question of what might be causing illness in ou
 
 In this course, we will be working with two types of statistics: *descriptive* and *inferential*.
 
--   **Descriptive** statistics--also called **summary** statistics--are ways of presenting, organizing, and summarizing data. Data visualization is often associated with descriptive statistics
+-   **Descriptive** statistics, also called **summary** statistics, are ways of presenting, organizing, and summarizing data.
 -   **Inferential** statistics help us draw reasonable conclusions about a population based on the data we observed in a sample.
 
 In Module 2, we will be focusing exclusively on descriptive statistics and data visualization techniques.
@@ -86,20 +90,29 @@ As a quick overview, descriptive statistics often include 3 elements:
 -   measures of *central tendency*: mean, median, and mode
 -   measures of *variation*: range and standard deviation
 
-The slide deck with more information about descriptive statistics is linked on D2L.
-
-For a nice overview of descriptive statistics, check out [this website](https://www.scribbr.com/statistics/descriptive-statistics/) I showed in class. It goes a bit further than we go in this class, but it is a nice place to review!
+For a nice overview of descriptive statistics, check out [this website](https://www.scribbr.com/statistics/descriptive-statistics/). It goes a bit further than we cover here, but it is a great place to review!
 
 ### Let's Practice
 
 Now that we have a more formalized understanding of measures of central tendency and measures of variation, let's put them into use to learn a little bit more about our data. In small groups, calculate the mean (`mean()`) and standard deviation (`sd()`) for the percent fish in our sick crew's diets.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 sick %>% 
   summarize(mean_fish = mean(perc_fish, na.rm = TRUE),
             sd_fish = sd(perc_fish, na.rm = TRUE))
 ```
 
+**Instructor Note:** This is a good moment to ask students what `na.rm = TRUE` does and why it might be necessary. They saw this in module 1.3.
+:::
+
 ## Data Visualization in R
 
 Combining data visualization with descriptive statistics is a great way to understand our data!
@@ -129,9 +142,17 @@ Discuss this histogram with your group members. Some questions to consider:
 
 Take about 5 minutes. Be ready to report out.
 
+::: instructor-only
+**Instructor Note:** Key takeaways: a histogram shows the distribution of a single continuous variable, in this case, what proportion of the crew eats various amounts of plants. The histogram alone can't tell us whether plants are causing illness, because it doesn't show any relationship with sickness. Use this to motivate the need for a two-variable plot (scatter plot), which comes next.
+:::
+
 ### Group Brainstorm
 
-We've covered one type of visualization that just shows one variable...but what we're really interested in is figuring out if fish or plants are the culprit in food poisoning. Spend about 5 minutes in your groups sketching out a visualization that might give us insight into this.
+We've covered one type of visualization that shows one variable at a time... but what we're really interested in is figuring out if fish or plants are the culprit in food poisoning. Spend about 5 minutes in your groups sketching out a visualization that might give us insight into this.
+
+::: instructor-only
+**Instructor Note:** Students should land on something like a scatter plot or bar chart comparing food consumption against doctor visits. Accept any reasonable idea and use it to transition into the scatter plot section.
+:::
 
 ## Scatter plots
 
@@ -148,8 +169,19 @@ This isn't super informative, because there isn't really a relationship between
 
 Create a scatter plot to determine whether there is a correlation between the percentage of fish eaten and the number of trips to the doctor in the past 6 months.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 plot(x = sick$perc_fish, y = sick$doctor_trips)
 ```
 
+**Instructor Note:** Students should observe a positive relationship, crew members with more fish in their diet tend to have more doctor visits. This is the key finding of the lesson: fish appears to be the culprit. Use "How do we interpret this plot?" as a class discussion prompt before moving on. Ask students: what would this plot look like if there were no relationship? What if plants were the culprit instead?
+:::
+
 How do we interpret this plot?
\ No newline at end of file
diff --git a/scripts/toggle-view.js b/scripts/toggle-view.js
new file mode 100644
index 0000000..116045d
--- /dev/null
+++ b/scripts/toggle-view.js
@@ -0,0 +1,76 @@
+(function () {
+  const STORAGE_KEY = 'ditw-view-mode';
+
+  function isTogglePage() {
+    const path = window.location.pathname;
+    // Lesson pages: /modules/module_1/module1_1.html etc.
+    const isLesson = /\/modules\/module_\d+\/module\d+_\d+\.html/.test(path);
+    // Instructors page
+    const isInstructors = /\/instructors\.html/.test(path);
+    return isLesson || isInstructors;
+  }
+
+  function setView(mode) {
+    const isInstructor = mode === 'instructor';
+    document.body.classList.toggle('instructor-view', isInstructor);
+    localStorage.setItem(STORAGE_KEY, mode);
+    updateButton(isInstructor);
+    updateBanner(isInstructor);
+  }
+
+  function updateButton(isInstructor) {
+    const btn = document.getElementById('view-toggle-btn');
+    if (!btn) return;
+    btn.innerHTML = isInstructor
+      ? '<span class="toggle-opt">Student</span> <span class="toggle-divider">|</span> <span class="toggle-opt active">Instructor</span>'
+      : '<span class="toggle-opt active">Student</span> <span class="toggle-divider">|</span> <span class="toggle-opt">Instructor</span>';
+    btn.title = isInstructor
+      ? 'Currently in Instructor View - click to switch to Student View'
+      : 'Currently in Student View - click to switch to Instructor View';
+  }
+
+  function updateBanner(isInstructor) {
+    let banner = document.getElementById('instructor-banner');
+    if (!banner) {
+      banner = document.createElement('div');
+      banner.id = 'instructor-banner';
+      banner.textContent = 'INSTRUCTOR VIEW - answers and instructor notes are visible';
+      const navbar = document.querySelector('.navbar');
+      if (navbar && navbar.nextSibling) {
+        navbar.parentNode.insertBefore(banner, navbar.nextSibling);
+      } else {
+        document.body.insertBefore(banner, document.body.firstChild);
+      }
+    }
+    banner.style.display = isInstructor ? 'block' : 'none';
+  }
+
+  function createButton() {
+    const btn = document.createElement('button');
+    btn.id = 'view-toggle-btn';
+    btn.setAttribute('aria-label', 'Toggle between Student and Instructor view');
+    btn.addEventListener('click', function () {
+      const current = document.body.classList.contains('instructor-view')
+        ? 'instructor' : 'student';
+      setView(current === 'instructor' ? 'student' : 'instructor');
+    });
+
+    const toc = document.getElementById('quarto-margin-sidebar');
+    if (toc) {
+      toc.insertBefore(btn, toc.firstChild);
+    } else {
+      btn.classList.add('toggle-btn-fallback');
+      document.body.appendChild(btn);
+    }
+    return btn;
+  }
+
+  document.addEventListener('DOMContentLoaded', function () {
+    // Only show the toggle on lesson pages and the instructors page
+    if (!isTogglePage()) return;
+
+    const saved = localStorage.getItem(STORAGE_KEY) || 'student';
+    createButton();
+    setView(saved);
+  });
+})();
\ No newline at end of file
diff --git a/styles.scss b/styles.scss
index 90a0f30..0603da6 100644
--- a/styles.scss
+++ b/styles.scss
@@ -25,7 +25,8 @@ $link-color: $magenta;
 
 /*-- scss:rules --*/
 
-// Title
+// text
+
 .title {
   font-family: "Vollkorn";
   font-size: 45px;
@@ -36,25 +37,44 @@ $link-color: $magenta;
   font-weight: 500;
 }
 
-// Headers
 h1, h2, h3 {
   font-family: "Vollkorn";
   color: $darkest-blue;
 }
 
-// Navbar for mobile only
+body {
+  font-family: "Raleway";
+  color: $dark-blue;
+  font-weight: 500;
+}
+
+// links
+
+a {
+  color: $magenta;
+  text-decoration: none;
+
+  &:hover {
+    color: darken($magenta, 10%);
+    text-decoration: underline;
+  }
+}
+
+// navbar
+
 .navbar {
   font-family: "Righteous";
 }
 
-// Hide navbar logo on desktop since sidebar has the logo
+// Hide navbar logo on desktop — sidebar logo takes over
 @media (min-width: 992px) {
   .navbar-brand img {
     display: none;
   }
 }
 
-// Sidebar
+// sidebar
+
 .sidebar {
   font-family: "Righteous";
   font-size: 1rem;
@@ -75,7 +95,6 @@ h1, h2, h3 {
   padding-top: 1em;
 }
 
-// Sidebar logo
 .sidebar-logo {
   width: 160px;
   height: 160px;
@@ -84,49 +103,141 @@ h1, h2, h3 {
   margin: 1em auto;
 }
 
-// Body
-body {
-  font-family: "Raleway";
-  color: $dark-blue;
-  font-weight: 500;
-}
+// pdf download button
 
-// Links
-a {
-  color: $magenta;
+.toc-pdf-button {
+  display: block;
+  margin-top: 1rem;
+  padding: 0.5rem 0.75rem;
+  border-radius: 8px;
+  text-align: center;
+  font-family: "Righteous";
+  background: $magenta;
+  color: $blanched-almond !important;
   text-decoration: none;
+
+  &:hover {
+    background: darken($magenta, 10%);
+    text-decoration: none;
+  }
 }
 
-a:hover {
-  color: darken($magenta, 10%);
-  text-decoration: underline;
+// instructor/student toggle
+
+// by default, student view
+.instructor-only {
+  display: none;
 }
 
-// Mobile
-@media (max-width: 768px) {
-  .title {
-    font-size: 32px;
+// instructor view
+body.instructor-view .instructor-only {
+  display: block;
+  border-left: 4px solid $magenta;
+  background: rgba(138, 22, 101, 0.05);
+  padding: 0.5rem 1rem;
+  margin: 0.75rem 0;
+  border-radius: 0 8px 8px 0;
+
+  &::before {
+    content: "INSTRUCTOR";
+    display: block;
+    font-family: "Righteous";
+    font-size: 0.65rem;
+    letter-spacing: 0.12em;
+    color: $magenta;
+    margin-bottom: 0.35rem;
   }
+}
 
-  body {
-    font-size: 14px;
-  }
+// banner below navbar when instructor mode is active
+#instructor-banner {
+  display: none;
+  width: 100%;
+  background: $magenta;
+  color: $blanched-almond;
+  font-family: "Righteous";
+  font-size: 0.8rem;
+  letter-spacing: 0.08em;
+  text-align: center;
+  padding: 0.4rem 1rem;
+  position: sticky;
+  top: 0;
+  z-index: 999;
 }
 
-/* PDF button in the sidebar */
-.toc-pdf-button {
+// toggle button, sits lh side of rh sidebar
+#view-toggle-btn {
   display: block;
-  margin-top: 1rem;
+  width: 100%;
+  margin-bottom: 1.25rem;
   padding: 0.5rem 0.75rem;
+  border: 2px solid $dark-blue;
   border-radius: 8px;
-  text-align: center;
+  cursor: pointer;
   font-family: "Righteous";
-  background: $magenta;
-  color: $blanched-almond !important;
-  text-decoration: none;
+  font-size: 0.78rem;
+  letter-spacing: 0.03em;
+  background: $blanched-almond;
+  color: $dark-blue;
+  text-align: center;
+  transition: border-color 0.2s ease, background 0.2s ease;
+
+  &:hover {
+    background: darken($blanched-almond, 5%);
+  }
+
+  .toggle-label {
+    font-size: 0.7rem;
+    letter-spacing: 0.08em;
+    opacity: 0.6;
+    margin-right: 0.2rem;
+  }
+
+  .toggle-divider {
+    opacity: 0.35;
+    margin: 0 0.3rem;
+  }
+
+  .toggle-opt {
+    opacity: 0.45;
+
+    &.active {
+      opacity: 1;
+      font-weight: 700;
+      text-decoration: underline;
+      text-underline-offset: 3px;
+    }
+  }
 }
 
-.toc-pdf-button:hover {
-  background: darken($magenta, 10%);
-  text-decoration: none;
+// button border turns purple in instructor mode
+body.instructor-view #view-toggle-btn {
+  border-color: $magenta;
+  color: $magenta;
+
+  &:hover {
+    background: rgba(138, 22, 101, 0.06);
+  }
+}
+
+// for accessibility, if no TOC sidebar exists, fix to bottom-right
+#view-toggle-btn.toggle-btn-fallback {
+  position: fixed;
+  bottom: 2rem;
+  right: 2rem;
+  width: auto;
+  z-index: 1000;
+  box-shadow: 0 2px 10px rgba(0, 0, 0, 0.2);
+}
+
+// mobile accessibility
+
+@media (max-width: 768px) {
+  .title {
+    font-size: 32px;
+  }
+
+  body {
+    font-size: 14px;
+  }
 }
\ No newline at end of file

From b52c016fd1a80ac2f31136b6a783a1e920e18a33 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Mon, 11 May 2026 14:17:24 -0700
Subject: [PATCH 14/15] major updates to all lessons - implementing
 student/instructor toggle

---
 modules/module_1/module1_1.qmd                |   5 +-
 modules/module_1/module1_2.qmd                |   5 +-
 modules/module_1/module1_3.qmd                |  38 ++---
 modules/module_1/module1_home.qmd             |  12 +-
 modules/module_2/module2_1.qmd                |  23 +++-
 modules/module_2/module2_2.qmd                |  78 ++++++++---
 modules/module_2/module2_3.qmd                |  57 ++++++--
 modules/module_2/module2_4.qmd                |  96 ++++++++++---
 modules/module_2/module2_5.qmd                | 104 +++++++++++---
 modules/module_2/module2_home.qmd             |  11 +-
 modules/module_3/module3_1.qmd                |  65 +++++++--
 modules/module_3/module3_2.qmd                |  85 ++++++++----
 modules/module_3/module3_3.qmd                | 119 +++++++++++++---
 modules/module_3/module3_home.qmd             |  12 +-
 modules/module_4/module4_1.qmd                |  67 ++++++---
 modules/module_4/module4_2.qmd                |  42 ++++--
 modules/module_4/module4_3.qmd                | 130 ++++++++++++++----
 modules/module_4/module4_4.qmd                |  65 +++++++--
 modules/module_4/module4_home.qmd             |  12 +-
 ..._and_logisitc.png => exp_and_logistic.png} | Bin
 modules/module_5/module5_1.qmd                |  81 ++++++++---
 modules/module_5/module5_2.qmd                |  53 +++++--
 modules/module_5/module5_3.qmd                |  78 +++++++++--
 modules/module_5/module5_home.qmd             |  11 +-
 24 files changed, 982 insertions(+), 267 deletions(-)
 rename modules/module_5/assets/{exp_and_logisitc.png => exp_and_logistic.png} (100%)

diff --git a/modules/module_1/module1_1.qmd b/modules/module_1/module1_1.qmd
index e980c5a..2faba26 100644
--- a/modules/module_1/module1_1.qmd
+++ b/modules/module_1/module1_1.qmd
@@ -22,7 +22,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ## Check-in (5 minutes)
 
-Let's all sign-up for [Posit Cloud!](https://posit.cloud/) We use Posit Cloud as our coding environment so everyone has access to R and RStudio through a web browser.
+Let's all sign up for [Posit Cloud!](https://posit.cloud/) We use Posit Cloud as our coding environment so everyone has access to R and RStudio through a web browser.
 
 ## RStudio Mini-tour
 
@@ -78,7 +78,8 @@ A quick shortcut for adding a code chunk is `Ctrl + Alt + i` (`Cmd + Opt + i` on
 Let's work with an example code chunk.
 
 ```{r}
-# The `#` symbol is used to write a comment. Comments are notes to yourself or others reading your code. R does not run anything written after `#` on a line.
+# The `#` symbol is used to write a comment. 
+# Comments are notes to yourself or others reading your code. R does not run anything written after `#` on a line.
 
 432 - 11     # subtraction
 
diff --git a/modules/module_1/module1_2.qmd b/modules/module_1/module1_2.qmd
index f0d0505..d5689e5 100644
--- a/modules/module_1/module1_2.qmd
+++ b/modules/module_1/module1_2.qmd
@@ -53,10 +53,11 @@ All elements in a vector must be the same type (all numbers, all text, etc.).
 ```{r}
 weight_kg <- c(3, 2, 4, 9, 7, 3, 6)
 weight_kg
+```
 
+```{r}
 animals <- c("cat", "rat", "bat", "rat")
 animals
-# R does everything in vectors
 ```
 
 ## Data classes
@@ -302,7 +303,7 @@ sd(plants[plants$nitrogen == "N", 1])
 
 ## Helpful Functions
 
-Below are some functions that I often find very helpful when working with vectors and data frames:
+Below are some particularly useful functions when working with vectors and data frames:
 
 -   `str()`: shows the structure of the object (e.g., rows and columns)
 -   `head()` and `tail()`: shows the first six and last six rows, respectively
diff --git a/modules/module_1/module1_3.qmd b/modules/module_1/module1_3.qmd
index c2f0dce..c1e2fcf 100644
--- a/modules/module_1/module1_3.qmd
+++ b/modules/module_1/module1_3.qmd
@@ -17,7 +17,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 -   Students will be able to load packages.
 -   Students will be able to use some functions of the tidyverse: select, filter, the pipe, mutate, summarize, and group by.
--   Students will compare and contrast base R and tidyverse methodology for sub-setting data frames.
+-   Students will compare and contrast base R and tidyverse methodology for subsetting data frames.
 -   Students will be able to use tidyverse functions to summarize real-world data.
 
 ## The `tidyverse`: What is it?
@@ -26,7 +26,7 @@ Different programming languages have different syntax (language structure). The
 
 Until now, all of the coding we have done is in the original R language, which is often called "base R." The syntax in the `tidyverse` is often pretty different from base R. Both are useful, and many people often combine them, which is why we start with base R.
 
-That said, we will be primarily using the `tidyverse` for the rest of the semester.
+That said, we will be primarily using the `tidyverse` for the rest of the course.
 
 ### Wait, what is a package??
 
@@ -36,7 +36,7 @@ You *install* a package once, but you *load* it with `library()` every time you
 
 ### Explore the `tidyverse`
 
-If you want to learn more about the tidyverse, head over to [www.tidyverse.org](www.tidyverse.org) and browse the site. Below is a brief summary of *some* of the packages I think you might find the most useful.
+If you want to learn more about the tidyverse, head over to [www.tidyverse.org](www.tidyverse.org) and browse the site. Below is a brief summary of *some* of the packages that are particularly useful for this course.
 
 -   `tidyr`: creating data that is consistent in form/shape
 -   `dplyr`: creating data that is clean, easily wrangled, and summarized
@@ -50,7 +50,7 @@ If you want to learn more about the tidyverse, head over to [www.tidyverse.org](
 
 ### Download and Install
 
-In most scenarios, you will need to download a package from the internet onto your computer before you can use it in RStudio. However, with Posit Cloud, I've already done this step for you!
+In most scenarios, you will need to download a package from the internet onto your computer before you can use it in RStudio. However, with Posit Cloud, this step has already been done for you!
 
 For future reference, though:
 
@@ -94,7 +94,7 @@ climate_df <- read_csv("data/global_temps.csv")
 climate_df
 ```
 
-The `tidyverse` converts 2D data into something called a tibble! For our intents and purposes, it is basically the same as a data frame (and I'll probably call it a data frame, in reality).
+The `tidyverse` converts 2D data into something called a tibble! For our intents and purposes, it is basically the same as a data frame (and we'll use the terms interchangeably).
 
 As a quick reminder, *columns* represent variables and are vertical. *Rows* represent observations and are horizontal.
 
@@ -120,7 +120,7 @@ First, let's remind ourselves how we would accomplish this in base R.
 climate_df[,1:2]
 ```
 
-The `select()` function does the same thing but with more power (and, in my opinion, more easily). The first argument in the function is the data frame. Any following arguments are the columns we want to select.
+The `select()` function does the same thing but with more power and more readable syntax. The first argument in the function is the data frame. Any following arguments are the columns we want to select.
 
 ```{r}
 # first argument is the data frame, then the columns
@@ -143,7 +143,7 @@ Write a line of code to select the following data from the `climate_df`: average
 
 ```
 
-::: {.instructor-only}
+::: instructor-only
 **Answer:**
 
 ```{r}
@@ -162,7 +162,7 @@ It is important to remember that the computer interprets everything literally. W
 climate_df[climate_df$AverageTemperature > 25, ]
 ```
 
-The code above is, in my opinion, a bit unwieldy. Filter feels more intuitive. We still need the double equal signs, though!
+The code above is a bit unwieldy. Filter feels more intuitive. We still need the double equal signs, though!
 
 ```{r}
 # filter
@@ -184,7 +184,7 @@ us_df <- filter(climate_df, AverageTemperature > 25)
 
 ## Let's practice using `select()` and `filter()`
 
-Work with the climate data we've been using this class period. Construct a small set of code that does the following:
+Work with the climate data we've been using in this lesson. Construct a small set of code that does the following:
 
 1.  Slims down the full data frame to one that has the date, average temperatures, and the city names. Assign this to an object called `slim`.
 2.  Filters the data for Paris with an average temperature less than 22.
@@ -195,7 +195,7 @@ Work with the climate data we've been using this class period. Construct a small
 
 ```
 
-::: {.instructor-only}
+::: instructor-only
 **Answer:**
 
 ```{r}
@@ -204,7 +204,7 @@ filtered <- filter(slim, City == "Paris", AverageTemperature < 22)
 cold_paris <- filtered
 ```
 
-**Instructor Note:** Students haven't learned the pipe yet so the unpiped version is correct here. 
+**Instructor Note:** Students haven't learned the pipe yet so the unpiped version is correct here.
 :::
 
 ## The Pipe `%>%`
@@ -222,6 +222,10 @@ This is helpful for a lot of reasons, including:
 
 The shortcut for typing a pipe is `Ctrl` + `Shift` + `M` (or `Cmd` + `Shift` + `M` on a Mac)
 
+::: instructor-only
+**Instructor Note:** Newer versions of RStudio (2022 and later) default this shortcut to the native pipe `|>` instead of `%>%`. Both work for everything in this course, but if students get `|>` when they expected `%>%`, go to **Tools → Global Options → Code** and check "Use native pipe operator" - uncheck it to default back to `%>%`, or reassure students that `|>` is fine to use. It's worth deciding which one you want the class to use and making it consistent.
+:::
+
 ```{r}
 climate_df %>% 
   select(dt, City)
@@ -243,7 +247,7 @@ In small groups, use pipes to create a new data frame called `warm_nigeria` that
 
 ```
 
-::: {.instructor-only}
+::: instructor-only
 **Answer:**
 
 ```{r}
@@ -257,7 +261,7 @@ warm_nigeria <- climate_df %>%
 
 ## Turning Data into Information with `summarize()`
 
-Like we have talked about in previous classes, some of the best ways for us to understand our data is through what we call summary statistics such as the mean (average), standard deviation, minimums, maximums, etc. Fortunately, the `tidyverse` has a handy-dandy function to make this easy to do with data frames.
+Some of the best ways to understand data are through what we call summary statistics such as the mean (average), standard deviation, minimums, maximums, etc. Fortunately, the `tidyverse` has a handy-dandy function to make this easy to do with data frames.
 
 The `summarize()` function creates a new data frame with columns and values we give it.
 
@@ -274,9 +278,7 @@ climate_df %>%
 
 Wait a second! Those are some weird values!
 
-`NA` is used to represent missing data. So what is happening here? We know that there are numbers to calculate these values.
-
-It is important to note that if any of the values in the column that you are trying to summarize are missing (`NA`), you might get some wonky values, like you did above.
+`NA` is how R represents missing data. The problem is that even one `NA` in a column causes `mean()` and `sd()` to return `NA` by default. R won't assume you want to ignore the missing values without being told to.
 
 Fortunately, `mean()` and `sd()` and some other functions have an argument to remove the missing values: `na.rm = TRUE`
 
@@ -316,7 +318,7 @@ Practice using the combination of `group_by()` and `summarize()` to calculate th
 
 ```
 
-::: {.instructor-only}
+::: instructor-only
 **Answer:**
 
 ```{r}
@@ -366,7 +368,7 @@ Write some code that uses the `round()` function to create a new column of avera
 
 ```
 
-::: {.instructor-only}
+::: instructor-only
 **Answer:**
 
 ```{r}
diff --git a/modules/module_1/module1_home.qmd b/modules/module_1/module1_home.qmd
index 5f9c7bc..b3f06f6 100644
--- a/modules/module_1/module1_home.qmd
+++ b/modules/module_1/module1_home.qmd
@@ -20,16 +20,24 @@ By the end of this module, students will be able to:
 -   subset vectors and data frames using both index- and condition-based approaches
 -   use functions and interpret function arguments in R
 -   use core `tidyverse` tools such as `select()`, `filter()`, `mutate()`, `summarize()`, and `group_by()` to explore and organize data
+-   use the pipe operator (`%>%`) to chain `tidyverse` functions together
 
 ## Lessons & Assignments
 
-Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+### Lessons
+
+Each lesson page displays content in **student view** by default. Use the toggle button in the top right corner of the page to switch to **instructor view**, which reveals embedded answer keys and instructor notes. The QMD file, PDF, and any associated data can be downloaded from each lesson's page in either view.
 
 -   [1.1: Introduction to R and RStudio](module1_1.qmd)
 -   [1.2: Intro to Coding in R](module1_2.qmd)
 -   [1.3: Introduction to the `tidyverse`](module1_3.qmd)
+
+### Assignments
+
+Assignment pages are publicly available in **student view only**. The QMD file, PDF, and any associated data can be downloaded from each assignment's page. Instructor versions with answer keys are only available to educators. To request access, visit the [Instructors page](../../instructors.qmd).
+
 -   [Assignment 1: Getting to Know the Team](assignments/a1/m1a1.qmd)
--   [Assignment 2: Getting to Know Your Antarctic Home](assignments/a2/m1a2.qmd) Another assignment?
+-   [Assignment 2: Getting to Know Your Antarctic Home](assignments/a2/m1a2.qmd)
 
 ## GitHub
 
diff --git a/modules/module_2/module2_1.qmd b/modules/module_2/module2_1.qmd
index 84a6ae5..4251d4c 100644
--- a/modules/module_2/module2_1.qmd
+++ b/modules/module_2/module2_1.qmd
@@ -43,6 +43,7 @@ First we're going to pull in the data and give it a quick inspection/exploration
 As usual, we start by calling the `tidyverse`.
 
 ```{r}
+#| message: false
 library(tidyverse)
 ```
 
@@ -61,6 +62,14 @@ Let's talk about the data we have:
 
 Which variables are we particularly interested in? Are they continuous or categorical? Does it matter?
 
+::: instructor-only
+**Instructor Note:** Each row represents one team member. The character columns are `last`, `first`, `sex`, and `specialties`; the numeric columns are `age`, `height_cm`, `weight_kg`, `perc_fish`, `perc_plant`, and `doctor_trips`.
+
+The key variables for this lesson are `perc_fish`, `perc_plant`, and `doctor_trips`. Point out that `perc_fish` and `perc_plant` are **proportions** (0 to 1), not percentages (0 to 100). Row 1 shows `perc_fish = 0.994` and `perc_plant = 0.006`, which sum to 1. Students may call them "percent" informally, which is fine, but make sure they understand the scale when interpreting plots.
+
+`doctor_trips` is a count (discrete) but is treated as continuous for the purposes of scatter plots here. This is worth a brief mention if students ask.
+:::
+
 ### Group Discussion
 
 How might we figure out what is causing the problem? Try to focus on potential solutions that involve the data we already have.
@@ -119,7 +128,7 @@ Combining data visualization with descriptive statistics is a great way to under
 
 There are two main ways to make data visualizations in R: through base R, which is the syntax we learned in our first week of coding, and through `ggplot2`, which is a package in the `tidyverse`.
 
-For the majority of the semester, we will be plotting in with `ggplot2`, which is a fun and powerful tool. However, to plot even a simple plot, `ggplot2` takes some explanation. We will talk about `ggplot2` in our *next class* but for today, let's first make some quick-and-dirty plots in *base R*.
+For the majority of this course, we will be plotting with `ggplot2`, which is a fun and powerful tool. However, to plot even a simple plot, `ggplot2` takes some explanation. We will cover `ggplot2` in the next lesson. For now, let's make some quick plots in *base R*.
 
 ### Histograms
 
@@ -180,8 +189,14 @@ Create a scatter plot to determine whether there is a correlation between the pe
 ```{r}
 plot(x = sick$perc_fish, y = sick$doctor_trips)
 ```
-
-**Instructor Note:** Students should observe a positive relationship, crew members with more fish in their diet tend to have more doctor visits. This is the key finding of the lesson: fish appears to be the culprit. Use "How do we interpret this plot?" as a class discussion prompt before moving on. Ask students: what would this plot look like if there were no relationship? What if plants were the culprit instead?
 :::
 
-How do we interpret this plot?
\ No newline at end of file
+How do we interpret this plot? What does the pattern tell us about the relationship between fish consumption and illness?
+
+::: instructor-only
+**Instructor Note:** The plot shows a clear positive relationship. Team members with a low proportion of fish in their diet have low doctor visits. As fish proportion increases, doctor visits jump.
+
+Key discussion questions: - What would this plot look like if there were *no* relationship? (Points scattered randomly with no directional trend) - What would it look like if *plants* were the culprit instead? (The pattern would flip, high `perc_plant` values would pair with high doctor trips, but since `perc_fish` and `perc_plant` sum to 1, this plot would actually show the opposite pattern) - Does this plot prove fish is contaminated? (No, it shows correlation, not causation. We haven't done any statistical testing yet. This is a good preview of what Module 3 will cover)
+
+Close by connecting back to the research question students wrote at the start of the lesson. Did their data support or contradict what they predicted?
+:::
\ No newline at end of file
diff --git a/modules/module_2/module2_2.qmd b/modules/module_2/module2_2.qmd
index 1ed7e75..3c2d3c8 100644
--- a/modules/module_2/module2_2.qmd
+++ b/modules/module_2/module2_2.qmd
@@ -13,15 +13,22 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Intro to `ggplot2`
 
+## Learning Outcomes
+
+-   Students will be able to describe the structure of a `ggplot2` plot.
+-   Students will be able to build a scatter plot using `ggplot2` by adding layers.
+-   Students will be able to label axes and apply a theme to improve plot readability.
+
 So far, we've used the base R plotting syntax. While quick plots in base R can still be really useful ways to do preliminary data exploration and visualization, we often want plots that go beyond the basics without too much additional effort. This is where `ggplot2` comes in and really shines!
 
 ## Example
 
-Before we get into the nitty-gritty of how `ggplot2` works, Let's run an example using the data about our sick crew members from earlier.
+Before we get into the nitty-gritty of how `ggplot2` works, let's run an example using the data about our sick crew members from earlier.
 
 First, we need to load in both the `tidyverse` package and our data. We can remind ourselves what the data look like using the `head()` function.
 
-```{r, message = FALSE}
+```{r}
+#| message: false
 # load package
 library(tidyverse)
 
@@ -30,17 +37,17 @@ sick <- read_csv("data/sick_data.csv")
 head(sick)
 ```
 
-Here is code to make a scatter plot of the relationship between percent fish in diets and how many trips to the doctor.
+Here is code to make a scatter plot of the relationship between proportion of fish in diets and how many trips to the doctor.
 
 ```{r}
 ggplot(sick, aes(x = perc_fish, y = doctor_trips)) +
   geom_point() +
-  labs(x = "Percent Fish in Diet",
+  labs(x = "Proportion of Fish in Diet",
        y = "Number of Trips to the Doctor") +
   theme_light()
 ```
 
-Nice, right? In the next few classes, we will really start to see the power of `ggplot`. For now, though, let's focus on how this works.
+Nice, right? In the next few lessons, we will really start to see the power of `ggplot2`. For now, though, let's focus on how this works.
 
 ## `ggplot2`
 
@@ -56,54 +63,58 @@ The `gg` in `ggplot2` stands for "Grammar of Graphics." The "grammar" part is ba
 
 The design is that you work iteratively, building up layer upon layer until you have your final plot.
 
-The typical structure looks like this:
-
-| `ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +`
-| `<GEOM_FUNCTION>()`
-
-Let's iteratively build up to the plot we have made above:
-
 1)  Specify the data
 
-```{r fig.width=4.5, fig.height=3}
+```{r}
+#| fig-width: 4.5
+#| fig-height: 3
 ggplot(data = sick)
 
-# ggplot always starts by drawing a blank coordinate system. Nothing will appear until you add at least one geometric layer. (geom())
+# ggplot() always starts by drawing a blank coordinate system.
+# Nothing will appear until you add at least one geometric layer (a geom function).
 ```
 
-As we've previously seen, most plots that we make display data in some way relative to two different axes: the x-axis (horizontal) and the y-axis (vertical).
+Most plots display data relative to two axes: the x-axis (horizontal) and the y-axis (vertical).
 
 You will definitely want to memorize which axis is which!
 
 2)  Specify the x-axis (horizontal) and the y-axis (vertical) in the `aes()` function.
 
-```{r fig.width=4.5, fig.height=3}
+```{r}
+#| fig-width: 4.5
+#| fig-height: 3
 ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips))
 ```
 
 3)  Add the type of plot we want using a `geom` function. For a scatter plot, we use `geom_point()`.
 
-```{r fig.width=4.5, fig.height=3}
+```{r}
+#| fig-width: 4.5
+#| fig-height: 3
 ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips)) +
   geom_point()
 ```
 
 4)  Clean up the axis labels with the `labs()` function so they are more easily interpreted.
 
-```{r fig.width=4.5, fig.height=3}
+```{r}
+#| fig-width: 4.5
+#| fig-height: 3
 ggplot(data = sick, mapping = aes(x = perc_fish, y = doctor_trips)) +
   geom_point() +
-  labs(x = "Percent Fish in Diet",
+  labs(x = "Proportion of Fish in Diet",
        y = "Number of Trips to the Doctor") 
 ```
 
 5)  Choose a `theme` function to make the plot more aesthetically pleasing.
 
-```{r fig.width=4.5, fig.height=3}
+```{r}
+#| fig-width: 4.5
+#| fig-height: 3
 # theme_bw(), theme_classic(), and theme_light() are my favorites
 ggplot(sick, aes(x = perc_fish, y = doctor_trips)) +
   geom_point() +
-  labs(x = "Percent Fish in Diet",
+  labs(x = "Proportion of Fish in Diet",
        y = "Number of Trips to the Doctor") +
   theme_light()
 ```
@@ -116,4 +127,27 @@ In Summary:
 -   we use a `+` to add layers
 -   we specify the type of plot, or `geom` using one of many possible geom functions
 -   we use the `labs()` function to clean up the labels
--   we add a `theme` function to make it more visually readable
\ No newline at end of file
+-   we add a `theme` function to make it more visually readable
+
+## Let's Practice
+
+Using the `sick` data, build a `ggplot2` scatter plot that shows the relationship between the proportion of plants in a crew member's diet (`perc_plant`) and their number of doctor trips (`doctor_trips`). Make sure to label your axes clearly and apply a theme.
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
+```{r}
+ggplot(sick, aes(x = perc_plant, y = doctor_trips)) +
+  geom_point() +
+  labs(x = "Proportion of Plants in Diet",
+       y = "Number of Trips to the Doctor") +
+  theme_light()
+```
+
+**Instructor Note:** The plant plot shows the inverse pattern from the fish plot, higher plant consumption pairs with fewer doctor visits. Since `perc_fish` and `perc_plant` sum to 1, this is expected. This is a good moment to point out that both plots tell the same story from opposite angles, and to ask students which variable they think is actually driving the illness.
+:::
\ No newline at end of file
diff --git a/modules/module_2/module2_3.qmd b/modules/module_2/module2_3.qmd
index b485b39..bfa9f4e 100644
--- a/modules/module_2/module2_3.qmd
+++ b/modules/module_2/module2_3.qmd
@@ -13,19 +13,30 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # A Visualization Primer
 
+## Learning Outcomes
+
+-   Students will be able to explain why data visualization is important.
+-   Students will be able to identify different types of data visualizations and describe when each is appropriate.
+-   Students will be able to match a dataset's structure to an appropriate visualization type.
+-   Students will be able to critique a visualization.
+
 ## Why Does Data Visualization Matter?
 
 ### Small Groups
 
 As a group, take 5 minutes to compare your notes and come to a group consensus on your top 3 reasons why data visualization matters. Be sure to choose someone to report out!
 
+::: instructor-only
+**Instructor Note:** Some strong answers include: patterns in data become visible that are invisible in a table; complex information can be communicated quickly; visualizations help identify outliers and anomalies; they are essential for communicating findings to non-technical audiences; and they support exploratory data analysis before formal testing.
+:::
+
 ## Types of Visualization
 
 ### Visual cues for communicating data
 
 [Here](https://rafalab.dfci.harvard.edu/dsbook-part-1/dataviz/dataviz-principles.html) is a decent overview of some of the core concepts of data visualization.
 
-This website is pretty great and goes into a lot of detail about good practices in data visualization. If this is something that really piques your interest, I encourage you to check it out!
+This website is pretty great and goes into a lot of detail about good practices in data visualization. If this is something that really piques your interest, it is well worth exploring further!
 
 In this course, our main take-away from this website is the use of visual cues to communicate data and which ones are better than others.
 
@@ -39,20 +50,27 @@ Types of data visualizations:
 
 We will talk more about how to choose the right visualization for your data now and also in the rest of the module.
 
+::: instructor-only
+**Instructor Note:** Students commonly name: bar charts, pie charts, line graphs, scatter plots, histograms. Accept any reasonable answer and write them on the board (if you have one).
+:::
+
 ### Data matching activity
 
-Still in your groups, open the PDF called "data_viz_matching"
+Complete the data matching activity with your group. Your instructor will provide the worksheet.
 
 Spend a few minutes seeing if you can match the data descriptions to the types of data visualizations.
 
 Each “dataset” is pulled from the same overall dataset: measurements from 344 individual penguins from different species and on different islands over 3 years. Some datasets created multiple plots.
 
+#### Data descriptions
+
 A.  Penguin body mass and flipper lengths
 B.  Penguin flipper lengths
 C.  Penguin species and flipper lengths
 D.  Penguins body mass, species, and flipper lengths
 
-#### Matches
+::: instructor-only
+**Answers:**
 
 1.  A
 2.  C
@@ -60,33 +78,42 @@ D.  Penguins body mass, species, and flipper lengths
 4.  B
 5.  C
 6.  D
-
-See the PowerPoint on D2L for full details!
+:::
 
 ## Figure Critique
 
 First, read through [this blog post](https://humansofdata.atlan.com/2019/02/dos-donts-data-visualization/) on the "Dos and Don'ts of Data Visualization"
 
-Now that you have some insights, let's critique these figures below. Yes, these are actual figures in the wild...
+Now that you have some insights, let's critique these figures below. Yes, these are actual figures in the wild!
 
--   what aspects don't work
--   what aspects do work
--   how would you present the data?
+For each one, consider:
 
-Figures:
+-   What aspects don't work?
+-   What aspects do work?
+-   How would you present this data differently?
 
-1.  
+**Figure 1:**
 
 ![](assets/image1.png){width="50%"}
 
-2.  
+**Figure 2:**
 
 ![](assets/image2.jpg){width="50%"}
 
-3.  
+**Figure 3:**
 
 ![](assets/image3.jpg){width="50%"}
 
-4.  
+**Figure 4:**
+
+![](assets/image4.png){width="50%"}
+
+::: instructor-only
+**Instructor Note:** These figures share several problems that are worth talking about:
 
-![](assets/image4.png){width="50%"}
\ No newline at end of file
+-   Truncated axes exaggerate differences by not starting the y-axis at zero. The GNH gender gap looks dramatic visually but is only 0.079 on a 0–1 scale.
+-   Pie charts make proportion comparison difficult because humans are poor at judging angles. A bar chart is almost always clearer.
+-   3D effects are adding visual distortion without adding information. 3D bars and cylinders make it impossible to accurately read values off an axis.
+-   Showing too many groups in the same overlapping plot makes it impossible to find meaning from any of them.
+-   Bins of unequal length in a histogram.
+:::
\ No newline at end of file
diff --git a/modules/module_2/module2_4.qmd b/modules/module_2/module2_4.qmd
index c38b575..cc89780 100644
--- a/modules/module_2/module2_4.qmd
+++ b/modules/module_2/module2_4.qmd
@@ -12,17 +12,26 @@ knitr::opts_chunk$set(
     echo = TRUE,
     fig.width = 5,
     fig.height = 4,
-    fig.aligh = "center"
+    fig.align = "center"
 )
 ```
 
 # Plotting in `ggplot2`
 
+## Learning Outcomes
+
+-   Students will be able to build histograms, multiple histograms, scatter plots, and box plots using `ggplot2`.
+-   Students will be able to select an appropriate plot type based on the number and type of variables.
+-   Students will be able to layer multiple `geom` functions in a single plot.
+-   Students will be able to use `aes()` arguments correctly to map variables to visual properties such as color and fill.
+-   Students will be able to add labels and a theme to a `ggplot2` plot.
+
 ## Data Visualization Types and When to Use Them
 
 Let's practice making different kinds of plots with various `geom` functions to see how they work.
 
 ```{r}
+#| message: false
 library(tidyverse)
 fish <- read_csv("data/fish_sick_data.csv")
 ```
@@ -32,7 +41,7 @@ fish <- read_csv("data/fish_sick_data.csv")
 First, let's remind ourselves of the general structure of how we make plots using the `ggplot2` syntax.
 
 ```{r}
-# ggplot(data = <DATA>, mapping = aes(x = <COLUMN1>, y = <COLUMN2)) +
+# ggplot(data = <DATA>, mapping = aes(x = <COLUMN1>, y = <COLUMN2>)) +
   # geom_function() +
   # labs() +
   # theme()
@@ -40,7 +49,7 @@ First, let's remind ourselves of the general structure of how we make plots usin
 
 ## Histograms
 
-As we learned last modules, histograms are plots which let us look at *one numeric variable.*
+As we covered in the previous lesson, histograms are plots that let us look at *one numeric variable.*
 
 They help us get a feel for the distribution of that data. To make histograms in `ggplot2`, we use the `geom_histogram()` function. Let's look at the number of fish.
 
@@ -61,10 +70,19 @@ ggplot(fish, aes(x = num_fish)) +
 
 Make a histogram of the number of sick fish in the tanks. Create the histogram with 10 bins (10 groupings).
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 ggplot(fish, aes(x = num_sick)) +
   geom_histogram(bins = 10)
 ```
+:::
 
 ## Multiple Histograms (with `geom_histogram()`)
 
@@ -75,7 +93,7 @@ ggplot(fish, aes(x = num_fish, fill = species)) +
   geom_histogram(bins = 10)
 ```
 
-What is happening in the column with both green and red? Perhaps the teal histogram is in front of the red histogram and as blocking us from seeing some red?
+What is happening in the column with both green and red? Perhaps the teal histogram is in front of the red histogram and is blocking us from seeing some red?
 
 Let's change the transparency using an argument called `alpha`, which allows us to make layers transparent. The scale for `alpha` goes from 0 (completely transparent) to 1 (not transparent at all).
 
@@ -97,18 +115,27 @@ ggplot(fish, aes(x = num_fish, fill = species)) +
 
 Aha! This is different from above. Instead of red being stacked vertically on top of teal, we can now see that the red values start at 0 on the y-axis and are overlapping with the teal.
 
-The `position = "identity"` argument tell `geom_histogram` to plot the data for each group starting from 0 in the y-axis rather than stacking values from the same group, which is the default.
+The `position = "identity"` argument tells `geom_histogram()` to plot the data for each group starting from 0 in the y-axis rather than stacking values from the same group, which is the default.
 
-Any time we plot a multiple histogram, we need to change the transparency and add `position = "identity"` when creating a multiple histogram; if we don't, we won't see potential overlap!
+Any time we create a multiple histogram, we need to add transparency and `position = "identity"`; if we don't, we risk missing overlap between groups!
 
 ### Let's Practice!
 
 Make a multiple histogram of the number of sick fish per species. Make sure your plot has 10 bins, is partially transparent, and the data are *not* stacked.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 ggplot(fish, aes(x = num_sick, fill = species)) +
   geom_histogram(bins = 10, alpha = 0.5, position = "identity")
 ```
+:::
 
 ------------------------------------------------------------------------
 
@@ -116,7 +143,7 @@ ggplot(fish, aes(x = num_sick, fill = species)) +
 
 A quick note about the `aes()` function. It's one of the more confusing bits of `ggplot2`.
 
-When do I put the `color` (or `size` or `linetype` or `fill` or whatever) inside the `aes()` function versus in the `geom` function but outside of `aes()`? When we made our multiple histogram plots, why did the `fill` argument go inside of `aes()` but `alpha` went outside?
+When should you put `color` (or `size`, `linetype`, `fill`, etc.) inside the `aes()` function versus in the `geom` function outside of `aes()`? Looking back at the multiple histogram plots, why did `fill` go inside `aes()` but `alpha` went outside?
 
 Essentially, it boils down to this:
 
@@ -143,16 +170,25 @@ Using what you've learned about making histograms, see if you can create a "mult
 
 *Hint: you'll want to use an argument called `color`*.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 ggplot(fish, aes(avg_daily_temp, num_fish, color = species)) +
   geom_point()
 ```
+:::
 
 ## Box-and-Whisker Plots
 
 Box-and whisker-plots (also known as box plots) are another great option for looking at one *numeric* variable and one or more *categorical* variables. They are particularly nice when you want to see measures of central tendency and variation in the same plot.
 
-Let's build one and then talk through what each component means. We use `geom_boxplot` to make these types of plots.
+Let's build one and then talk through what each component means. We use `geom_boxplot()` to make these types of plots.
 
 ```{r}
 ggplot(fish, aes(species, num_fish, color = species)) +
@@ -163,7 +199,7 @@ So what does the box represent? And the whiskers?
 
 -   the **box** represents the *middle 50% of the values* in the data set.
 -   the line that runs through the middle of the box represents the *median (**middle value**)* of the data
--   the **whiskers** represent the *spread of the data* (we won't get into the mathematical details of exactly how they are calculated) and are roughly comparable to 95% confidence intervals (we will cover this in another module)
+-   the **whiskers** extend to 1.5 times the height of the box (the interquartile range) in each direction, giving a sense of the overall spread of the data
 -   values that fall outside of the whiskers can be considered outliers and are plotted individually
 
 ### Layering
@@ -180,11 +216,11 @@ ggplot(fish, aes(species, num_fish, color = species)) +
   geom_point(alpha = 0.5)
 ```
 
-This is nice, but there is still some overlap in points that makes it hard for us to see exactly how many points are there.
+This is nice, but there is still some overlap in points that makes it hard to see exactly how many points there are.
 
 The `geom_jitter()` function is a special version of `geom_point()`. It adds a little bit of randomness to the points (both horizontally and vertically) so that they don't overlap as much.
 
-We can control how much randomness we allow with the `width` and/or `height` arguments. I usually ignore `height` and set the `width` argument to 0.1.
+We can control how much randomness we allow with the `width` and/or `height` arguments. A good starting point is to leave `height` at its default and set `width` to 0.1.
 
 ```{r}
 ggplot(fish, aes(species, num_fish, color = species)) +
@@ -208,13 +244,13 @@ ggplot(fish, aes(species, num_fish, color = species)) +
 
 ### `color` and `fill`
 
-We have used two different arguments in the `aes()` function to specify that we want to colors in our plots to change based on one of the columns in our dataset. When do you want to use which one?
+We have used two different arguments in the `aes()` function to specify that we want the colors in our plots to change based on one of the columns in our dataset.
 
 #### `color`
 
 For the most part, the `color` argument refers to the color of the lines in a plot (e.g., in box plots, the colors of the lines change).
 
-The one exception to this is in `geom_point()`. We change the color of the points with the `color` argument, as well.
+The one exception is `geom_point()`, where `color` controls the color of the points themselves.
 
 #### `fill`
 
@@ -244,39 +280,61 @@ Based on those answers, determine which plot types you can produce for those var
 
 Now, make your two plots! To each, add labels and a theme.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 # boxplot
 ggplot(fish, aes(species, avg_daily_temp, color = species)) +
   geom_boxplot() +
   geom_jitter(alpha = 0.5, width = 0.1) +
-  labs(x = "Average Daily Temp (C)",
-       y = "Frequency", 
+  labs(x = "Species",
+       y = "Average Daily Temperature (°C)", 
        color = "Fish Species") +
   theme_bw()
 
 # histogram
 ggplot(fish, aes(avg_daily_temp, fill = species)) +
   geom_histogram(bins = 10, alpha = 0.5, position = "identity") + 
-  labs(x = "Average Daily Temp (C)", 
+  labs(x = "Average Daily Temperature (°C)", 
        y = "Frequency",
        fill = "Fish Species") +
   theme_bw()
 ```
 
-Finished? Let's add another variable. Now, we want to plot average daily temperatures, the number of sick fish, and the fish species. Work through the same steps as above.
+**Instructor Note:** Categorical variable (species) goes on the x-axis and the numeric variable goes on the y-axis.
+:::
+
+Finished? Let's add another variable. Now, we want to plot average daily temperatures, the number of sick fish, and the fish species. Work through the same steps as above — how many variables are there, what types are they, and which plot type makes sense?
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
 ```{r}
 ggplot(fish, aes(avg_daily_temp, num_sick, color = species)) +
   geom_point(alpha = 0.5) +
-  labs(x = "Average Daily Temp (C)",
+  labs(x = "Average Daily Temperature (°C)",
        y = "Number of Sick Fish per Tank",
        color = "Fish Species") +
   theme_light()
 ```
 
+**Instructor Note:** Three variables: two numeric (avg_daily_temp, num_sick) and one categorical (species). A scatter plot is the right choice, two numeric variables on the axes, categorical variable mapped to color.
+:::
+
 # Summary
 
-Let's summarize some of what we've learned in this module
+Let's summarize some of what we've learned in this lesson
 
 ## Data Visualization Types and When to Use Them
 
diff --git a/modules/module_2/module2_5.qmd b/modules/module_2/module2_5.qmd
index 6f6decb..3f4eda3 100644
--- a/modules/module_2/module2_5.qmd
+++ b/modules/module_2/module2_5.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # What is causing the food poisoning?
 
+## Learning Outcomes
+
+-   Students will be able to apply descriptive statistics and data visualization skills to investigate a problem.
+-   Students will be able to create a new variable using `mutate()`.
+-   Students will be able to use `group_by()` and `summarize()` to compare groups.
+-   Students will be able to select and interpret appropriate plot types to identify patterns across environmental variables.
+
 At the beginning of Module 2, we set out to discover what was causing food poisoning among our colleagues at our Antarctic base. Let's put everything we've learned about descriptive statistics and data visualization to use to try to hunt down what the problem is.
 
 ## Set-up
@@ -21,7 +28,8 @@ Let's load the package and data we will need.
 
 ### Packages
 
-```{r message = FALSE}
+```{r}
+#| message: false
 library(tidyverse)
 ```
 
@@ -30,6 +38,7 @@ library(tidyverse)
 First, we need our dataset!
 
 ```{r}
+#| message: false
 sick_fish <- read_csv("data/fish_sick_data.csv") 
 ```
 
@@ -41,7 +50,15 @@ head(sick_fish)
 
 ## Which Fish?
 
-Last class, we plotted the number of sick fish. Let's remind ourselves what that looked like. Make a plot that compares the numbers of sick fish per species. We actually have a few options!
+In the previous lesson, we plotted the number of sick fish. Let's remind ourselves what that looked like. Make a plot that compares the numbers of sick fish per species. We actually have a few options!
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
 ```{r}
 ggplot(sick_fish, aes(num_sick, fill = species)) +
@@ -51,6 +68,7 @@ ggplot(sick_fish, aes(num_sick, fill = species)) +
        fill = "Species") +
   theme_classic()
 ```
+:::
 
 ### Density
 
@@ -58,7 +76,7 @@ Wait a second! Take a look back at the data. There is a "number of fish" column,
 
 We should probably take into account how many fish there are in the tank to begin with. 12 sick fish out of 50 is probably a bigger deal than 12 sick fish out of 100!
 
-What we need to do is calculate a density of fish--number of sick fish / number of total fish.
+What we need to do is calculate a density: the number of sick fish divided by the total number of fish in the tank.
 
 ```{r}
 # take into account the number of fish in the tank: density of sick fish
@@ -75,12 +93,23 @@ Work in small groups to do the following:
 -   find the average number and standard deviation of sick fish for both species
 -   make a plot that compares the distributions of sick fish numbers for both species (you have multiple options here!)
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 sick_fish %>% 
   group_by(species) %>% 
   summarize(mean_sick_fish = mean(density),
             sd_sick_fish = sd(density))
+```
 
+```{r}
+# boxplot option
 ggplot(sick_fish, aes(species, density, color = species)) +
   geom_boxplot() + 
   geom_jitter(alpha = 0.5, width = 0.1) +
@@ -88,44 +117,85 @@ ggplot(sick_fish, aes(species, density, color = species)) +
        y = "Density of Sick Fish in Tanks",
        color = "Species") +
   theme_light()
+```
 
-# histogram
+```{r}
+# histogram option
 ggplot(sick_fish, aes(density, fill = species)) +
   geom_histogram(alpha = 0.5, bins = 15)
-
 ```
 
-Uh oh...the trout densities look even worse than just the number of sick fish. We need to take a closer look at what is going on in the trout tanks!
+**Instructor Note:** Trout have a mean sick fish density of 0.193, roughly 5.7× higher than tilapia (0.034). The boxplot makes this immediately visible.
+:::
+
+Uh oh... the trout densities look even worse than just the number of sick fish.
 
 We should create a data frame that only contains trout to work with for the rest of our analyses. Take a few minutes to work on that; call it `sick_trout`.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 sick_trout <- sick_fish %>% 
   filter(species == "trout")
+sick_trout
 ```
+:::
 
 ## What Environmental Factor?
 
-Let's talk about fish physiology for a minute.
-
 Take a look back at the data frame. Which columns are environmental variables that could be driving the issues?
 
 Are those columns numeric or categorical? What plot type have we talked about that might help us find a relationship between density and each of these variables (one at a time...)?
 
-In small groups, make plots using `density` column in the `sick_trout` data to try to figure out which environmental factor is causing problems in the trout. Treat your variables as numeric.
+::: instructor-only
+**Instructor Note:** The environmental columns in the trout data are `avg_daily_temp`, `num_fish`, `day_length`, and `tank_volume`.
+
+All are numeric, so scatter plots with `density` on the y-axis are the right choice.
+:::
+
+In small groups, make plots using the `density` column in the `sick_trout` data to try to figure out which environmental factor is causing problems in the trout.
+
+```{r}
+# Write your code here
+
+```
+
+What do we think is the environmental driver causing issues with the trout?
+
+::: instructor-only
+**Answer:**
 
 ```{r}
 ggplot(sick_trout, aes(x = num_fish, y = density)) +
-  #geom_smooth(method = 'lm', se = FALSE) +
-  geom_point() 
+  geom_smooth(method = "lm", se = FALSE) +
+  geom_point() +
+  labs(x = "Number of Fish in Tank", y = "Density of Sick Fish") +
+  theme_light()
 
 ggplot(sick_trout, aes(x = avg_daily_temp, y = density)) +
-  #geom_smooth(method = 'lm', se = FALSE) +
-  geom_point()
+  geom_smooth(method = "lm", se = FALSE) +
+  geom_point() +
+  labs(x = "Average Daily Temperature (°C)", y = "Density of Sick Fish") +
+  theme_light()
 
-ggplot(sick_trout, aes(day_length, density)) +
-  #geom_smooth(method = 'lm', se = FALSE) +
-  geom_point() 
+ggplot(sick_trout, aes(x = day_length, y = density)) +
+  geom_smooth(method = "lm", se = FALSE) +
+  geom_point() +
+  labs(x = "Day Length", y = "Density of Sick Fish") +
+  theme_light()
 ```
 
-What do we think is the environmental driver causing issues with the trout?
\ No newline at end of file
+**Instructor Note:** The clearest signal is in `avg_daily_temp`, there is a positive relationship between temperature and sick fish density. As temperature increases, density tends to increase. This makes biological sense: rainbow trout are cold-water fish and even modest temperature increases toward the upper end of their tolerance range stress their immune function.
+
+`day_length` shows essentially no relationship
+
+`num_fish` shows a slight negative trend, as the number of fish in a tank increases, density of sick fish decreases slightly. This is likely a dilution effect in the density calculation (more total fish in the denominator) rather than a real biological pattern, and students should note that the scatter is wide.
+
+Temperature is looking like the primary driver. Close by asking: what would you recommend to the Antarctic base team? This sets up the transition into Module 3.
+:::
\ No newline at end of file
diff --git a/modules/module_2/module2_home.qmd b/modules/module_2/module2_home.qmd
index f03a1e6..c2cf8b0 100644
--- a/modules/module_2/module2_home.qmd
+++ b/modules/module_2/module2_home.qmd
@@ -24,13 +24,20 @@ By the end of this module, students will be able to:
 
 ## Lessons & Assignments
 
-Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+### Lessons
+
+Each lesson page displays content in **student view** by default. Use the toggle button in the top right corner of the page to switch to **instructor view**, which reveals embedded answer keys and instructor notes. The QMD file, PDF, and any associated data can be downloaded from each lesson's page in either view.
 
 -   [2.1: Good Food Gone Bad](module2_1.qmd)
 -   [2.2: Plotting with `ggplot2`](module2_2.qmd)
 -   [2.3: Data Visualization](module2_3.qmd)
 -   [2.4: Exploring `geom` Functions](module2_4.qmd)
 -   [2.5: Module 2 Wrap-Up](module2_5.qmd)
+
+### Assignments
+
+Assignment pages are publicly available in student view only. The QMD file, PDF, and any associated data can be downloaded from each assignment's page. Instructor versions with answer keys are only available to educators. To request access, visit the [Instructors page](../../instructors.qmd).
+
 -   Assignment 1: Scatter Plots
 -   Assignment 2: From Data to Plot
 -   Assignment 3: Final Project Kickoff
@@ -39,4 +46,4 @@ Downloads for each lesson and assignment - including the QMD file, PDF, and any
 
 To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
 
-**Links are not yet live!**
\ No newline at end of file
+**Links are not yet live.**
\ No newline at end of file
diff --git a/modules/module_3/module3_1.qmd b/modules/module_3/module3_1.qmd
index 4422ac0..a379df6 100644
--- a/modules/module_3/module3_1.qmd
+++ b/modules/module_3/module3_1.qmd
@@ -13,13 +13,20 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Introduction to the Problem
 
+## Learning Outcomes
+
+-   Students will be able to use `group_by()` and `summarize()` to compare summary statistics across groups.
+-   Students will be able to build and interpret histograms and scatter plots using `ggplot2`.
+-   Students will be able to add a vertical line to a `ggplot2` plot using `geom_vline()` with a secondary data frame.
+-   Students will be able to describe the trade-off between signal distance and battery life in wildlife tracking collars.
+
 ## The Challenge
 
 Given the issues with our farmed fish, we will need to supplement our diets with some fish that are wild-caught for a while. These fish are caught by members of Team Antarctica, but there is quite a bit of risk involved.
 
 ![](assets/seal2.jpeg){width="50%"}
 
-One of the main hazards to these fishing teams are [leopard seals](https://en.wikipedia.org/wiki/Leopard_seal). They're apex predators in this ecosystem and are quite large, averaging 2.4--3.5 m (7.9--11.5 ft) in length and 200-600 kilograms (440-1,320 lb) in weight. Due to their large size and predatory nature, they can cause serious injury, especially when large schools of fish are involved ([a researcher was killed by a leopard seal in Antarctica in 2003](https://www.theguardian.com/uk/2003/jul/24/science.highereducation)).
+One of the main hazards to these fishing teams is [leopard seals](https://en.wikipedia.org/wiki/Leopard_seal). They're apex predators in this ecosystem and are quite large, averaging 2.4--3.5 m (7.9--11.5 ft) in length and 200-600 kilograms (440-1,320 lb) in weight. Due to their large size and predatory nature, they can cause serious injury, especially when large schools of fish are involved ([a researcher was killed by a leopard seal in Antarctica in 2003](https://www.theguardian.com/uk/2003/jul/24/science.highereducation)).
 
 ![](assets/seal3.jpeg){width="50%"}
 
@@ -35,9 +42,10 @@ We've been tasked with determining why the units are failing and if we can tie i
 
 ## The Data
 
-Our data on collars is in a file called "collar_data.csv."
+Our data on collars is in a file called "collar_data.csv." We are calling it `collars`.
 
-```{r message = FALSE}
+```{r}
+#| message: false
 library(tidyverse)
 collars <- read_csv("data/collar_data.csv")
 ```
@@ -57,8 +65,17 @@ Let's practice. Write some code to do the following:
     -   battery life
     -   signal distance
 -   do this for both manufacturers
+-   save the result as an object called `bat_life_and_signal` (we will use it later!)
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
-```{r summarize}
+```{r}
 bat_life_and_signal <- collars %>%
   group_by(maker) %>%
   summarize(bat_life_mean = mean(battery_life),
@@ -67,9 +84,18 @@ bat_life_and_signal <- collars %>%
             signal_sd = sd(signal_distance)) 
 bat_life_and_signal
 ```
+:::
 
 We also might want to count up how many collar failures are attributed to each maker.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 collars %>% 
   group_by(maker) %>% 
@@ -80,6 +106,9 @@ collars %>%
   count(fail)
 ```
 
+**Instructor Note:** Budget Collars LLC has 16 failures compared to just 4 for Collarium Inc. Budget Collars LLC is clearly the problem maker.
+:::
+
 Looks like one of these makers is definitely the one having some problems!
 
 ## Visualizing the Data
@@ -88,7 +117,7 @@ After summarizing our data, our next step in our data exploration is usually dat
 
 ### One Continuous Variable at a Time
 
-What types of plots can we use to explore one continuous variable (and one categorical variable--in this case, maker)?
+What types of plots can we use to explore one continuous variable (and one categorical variable, in this case, maker)?
 
 -   multiple histogram
 -   box-and-whisker plot
@@ -115,7 +144,8 @@ We will use the `geom_vline()` function to add a vertical line onto our plot at
 
 Let's take a look at the help file for `geom_vline()` to see what arguments it takes. Scroll down to the "Aesthetics" section.
 
-```{r, eval=FALSE}
+```{r}
+#| eval: false
 ?geom_vline
 ```
 
@@ -144,7 +174,15 @@ ggplot(collars, aes(battery_life, fill = maker)) +
 
 #### Group Challenge!
 
-Make the same plot that we made above expect for the signal distance.
+Make the same plot that we made above except for the signal distance.
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
 ```{r}
 ggplot(collars, aes(signal_distance, fill = maker)) +
@@ -156,10 +194,11 @@ ggplot(collars, aes(signal_distance, fill = maker)) +
        fill = "Maker") +
   theme_light()
 ```
+:::
 
 ### Two Continuous Variables at a Time
 
-We've explored both battery life and signal distance individually. Now let's bring them together into one plot to see what the relationship between them is like. What type of plot will be use for that?
+We've explored both battery life and signal distance individually. Now let's bring them together into one plot to see what the relationship between them is like. What type of plot will we use for that?
 
 ```{r}
 ggplot(collars, aes(x = signal_distance, y = battery_life, color = maker)) +
@@ -168,4 +207,12 @@ ggplot(collars, aes(x = signal_distance, y = battery_life, color = maker)) +
 
 How do we interpret this plot? What is it telling us about the relationship between signal distance and battery life? What is it telling us about that relationship between the two collar makers?
 
-As it turns out, this trade-off between signal distance and battery length is very real when it comes to wildlife tracking! Why do you think that is?
\ No newline at end of file
+As it turns out, this trade-off between signal distance and battery length is very real when it comes to wildlife tracking! Why do you think that is?
+
+::: instructor-only
+**Instructor Note:** The plot shows two distinct clusters with almost no overlap. Collarium Inc. (teal) has higher battery life and lower signal distance. Budget Collars LLC (pink) has lower battery life and higher signal distance.
+
+Budget Collars LLC appears to have designed their collars for longer signal range at the cost of battery life. The reason students should suspect is that transmitting a signal over a longer distance requires more power, which drains the battery faster.
+
+This also sets up a key question for the next lesson. Is the difference in battery life between the two makers statistically meaningful, or could it be due to chance?
+:::
\ No newline at end of file
diff --git a/modules/module_3/module3_2.qmd b/modules/module_3/module3_2.qmd
index 4429cc7..47dcd58 100644
--- a/modules/module_3/module3_2.qmd
+++ b/modules/module_3/module3_2.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Comparing Two Means
 
+## Learning Outcomes
+
+-   Students will be able to identify appropriate independent and dependent variables for a t-test.
+-   Students will be able to write null and alternative hypotheses.
+-   Students will be able to run a t-test in R using `t.test()`.
+-   Students will be able to interpret t-test output including the t-statistic, p-value, and confidence interval.
+
 When we are comparing data from two groups, we often want to compare the mean values of the different groups to see if there are differences. We can do this in a number of ways:
 
 -   Numerically (descriptive statistics)
@@ -21,17 +28,25 @@ When we are comparing data from two groups, we often want to compare the mean va
 
 In this course so far, we have done the first 2: by calculating the mean values of groups and by plotting histograms, density plots, and box-plots.
 
-Today, we will be exploring the statistical side, using inferential statistics. Once again, we will be using our collars data, focusing on the battery life and the signal distance.
+In this lesson, we will be exploring the statistical side using inferential statistics. Once again, we will be using our collars data, focusing on the battery life and the signal distance.
 
 Let's get set-up by loading the tidyverse and reading in our data.
 
-```{r, message=FALSE, warning=FALSE}
-# Load library
-library(tidyverse)
+```{r}
+#| message: false
+#| warning: false
+# Write your code here
+
+```
 
-# Load data
+::: instructor-only
+**Answer:**
+
+```{r}
+library(tidyverse)
 collars <- read_csv("data/collar_data.csv")
 ```
+:::
 
 Let's take a quick look at the data structure to remind ourselves what data we are using.
 
@@ -44,7 +59,7 @@ Before we dive in, if you need a refresher on inferential statistics, check out
 
 ## T-Tests
 
-When we have a categorical variable with 2 categories, the statistical test that we use to determine if the two categories are *statistically significantly different* from each other is called a **t-test**.
+When we have a categorical variable with 2 categories, the statistical tests we use to determine if the two categories are *statistically significantly different* from each other are called **t-tests**.
 
 ### When to Use a t-test
 
@@ -75,23 +90,23 @@ In this case, we want to know if there is a *meaningful* (read: statistical) dif
 
 Based on our question, we can now set up two different statistical hypotheses: the null hypothesis and the alternative hypothesis.
 
-The null hypothesis always states that there is no difference or no relationship. We can think of the null hypothesis as our starting place---this is our default assumption. The alternative hypothesis is, well, the alternative to the null hypothesis. For example:
+The null hypothesis always states that there is no difference or no relationship. We can think of the null hypothesis as our starting place or our default assumption. The alternative hypothesis is, well, the alternative to the null hypothesis. For example:
 
 -   **Null Hypothesis** ($H_{0}$): There is *no* difference in the means of battery life between the two collar makers
 -   **Alternative Hypothesis** ($H_{A}$): There *is* a difference in the mean battery life between the two collar makers
 
 How can we determine whether we should *not reject* ("accept") or *reject* the null hypothesis? That's where statistics come in.
 
-*(Note: For reasons I won't go into, we never accept the alternative hypothesis, we only reject the null hypothesis.)*
+*(Note: For statistical reasons beyond the scope of this course, we never say we "accept" the alternative hypothesis, we only say we "reject" or "fail to reject" the null hypothesis.)*
 
 ### Running a t-test
 
-There is a set of statistical tools that can help us determine whether or not there is a difference between 2 means. This group of tools are called t-tests.
+There is a set of statistical tools that can help us determine whether or not there is a difference between 2 means. These tools are called t-tests.
 
 Let's briefly remind ourselves of the logic here.
 
 1.  Our data are a *sample* of a larger *population* (think of the population as all of the collars ever produced by both companies).
-2.  We're interested in the the difference in the means between the two groups.
+2.  We're interested in the difference in the means between the two groups.
     -   If they're exactly the same, the difference in means would be 0.
     -   If they're different, the difference between the means will be something either larger or smaller than 0.
 3.  However, because we only have data from a random *sample* of collars, there will be some variation in our numbers due to sampling error.
@@ -119,10 +134,10 @@ Let's talk through it:
 
 -   `t`: this is what we call the t-value, or t-statistic.
     -   Here, it is a metric of how different the difference of the two means is from 0.
-    -   Note that it doesn't correspond *directly* to the difference --- it is taking into account the actual difference as well as the variation in the data sets.
-    -   Take home: big values (can be positive or negative) indicate a big difference from 0 while small values are mean the difference is close to 0.
+    -   Note that it doesn't correspond *directly* to the difference, it is taking into account the actual difference as well as the variation in the data sets.
+    -   Take home: big values (can be positive or negative) indicate a big difference from 0 while small values mean the difference is close to 0.
 -   `df`: stands for *degrees of freedom*
-    -   It's a measure of your sample size and some other stuff --- honestly, this is not something we're really going to focus on here.
+    -   It's a measure of your sample size and some other stuff, honestly, this is not something we're really going to focus on here.
 -   `p-value`: a measure of certainty of the difference outlined in the `t-statistic` above
     -   For the test above, our p-value is *very* small: 0.00000000000000022
         -   Wait, how did I get from `2.2e-16` to 0.00000000000000022?
@@ -132,7 +147,7 @@ Let's talk through it:
     -   There are a lot of benchmarks in different fields for what this value should be below to consider the difference *significant*; typically a p-value below 0.05 is considered significant.
 -   `95 percent confidence interval`: this is the range that we can expect the test statistic (difference in means) to fall in 95% of the time given the data.
     -   Our test statistic is the difference in means of Budget Collars and Collarium, or 86.8 - 121.5 = -34.7
-    -   If we randomly sample a group of collars from the "population", the difference in means will fall between -30.35 and -38.98 about 95% of the time
+    -   If we randomly sample a group of collars from the "population", the difference in means will fall between -38.98 and -30.35 about 95% of the time
 
 Let's quickly remind ourselves how p-values work:
 
@@ -151,24 +166,42 @@ We want to know if there is a significant difference in the average signal dista
 
 1.  Identify your independent and dependent variables.
 
--   **Independent:** maker, categorical
--   **Dependent:** signal_distance, continuous
-
 2.  Write out your null and alternative hypotheses.
 
--   **Null Hypothesis** ($H_{0}$): there is *no* difference in average signal distance between collar makers
--   **Alternative Hypothesis** ($H_{A}$): there *is* a difference in average signal distance between collar makers
+3.  Run the t-test.
 
-3.  Run the t-test
+```{r}
+# Write your code here
+
+```
+
+4.  Interpret the t-test:
+
+-   Is the p-value below our cut-off for significance (0.05)?
+-   Is our result statistically significant?
+-   Does that mean we should or should not reject the null hypothesis?
+-   So... is there a "real" difference in the means between the two companies or not?
+
+::: instructor-only
+**Answers:**
+
+1.  **Independent:** maker (categorical) \| **Dependent:** signal_distance (numeric)
+
+2.  **Null Hypothesis** ($H_{0}$): There is *no* difference in average signal distance between collar makers\
+    **Alternative Hypothesis** ($H_{A}$): There *is* a difference in average signal distance between collar makers
+
+3.  
 
 ```{r}
-# A t-test for signal distance
 t.test(signal_distance ~ maker, data = collars)
 ```
 
-4.  Interpret the t-test
+4.  
+
+-   p-value below 0.05? **Yes**
+-   Statistically significant? **Yes**
+-   Reject or fail to reject null? **Reject**
+-   Real difference between companies? **Yes**
 
--   Is the p-value below our cut-off for significance (0.05)? ***Yes, p \< 0.05***
--   Is our result statistically significant? ***Yes!***
--   Does that mean we should or should not reject the null hypothesis? ***REJECT***
--   So... is there a "real" difference in the means between the two companies or not? ***Yes, there is a real difference***
\ No newline at end of file
+**Instructor Note:** Students should note that signal distance IS significantly different between makers — Budget Collars LLC transmits farther on average (\~4302) vs Collarium Inc. (\~4195). Combined with the battery life result, this confirms the engineering trade-off seen visually in 3.1: Budget Collars LLC trades battery life for signal range.
+:::
\ No newline at end of file
diff --git a/modules/module_3/module3_3.qmd b/modules/module_3/module3_3.qmd
index 4635ffa..224bf33 100644
--- a/modules/module_3/module3_3.qmd
+++ b/modules/module_3/module3_3.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Comparing (Multiple) Means
 
+## Learning Outcomes
+
+-   Students will be able to explain when to use an ANOVA versus a t-test.
+-   Students will be able to write null and alternative hypotheses for an ANOVA.
+-   Students will be able to run an ANOVA using `aov()` and interpret the output.
+-   Students will be able to run a Tukey HSD post hoc test and interpret pairwise comparisons.
+
 So far in this module, we have discussed comparing the means of two groups in multiple ways: numerically, visually, and statistically. Specifically, we learned about running t-tests to statistically compare the means of two groups.
 
 What if we have more than 2 groups, though? Often, we have 3, 4, 12...any number of groups that we might want to compare against each other. How do we do that?
@@ -23,7 +30,7 @@ What is an ANOVA? It stands for **AN**alysis **O**f **VA**riance.
 
 Why is it called that? Well, ANOVAs compare the amount of variability in the data *between* and *within* groups. In plainer terms, it is looking at how different the data in each group are from each other (within) and also how different the groups are from the other groups (between).
 
-The group of statistical tests called ANOVAs are extensions of the t-test, using the same concepts but allowing us to compare the means of more than two groups at a time. There are lots of different types of ANOVAs, but we are going to stick with the most simple version.
+The group of statistical tests called ANOVAs are extensions of the t-test, using the same concepts but allowing us to compare the means of more than two groups at a time. There are lots of different types of ANOVAs, but we are going to stick with the simplest version.
 
 ### When to use an ANOVA
 
@@ -46,20 +53,37 @@ Like t-tests, ANOVAs are another example of statistical hypothesis testing. We a
 
 ## Let's Run an Example
 
-Last week, we ran t-tests on the battery life and signal distance variables in order to determine if there was a statistically significant difference between two groups (the collar makers).
+In the previous lesson, we ran t-tests on the battery life and signal distance variables in order to determine if there was a statistically significant difference between two groups (the collar makers).
 
 What if our large mammal team had deployed collars from *three* different companies rather than just two? How would we compare the means numerically, visually, and statistically? Let's find out.
 
-First up, in small groups, work through the set-up, numeric, and visual sections. We'll spend 15-20 minutes on this.
+In small groups, work through the set-up, numeric, and visual sections.
+
+::: instructor-only
+**Instructor Note:** Plan for 15–20 minutes on the set-up, numeric, and visual sections before bringing the class back together for the ANOVA section.
+:::
 
 ### Set-Up
 
 First, as always, we need to load the `tidyverse` and the read in our collars data. This time, use the file `more_collars.csv`.
 
 ```{r}
+#| message: false
+#| warning: false
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
+```{r}
+#| message: false
+#| warning: false
 library(tidyverse)
 collars <- read_csv("data/more_collars.csv")
 ```
+:::
 
 ### Conceptual
 
@@ -85,6 +109,14 @@ Let's calculate some summary values for our data to get some preliminary informa
 
 Calculate the means and the standard deviations (`sd()`) for the battery life and the signal distance for *each* collar maker.
 
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
 ```{r}
 summary_stats <- collars %>% 
   group_by(maker) %>% 
@@ -95,6 +127,7 @@ summary_stats <- collars %>%
 
 summary_stats
 ```
+:::
 
 Let's examine the data. Do you think the collar companies are all different from each other? Are there pairs of companies that look more similar?
 
@@ -105,19 +138,34 @@ As always, we love (\<3) visualizing our data, especially as a form of data expl
 First, explore the distribution of battery life by maker using a multiple histogram (no need to add a line for the mean, but you can if you really want to!).
 
 ```{r}
-# battery life
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
+```{r}
 ggplot(collars, aes(battery_life, fill = maker)) +
   geom_histogram(alpha = 0.5, position = "identity") +
-  labs(x = "Battery Life", 
+  labs(x = "Battery Life (days)", 
        y = "Frequency",
        fill = "Collar Maker") +
   theme_bw()
 ```
+:::
 
-Let's do a different type of visualization for signal distance. What type of data visualization have we covered that does a good job of showing both central tendency (median, in this case) and variation (middle 50% and 95% confidence intervals) in one continuous variable?
+Now let's do a different type of visualization for signal distance. What type of data visualization have we covered that does a good job of showing both central tendency (median, in this case) and the spread of the data (the middle 50% and the overall range) in one continuous variable?
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
 ```{r}
-# signal distance
 ggplot(collars, aes(maker, signal_distance)) +
   geom_boxplot() +
   geom_jitter(width = 0.1, alpha = 0.5) +
@@ -125,9 +173,14 @@ ggplot(collars, aes(maker, signal_distance)) +
        y = "Signal Distance") +
   theme_classic()
 ```
+:::
 
 Based on the numbers and your visualizations, what is your prediction? Are the 3 groups different *enough*, whatever that means?
 
+::: instructor-only
+**Instructor Note:** Accept any reasonable prediction here. The goal is to get students thinking about statistical vs. practical significance before running the test. Revisit their predictions after the ANOVA results are in.
+:::
+
 ### Statistic
 
 Now that we have explored the data numerically and visually, let's interrogate the data statistically.
@@ -151,20 +204,31 @@ We want to pay attention to the following:
 -   `F-value`: this is our test statistic. It determines our `p-value` (you don't need to know how)
 -   `Pr(>F)`: this is our `p-value`, which has roughly the same meaning as with a t-test.
     -   We want to know if this value is larger or smaller than our cut-off value. We will stick with 0.05.
-    -   The notation here, `Pr(>F)`, is saying "What is the probability of us getting an F-value larger than the one we have by change alone?" If that probability is less that 0.05 (or 5%), then we reject the null hypothesis that the means are the same.
+    -   The notation here, `Pr(>F)`, is saying "What is the probability of us getting an F-value larger than the one we have by chance alone?" If that probability is less than 0.05 (or 5%), then we reject the null hypothesis that the means are the same.
 -   `***`: the asterisks are reiterating what the p-value tells us, indicating just how small the p-value is.
 
 What is our conclusion? Are these means different?
 
 #### Practice
 
-How about for signal life? What is your interpretation of these results?
+How about for signal distance? What is your interpretation of these results?
+
+```{r}
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
 ```{r}
 signal_model <- aov(data = collars, signal_distance ~ maker)
 summary(signal_model)
 ```
 
+**Instructor Note:** The result should also be significant. Ask students to state a conclusion in plain language before moving to pairwise comparisons.
+:::
+
 ### Pairwise Comparisons
 
 ANOVAs are incredibly useful to tell you if there is a difference in the means of *any* of the groups. However, they do not tell you *which* means differ from another.
@@ -186,20 +250,43 @@ What do these different columns represent?
 
 How should we interpret these results?
 
--   Are Collarium and Bugdet significantly different in battery life? **Yes (p \< 0.05)**
--   Are Darn Tuff and Budget significantly different in battery life? **Yes (p \< 0.05)**
--   Are Darn Tuff and Collarium significantly different in battery life? **No (p \> 0.05)**
+-   Are Collarium and Budget significantly different in battery life?
+-   Are Darn Tuff and Budget significantly different in battery life?
+-   Are Darn Tuff and Collarium significantly different in battery life?
+
+::: instructor-only
+**Answers:**
+
+-   Collarium vs. Budget: **Yes (p \< 0.05)**
+-   Darn Tuff vs. Budget: **Yes (p \< 0.05)**
+-   Darn Tuff vs. Collarium: **No (p \> 0.05)**
+:::
 
 #### Practice
 
 In your groups, run the Tukey's HSD test for signal distance and interpret the results.
 
 ```{r}
-TukeyHSD(signal_model)
+# Write your code here
+
 ```
 
 How should we interpret these results?
 
--   Are Collarium and Budget significantly different in signal distance? **Yes (p \< 0.05)**
--   Are Darn Tuff and Budget significantly different in signal distance? **Yes (p \< 0.05)**
--   Are Darn Tuff and Collarium significantly different in signal distance? **Yes (p \< 0.05)**
\ No newline at end of file
+-   Are Collarium and Budget significantly different in signal distance?
+-   Are Darn Tuff and Budget significantly different in signal distance?
+-   Are Darn Tuff and Collarium significantly different in signal distance?
+
+::: instructor-only
+**Answers:**
+
+```{r}
+TukeyHSD(signal_model)
+```
+
+-   Collarium vs. Budget: **Yes (p \< 0.05)**
+-   Darn Tuff vs. Budget: **Yes (p \< 0.05)**
+-   Darn Tuff vs. Collarium: **Yes (p \< 0.05)**
+
+**Instructor Note:** Unlike battery life, all three makers differ significantly in signal distance. This means every pairwise combination is statistically different — no two makers are comparable in terms of signal range.
+:::
\ No newline at end of file
diff --git a/modules/module_3/module3_home.qmd b/modules/module_3/module3_home.qmd
index 8b06a1d..f4dd19b 100644
--- a/modules/module_3/module3_home.qmd
+++ b/modules/module_3/module3_home.qmd
@@ -19,14 +19,22 @@ By the end of this module, students will be able to:
 -   run a t-test using the `t.test()` function and interpret the output, including t-value, p-value, and confidence interval
 -   run a one-way ANOVA using the `aov()` and `summary()` functions and interpret the F-value and p-value
 -   run a Tukey's HSD post hoc test using `TukeyHSD()` and interpret pairwise comparisons
+-   add a vertical reference line to a `ggplot2` plot using `geom_vline()`
 
 ## Lessons & Assignments
 
-Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+### Lessons
+
+Each lesson page displays content in **student view** by default. Use the toggle button in the top right corner of the page to switch to **instructor view**, which reveals embedded answer keys and instructor notes. The QMD file, PDF, and any associated data can be downloaded from each lesson's page in either view.
 
 -   [3.1: Leopard Seals](module3_1.qmd)
 -   [3.2: T-Tests](module3_2.qmd)
 -   [3.3: Comparing (Multiple) Means](module3_3.qmd)
+
+### Assignments
+
+Assignment pages are publicly available in student view only. The QMD file, PDF, and any associated data can be downloaded from each assignment's page. Instructor versions with answer keys are only available to educators. To request access, visit the [Instructors page](../../instructors.qmd).
+
 -   Assignment 1: Comparing Two Means
 -   Assignment 2: Comparing Multiple Means
 
@@ -34,4 +42,4 @@ Downloads for each lesson and assignment - including the QMD file, PDF, and any
 
 To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
 
-**Links are not yet live!**
\ No newline at end of file
+**Links are not yet live.**
\ No newline at end of file
diff --git a/modules/module_4/module4_1.qmd b/modules/module_4/module4_1.qmd
index 4063feb..8d6e78d 100644
--- a/modules/module_4/module4_1.qmd
+++ b/modules/module_4/module4_1.qmd
@@ -11,13 +11,22 @@ format:
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
+# Combining Datasets (Joins & Binds)
+
+## Learning Outcomes
+
+-   Students will be able to explain the difference between joins and binds.
+-   Students will be able to use joins to merge two datasets by a shared identifier.
+-   Students will be able to use `bind_rows()` to append rows from one dataset to another.
+-   Students will be able to export a combined dataset using `write_csv()`.
+
 ## The Context
 
 After the series of incidents where a number of the collars made by Budget Collars LLC seem to be failing, our team decided to try and replace as many of them as possible. Besides, their battery life is far inferior.
 
 We're placing as many collars on as many seals as we can, and we are starting to run short on collars.
 
-One of our intrepid data science team members found an old box of collars and some data on all of the collars (it was a real challenge getting this data off of an old floppy drive, but she managed).
+One of our intrepid data science team members found an old box of collars and some data on all of the collars (it was a real challenge getting this data off of an old floppy drive, but we managed).
 
 ## Our Tasks
 
@@ -27,32 +36,29 @@ We're going to spend the next lessons tackling two tasks:
 
 Our main goal is to create a single data set that includes the data we have previously looked at for collars, the data on the new collars, as well as the additional data on the old collars.
 
-This is a little tricky, as the collar IDs need to be matched up with their counterparts across datasets, and new collars need be added. Unfortunately, the new collars have IDs and some additional data, but we don't know the maker of the new collars.
+This is a little tricky, as the collar IDs need to be matched up with their counterparts across datasets, and new collars need to be added. Unfortunately, the new collars have IDs and some additional data, but we don't know the maker of the new collars.
 
 **2) Second, we will try to identify the maker of mystery collars.**
 
-In order to do this, we will skim the surface of machine learning. We can use a common classification algorithm, K-Nearnest Neighbors (KNN), to predict which maker made which of our unidentified collars. Don't worry, we won't go into *too* much detail here, but I want to you to at least have a general idea of how it works.
+In order to do this, we will skim the surface of machine learning. We can use a common classification algorithm, K-Nearest Neighbors (KNN), to predict which maker made which of our unidentified collars. Don't worry, we won't go into *too* much detail, but having a general idea of how it works will be useful.
 
 ## Combining Data (Joins and Binds)
 
-Often times, we have a lot of data for one project that are related but storing all of the data in one file would add unnecessary redundancy (e.g., data in certain rows would need to be repeated too often). Other times, data has been collected separately and needs to be combined before analysis.
+Oftentimes, we have a lot of data for one project that are related but storing all of the data in one file would add unnecessary redundancy (e.g., data in certain rows would need to be repeated too often). Other times, data has been collected separately and needs to be combined before analysis.
 
 Being able to join together data from related tables is a key skill in data science, and for working with larger data structures (databases with their own languages, like SQL).
 
 ### The Data
 
-Let's load in the `tidyverse` and the data we're working.
+Let's load in the `tidyverse` and the data we're working with.
 
-```{r message=FALSE}
-# load the tidyverse
+```{r}
+#| message: false
+#| warning: false
 library(tidyverse)
-
-#loading in the data
 collars <- read_csv("data/collar_data.csv")
 new_collars <- read_csv("data/new_collars.csv")
 old_collars_new_data <- read_csv("data/old_collars_new_data.csv")
-
-# Tell me about these data, how are we going to join them?
 ```
 
 First, let's explore our data. We want to focus on 2 things here:
@@ -76,6 +82,14 @@ In small groups, talk through the process of combining these three datasets. Thi
 
 Draw out a diagram that represents how this process might go.
 
+::: instructor-only
+**Instructor Note:** `collars` and `old_collars_new_data` share `collar_id` and `maker`, these get joined.
+
+`new_collars` has different collars entirely, these get appended with `bind_rows()`.
+
+The order matters: join first (to enrich the existing collar data), then bind (to add the new collars). If they bind first they will lose the ability to match on `collar_id`.
+:::
+
 ## Joins vs. Binds
 
 Now that we've decided on a process for how to combine our data, let's figure out which functions we are going to use to accomplish this task.
@@ -86,7 +100,7 @@ We have 2 main methods of combining datasets, and they work in different ways.
 
 Joins are arguably the more complicated of the two types of ways to combine data, but they are, therefore, the more flexible and useful.
 
-The magic of comes comes because they match up columns of data based on unique identifiers in each row of data.
+The magic of joins comes because they match up columns of data based on unique identifiers in each row of data.
 
 In the following diagram, the two example data frames have the column `x1` in common, and each of the values in `x1` are unique (no repeats in the same data frame). When combining the datasets, all of the columns are added, and their rows are matched up to their respective values in the `x1` column.
 
@@ -94,21 +108,34 @@ This can happen a couple ways, depending on which data frame is the reference an
 
 ![](assets/joins.png){width="50%"}
 
+There are four main types of joins, and they differ in which rows they keep:
+
+-   `left_join()`: keeps all rows from the left (first) dataset and adds matching columns from the right dataset. (Rows in the right with no match are dropped, rows in the left with no match get `NA` for the right-side columns.)
+-   `right_join()`: keeps all rows from the right dataset.
+-   `inner_join()`: keeps only rows that have a match in both datasets.
+-   `full_join()`: keeps all rows from both datasets, filling `NA` where there is no match.
+
+For our task in Step 1, we are using `left_join()` because we want to keep all of our original collars (the left dataset) and add the new measurements from `old_collars_new_data` (the right dataset). Since every collar in `collars` has a matching entry in `old_collars_new_data`, a `full_join()` would actually give the same result here.
+
 ### Binds
 
-The other way we can combine data sets in through binds. Binds act similarly to gluing datasets together.
+The other way we can combine data sets is through binds. Binds act similarly to gluing datasets together.
 
-They don't match up data based on unique identifiers; instead they match up data by column name (`bind_rows`) or row position (`bind_cols`)
+They don't match up data based on unique identifiers; instead they match up data by column name (`bind_rows()`) or row position (`bind_cols()`)
 
 ![](assets/binds.png){width="50%"}
 
 How should we go about combining our three datasets? Come up with a plan that you think will work.
 
+::: instructor-only
+**Instructor Note:** The correct order is: (1) left_join collars with old_collars_new_data on collar_id and maker to add antenna_length and weight to existing collars, then (2) bind_rows to append new_collars.
+:::
+
 ## Task 1: Combine Our Data
 
 ### Step 1
 
-Our first step in to merge the old collar data with the new data about those old collars.
+Our first step is to merge the old collar data with the new data about those old collars.
 
 ```{r}
 # use a left join
@@ -118,10 +145,14 @@ old_collars_combined <- collars %>%
 
 ```
 
-If we take a look our new data frame, we should hopefully see that the values for `antenna_length` and `weight` have been matched up with their respective collar id.
+If we take a look at our new data frame, we should hopefully see that the values for `antenna_length` and `weight` have been matched up with their respective collar ID.
 
 Could we have used another tactic to combine these two datasets? What would the pros and cons be?
 
+::: instructor-only
+**Instructor Note:** A `full_join()` would produce the same result here since all collar IDs appear in both datasets. The key advantage of `left_join()` is that it explicitly matches on `collar_id` and `maker`.
+:::
+
 ### Step 2
 
 Now we need to add the new collars to our dataset. What is our best method for combining?
@@ -141,9 +172,9 @@ tail(full)
 
 Now that we've accomplished our first task, we are going to want to use this combined dataset to accomplish our next task, which is using a classification algorithm to help us predict which maker made the mystery collars.
 
-To save our data as a .csv file that we can use in another analysis, we are going to use a function that exports the dataset (`write_csv`) instead of importing it (`read_csv`).
+To save our data as a .csv file that we can use in another analysis, we are going to use a function that exports the dataset (`write_csv()`) instead of importing it (`read_csv()`).
 
-The `write_csv` function requires the name of the dataframe to export as the first argument and the name of the file we want to create as the second argument.
+The `write_csv()` function requires the name of the dataframe to export as the first argument and the name of the file we want to create as the second argument.
 
 ```{r}
 write_csv(full, "data/all_collar_data.csv")
diff --git a/modules/module_4/module4_2.qmd b/modules/module_4/module4_2.qmd
index 7c25ad9..242cbc1 100644
--- a/modules/module_4/module4_2.qmd
+++ b/modules/module_4/module4_2.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # K-Nearest Neighbors
 
+## Learning Outcomes
+
+-   Students will be able to explain the general concept of K-Nearest Neighbors classification.
+-   Students will be able to describe the purpose of training and testing data in machine learning.
+-   Students will be able to interpret the output of a KNN model including the confusion matrix and predictions.
+-   Students will be able to scale data and explain why scaling is necessary for distance-based algorithms.
+
 ## Machine Learning
 
 Now, we're going to work through an example of machine learning, which is used in a variety of ways in data science, across industries and disciplines.
@@ -21,7 +28,7 @@ Machine learning is a subfield of artificial intelligence (AI). Machine learning
 
 Remember, we have 50 unlabeled collars, and we want to be able to predict who made them so we can increase the safety of our fishers. We need to throw out any collars that we think were made by Budget Collars LLC, and only incorporate new collars from our unlabeled batch that we think were made by Collarium Inc.
 
-Given the data we have from the collars we know were created by Budget Collars LLC and Collarium Inc, our goal is to train a model on collars with known makers to then apply to our unknown collars.
+Given the data we have from the collars we know were created by Budget Collars LLC and Collarium Inc., our goal is to train a model on collars with known makers to then apply to our unknown collars.
 
 ## KNN in R
 
@@ -33,13 +40,13 @@ You don't need to know the inner-workings of the model or even the code below; a
 
 ### How does KNN work?
 
-Let's start with watch [a video](https://www.youtube.com/watch?v=MDniRwXizWo&ab_channel=AugmentedStartups) that does a nice job explaining the basics of KNN.
+Let's start by watching [a video](https://www.youtube.com/watch?v=MDniRwXizWo&ab_channel=AugmentedStartups) that does a nice job explaining the basics of KNN.
 
 What are your takeaways?
 
 ### Visualizing
 
-Creating plots that visualize KNN is pretty complicated, so we won't be doing that as a class. However, you can find a nice interactive data visualization [here](http://vision.stanford.edu/teaching/cs231n-demos/knn/).
+Creating plots that visualize KNN is pretty complicated, so we won't be doing that in this lesson. However, you can find a nice interactive data visualization [here](http://vision.stanford.edu/teaching/cs231n-demos/knn/).
 
 Play around with the data visualization. Can you figure out what it is telling you?
 
@@ -53,10 +60,25 @@ First, we need our data!
 
 ### Data Cleaning and Prepping
 
-```{r message=FALSE}
+Load the tidyverse and read in "data/all_collar_data.csv", saving it as `full_collars`.
+
+```{r}
+#| message: false
+#| warning: false
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
+```{r}
+#| message: false
+#| warning: false
 library(tidyverse)
 full_collars <- read_csv("data/all_collar_data.csv")
 ```
+:::
 
 Next, we need to scale our data.
 
@@ -94,7 +116,7 @@ Right now, we have 3 collar categories because we haven't separated the unknown
 
 ### Known and Unknown Labels
 
-Before we continue, we need to make separate data frames to work with, one with collars with known makers and one with collars with unknown labels
+Before we continue, we need to make separate data frames to work with, one with collars with known makers and one with collars with unknown labels.
 
 ```{r}
 # we also need to remove the collar_id labels
@@ -113,7 +135,7 @@ labeled_collars <- knn_data %>%
 
 ### Training and Testing Data
 
-First, we need to train and test our model. We will do that with the data with known collars makers.
+First, we need to train and test our model. We will do that with the data with known collar makers.
 
 Note: this is where the code starts getting a bit complicated. It's ok if you aren't entirely clear on what everything is doing.
 
@@ -141,6 +163,8 @@ Now that we have training and testing data, we can perform the first phase of KN
 We want to know how many nearest neighbors we should use and how well our model works on the testing data. We will use the `caret` package to run these analyses.
 
 ```{r}
+#| message: false
+#| warning: false
 #loading package to do knn
 library(caret)
 
@@ -151,7 +175,7 @@ knn_training_model <- train(maker ~ ., data = training, method = "knn")
 knn_training_model # best k value is 5
 ```
 
-Ok, we have determined that our best k value is 5. Now we can use our training model on the testing data to see if it makes any mistakes. We use the `predict` function from `caret` to do this.
+Okay, we have determined that our best k value is 5. Now we can use our training model on the testing data to see if it makes any mistakes. We use the `predict` function from `caret` to do this.
 
 ```{r}
 # making predictions with the test data
@@ -180,4 +204,6 @@ new_labels <- bind_cols(unknown_collar_ids, knn_predict)
 new_labels
 ```
 
-Typically, you would want to replace the columns in the entire dataframe, but we will end here for today :) Our goal of labeling our unlabeled collars is complete! We will deploy any that are now labeled as Collarium LLC.
\ No newline at end of file
+Typically, you would want to replace the columns in the entire dataframe, but we will end here for today :)
+
+Our goal of labeling our unlabeled collars is complete! We will deploy any that are now labeled as Collarium Inc.
\ No newline at end of file
diff --git a/modules/module_4/module4_3.qmd b/modules/module_4/module4_3.qmd
index 7c2b2df..31f723f 100644
--- a/modules/module_4/module4_3.qmd
+++ b/modules/module_4/module4_3.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Roads and Regressions
 
+## Learning Outcomes
+
+-   Students will be able to create a scatter plot with a line of best fit using `geom_smooth()`.
+-   Students will be able to calculate and interpret a correlation coefficient and R-squared value.
+-   Students will be able to run a linear regression using `lm()` and interpret the output including slope, intercept, and p-value.
+-   Students will be able to use regression results to inform a data-driven decision.
+
 ## The Scenario
 
 In this module, we have tried to figure out which bays we should prioritize for fishing based on the following data sets:
@@ -64,12 +71,23 @@ For this lesson, we are going to focus on two: *nitrogen (N) content* and *soil
 
 ### Set-Up
 
-As usual, we start with loading our packages and our data.
+As usual, we start with loading tidyverse and our data. Read in "data/hairgrass_data.csv" and save it as an object called `hairgrass_data`.
+
+```{r}
+#| message: false
+#| warning: false
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
 
-```{r message=FALSE}
+```{r}
 library(tidyverse)
 hairgrass_data <- read_csv("data/hairgrass_data.csv")
 ```
+:::
 
 Let's take a look at the data in the data set. What does each row represent?
 
@@ -84,12 +102,14 @@ Let's start by investigating any relationship between hair grass density and nit
 
 First, we should spend a little time thinking about our variables. Spend a few minutes in small groups discussing the answer to the questions below.
 
--   Which columns are we interested in right now? soil_nitrogen, hairgrass_density
+-   Which columns are we interested in right now?
 -   Which one is the independent variable and which is the dependent variable?
-    -   independent: soil_nitrogen
-    -   dependent: hairgrass_density
--   Are the variables categorical or continuous? both are continuous
--   Which type of data visualization should we use? scatterplot
+-   Are the variables categorical or continuous?
+-   Which type of data visualization should we use?
+
+::: instructor-only
+**Answers:** - soil_nitrogen and hairgrass_density - independent = soil_nitrogen, dependent = hairgrass_density - both continuous - scatter plot
+:::
 
 Next, calculate the mean (`mean()`) and standard deviation (`sd()`) for the nitrogen content.
 
@@ -135,7 +155,7 @@ ggplot(hairgrass_data, aes(soil_nitrogen, hairgrass_density)) +
   geom_point() +
   geom_smooth(method = "lm", se = FALSE) +
   labs(x = "N Content",
-       y = "Hairgrass Density (per m^2)") +
+       y = "Hairgrass Density (per 25 m^2)") +
   theme_bw()
 
 # method = "lm" is required!
@@ -160,7 +180,7 @@ It is also important to recognize that the correlation coefficient has nothing t
 
 ![](assets/steepness.jpeg){width="50%"}
 
-Based on the hairgrass plot we did above, do you expect the correlation coefficient to be positive or negative? For this class, we'll say that any `r` value with 0.1 of 0 means there is no relationship.
+Based on the hairgrass plot we did above, do you expect the correlation coefficient to be positive or negative? For this class, we'll say that any `r` value within 0.1 of 0 means there is no relationship.
 
 To calculate the correlation coefficient, we need to go back to base R and indicate which columns we are referencing with the `$` operator. We use the `cor()` function.
 
@@ -185,6 +205,10 @@ r_nitrogen^2
 
 How would we interpret this `r^2` value?
 
+::: instructor-only
+**Instructor Note:** r = 0.595. a moderate positive relationship. r\^2 = 0.354, meaning nitrogen content explains about 35% of the variation in hairgrass density. This is a meaningful relationship for ecological data.
+:::
+
 One way to think of the `r^2` value that might be helpful is to convert it from a proportion to a percentage—what *percentage* of the variation in the dependent variable is explained by the independent variable?
 
 To convert the proportion to a percentage, we can multiply the value by 100.
@@ -201,7 +225,7 @@ Since we are using linear regressions in this course, the regression model will
 
 -   `y` = dependent variable (y-axis)
 -   `x` = independent variable (x-axis)
--   `m` = slope of the line is the slope
+-   `m` = slope of the line
 -   `b` = y-intercept
 
 When we have a line with the specific slope and y-intercept, this equation lets us calculate the expected y-axis value for any given point on the x-axis.
@@ -218,6 +242,10 @@ What is the null hypothesis? What is the alternative hypothesis?
 
 What do you think this means for the slopes?
 
+::: instructor-only
+**Instructor Note:** The null hypothesis of no relationship corresponds to a slope of 0. If there's no relationship, the line of best fit would be flat. The alternative hypothesis implies a non-zero slope (either positive or negative).
+:::
+
 ### Regression Analysis
 
 Thankfully, R can calculate the slope (`m`) and y-intercept (`b`) of the line of best fit for us.
@@ -249,46 +277,85 @@ This means our equation for the line of best fit is: y = 1.8x + 84.24 and there
 
 ## Soil pH
 
-Let's do the same series of steps to determine how soil pH impacts hair grass densities. Work on this in your small groups, and we will go over it in about 10 minutes.
+Let's do the same series of steps to determine how soil pH impacts hair grass densities.
+
+Work on this in your small groups.
+
+::: instructor-only
+**Instructor Note:** Work in small groups for about 10 minutes, then review as a class.
+:::
 
 Start with summarizing the data: mean and standard deviation.
 
+```{r}
+# Write your code here
+
+```
+
+Visualize the data. Remember to add in the line of best fit using `ggplot2`. Also add labels and a theme.
+
+```{r}
+# Write your code here
+
+```
+
+Calculate the correlation coefficient (`r`). What does this tell us?
+
+```{r}
+# Write your code here
+
+```
+
+How much variation does soil pH explain in the hair grass density data?
+
+```{r}
+# Write your code here
+
+```
+
+Write out the model equation for our question about soil pH (without values).
+
+Run the regression model and write out the equation for the line of best fit.
+
+```{r}
+# Write your code here
+
+```
+
+Interpret the results of the regression. What do we conclude about the relationship between soil pH and hair grass density, and why?
+
+::: instructor-only
+**Answers:**
+
 ```{r}
 hairgrass_data %>% 
   summarize(mean_pH = mean(soil_ph),
             sd_pH = sd(soil_ph))
 ```
 
-Visualize the data. Remember to add in the line of best fit using `ggplot2`. Also add labels and a theme for practice.
-
 ```{r}
 ggplot(hairgrass_data, aes(soil_ph, hairgrass_density)) +
   geom_point() +
   geom_smooth(method = "lm", se = FALSE) +
   labs(x = "Soil pH",
-       y = "Hairgrass Density per m^2") + 
+       y = "Hairgrass Density (per 25 m^2)") + 
   theme_bw()
 ```
 
-Calculate the correlation coefficient (`r`)? What does this tell us?
-
 ```{r}
 r_ph <- cor(x = hairgrass_data$soil_ph, y = hairgrass_data$hairgrass_density)
+r_ph
 ```
 
-How much variation does soil pH explain in the hair grass density data?
+r = -0.131. a weak negative relationship, barely outside the "no relationship" threshold of 0.1.
 
 ```{r}
 r_ph^2
 ```
 
-Write out the model for our question about soil pH (without values)?
+`r^2` = 0.017. soil pH explains only about 1.7% of the variation in hairgrass density.
 
-```{r}
-# hairgrass density  = m(soil_ph) + b
-```
-
-Run the regression model and write out the equation for the line of best fit.
+Model form: hairgrass_density = m(soil_ph) + b
 
 ```{r}
 ph_model <- lm(hairgrass_density ~ soil_ph, data = hairgrass_data)
@@ -297,12 +364,23 @@ summary(ph_model)
 
 Equation for line of best fit: y = -4.648x + 150.24
 
-Interpret the results of the regression (our p-value cutoff is still = 0.05). What do we conclude about the relationship between soil pH and hair grass density? Why?
+p = 0.0421, which is less than 0.05, so we reject the null hypothesis. There is a statistically significant relationship between soil pH and hairgrass density.
+
+However, look at `r^2` = 0.017. That means soil pH explains only 1.7% of the variation in hairgrass density. The relationship is real but extremely weak.
 
-Because p \< 0.05 for the regression model, we reject the null hypothesis and conclude that there is a relationship between soil pH and hairgrass density.
+**A p-value tells us whether a relationship exists, r\^2 tells us how strong it is. Here, the relationship technically exists but is far too weak to be a meaningful predictor. Nitrogen (r\^2 = 0.354, explaining 35% of variation) is a way better predictor of where hairgrass thrives.**
+:::
 
 ## Data-driven Decision Making
 
 The reason we are using regression analysis is to inform where we should (or should not) build our road so we don't harm the sensitive hair grass or take away their prime habitat.
 
-What do results above for nitrogen content and soil pH mean for the road we are building?
\ No newline at end of file
+What do results above for nitrogen content and soil pH mean for the road we are building?
+
+::: instructor-only
+**Instructor Note:** Nitrogen content is by far the stronger predictor. r\^2 = 0.354, meaning it explains 35% of the variation in hairgrass density (p \< 2.2e-16). Higher nitrogen strongly predicts higher hairgrass density. **Road planning should prioritize avoiding high-nitrogen areas.**
+
+Soil pH is statistically significant (p = 0.0421) but practically very weak. r\^2 = 0.017 means it explains only 1.7% of the variation. While the negative slope suggests more acidic soils have slightly higher density, this relationship is too weak to be a reliable guide on its own.
+
+**p \< 0.05 tells us a relationship exists, but r\^2 tells us whether it is actually strong enough to be useful.** Soil pH passes the significance threshold but fails the usefulness test.
+:::
\ No newline at end of file
diff --git a/modules/module_4/module4_4.qmd b/modules/module_4/module4_4.qmd
index 50e8154..ba34daf 100644
--- a/modules/module_4/module4_4.qmd
+++ b/modules/module_4/module4_4.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Multiple Regression
 
+## Learning Outcomes
+
+-   Students will be able to explain when and why a multiple regression is used.
+-   Students will be able to run a multiple regression in R and interpret the output for each variable.
+-   Students will be able to explain what an interaction term means and identify it visually from a plot.
+-   Students will be able to run a model with an interaction term and interpret the results.
+
 Sometimes (often!), we have multiple independent variables that we think might be influencing our dependent variable. In order to take into account more than one variable, we can perform what is called a "multiple regression."
 
 Multiple regressions can become complicated beasts, but we are going to focus on adding in just one additional variable and how it can impact our interpretations.
@@ -21,16 +28,32 @@ Let's use some penguins nesting site data to demonstrate.
 
 ## Set-Up
 
-As usual, we want to load our packages and our data before we begin.
+Load the tidyverse and read in "data/site_changes.csv", saving it as `site_data`
 
-```{r message = FALSE}
+```{r}
+#| message: false
+#| warning: false
+# Write your code here
+
+```
+
+::: instructor-only
+**Answer:**
+
+```{r}
 library(tidyverse)
-site_data <- read_csv("data/site_changes.csv") %>% 
-  mutate(year = as.factor(year)) # don't worry about this code too much
-# we want the year column treated as a category, not a number
+site_data <- read_csv("data/site_changes.csv")
 ```
+:::
 
-We want to understand what factors influence penguin nesting---we wouldn't want to build our road through important penguin nesting territories!
+```{r}
+# This converts the year column to a categorical variable rather than a number
+# We need to run this for the regression models below to work!
+site_data <- site_data %>% 
+  mutate(year = as.factor(year))
+```
+
+We want to understand what factors influence penguin nesting. We wouldn't want to build our road through important penguin nesting territories!
 
 Let's quickly take a look at the data we have.
 
@@ -64,6 +87,10 @@ summary(tussocks_model)
 
 Our model is telling us that the relationship between the number of nests and number of tussocks is highly significant.
 
+::: instructor-only
+**Instructor Note:** p = 8.81e-05, highly significant. The positive coefficient (1.33) means more grass tussocks predict more nests. R\^2 = 0.075, so tussocks explains about 7.5% of variation on its own.
+:::
+
 ### Running a Multiple Regression
 
 What if we incorporate the year the data were collected into our model? Adding an additional independent variable to our model can change how we interpret the results.
@@ -89,12 +116,16 @@ tussocks_model <- lm(num_nests ~ tussocks + year, data = site_data)
 summary(tussocks_model)
 ```
 
-We have a bunch of estimates now! Don't worry, I won't be asking you to put them into an equation :) We are only focusing on significance values.
+We have a bunch of estimates now! Don't worry about putting these into an equation :) We are only focusing on significance values.
 
 -   `tussocks`: the p-value for the tussocks row (p = 0.7) shows whether the number of tussocks is a significant driver of nest numbers (it isn't!)
 -   `year2011`: this p-value is significant (p = 4.69e-05)---year does seem to impact how many nests there are
     -   (you don't need to know this, but if you are curious...): the reason this says `year2011` and not just `year` is because year is acting as a category, and we can get different coefficient estimates for year. The first option in the categorical variable (1971, in this case) is incorporated into the current intercept estimate, so `year2011` indicates the difference from the original.
 
+::: instructor-only
+**Instructor Note:** Once the model accounts for the fact that nest numbers changed between years, tussocks no longer explains any additional variation. The tussocks effect in the simple model was being driven by the year difference, not by tussocks themselves.
+:::
+
 ### Interactions
 
 Sometimes, we might expect that there will be in interactive effect between the two independent variables we are including in our model. This means that the combinations of independent variables impact the dependent variable in different ways.
@@ -124,9 +155,9 @@ It turns out that adding in the year variable has changed the significance in ou
 -   The positive relationship we originally observed is no longer present when we account for the year.
 -   There is no significant interaction, meaning that penguins respond to the number of tussocks the same way in each year.
 -   Interestingly (and maybe frustratingly), the significant effect of year that we saw before is also gone.
-    -   This is a confusing bit of statistics, and I won't go into depth here about why this happens. Similar things sometimes happen when we have a significant overall ANOVA but no significant pairwise comparisons.
+    -   This is a confusing bit of statistics, and the full explanation is beyond the scope of this course. Similar things sometimes happen when we have a significant overall ANOVA but no significant pairwise comparisons.
     -   What is happening behind the scenes has to do with how the information for each variable is being partitioned. Adding new variables (including an interaction) decreases the likelihood of any individual variable being significant.
-    -   I will never ask you to explain this! Only to correctly interpret the significance values.
+    -   For this course, the goal is to correctly interpret the significance values, not to explain why this happens.
 
 Let's explore another example to get a better understanding.
 
@@ -153,6 +184,10 @@ summary(stone_size_mod)
 
 How do we interpret this model?
 
+::: instructor-only
+**Instructor Note:** Stone size is a significant positive predictor of nest numbers (p = 1.64e-05). Larger stones are associated with more nests. R\^2 = 0.090, so stone size explains about 9% of variation. Significant, but a weak predictor on its own.
+:::
+
 Next, let's add year in the mix. First, plot the relationship between the number of nests and stone size by year.
 
 ```{r}
@@ -170,6 +205,10 @@ summary(stone_size_mod)
 
 How do we interpret this multiple regression model?
 
+::: instructor-only
+**Instructor Note:** Both stone size (p = 5.6e-07) and year (p = 6.1e-10) are highly significant. Unlike tussocks, stone size remains a significant predictor even after accounting for year. The model improved substantially. R\^2 went from 0.090 to 0.251, meaning the two-variable model explains 25% of variation vs. 9% for stone size alone.
+:::
+
 From the ggplot above, we can see that the slopes for the two different years are quite different: one is pretty flat while the other is definitely positive. Different slopes often indicate that an *interaction* might be at work. Let's find out. Run another multiple regression model, this time adding in the interactive effect (`*`).
 
 ```{r}
@@ -179,10 +218,16 @@ summary(stone_size_mod)
 
 Wow, a bunch of significant variables! What does that mean, exactly?
 
+::: instructor-only
+**Instructor Note:** All three terms are significant: stone_size (p = 9.35e-12), year2011 (p = 2.51e-07), and the interaction stone_size:year2011 (p = 2.57e-06).
+
+The negative interaction coefficient (-1.13) means the effect of stone size on nest numbers is weaker in 2011 than in 1971.
+:::
+
 Not only are stone size and year significant, but so is the interaction between stone size and year. What does this mean? It means that penguins in different years seems to respond differently to stone size. In 1971, the number of nests increases with stone size, but in 2011, the number of nests doesn't seem impacted by stone size. Therefore, there is an *interaction* between stone size and year.
 
 ### Resources
 
-Still feeling a bit confused about multiple regression and interactions? I recommend checking out [this website](https://stattrek.com/multiple-regression/interaction?tutorial=reg). You can skip all the equation stuff at the beginning. Below that, though, I think they do a nice job of laying out what interactions are.
+Still feeling a bit confused about multiple regression and interactions? [This website](https://stattrek.com/multiple-regression/interaction?tutorial=reg) does a nice job of laying out what interactions are.
 
 Also, if you are interested in how we select which model is the best model, [this article](https://www.statology.org/aic-in-r/) is a decent place to start.
\ No newline at end of file
diff --git a/modules/module_4/module4_home.qmd b/modules/module_4/module4_home.qmd
index d400d62..9f43e81 100644
--- a/modules/module_4/module4_home.qmd
+++ b/modules/module_4/module4_home.qmd
@@ -21,16 +21,22 @@ By the end of this module, students will be able to:
 -   add a regression line to a `ggplot2` scatter plot using `geom_smooth()`
 -   explain when and why multiple regression is preferable to simple linear regression
 -   run and interpret a multiple regression with more than one independent variable
--   select the appropriate statistical test for a given research question and dataset
 
 ## Lessons & Assignments
 
-Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+### Lessons
+
+Each lesson page displays content in **student view** by default. Use the toggle button in the top right corner of the page to switch to **instructor view**, which reveals embedded answer keys and instructor notes. The QMD file, PDF, and any associated data can be downloaded from each lesson's page in either view.
 
 -   [4.1: Combining Datasets (Joins & Binds)](module4_1.qmd)
 -   [4.2: K-Nearest Neighbor](module4_2.qmd)
 -   [4.3: Roads and Regressions](module4_3.qmd)
 -   [4.4: Multiple Regression](module4_4.qmd)
+
+### Assignments
+
+Assignment pages are publicly available in **student view only**. The QMD file, PDF, and any associated data can be downloaded from each assignment's page. Instructor versions with answer keys are only available to educators. To request access, visit the [Instructors page](../../instructors.qmd).
+
 -   Assignment 1: Linear Regression
 -   Assignment 2: More Regressions
 -   Assignment 3: Multiple Regressions
@@ -40,4 +46,4 @@ Downloads for each lesson and assignment - including the QMD file, PDF, and any
 
 To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
 
-**Links are not yet live!**
\ No newline at end of file
+**Links are not yet live.**
\ No newline at end of file
diff --git a/modules/module_5/assets/exp_and_logisitc.png b/modules/module_5/assets/exp_and_logistic.png
similarity index 100%
rename from modules/module_5/assets/exp_and_logisitc.png
rename to modules/module_5/assets/exp_and_logistic.png
diff --git a/modules/module_5/module5_1.qmd b/modules/module_5/module5_1.qmd
index 26511da..72c52c2 100644
--- a/modules/module_5/module5_1.qmd
+++ b/modules/module_5/module5_1.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Introduction to Population Growth
 
+## Learning Outcomes
+
+-   Students will be able to distinguish between exponential and logistic population growth models.
+-   Students will be able to interpret the intrinsic rate of increase (`r`) and carrying capacity (`K`) and explain their ecological significance.
+-   Students will be able to use `filter()` and `ggplot2` to explore and visualize population data.
+-   Students will be able to evaluate which fish population characteristics make a population a sustainable fishing target.
+
 Let's use descriptive statistics and data visualization to better understand population dynamics.
 
 ### Exponential Growth
@@ -41,7 +48,7 @@ We use the following equation to calculate the population size:
 
 As a reminder, this is what each type of population growth model looks like:
 
-![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
+![](assets/exp_and_logistic.png){width="75%" fig-align="center"}
 
 # What Population Factors Matter?
 
@@ -58,14 +65,25 @@ Let's look at fish data from around our station in Antarctica over the past 20 y
 
 These aren't necessarily our targets for fishing, particularly since it would be difficult and potentially disruptive to fish in the bay. Right now our goal is just to learn so we know what to look for when we are making our decision to conduct sustainable fishing.
 
-![Adapted from Figure 2 from Zheng et al. (2022)](assets/fish_data_fish.png){fig-align="center"}
+![Adapted from Figure 2 from Zhang et al. (2022)](assets/fish_data_fish.png){fig-align="center"}
+
+As always, first we load the `tidyverse` and read in the data: "fish_pops.csv" as `fish_pops`.
+
+```{r}
+#| message: false
+#| warning: false
+# Write your code here
+
+```
 
-As always, first we load the `tidyverse` and read in the data.
+::: instructor-only
+**Answer:**
 
 ```{r}
 library(tidyverse)
 fish_pops <- read_csv("data/fish_pops.csv")
 ```
+:::
 
 ### Largest Population
 
@@ -86,11 +104,13 @@ popB <- fish_pops %>%
 popB
 ```
 
-**What type of growth is the population exhibiting?**\
-*Answer: Exponential.*
+**What type of growth is the population exhibiting?**
 
-**Why is there no carrying capacity in this population data set? What does this mean about the resources Population B needs?**\
-*Answer: Exponential models assume unlimited resources, so no carrying capacity (K) is included.*
+**Why is there no carrying capacity in this population data set? What does this mean about the resources Population B needs?**
+
+::: instructor-only
+**Answers:** - Growth type: exponential - No carrying capacity because exponential models assume unlimited resources. Population B is not yet limited by available resources.
+:::
 
 ### Exponential Growth Populations
 
@@ -122,8 +142,11 @@ popA <-fish_pops %>%
 popA
 ```
 
-**What do you notice about Population A that is different than Population B?**\
-*Answer: The intrinsic growth rate (r) for Population A is negative.*
+**What do you notice about Population A that is different than Population B?**
+
+::: instructor-only
+**Answer:** The intrinsic growth rate (r) for Population A is negative.
+:::
 
 The intrinsic rate of increase (r) for Population A is negative. Let's see what that means for Population A and plot it by itself:
 
@@ -135,17 +158,23 @@ ggplot(popA, aes(x = year, y = N)) +
   theme_light()
 ```
 
-**What has happened to this population?**\
-*Answer: Population A crashes and reaches zero.*
+**What has happened to this population?**
+
+::: instructor-only
+**Answer:** Population A crashes and reaches zero. A negative r means more individuals are dying than being born, so the population declines until it goes extinct.
+:::
 
 Even with "exponential growth", the population has crashed (gone to 0), because *r* is negative. It took approximately 7 years for this to happen.
 
-**Between Population A and Population B, which is a better target population for fishing? Why?**\
-*Answer: Population B, because r \> 0 and it grows quickly.*
+**Between Population A and Population B, which is a better target population for fishing? Why?**
 
-**Logistic growth populations**
+::: instructor-only
+**Answer:** Population B: r \> 0 and it grows quickly, meaning it can recover from harvesting pressure. Population A is already declining and would collapse under any additional pressure.
+:::
 
-Ok, so we can't rely only on the type of growth as a way to make our fishing decisions. But, we're still curious about our populations that are limited by resources (i.e., exhibiting logistic growth).
+### Logistic Growth Populations
+
+Okay, so we can't rely only on the type of growth as a way to make our fishing decisions. But, we're still curious about our populations that are limited by resources (i.e., exhibiting logistic growth).
 
 ```{r}
 log_pops <- fish_pops %>%
@@ -165,8 +194,11 @@ ggplot(log_pops, aes(x = year, y = N, color = population))+
   theme_light()
 ```
 
-**Which population gets the largest?**\
-*Answer: Population D*
+**Which population gets the largest?**
+
+::: instructor-only
+**Answer:** Population D.
+:::
 
 ### Finding the Carrying Capacity
 
@@ -174,13 +206,20 @@ When we have a logistic growth model, we often will want to find the carrying ca
 
 **What values does each population level off at?**
 
--   Population C: *20*
+-   Population C:
+-   Population D:
+
+::: instructor-only
+**Answers:** Pop C: 20 Pop D: 100 (carrying capacity (K) values in the data)
+:::
 
--   Population D: *100*
+**Which population do you think might be the best candidate to fish between these two?**
 
-These are the carrying capacities. If you look back at the data, you will see Population C has a carrying capacity of 20 individuals, whereas Population D has a carrying capacity of 100 individuals. **Which population do you think might be the best candidate to fish between these two?** *Answer: Population D is more abundant and has a higher carrying capacity.*
+::: instructor-only
+**Answer:** Population D. It is more abundant and has a higher carrying capacity, meaning it can support more individuals and better withstand fishing.
+:::
 
-**A review of what we have learned**
+### A Review of What We Have Learned
 
 The following factors are important to know about a population before fishing:
 
diff --git a/modules/module_5/module5_2.qmd b/modules/module_5/module5_2.qmd
index d8003d5..738530f 100644
--- a/modules/module_5/module5_2.qmd
+++ b/modules/module_5/module5_2.qmd
@@ -13,6 +13,13 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Sustainable Fishing
 
+## Learning Outcomes
+
+-   Students will be able to explain why logistic growth populations are preferable targets for sustainable fishing.
+-   Students will be able to fit a logistic growth model using the `drc` package and extract the carrying capacity estimate.
+-   Students will be able to calculate the maximum sustainable yield (MSY) from a carrying capacity estimate.
+-   Students will be able to add a horizontal reference line to a `ggplot2` plot using `geom_hline()`.
+
 ## Applying Principles of Population Growth
 
 We have decided that the Antarctic toothfish (*Dissostichus mawsoni*) may be a good candidate for our fishing.
@@ -29,13 +36,23 @@ Let's keep exploring our fish populations to help us understand what we should b
 
 ## Set-Up
 
-As usual, we start with loading our packages and our data.
+As usual, we start with loading our packages and our data. Read in "fish_pops.csv" as `fish`.
 
 ```{r}
-library(tidyverse)
+#| message: false
+#| warning: false
+# Write your code here
+
+```
 
+::: instructor-only
+**Answer:**
+
+```{r}
+library(tidyverse)
 fish <- read_csv("data/fish_pops.csv")
 ```
+:::
 
 Let's take a look at the data and remind ourselves what the data look like.
 
@@ -47,7 +64,7 @@ head(fish)
 
 Let's remind ourselves about exponential growth and logistic growth.
 
-**Expotential growth** takes births and deaths (in the form of the intrinsic growth rate, `r`) into account:
+**Exponential growth** takes births and deaths (in the form of the intrinsic growth rate, `r`) into account:
 
 ![](assets/clipboard-4205079346.png){width="83" fig-align="center"}
 
@@ -57,7 +74,7 @@ Let's remind ourselves about exponential growth and logistic growth.
 
 This is what each type of population growth model looks like:
 
-![](assets/exp_and_logisitc.png){width="75%" fig-align="center"}
+![](assets/exp_and_logistic.png){width="75%" fig-align="center"}
 
 ### Which Population to Fish?
 
@@ -86,10 +103,12 @@ While the dataset contains carrying capacity estimates, those are only visual es
 
 To do so, we are actually going to use a new package, `drc`.
 
-This package will let us fit a logistic model and provide an estimate of the asymptote of the curve, or the value where the curve levels off and not longer increases. This is our estimate of `K`, the carrying capacity of the population.
+This package will let us fit a logistic model and provide an estimate of the asymptote of the curve, or the value where the curve levels off and no longer increases. This is our estimate of `K`, the carrying capacity of the population.
 
-```{r message=FALSE}
-# load the drc package; text when you load it is fine (as long as it doesn't say error)
+```{r}
+#| message: false
+#| warning: false
+# load the drc package; any loading messages in your console are fine
 library(drc)
 ```
 
@@ -97,7 +116,21 @@ Now, we are going to use a function from the `drc` package called `drm()` to fit
 
 It works very similarly to the inferential statistics models from Module 3 except instead of finding a straight line of best fit, this line of best fit is allowed to curve like the logistic growth model.
 
-What kind of growth do we think we have based on this plot? Logistic
+Let's take a look at the data to confirm what type of growth we're working with.
+
+```{r}
+ggplot(log_pop, aes(x = year, y = N)) +
+  geom_point() +
+  geom_line() +
+  labs(x = "Year", y = "Population") +
+  theme_bw()
+```
+
+What kind of growth is this population exhibiting?
+
+::: instructor-only
+**Answer:** Logistic. The population increases quickly at first, then levels off as it approaches the carrying capacity.
+:::
 
 ```{r}
 # Fit the model to the data
@@ -106,7 +139,7 @@ What kind of growth do we think we have based on this plot? Logistic
 model <- drm(N ~ year, data = log_pop, fct = LL.4())
 ```
 
-This time, unlike with the linear model, we are interested not in a p-value but in the estimate of a specific value from the model--the carrying capacity.
+This time, unlike with the linear model, we are interested not in a p-value but in the estimate of a specific value from the model: the carrying capacity.
 
 To get all of the estimated values, we will use a function called `coef`, which is short for "coefficient," to give us the estimates from the `model` object.
 
@@ -147,7 +180,7 @@ ggplot(log_pop, aes(x = year, y = N)) + # Plot the original data points (year vs
   theme_bw()
 ```
 
-To add the a line representing the carrying capacity, we can use `geom_hline()` to add a horizontal line where the y-intercept is equal to the carrying capacity.
+To add a line representing the carrying capacity, we can use `geom_hline()` to add a horizontal line where the y-intercept is equal to the carrying capacity.
 
 This is very similar to how we used `geom_vline()` to add a vertical line to histograms to represent the mean value.
 
diff --git a/modules/module_5/module5_3.qmd b/modules/module_5/module5_3.qmd
index 3c3de21..c50b9d6 100644
--- a/modules/module_5/module5_3.qmd
+++ b/modules/module_5/module5_3.qmd
@@ -13,11 +13,18 @@ knitr::opts_chunk$set(echo = TRUE)
 
 # Comparing Populations
 
+## Learning Outcomes
+
+-   Students will be able to add logistic growth model curves to a `ggplot2` visualization using `predict()` and `geom_line()`.
+-   Students will be able to estimate and compare carrying capacities across multiple populations.
+-   Students will be able to select and run an appropriate statistical test to compare mean population sizes across groups.
+-   Students will be able to make a data-driven recommendation about which population to fish.
+
 Throughout this module, we have learned how populations demonstrating exponential growth and logistic growth differ from one another, both conceptually and visually.
 
 While we have fit models to some of these populations (specifically, logistic growth models), we have done so in order to estimate the carrying capacity of the population rather than to use inferential statistics to determine if the populations are statistically significantly different from one another.
 
-In today's lesson, we are going to learn how to (a) add those logistic growth models to our data visualizations and (b) compare populations statistically.
+In this lesson, we are going to learn how to (a) add those logistic growth models to our data visualizations and (b) compare populations statistically.
 
 ## Set Up
 
@@ -25,16 +32,24 @@ As always, we need to start by loading the packages we are going to use along wi
 
 First, let's load our packages. We are going to be using both the `tidyverse` and `drc` packages in this lesson, so we need to load both of them.
 
+Then, load in our data "comparing_populations.csv" as `pops`.
+
 ```{r}
-library(tidyverse)
-library(drc)
+#| message: false
+#| warning: false
+# Write your code here
+
 ```
 
-Next, we want to bring in our dataset.
+::: instructor-only
+**Answer:**
 
 ```{r}
+library(tidyverse)
+library(drc)
 pops <- read_csv("data/comparing_populations.csv")
 ```
+:::
 
 We can use the `head()` function to take a look at the data. What data do we have?
 
@@ -70,6 +85,12 @@ Based on the plot, what do you notice about these populations?
 
 Given your answers to the questions above, what might be a good next step?
 
+::: instructor-only
+**Instructor Note:** All three populations show logistic growth. The NE population appears to reach the highest abundance, SW the lowest, with SE in between.
+
+Students should suggest fitting logistic models to each population to estimate carrying capacities, then running a statistical test to compare them.
+:::
+
 ## Numerically
 
 When comparing different categories or groups (in this case, comparing different populations), we typically compare them both visually and numerically before conducting statistical tests.
@@ -78,6 +99,12 @@ Since we have already made visual comparisons, let's compare the populations num
 
 Typically, we have compared groups with measures of central tendency and measures of variability. Do you think that is what we should do here? Why or why not?
 
+::: instructor-only
+**Instructor Note:** Not quite, calculating a mean across all time points would mix the growth phase with the stable phase and give a misleading number.
+
+A better approach is to compare the carrying capacities from the logistic models. Once we restrict to the leveled-off period (year 15+), then comparing means with an ANOVA makes sense.
+:::
+
 ### Separate Populations
 
 Unlike our typical procedure, in order to fit logistic models to each population, our simplest way forward will be to create one data frame for each population.
@@ -87,13 +114,13 @@ Unlike our typical procedure, in order to fit logistic models to each population
 We can use the `filter` function to select each population, one at a time.
 
 ```{r}
-pop_NE <- pops |> 
+pop_NE <- pops %>% 
   filter(population == "NE")
 
-pop_SE <- pops |> 
+pop_SE <- pops %>% 
   filter(population == "SE")
 
-pop_SW <- pops |> 
+pop_SW <- pops %>% 
   filter(population == "SW")
 ```
 
@@ -101,7 +128,7 @@ pop_SW <- pops |>
 
 Now that we have data frames for each of the populations, we can fit the logistic model to each population and determine the estimate of the carrying capacity for each.
 
-As we run the code to fit the model to each population, we might receive some warnings: "Warning :NaNs produced". Don't worry--that's ok! Keep going!
+As we run the code to fit the model to each population, we might receive some warnings: "Warning :NaNs produced". Don't worry, that's ok! Keep going!
 
 ```{r}
 # NE population
@@ -173,14 +200,14 @@ If we compare these numbers to the numbers in the `abund` column of `pop_NE`, we
 Let's use the `mutate` function to add these model values to each dataframe.
 
 ```{r}
-pop_NE <- pop_NE |> 
+pop_NE <- pop_NE %>% 
   mutate(logistic_model = predict(model_NE))
 pop_NE
 
-pop_SE <- pop_SE |> 
+pop_SE <- pop_SE %>% 
   mutate(logistic_model = predict(model_SE))
 
-pop_SW <- pop_SW |> 
+pop_SW <- pop_SW %>% 
   mutate(logistic_model = predict(model_SW))
 ```
 
@@ -223,16 +250,23 @@ What type of statistical test should we use to answer that question?
 
 What would the null and alternative hypotheses be for that statistical test?
 
+::: instructor-only
+**Instructor Note:** An ANOVA. We have one numeric dependent variable (abundance) and one categorical independent variable (population) with three groups.
+
+-   **Null hypothesis:** The mean abundance is the same across all three populations during the leveled-off period.
+-   **Alternative hypothesis:** At least one population has a significantly different mean abundance.
+:::
+
 ### Filtering the Data
 
 To most accurately compare the populations, we likely want to restrict the comparison to when the populations (or the models of best fit, at least) are fairly leveled off.
 
-(Again, this is more of an estimation than an exact science, though there are more precise--and complicated--tools that could be used.)
+(Again, this is more of an estimation than an exact science, though there are more precise and complicated tools that could be used.)
 
 A rough estimation would be that the models mostly level off around year 15; let's filter our data to include only abundance from year 15 and later.
 
 ```{r}
-pops_15_25 <- pops_models |> 
+pops_15_25 <- pops_models %>% 
   filter(year >= 15)
 pops_15_25
 ```
@@ -254,12 +288,30 @@ TukeyHSD(model_aov)
 
 How do we interpret these results? What are our conclusions?
 
+::: instructor-only
+**Instructor Note:** The ANOVA is significant (p = 0.000704), meaning at least one population differs from the others. The Tukey HSD breaks it down:
+
+-   NE vs SE: not significantly different (p = 0.355)
+-   NE vs SW: significantly different (p = 0.0006) NE has higher abundance
+-   SE vs SW: significantly different (p = 0.021) SE has higher abundance
+
+Conclusion: SW is significantly lower in abundance than both NE and SE. NE and SE are statistically similar to each other, but NE has a slightly higher carrying capacity.
+:::
+
 ## Data-Driven Decision Making
 
 Our ultimate goal in this module has been to determine which population of fish is best for fishing sustainably. We are prioritizing large, stable populations.
 
 Given those requirements and what our numeric, visual, and statistical tests have told us, which of these populations would be the best for us to harvest? Why?
 
+::: instructor-only
+**Instructor Note:** NE is the best choice, it has the highest carrying capacity and is significantly more abundant than SW.
+
+SE is also a reasonable choice since it is not statistically different from NE and has a substantially higher carrying capacity than SW.
+
+SW should be avoided: it is significantly less abundant than the other two and has the lowest carrying capacity, so it is least able to withstand fishing pressure.
+:::
+
 ## References
 
 Golding, J.; Andersen, D.; Bledsoe, E. (2024). [Data-Driven Decision-Making: Antarctic Fisheries](http://dx.doi.org/10.25334/YECE-7M02). [Teaching Ecology for All Undergraduate Audiences](https://qubeshub.org/groups/teespring2024), QUBES Educational Resources. [doi:10.25334/YECE-7M02](http://dx.doi.org/10.25334/YECE-7M02)
\ No newline at end of file
diff --git a/modules/module_5/module5_home.qmd b/modules/module_5/module5_home.qmd
index f035286..4014250 100644
--- a/modules/module_5/module5_home.qmd
+++ b/modules/module_5/module5_home.qmd
@@ -27,11 +27,18 @@ By the end of this module, students will be able to:
 
 ## Lessons & Assignments
 
-Downloads for each lesson and assignment - including the QMD file, PDF, and any data - are available on their individual pages.
+### Lessons
+
+Each lesson page displays content in **student view** by default. Use the toggle button in the top right corner of the page to switch to **instructor view**, which reveals embedded answer keys and instructor notes. The QMD file, PDF, and any associated data can be downloaded from each lesson's page in either view.
 
 -   [5.1: Population Growth](module5_1.qmd)
 -   [5.2: Sustainable Fishing](module5_2.qmd)
 -   [5.3: Comparing Populations](module5_3.qmd)
+
+### Assignments
+
+Assignment pages are publicly available in **student view only**. The QMD file, PDF, and any associated data can be downloaded from each assignment's page. Instructor versions with answer keys are only available to educators. To request access, visit the [Instructors page](../../instructors.qmd).
+
 -   Assignment 1: Scientific Literature & Abstract Writing *(writing assignment)*
 -   Assignment 2: Choosing a Bay to Fish
 -   Assignment 3: Comparing Populations
@@ -40,4 +47,4 @@ Downloads for each lesson and assignment - including the QMD file, PDF, and any
 
 To clone the entire course or download multiple files at once, visit the [course GitHub repository]().
 
-**Links are not yet live!**
\ No newline at end of file
+**Links are not yet live.**
\ No newline at end of file

From f893a5e735e25c5c19bf62eb0c0eb2a002dbbcf5 Mon Sep 17 00:00:00 2001
From: lilymcmullen <lilymcmullen@arizona.edu>
Date: Sun, 17 May 2026 18:06:26 -0700
Subject: [PATCH 15/15] fixing assignment file structure

---
 modules/module_1/assignments/a1/m1a1.qmd      |    75 +-
 modules/module_1/assignments/a2/m1a2.qmd      |    44 +-
 .../assignments/a1/fish_sick_data.csv         |    51 +
 modules/module_2/assignments/a1/m2a1.qmd      |   139 +
 modules/module_2/assignments/a2/m2a2.qmd      |   189 +
 .../assignments/a2/tilapia_growth.csv         |   321 +
 .../assignments/a3/chinstraps_on_signy.csv    |  1909 ++
 modules/module_2/assignments/a3/fur_seals.csv |  3602 ++++
 .../assignments/a3/gentoos_and_temps.csv      |   174 +
 modules/module_2/assignments/a3/krill.csv     | 14544 ++++++++++++++++
 modules/module_2/assignments/a3/m2a3.qmd      |   224 +
 modules/module_3/assignments/a1/m3a1.qmd      |   134 +
 .../module_3/assignments/a1/wind_turbines.csv |    68 +
 modules/module_3/assignments/a2/fish.csv      |  1921 ++
 modules/module_3/assignments/a2/m3a2.qmd      |   203 +
 modules/module_3/assignments/a2/seals.csv     |   801 +
 16 files changed, 24340 insertions(+), 59 deletions(-)
 create mode 100644 modules/module_2/assignments/a1/fish_sick_data.csv
 create mode 100644 modules/module_2/assignments/a1/m2a1.qmd
 create mode 100644 modules/module_2/assignments/a2/m2a2.qmd
 create mode 100644 modules/module_2/assignments/a2/tilapia_growth.csv
 create mode 100644 modules/module_2/assignments/a3/chinstraps_on_signy.csv
 create mode 100644 modules/module_2/assignments/a3/fur_seals.csv
 create mode 100644 modules/module_2/assignments/a3/gentoos_and_temps.csv
 create mode 100644 modules/module_2/assignments/a3/krill.csv
 create mode 100644 modules/module_2/assignments/a3/m2a3.qmd
 create mode 100644 modules/module_3/assignments/a1/m3a1.qmd
 create mode 100644 modules/module_3/assignments/a1/wind_turbines.csv
 create mode 100644 modules/module_3/assignments/a2/fish.csv
 create mode 100644 modules/module_3/assignments/a2/m3a2.qmd
 create mode 100644 modules/module_3/assignments/a2/seals.csv

diff --git a/modules/module_1/assignments/a1/m1a1.qmd b/modules/module_1/assignments/a1/m1a1.qmd
index e2778b1..3f95863 100644
--- a/modules/module_1/assignments/a1/m1a1.qmd
+++ b/modules/module_1/assignments/a1/m1a1.qmd
@@ -1,11 +1,16 @@
 ---
-title: "Module 1 Assignment 1"
-format:
-  html:
-    toc: true
+title: "Module 1, Assignment 2: Getting to Know the Team"
+author: "your name here"
+date: today
+format: pdf
 ---
 
-## Getting to Know Team Antarctica!
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Description
 
 ### Purpose
 
@@ -19,17 +24,16 @@ Write R code to successfully answer each question below.
 
 -   Code is within the provided code chunks
 -   Code chunks run without errors
--   Code will be assessed as follows:
-    -   Code produces the correct result
+-   Code produces the correct result
     -   Code that produces the correct answer will receive full credit
     -   Code attempts with logical direction will receive partial credit
 -   Written answers address the questions in sufficient detail
 
-### Assignment Questions
+# Assignment Questions
 
 Each question is worth 2 points unless otherwise specified.
 
-### Vectors
+## Vectors
 
 Let's start working with vectors, or 1-dimensional data, first.
 
@@ -43,37 +47,37 @@ counts <- c(2, 9, 4, 3, 6, 7, 1, 0, 3)
 1.  Write a line of code that returns the 2nd value in the `counts` vector.
 
 ```{r}
-# your code here
+
 ```
 
 2.  Subset the `counts` vector using a condition (not index) that returns values from the vector that are greater than 5.
 
 ```{r}
-# your code here
+
 ```
 
 3.  Calculate the average number of penguins that were counted. (Hint: the `mean()` function will calculate the average.)
 
 ```{r}
-# your code here
+
 ```
 
-### Data Frames
+## Data Frames
 
 Now that we've practiced with vectors, let's move on to 2-dimensional data.
 
 Remember those questions about our trip to Antarctica from the survey you completed the first week of classes? It's time to play around with that data!
 
-The following code chunk will read the data into RStudio. Be sure to run it before trying to answer the questions! A bunch of stuff will pop up, but it should work just fine.
+The following code chunk will read the data into RStudio. Be sure to run it before trying to answer the questions!
 
 Once you run the code, you will see a data frame called `team_data` show up in the environment.
 
 ```{r}
-team_data <- read.csv("team_antarctica.csv")
+#| eval: false
+team_data <- read.csv("data/team_antarctica.csv")
 ```
 
-4.  Take a look at the data and answer the following questions (0.5 points each):
-
+4.  Take a look at the data and answer the following questions (0.5 points each).
     a.  What are the dimensions of the data (how many rows and how many columns)?
     b.  Name a column that has *categorical* data.
     c.  Name a column that has *numeric* data.
@@ -86,21 +90,21 @@ team_data <- read.csv("team_antarctica.csv")
     Modify one of the arguments in `head()` so that the line of code prints the first 10 rows. (If you aren't sure how to do that, remember how you can look for help about functions!)
 
 ```{r}
-# your code here
+
 ```
 
 6.  Using what you know about subsetting data frames, write a line of code that returns the parka color for Unique ID 4. (Hint: count the columns!)
 
 ```{r}
-# your code here
+
 ```
 
-When we have a large data set like this, it is often helpful to summarize the data in some way. The next few questions will help use get a better understanding of the content of the data set.
+When we have a large data set like this, it is often helpful to summarize the data in some way. The next few questions will help us get a better understanding of the content of the data set.
 
-7.  On average, how did people rate their ability to live in a remote location?
+7.  On average, how did people rate their ability to be in a remote location?
 
 ```{r}
-# your code here
+
 ```
 
 8.  What are the minimum and maximum distances that would be traveled by a team member to get to Antarctica? Use the `min()` and `max()` functions.
@@ -112,7 +116,7 @@ When we have a large data set like this, it is often helpful to summarize the da
 9.  Subset using a condition to return the rows of data only for people who rated their fishing skills as a 5. Retain all of the columns in those rows. (Hint: numbers do not need quotation marks around them).
 
 ```{r}
-# your code here
+
 ```
 
 10. Calculate the average cold tolerance of people who want blue parkas. Use these steps to think through how to answer this question.
@@ -124,30 +128,27 @@ When we have a large data set like this, it is often helpful to summarize the da
     c.  Finally, think about how to calculate the average
 
 ```{r}
-# your code here
+
 ```
 
-### Bonus (up to 2 points)!
+## Bonus (up to 2 points)!
 
 What animal should be on our team flag?
 
 -   First, create a vector called `mascot` that has only the values from the `team_flag` column.
--   Next, use the `table()` function on the `mascot`. Think through what this output likely means.
--   For full points, make sure you type out which animal should be our mascot below; providing only the code will result in partial credit.
+-   Next, use the `table()` function on the `mascot`.
+-   For full points, make sure you type out which animal that should be our mascot below; providing only the code will result in partial credit.
 
 *Answer:*
 
 ```{r}
-# your code here
-```
 
-## Turning in Your Assignment
+```
 
-Follow these steps to successfully turn in your assignment on D2L.
+# Turning in Your Assignment
 
-1.  Click the `Knit` button up near the top of this document. This should produce a PDF file that shows up in the `Files` panel on the bottom-right of your screen and/or pops up as a new window.
-2.  You might get a window that pops up that says to "Try Again." If you do, close the window and check whether there is a PDF in the Files tab--there usually is.
-3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files tab.
-4.  Click on the blue gear near the top of the `Files` panel and choose Export.
-5.  Put your last name at the front of the file name when prompted, then click the Download button. The PDF file of your assignment is now in your "Downloads" folder on your device.
-6.  Head over to D2L and navigate to Module 1. Submit the PDF file that you just downloaded.
\ No newline at end of file
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_1/assignments/a2/m1a2.qmd b/modules/module_1/assignments/a2/m1a2.qmd
index 75fd5ea..e69f654 100644
--- a/modules/module_1/assignments/a2/m1a2.qmd
+++ b/modules/module_1/assignments/a2/m1a2.qmd
@@ -1,11 +1,12 @@
 ---
-title: "Module 1 Assignment 2: Getting to Know Your Antarctic Home"
-author: "Ellen Bledsoe"     # <- your name here!
-date: "`r Sys.Date()`"      # <- uses the current date when rendered
-output: pdf_document
+title: "Module 1, Assignment 3: Getting to Know Your Home"
+author: "your name here"
+date: today
+format: pdf
 ---
 
-```{r setup, include=FALSE}
+```{r}
+#| include: false
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
@@ -30,7 +31,7 @@ Write R code using the `tidyverse` to successfully answer each question below.
 
 # Assignment Questions
 
-For this assignment, you'll be working with another real-world data set--a collection of data from climate stations scattered across Antarctica.
+For this final assignment for Module 1, you'll be working with another real-world data set: a collection of data from climate stations scattered across Antarctica.
 
 1.  In your own words, describe what the `tidyverse` is. Your answer should be between 1-3 sentences.
 
@@ -39,14 +40,14 @@ For this assignment, you'll be working with another real-world data set--a colle
 2.  Load in the `tidyverse` package.
 
 ```{r}
-# your code here
+
 ```
 
 3.  Load in the data file (called `aggregated_station_data.csv`) using the `read_csv()` function. Save the data as an object called `weather`.
 
 ```{r}
 # Note: use the read_csv function, not the read.csv function!
-# your code here
+
 ```
 
 4.  Take a look at the data in whichever way you would like (looking in the environment, viewing the data, typing a function into the console below, etc.). How many rows and columns are in the data? Type your answers below:
@@ -56,23 +57,24 @@ For this assignment, you'll be working with another real-world data set--a colle
 
 ```{r}
 # using code is optional for Question 4
+
 ```
 
-5.  Write some code to choose rows that only include temperatures which are above freezing (AKA greater than 0 because the temperature is currently in Celsius)
+5.  Write some code to choose rows that only includes temperatures which are above freezing (AKA greater than 0 because the temperature is currently in Celsius)
 
 ```{r}
-# your code here
+
 ```
 
 6.  Write some code to choose *only* columns with the following information: the year, day, month, temperature, and the station ID. Save these columns as a new object called `station_temp`.
 
 ```{r}
-# your code here
+
 ```
 
 7.  Using the data frame you created in Q6 above (`station_temp`), add a new column to that data frame that converts the temperature column (currently in Celsius) to Fahrenheit. Call the new column `tempF`.
 
-    *Hint: we did this in class--use that same equation!*
+    *Hint: we did this in class, use that same equation!*
 
 ```{r}
 
@@ -87,7 +89,7 @@ For this assignment, you'll be working with another real-world data set--a colle
     *Hint: think about months first (split) and then temperature (apply). You will also want to remove all the `NA` values.*
 
 ```{r}
-# your code here
+
 ```
 
 10. Again, using the *original* data frame, produce a data frame with the average temperature in the month of January for each station.
@@ -101,7 +103,7 @@ For this assignment, you'll be working with another real-world data set--a colle
     -   remember to remove the `NA` values!
 
 ```{r}
-# your code here
+
 ```
 
 ## Bonus! (up to 2 points)
@@ -109,15 +111,13 @@ For this assignment, you'll be working with another real-world data set--a colle
 Write code to determine how many unique stations are in the `weather` data set. (Hint: look up the help files for the `distinct()` and the `count()` functions).
 
 ```{r}
-# your code here
+
 ```
 
 # Turning in Your Assignment
 
-Follow these steps to successfully turn in your assignment on D2L.
-
-1.  Click the `Knit` button up near the top of this document. This should produce a PDF file that shows up in the `Files` panel on the bottom-right of your screen.
-2.  Click the empty box to the left of the PDF file.
-3.  Click on the blue gear near the top of the `Files` panel and choose Export.
-4.  Put your last name at the front of the file name when prompted, then click the Download button. The PDF file of your assignment is now in your "Downloads" folder on your device.
-5.  Head over to D2L and navigate to Module 1. Submit the PDF file that you just downloaded.
\ No newline at end of file
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_2/assignments/a1/fish_sick_data.csv b/modules/module_2/assignments/a1/fish_sick_data.csv
new file mode 100644
index 0000000..d93eee6
--- /dev/null
+++ b/modules/module_2/assignments/a1/fish_sick_data.csv
@@ -0,0 +1,51 @@
+tank_id,species,avg_daily_temp,num_fish,day_length,tank_volume,num_sick
+388,tilapia,24.2712251525129,93,10,399.454416952633,3
+425,tilapia,24.5901291496196,98,11,399.837578231131,4
+420,tilapia,22.9683745186346,103,9,399.450822325264,2
+819,trout,14.0645280057374,85,11,401.12288566145,14
+176,tilapia,23.3395955019226,98,10,400.01435930689104,3
+926,trout,13.8,79,12,399.744465726852,10
+454,tilapia,23.8262819090694,104,10,399.931091280505,0
+204,tilapia,24.0265009468057,99,10,398.989507931594,6
+851,trout,15.6562757634324,79,12,401.004259254191,17
+515,tilapia,23.7882354976847,100,10,399.285493280529,7
+996,trout,15.7363386790295,86,13,399.743891759719,19
+715,tilapia,24.0467271520252,102,11,401.483269666681,5
+186,tilapia,24.3520806905395,100,10,401.57020346624,0
+678,tilapia,23.8022179755021,102,10,400.028021879009,4
+776,trout,15.1999323316343,69,11,399.57344818817904,13
+766,trout,15.1193874663199,79,12,399.96593686649,13
+964,trout,14.9337938675402,74,11,400.275117611875,12
+892,trout,15.1120851149759,87,13,400.384046652314,19
+534,tilapia,23.3824000421765,98,8,399.449546543012,4
+793,trout,14.7111345797935,82,11,399.716356434193,16
+751,trout,14.8425305053541,75,11,400.47152711948,20
+615,tilapia,23.7564429417007,103,10,400.073308580614,3
+317,tilapia,22.8513009895559,98,11,402.061963482953,4
+945,trout,14.6949793202414,66,14,398.41248630442,12
+227,tilapia,23.1036228789566,109,12,400.144621396201,6
+944,trout,15.1888140247927,62,12,399.811970483946,15
+233,tilapia,23.19692025004,100,10,400.46690114412,5
+113,tilapia,23.1000706683298,97,11,400.112271784797,3
+844,trout,15.2626278642247,77,13,399.734275989956,16
+973,trout,15.7140122531928,94,12,400.335309428759,16
+920,trout,14.2742371991973,63,12,399.379537208081,13
+382,tilapia,23.9243452330349,103,9,400.643923847699,3
+383,tilapia,24.4351987337161,100,10,400.61596491984494,4
+224,tilapia,24.3774649537342,103,10,400.56261837841504,0
+980,trout,13.8893099705121,79,11,400.17733825934,14
+244,tilapia,22.9291364850189,102,10,398.455334024837,6
+816,trout,14.5447122042087,71,12,399.878260395494,15
+67,tilapia,23.9726290395022,96,10,401.478339163685,1
+747,tilapia,23.3578023258169,104,10,399.67896330779297,4
+783,trout,14.8000165720383,73,13,401.978303476148,12
+710,tilapia,24.3958023019485,100,9,399.811656719429,1
+547,tilapia,23.3351489672318,98,10,400.525239509459,5
+909,trout,14.3139021925586,76,13,400.683839750471,14
+191,tilapia,22.9314048985264,102,10,400.896187238974,4
+252,tilapia,23.5628550849159,103,10,399.500397785847,7
+593,tilapia,23.7452191546067,102,10,401.619244789286,5
+480,tilapia,24.2291034528968,100,9,399.533964981092,4
+536,tilapia,23.8516170432863,100,10,399.176063949458,0
+566,tilapia,23.8184119559402,101,10,399.6061883996,2
+696,tilapia,24.0485489329341,101,12,400.67973733967,0
diff --git a/modules/module_2/assignments/a1/m2a1.qmd b/modules/module_2/assignments/a1/m2a1.qmd
new file mode 100644
index 0000000..55ba842
--- /dev/null
+++ b/modules/module_2/assignments/a1/m2a1.qmd
@@ -0,0 +1,139 @@
+---
+title: "Module 2, Assignment 1: Scatter Plots"
+author: "your name here"
+date: today
+format: pdf
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Details
+
+### Purpose
+
+The goal of this assignment is to assess your ability to produce and interpret scatter plots in both base R and `ggplot2`.
+
+### Task
+
+Write R code which produces the correct answers and text to correctly interpret the plots produced.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit Code used is based on what we have learned in this course
+-   Written answers address the questions in sufficient detail
+
+# Assignment Questions
+
+For this assignment, we are going to be making plots! We are going to use a data set containing data from a sample of our fish tanks. The data contains information about the tanks sampled and how many sick fish are contained in each tank.
+
+Questions are worth 1 point unless otherwise noted.
+
+First, let's prepare our data.
+
+1.  Load the `tidyverse` package into the work space.
+
+```{r}
+
+```
+
+2.  Read in the data using the `read_csv()` function. Name the data frame `sick_fish`.
+
+```{r}
+
+```
+
+3.  How many rows does the `sick_fish` data frame have? How many columns?
+
+    *Answer*:
+
+4.  Use a function (`head()`, `str()`, or another function you prefer) to take a look at the data points in the data frame.
+
+```{r}
+
+```
+
+5.  What does one row (observation) represent (e.g., an individual fish?, all fish of a certain species? all tanks of a certain species? etc.)?
+
+    *Answer*:
+
+6.  Based on the column names, briefly describe what data is included in the data frame.
+
+    *Answer:*
+
+7.  Take a look at the columns that have the total number of fish in the tank and the number of sick fish per tank. Determine whether these two columns are numeric or categorical.
+
+    *Answer*:
+
+### Plotting with base R
+
+8.  Using *base R* (the code we learned in lesson 2.1), create a histogram of the number of sick fish in each tank.
+
+```{r}
+
+```
+
+9.  In 2-3 sentences, describe what the histogram tells you about the distribution of the number of sick fish. (2 points)
+
+    *Answer*:
+
+10. Using *base R* (the code we learned in lesson 2.1), create a scatter plot. Put the total number of fish on the x-axis and the number of sick fish on the y-axis.
+
+```{r}
+
+```
+
+11. Interpret the scatter plot. What is the plot telling you? Is there a positive or negative relationship between the two variables. What does that mean? (2 points)
+
+```         
+*Answer*:
+```
+
+### Using `ggplot2`
+
+Now, let's use what we've learned about `ggplot2` to recreate the same scatter plot. We will do this in 2 steps.
+
+-   In the first step, we will add the data, axes, and geom.
+-   In the second step, we will modify the plot to increase interpretability by renaming axes and adding a theme.
+
+12. Create the scatter plot using the proper `geom` function.
+
+```{r}
+
+```
+
+13. Now, recreate the plot from Question 12 and add better axes labels and choose a theme to make it clearer to understand.
+
+```{r}
+
+```
+
+14. For *each* fish species, calculate the average and the standard deviation for the number of sick fish.
+
+    *Hint: think back to Module 1 and which function we can use to "split, apply, combine"*
+
+```{r}
+
+```
+
+15. Based on the summary data you've calculated above, do you think the two clumps of data correspond to the two species? Which species seems to be the one that is causing the most problems? (2 points)
+
+    *Answer*:
+
+16. Do some brainstorming. Based on the plots we've made and the calculations we've done so far, are we *sure* that the two clumps of data we see in the data visualization do, in fact, correlate to the two different species? How could we confirm? How might we include that information in the plot? (2 points)
+
+    *Answer*:
+
+# Turning in Your Assignment
+
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_2/assignments/a2/m2a2.qmd b/modules/module_2/assignments/a2/m2a2.qmd
new file mode 100644
index 0000000..c1fe57c
--- /dev/null
+++ b/modules/module_2/assignments/a2/m2a2.qmd
@@ -0,0 +1,189 @@
+---
+title: "Module 2, Assignment 2: From Data to Plot"
+author: "your name here"
+date: today
+format: pdf
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Details
+
+### Purpose
+
+The goal of this assignment is to practice developing successful workflows to get from the starting data to a plot.
+
+### Task
+
+Write R code which produces the correct answers and reflect on workflow.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code is commented with brief descriptions of what the code does
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit
+-   Written answers address the questions in sufficient detail
+
+# Assignment Questions
+
+Our goal for this assignment is to start with a data frame of data, summarize the data in constructive ways, and plot the data to answer some questions. To get from Point A to Point D requires some planning.
+
+In this assignment, we will first make a plan, execute how we would actually go about the process, and then evaluate how well our original plan matches with the path we actually used.
+
+## Data Summary
+
+The aquaculture scientists on Team Antarctica have been working on developing a new diet for tilapia (a type of fish) based on soy-protein, and they are interested in whether incorporating this into fish diets will result in faster growth rates.
+
+They are measuring the amount of growth based on the change in the weight of each fish after 30 days on the new diet.
+
+They've provided us with the data below to analyze.
+
+First, let's take a look at the data we will be using for the assignment.
+
+1.  As always, we start by loading the `tidyverse`. (1 point)
+
+```{r}
+     
+```
+
+2.  Next, we need to load in our tilapia growth dataset. Call the data frame `growth`. Then use both the `head` and `tail` functions to take a look at the data. (1 point)
+
+```{r}
+     
+```
+
+3.  Before we can plan any strategy, we need to understand the data that we have. Answer the following questions about the data. (0.5 points each, 2 points total)
+
+    a\. What does one row represent? (One tank? One temperature? One treatment? One fish?)
+
+    b\. How many tanks of fish were sampled?
+
+    c\. How many fish were sampled?
+
+    d\. How many different soy protein levels (treatments) are there?
+
+## Our Tasks
+
+We have been asked by our aquaculture specialists to provide them with the following:
+
+a.  a data frame with the average growth of fish per diet treatment (`perc_soy_protein`); growth is being measured by the change in weight of each fish after 30 days on the diet (`day_30_weight`).
+b.  a plot that shows the relationship (or lack thereof) between the amount of soy protein in the diet and how much the fish have grown
+c.  a data frame with the average growth for each combination of diet treatment *and* tank category (warm or cold)
+d.  a plot that shows the relationship (or lack thereof) between average tank temperature, the amount of growth, and the amount of soy in the diet.
+
+They've also asked that we provide all weights in kilograms instead of grams (the `day_30_weight` is currently in grams).
+
+## Prediction
+
+4.  Spend some time thinking about each one of these steps. What steps will you need to take to produce the end result? What data frame will you use? What columns will you use? What functions will you use? How will you plot things?
+
+    For each of the 4 tasks listed above in "Our Tasks," *describe* (do NOT code) how you will get from the starting point (a data frame) to the result (another data frame or a plot).
+
+    This question will be grade ***only on completion***, not on whether or not your plan is *correct*. (2 points)
+
+    *Task (a)*:
+
+    *Task (b)*:
+
+    *Task (c)*:
+
+    *Task (d)*:
+
+## Execution
+
+Now let's actually go ahead and complete our tasks with code.
+
+**NOTE! We need to run the line of code below. This line of code tells R to treat the soy protein column as a category rather than a number, and to display the levels in order from lowest to highest.**
+
+```{r}
+growth <- growth %>% 
+  mutate(perc_soy_protein = factor(perc_soy_protein, levels = c(0.2, 0.4, 0.6, 0.8)))
+```
+
+Because the aquaculture team has asked for everything to use kilograms instead of grams, it makes sense for us to add a column with the fish weights in kilograms (kg) instead of grams (g) to our data frame before we tackle any of the specific tasks.
+
+5.  Use the `mutate()` function to add a `day_30_weight_kg` column to the growth data frame with the weights in kilograms. (Hint: there are 1000 grams in 1 kilogram)
+
+    Save the output as `growth_kg` (2 points)
+
+    **NOTE: This is the data frame you will use for the remainder of the assignment.**
+
+```{r}
+     
+```
+
+### Task (a)
+
+**A data frame with the average growth of fish per diet treatment**
+
+6.  Create a new data frame called `growth_by_treatment` with the average growth for each level of soy protein. Remember to use the `growth_kg` data frame as your starting point. (2 points)
+
+```{r}
+     
+```
+
+### Task (b)
+
+**A box plot that shows the relationship (or lack thereof) between the amount of soy protein in the diet and the weight at 30 days (growth)**
+
+Based on the values we calculated in task (a), it looks like there is probably a positive relationship between the percent of soy protein in tilapia diet and growth. Let's plot the data to confirm.
+
+7.  Make a box plot to show this relationship. Change the axis labels to be more easily understood and add a theme. (2 point)
+
+    (Hint: because we want to plot *all* of the values, not just the mean values, we need to use the original `growth_kg` data frame, not the data frame we created in task (a))
+
+```{r}
+     
+```
+
+### Task (c)
+
+**A data frame with the average growth for each combination of diet treatment *and* tank category**
+
+8.  Create a new data frame called `growth_by_temp_treatment`. It should have groups for each combination of the amount of soy protein and whether tanks are warm or cold. Remember, you can create groups with multiple columns! Calculate the average growth for each combination. (2 points)
+
+```{r}
+     
+```
+
+### Task (d)
+
+**A scatter plot that shows the relationship (or lack thereof) between average tank temperature and the weight at 30 days**
+
+Based on our results above, do we think the tank being warm or cold has much of an influence? Let's plot our data to confirm.
+
+9.  Make a multiple scatter plot with the average water temperature in the tank on the x-axis (the actual number, not whether the tank is "cold" or "warm") and the amount of growth on the y-axis (vertical). Change the color of each point so that they represent the amount of soy protein in the diet. Change the axis labels to be more easily understood and add a theme. (2 point)
+
+    (Hint: because we want to plot *all* of the values, not just the mean values, we need to use the original `growth_kg` data frame)
+
+```{r}
+     
+```
+
+As suspected, there doesn't seem to be much of a difference based on tank temperatures.
+
+## Reflection
+
+10. Imagine we had decided to treat `perc_soy_protein` as a continuous variable. What type of plot would we have used for task (b)? Would we have been able to complete task (d)? Why or why not? (2 points)
+
+    *Answer*:
+
+11. Write 3-5 sentences about if and how your predictions and execution differed and what you learned through the process. (3 points)
+
+    *Examples of questions to answer: How did your initial prediction of how you expected to accomplish the 4 tasks match up with how we actually went about doing it? Were they similar? Were there common mistakes that you made beforehand? Did you plan a different execution from what we did above that you think would also work?*
+
+    *Answer*:
+
+# Turning in Your Assignment
+
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_2/assignments/a2/tilapia_growth.csv b/modules/module_2/assignments/a2/tilapia_growth.csv
new file mode 100644
index 0000000..7b37fe0
--- /dev/null
+++ b/modules/module_2/assignments/a2/tilapia_growth.csv
@@ -0,0 +1,321 @@
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diff --git a/modules/module_2/assignments/a3/chinstraps_on_signy.csv b/modules/module_2/assignments/a3/chinstraps_on_signy.csv
new file mode 100644
index 0000000..3b4470f
--- /dev/null
+++ b/modules/module_2/assignments/a3/chinstraps_on_signy.csv
@@ -0,0 +1,1909 @@
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diff --git a/modules/module_2/assignments/a3/fur_seals.csv b/modules/module_2/assignments/a3/fur_seals.csv
new file mode 100644
index 0000000..4b2050f
--- /dev/null
+++ b/modules/module_2/assignments/a3/fur_seals.csv
@@ -0,0 +1,3602 @@
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+2013-01-08,Beach,M,6.75,0,2013,Jan
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+2009-02-05,Tussock,M,10.2,1,2009,Feb
+2009-02-05,Tussock,F,10.2,1,2009,Feb
+2009-02-05,Tussock,M,14,1,2009,Feb
+2009-02-05,Tussock,F,9.9,1,2009,Feb
+2009-02-05,Tussock,F,14.6,1,2009,Feb
+2009-02-05,Tussock,F,8.5,1,2009,Feb
+2009-02-05,Tussock,M,8.9,0,2009,Feb
+2009-02-05,Tussock,F,9.4,1,2009,Feb
+2009-02-05,Tussock,M,5.5,1,2009,Feb
+2009-02-05,Tussock,F,10,1,2009,Feb
+2009-02-05,Tussock,M,11,1,2009,Feb
+2009-02-05,Tussock,F,11.4,1,2009,Feb
+2009-02-05,Tussock,M,13.25,1,2009,Feb
+2009-02-05,Tussock,M,11,1,2009,Feb
+2009-02-05,Tussock,F,8.8,1,2009,Feb
+2009-02-05,Tussock,M,9.2,1,2009,Feb
+2009-02-05,Tussock,M,13.5,0,2009,Feb
+2009-02-05,Tussock,F,9.5,1,2009,Feb
+2009-02-05,Tussock,M,11.5,1,2009,Feb
+2009-02-05,Tussock,M,15,1,2009,Feb
+2009-02-05,Tussock,F,8.5,0,2009,Feb
+2009-02-05,Tussock,M,10.8,0,2009,Feb
+2009-02-05,Tussock,M,11.75,2,2009,Feb
+2009-02-05,Tussock,M,12.2,0,2009,Feb
+2009-02-05,Tussock,M,9.8,0,2009,Feb
+2009-02-05,Tussock,M,13.6,1,2009,Feb
+2009-02-05,Tussock,M,8.8,1,2009,Feb
+2009-02-05,Tussock,F,9,1,2009,Feb
+2009-02-05,Tussock,M,6.6,0,2009,Feb
+2009-02-05,Tussock,F,8,1,2009,Feb
+2009-02-05,Tussock,M,9.4,0,2009,Feb
+2009-02-05,Beach,F,10.25,1,2009,Feb
+2009-02-05,Beach,F,11,1,2009,Feb
+2009-02-05,Beach,M,6.6,0,2009,Feb
+2009-02-05,Beach,M,10.5,1,2009,Feb
+2009-02-05,Beach,F,11,0,2009,Feb
+2009-02-05,Beach,F,9.2,1,2009,Feb
+2009-02-05,Beach,F,8,0,2009,Feb
+2009-02-05,Beach,F,8.9,1,2009,Feb
+2009-02-05,Beach,M,12.5,1,2009,Feb
+2009-02-05,Beach,F,9.2,1,2009,Feb
+2009-02-05,Beach,M,9.6,1,2009,Feb
+2009-02-05,Beach,F,9.5,1,2009,Feb
+2009-02-05,Beach,M,7,1,2009,Feb
+2009-02-05,Beach,M,9,0,2009,Feb
+2009-02-05,Beach,F,6.7,0,2009,Feb
+2009-02-05,Beach,M,10,1,2009,Feb
+2009-02-05,Beach,F,10.2,1,2009,Feb
+2009-02-05,Beach,M,12.5,1,2009,Feb
+2009-02-05,Beach,M,10.5,0,2009,Feb
+2009-02-05,Beach,F,8,0,2009,Feb
+2009-02-05,Beach,F,11.2,2,2009,Feb
+2009-02-05,Beach,F,8.9,1,2009,Feb
+2009-02-05,Beach,M,5.5,0,2009,Feb
+2009-02-05,Beach,F,9.5,1,2009,Feb
+2009-02-05,Beach,F,5.9,0,2009,Feb
+2009-02-05,Beach,M,9.8,0,2009,Feb
+2009-02-05,Beach,M,8.5,0,2009,Feb
+2009-02-05,Beach,M,14.5,1,2009,Feb
+2009-02-05,Beach,F,7.6,0,2009,Feb
+2009-02-05,Beach,F,8.1,0,2009,Feb
+2009-02-05,Beach,F,10,2,2009,Feb
+2009-02-05,Beach,F,12,1,2009,Feb
+2009-02-05,Beach,F,8.4,1,2009,Feb
+2009-02-05,Beach,M,12,1,2009,Feb
+2009-02-05,Beach,M,8,0,2009,Feb
+2009-02-05,Beach,M,9.6,1,2009,Feb
+2009-02-05,Beach,M,9.5,1,2009,Feb
+2009-02-05,Beach,M,9.2,0,2009,Feb
+2009-02-05,Beach,F,12.25,2,2009,Feb
+2009-02-05,Beach,M,12.2,1,2009,Feb
+2009-01-05,Tussock,M,5.8,0,2009,Jan
+2009-01-05,Tussock,M,6.2,0,2009,Jan
+2009-01-05,Tussock,F,6.6,0,2009,Jan
+2009-01-05,Tussock,M,8.8,0,2009,Jan
+2009-01-05,Tussock,M,9.3,0,2009,Jan
+2009-01-05,Tussock,M,7.7,0,2009,Jan
+2009-01-05,Tussock,M,5.9,0,2009,Jan
+2009-01-05,Tussock,F,6,0,2009,Jan
+2009-01-05,Tussock,F,5.5,0,2009,Jan
+2009-01-05,Tussock,F,7.4,0,2009,Jan
+2009-01-05,Tussock,M,6.1,0,2009,Jan
+2009-01-05,Tussock,M,9.1,0,2009,Jan
+2009-01-05,Tussock,F,7.1,0,2009,Jan
+2009-01-05,Tussock,F,8,0,2009,Jan
+2009-01-05,Tussock,M,9.8,0,2009,Jan
+2009-01-05,Tussock,F,7.6,0,2009,Jan
+2009-01-05,Tussock,M,10.6,0,2009,Jan
+2009-01-05,Tussock,F,7.5,0,2009,Jan
+2009-01-05,Tussock,F,7.1,0,2009,Jan
+2009-01-05,Tussock,M,10,0,2009,Jan
+2009-01-05,Tussock,M,8.1,0,2009,Jan
+2009-01-05,Tussock,F,6.2,0,2009,Jan
+2009-01-05,Tussock,M,9.9,0,2009,Jan
+2009-01-05,Tussock,M,8.2,0,2009,Jan
+2009-01-05,Tussock,F,6.4,0,2009,Jan
+2009-01-05,Tussock,M,10.1,0,2009,Jan
+2009-01-05,Tussock,F,7.4,0,2009,Jan
+2009-01-05,Tussock,M,11.3,0,2009,Jan
+2009-01-05,Tussock,M,9.3,0,2009,Jan
+2009-01-05,Tussock,M,9,0,2009,Jan
+2009-01-05,Tussock,F,8.2,0,2009,Jan
+2009-01-05,Tussock,M,7.6,0,2009,Jan
+2009-01-05,Tussock,M,6.4,0,2009,Jan
+2009-01-05,Tussock,M,6.5,0,2009,Jan
+2009-01-05,Tussock,M,7,0,2009,Jan
+2009-01-05,Tussock,M,7.4,0,2009,Jan
+2009-01-05,Beach,F,7,0,2009,Jan
+2009-01-05,Beach,M,5.1,0,2009,Jan
+2009-01-05,Beach,F,6,0,2009,Jan
+2009-01-05,Beach,M,7,0,2009,Jan
+2009-01-05,Beach,F,7.3,0,2009,Jan
+2009-01-05,Beach,F,4.6,0,2009,Jan
+2009-01-05,Beach,F,8.8,0,2009,Jan
+2009-01-05,Beach,F,5.3,0,2009,Jan
+2009-01-05,Beach,F,6,0,2009,Jan
+2009-01-05,Beach,M,8.2,0,2009,Jan
+2009-01-05,Beach,F,5.9,0,2009,Jan
+2009-01-05,Beach,M,6.8,0,2009,Jan
+2009-01-05,Beach,F,7.6,0,2009,Jan
+2009-01-05,Beach,F,6,0,2009,Jan
+2009-01-05,Beach,F,7,0,2009,Jan
+2009-01-05,Beach,F,8.3,0,2009,Jan
+2009-01-05,Beach,M,8.1,0,2009,Jan
+2009-01-05,Beach,F,6.9,0,2009,Jan
+2009-01-05,Beach,M,9,0,2009,Jan
+2009-01-05,Beach,F,9.8,0,2009,Jan
+2009-01-05,Tussock,M,5.5,0,2009,Jan
+2009-01-05,Tussock,M,7.8,0,2009,Jan
+2009-01-05,Tussock,F,8.6,0,2009,Jan
+2009-01-05,Tussock,M,7.3,0,2009,Jan
+2009-01-05,Tussock,F,9.8,0,2009,Jan
+2009-01-05,Tussock,F,7.8,0,2009,Jan
+2009-01-05,Tussock,F,7.3,0,2009,Jan
+2009-01-05,Tussock,F,6.8,0,2009,Jan
+2009-01-05,Tussock,M,6.7,0,2009,Jan
+2009-01-05,Tussock,M,10,0,2009,Jan
+2009-01-05,Tussock,M,7.2,0,2009,Jan
+2009-01-05,Tussock,F,4.8,0,2009,Jan
+2009-01-05,Tussock,F,8.9,0,2009,Jan
+2009-01-05,Tussock,M,8.3,0,2009,Jan
+2009-01-05,Beach,F,6.8,0,2009,Jan
+2009-01-05,Beach,F,4.9,0,2009,Jan
+2009-01-05,Beach,F,4,0,2009,Jan
+2009-01-05,Beach,M,9.3,0,2009,Jan
+2009-01-05,Beach,M,5.1,0,2009,Jan
+2009-01-05,Beach,F,5.6,0,2009,Jan
+2009-01-05,Beach,M,9.2,0,2009,Jan
+2009-01-05,Beach,M,11.5,0,2009,Jan
+2009-01-05,Beach,F,6.8,0,2009,Jan
+2009-01-05,Beach,M,11.9,0,2009,Jan
+2009-01-05,Beach,M,11.25,0,2009,Jan
+2009-01-05,Beach,M,8,0,2009,Jan
+2009-01-05,Beach,F,6,0,2009,Jan
+2009-01-05,Beach,M,9.1,0,2009,Jan
+2009-01-05,Beach,F,5.8,0,2009,Jan
+2009-01-05,Beach,F,5.5,0,2009,Jan
+2009-01-05,Beach,M,9.7,0,2009,Jan
+2009-01-05,Beach,M,11,0,2009,Jan
+2009-01-05,Beach,F,7.1,0,2009,Jan
+2009-01-05,Beach,M,9.2,0,2009,Jan
+2009-01-05,Beach,M,9.7,0,2009,Jan
+2009-01-05,Beach,M,8.2,0,2009,Jan
+2009-01-05,Beach,M,8.4,0,2009,Jan
+2009-01-05,Beach,M,11.6,0,2009,Jan
+2009-01-05,Beach,F,6.5,0,2009,Jan
+2009-01-05,Beach,F,7.8,0,2009,Jan
+2009-01-05,Beach,F,9.4,0,2009,Jan
+2009-01-05,Beach,F,8.3,0,2009,Jan
+2009-01-05,Beach,M,6.5,0,2009,Jan
+2009-01-05,Beach,M,6.6,0,2009,Jan
diff --git a/modules/module_2/assignments/a3/gentoos_and_temps.csv b/modules/module_2/assignments/a3/gentoos_and_temps.csv
new file mode 100644
index 0000000..bcb155e
--- /dev/null
+++ b/modules/module_2/assignments/a3/gentoos_and_temps.csv
@@ -0,0 +1,174 @@
+Species,Year,Colony,Num_Chicks,Dec_Temp_C
+Gentoo penguin,1982,Johnson,NA,-13.1
+Gentoo penguin,1982,Mountain Cwm,NA,-13.1
+Gentoo penguin,1982,Natural Arch,NA,-13.1
+Gentoo penguin,1982,Square Pond,NA,-13.1
+Gentoo penguin,1983,Johnson,1880,-15.7
+Gentoo penguin,1983,Mountain Cwm,767,-15.7
+Gentoo penguin,1983,Natural Arch,829,-15.7
+Gentoo penguin,1983,Square Pond,282,-15.7
+Gentoo penguin,1984,Johnson,636,-12.8
+Gentoo penguin,1984,Mountain Cwm,NA,-12.8
+Gentoo penguin,1984,Natural Arch,NA,-12.8
+Gentoo penguin,1984,Square Pond,227,-12.8
+Gentoo penguin,1985,Johnson,636,-15.7
+Gentoo penguin,1985,Mountain Cwm,622,-15.7
+Gentoo penguin,1985,Natural Arch,925,-15.7
+Gentoo penguin,1985,Square Pond,235,-15.7
+Gentoo penguin,1986,Johnson,875,-14.3
+Gentoo penguin,1986,Mountain Cwm,871,-14.3
+Gentoo penguin,1986,Natural Arch,1109,-14.3
+Gentoo penguin,1986,Square Pond,NA,-14.3
+Gentoo penguin,1987,Johnson,962,-14
+Gentoo penguin,1987,Mountain Cwm,714,-14
+Gentoo penguin,1987,Natural Arch,1069,-14
+Gentoo penguin,1987,Square Pond,311,-14
+Gentoo penguin,1988,Johnson,963,NA
+Gentoo penguin,1988,Mountain Cwm,654,NA
+Gentoo penguin,1988,Natural Arch,864,NA
+Gentoo penguin,1988,Square Pond,343,NA
+Gentoo penguin,1989,Johnson,1164,NA
+Gentoo penguin,1989,Mountain Cwm,1227,NA
+Gentoo penguin,1989,Natural Arch,2032,NA
+Gentoo penguin,1989,Square Pond,637,NA
+Gentoo penguin,1990,Johnson,957,NA
+Gentoo penguin,1990,Mountain Cwm,907,NA
+Gentoo penguin,1990,Natural Arch,1249,NA
+Gentoo penguin,1990,Square Pond,427,NA
+Gentoo penguin,1991,Johnson,43,-11
+Gentoo penguin,1991,Mountain Cwm,0,-11
+Gentoo penguin,1991,Natural Arch,NA,-11
+Gentoo penguin,1991,Square Pond,1,-11
+Gentoo penguin,1992,Johnson,1108,NA
+Gentoo penguin,1992,Mountain Cwm,1024,NA
+Gentoo penguin,1992,Natural Arch,1815,NA
+Gentoo penguin,1992,Square Pond,603,NA
+Gentoo penguin,1993,Johnson,1530,NA
+Gentoo penguin,1993,Mountain Cwm,1185,NA
+Gentoo penguin,1993,Natural Arch,2067,NA
+Gentoo penguin,1993,Square Pond,654,NA
+Gentoo penguin,1994,Johnson,60,-11.8
+Gentoo penguin,1994,Mountain Cwm,46,-11.8
+Gentoo penguin,1994,Natural Arch,116,-11.8
+Gentoo penguin,1994,Square Pond,0,-11.8
+Gentoo penguin,1995,Johnson,538,-13.9
+Gentoo penguin,1995,Mountain Cwm,593,-13.9
+Gentoo penguin,1995,Natural Arch,1010,-13.9
+Gentoo penguin,1995,Square Pond,368,-13.9
+Gentoo penguin,1996,Johnson,965,NA
+Gentoo penguin,1996,Mountain Cwm,867,NA
+Gentoo penguin,1996,Natural Arch,1375,NA
+Gentoo penguin,1996,Square Pond,587,NA
+Gentoo penguin,1997,Johnson,754,-10.5
+Gentoo penguin,1997,Mountain Cwm,768,-10.5
+Gentoo penguin,1997,Natural Arch,944,-10.5
+Gentoo penguin,1997,Square Pond,347,-10.5
+Gentoo penguin,1998,Johnson,0,-14.5
+Gentoo penguin,1998,Mountain Cwm,0,-14.5
+Gentoo penguin,1998,Natural Arch,1,-14.5
+Gentoo penguin,1998,Square Pond,2,-14.5
+Gentoo penguin,1999,Johnson,1188,-14.3
+Gentoo penguin,1999,Mountain Cwm,1038,-14.3
+Gentoo penguin,1999,Natural Arch,1246,-14.3
+Gentoo penguin,1999,Square Pond,392,-14.3
+Gentoo penguin,2000,Johnson,414,NA
+Gentoo penguin,2000,Mountain Cwm,468,NA
+Gentoo penguin,2000,Natural Arch,551,NA
+Gentoo penguin,2000,Square Pond,246,NA
+Gentoo penguin,2001,Johnson,1097,NA
+Gentoo penguin,2001,Mountain Cwm,886,NA
+Gentoo penguin,2001,Natural Arch,1015,NA
+Gentoo penguin,2001,Square Pond,413,NA
+Gentoo penguin,2002,Johnson,881,-14.3
+Gentoo penguin,2002,Mountain Cwm,979,-14.3
+Gentoo penguin,2002,Natural Arch,1280,-14.3
+Gentoo penguin,2002,Square Pond,472,-14.3
+Gentoo penguin,2003,Johnson,407,-13.1
+Gentoo penguin,2003,Mountain Cwm,759,-13.1
+Gentoo penguin,2003,Natural Arch,1323,-13.1
+Gentoo penguin,2003,Square Pond,213,-13.1
+Gentoo penguin,2004,Johnson,525,-14.2
+Gentoo penguin,2004,Landing Beach,14,-14.2
+Gentoo penguin,2004,Mountain Cwm,649,-14.2
+Gentoo penguin,2004,Natural Arch,982,-14.2
+Gentoo penguin,2004,Square Pond,254,-14.2
+Gentoo penguin,2005,Johnson,620,NA
+Gentoo penguin,2005,Landing Beach,19,NA
+Gentoo penguin,2005,Mountain Cwm,364,NA
+Gentoo penguin,2005,Natural Arch,1133,NA
+Gentoo penguin,2005,Square Pond,245,NA
+Gentoo penguin,2006,Johnson,1444,-14.4
+Gentoo penguin,2006,Landing Beach,71,-14.4
+Gentoo penguin,2006,Mountain Cwm,1086,-14.4
+Gentoo penguin,2006,Natural Arch,2073,-14.4
+Gentoo penguin,2006,Square Pond,567,-14.4
+Gentoo penguin,2007,Johnson,993,-14.1
+Gentoo penguin,2007,Landing Beach,156,-14.1
+Gentoo penguin,2007,Mountain Cwm,967,-14.1
+Gentoo penguin,2007,Natural Arch,1706,-14.1
+Gentoo penguin,2007,Square Pond,685,-14.1
+Gentoo penguin,2008,Johnson,1325,-15.9
+Gentoo penguin,2008,Landing Beach,118,-15.9
+Gentoo penguin,2008,Mountain Cwm,686,-15.9
+Gentoo penguin,2008,Natural Arch,1210,-15.9
+Gentoo penguin,2008,Square Pond,637,-15.9
+Gentoo penguin,2009,Johnson,2,-12.2
+Gentoo penguin,2009,Landing Beach,0,-12.2
+Gentoo penguin,2009,Mountain Cwm,18,-12.2
+Gentoo penguin,2009,Natural Arch,20,-12.2
+Gentoo penguin,2009,Square Pond,0,-12.2
+Gentoo penguin,2010,Johnson,407,-14.9
+Gentoo penguin,2010,Landing Beach,85,-14.9
+Gentoo penguin,2010,Mountain Cwm,303,-14.9
+Gentoo penguin,2010,Natural Arch,521,-14.9
+Gentoo penguin,2010,Square Pond,288,-14.9
+Gentoo penguin,2011,Johnson,1002,-14.8
+Gentoo penguin,2011,Landing Beach,193,-14.8
+Gentoo penguin,2011,Mountain Cwm,512,-14.8
+Gentoo penguin,2011,Natural Arch,1087,-14.8
+Gentoo penguin,2011,Square Pond,530,-14.8
+Gentoo penguin,2012,Johnson,813,-11.7
+Gentoo penguin,2012,Landing Beach,231,-11.7
+Gentoo penguin,2012,Mountain Cwm,513,-11.7
+Gentoo penguin,2012,Natural Arch,1103,-11.7
+Gentoo penguin,2012,Square Pond,464,-11.7
+Gentoo penguin,2013,Johnson,1384,-13.3
+Gentoo penguin,2013,Landing Beach,407,-13.3
+Gentoo penguin,2013,Mountain Cwm,729,-13.3
+Gentoo penguin,2013,Natural Arch,1512,-13.3
+Gentoo penguin,2013,Square Pond,629,-13.3
+Gentoo penguin,2014,Johnson,1223,-17.9
+Gentoo penguin,2014,Landing Beach,417,-17.9
+Gentoo penguin,2014,Mountain Cwm,663,-17.9
+Gentoo penguin,2014,Natural Arch,1215,-17.9
+Gentoo penguin,2014,Square Pond,666,-17.9
+Gentoo penguin,2015,Johnson,1794,-15.5
+Gentoo penguin,2015,Landing Beach,608,-15.5
+Gentoo penguin,2015,Mountain Cwm,759,-15.5
+Gentoo penguin,2015,Natural Arch,1499,-15.5
+Gentoo penguin,2015,Square Pond,704,-15.5
+Gentoo penguin,2016,Johnson,0,-12.9
+Gentoo penguin,2016,Landing Beach,0,-12.9
+Gentoo penguin,2016,Mountain Cwm,0,-12.9
+Gentoo penguin,2016,Natural Arch,0,-12.9
+Gentoo penguin,2016,Square Pond,0,-12.9
+Gentoo penguin,2017,Johnson,1217,-16.1
+Gentoo penguin,2017,Landing Beach,476,-16.1
+Gentoo penguin,2017,Mountain Cwm,607,-16.1
+Gentoo penguin,2017,Natural Arch,1027,-16.1
+Gentoo penguin,2017,Square Pond,580,-16.1
+Gentoo penguin,2018,Johnson,1331,-14.9
+Gentoo penguin,2018,Landing Beach,495,-14.9
+Gentoo penguin,2018,Mountain Cwm,649,-14.9
+Gentoo penguin,2018,Natural Arch,1166,-14.9
+Gentoo penguin,2018,Square Pond,594,-14.9
+Gentoo penguin,2019,Johnson,1451,-13.2
+Gentoo penguin,2019,Landing Beach,474,-13.2
+Gentoo penguin,2019,Mountain Cwm,498,-13.2
+Gentoo penguin,2019,Natural Arch,1133,-13.2
+Gentoo penguin,2019,Square Pond,839,-13.2
+Gentoo penguin,2020,Johnson,1742,-16.4
+Gentoo penguin,2020,Landing Beach,634,-16.4
+Gentoo penguin,2020,Mountain Cwm,676,-16.4
+Gentoo penguin,2020,Natural Arch,1421,-16.4
+Gentoo penguin,2020,Square Pond,879,-16.4
diff --git a/modules/module_2/assignments/a3/krill.csv b/modules/module_2/assignments/a3/krill.csv
new file mode 100644
index 0000000..5f0b6db
--- /dev/null
+++ b/modules/module_2/assignments/a3/krill.csv
@@ -0,0 +1,14544 @@
+Station,Date,Year,Month,Day_Night,Krill_per_1m2,Krill_per_1m2_log
+ake2008sars16,2008-01-15,2008,1,night,0.116,0.04766419460155998
+ake2008sars17,2008-01-15,2008,1,day,0,0
+ake2008sars22,2008-01-15,2008,1,day,1.454,0.3898745583909854
+ake2008sars23,2008-01-15,2008,1,day,38.784,1.5997084462241684
+ake2008sars24,2008-01-15,2008,1,day,305.411,2.486304352192778
+ake2008sars25,2008-02-14,2008,2,day,186.117,2.2721132459222484
+ake2008sars28,2008-02-14,2008,2,day,13.822,1.1709068089307477
+ake2008sars30,2008-02-14,2008,2,night,20.908,1.3406027322567475
+ake2008sars36,2008-02-14,2008,2,day,0,0
+ake2008sars37,2008-02-14,2008,2,night,0,0
+ake2008sars38,2008-02-14,2008,2,night,0.004,0.0017337128090005314
+ake2008sars4,2008-01-15,2008,1,day,0.22,0.08635983067474821
+ake2008sars41,2008-02-14,2008,2,day,13.763,1.1691746197537434
+ake2008sars42,2008-02-14,2008,2,day,0.09,0.03742649794062367
+ake2008sars46,2008-03-14,2008,3,day,458.825,2.6625925795189342
+ake2008sars47,2008-03-14,2008,3,day,529.482,2.7246706522650714
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+ake2008sars8,2008-01-15,2008,1,day,0.289,0.11025291735340301
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+aml1990feb1,1990-02-10,1990,2,NA,1.292,0.3602146132953523
+aml1990feb10,1990-02-10,1990,2,NA,0,0
+aml1990feb11,1990-02-10,1990,2,NA,0,0
+aml1990feb12,1990-02-10,1990,2,NA,0,0
+aml1990feb13,1990-02-10,1990,2,NA,0,0
+aml1990feb14,1990-02-10,1990,2,NA,0,0
+aml1990feb15,1990-02-10,1990,2,NA,7.752,0.9421073089893555
+aml1990feb16,1990-02-10,1990,2,NA,1.312,0.3639878297484915
+aml1990feb17,1990-02-10,1990,2,NA,0,0
+aml1990feb18,1990-02-10,1990,2,NA,1.292,0.3602146132953523
+aml1990feb19,1990-02-10,1990,2,NA,5.168,0.7901443650429005
+aml1990feb2,1990-02-10,1990,2,NA,0,0
+aml1990feb20,1990-02-24,1990,2,NA,0,0
+aml1990feb21,1990-02-24,1990,2,NA,1.456,0.39022836246913006
+aml1990feb22,1990-02-24,1990,2,NA,0,0
+aml1990feb23,1990-02-24,1990,2,NA,0,0
+aml1990feb24,1990-02-24,1990,2,NA,0,0
+aml1990feb25,1990-02-24,1990,2,NA,46.58,1.6774244377012475
+aml1990feb26,1990-02-24,1990,2,NA,5.92,0.8401060944567578
+aml1990feb27,1990-02-24,1990,2,NA,2.96,0.5976951859255123
+aml1990feb28,1990-02-24,1990,2,NA,0,0
+aml1990feb29,1990-02-24,1990,2,NA,0,0
+aml1990feb3,1990-02-10,1990,2,NA,7.87,0.9479236198317265
+aml1990feb30,1990-02-24,1990,2,NA,0,0
+aml1990feb31,1990-02-24,1990,2,NA,41.437,1.627744674722776
+aml1990feb32,1990-02-24,1990,2,NA,0,0
+aml1990feb33,1990-02-24,1990,2,NA,0,0
+aml1990feb34,1990-02-24,1990,2,NA,0,0
+aml1990feb35,1990-02-24,1990,2,NA,0,0
+aml1990feb36,1990-02-24,1990,2,NA,0,0
+aml1990feb37,1990-02-24,1990,2,NA,0,0
+aml1990feb4,1990-02-10,1990,2,NA,0,0
+aml1990feb5,1990-02-10,1990,2,NA,1.292,0.3602146132953523
+aml1990feb6,1990-02-10,1990,2,NA,1.292,0.3602146132953523
+aml1990feb7,1990-02-10,1990,2,NA,0,0
+aml1990feb8,1990-02-10,1990,2,NA,2.584,0.5543680009900875
+aml1990feb9,1990-02-10,1990,2,NA,2.623,0.5590683340345368
+aml1990jan1,1990-01-08,1990,1,NA,0,0
+aml1990jan10,1990-01-08,1990,1,NA,0,0
+aml1990jan11,1990-01-08,1990,1,NA,0,0
+aml1990jan12,1990-01-08,1990,1,NA,0,0
+aml1990jan13,1990-01-08,1990,1,NA,0,0
+aml1990jan14,1990-01-08,1990,1,NA,2.252,0.5121505369220305
+aml1990jan15,1990-01-08,1990,1,NA,2.281,0.5160062303860478
+aml1990jan16,1990-01-08,1990,1,NA,6.931,0.8993279498776541
+aml1990jan17,1990-01-08,1990,1,NA,0,0
+aml1990jan18,1990-01-08,1990,1,NA,9.125,1.0053950318867062
+aml1990jan19,1990-01-08,1990,1,NA,15.764,1.2243776521618068
+aml1990jan2,1990-01-08,1990,1,NA,11.406,1.0936317768289476
+aml1990jan20,1990-01-24,1990,1,NA,0,0
+aml1990jan21,1990-01-24,1990,1,NA,0,0
+aml1990jan22,1990-01-24,1990,1,NA,35.094,1.557435013998147
+aml1990jan23,1990-01-24,1990,1,NA,0,0
+aml1990jan24,1990-01-24,1990,1,NA,0,0
+aml1990jan25,1990-01-24,1990,1,NA,0,0
+aml1990jan26,1990-01-24,1990,1,NA,14.233,1.1827854420846833
+aml1990jan27,1990-01-24,1990,1,NA,0,0
+aml1990jan28,1990-01-24,1990,1,NA,0,0
+aml1990jan29,1990-01-24,1990,1,NA,0,0
+aml1990jan3,1990-01-08,1990,1,NA,6.931,0.8993279498776541
+aml1990jan30,1990-01-24,1990,1,NA,0,0
+aml1990jan31,1990-01-24,1990,1,NA,49.814,1.7059833832502254
+aml1990jan32,1990-01-24,1990,1,NA,0,0
+aml1990jan33,1990-01-24,1990,1,NA,7.116,0.9093420383613083
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+aml1990jan35,1990-01-24,1990,1,NA,0,0
+aml1990jan36,1990-01-24,1990,1,NA,0,0
+aml1990jan37,1990-01-24,1990,1,NA,4.809,0.7641013764762288
+aml1990jan4,1990-01-08,1990,1,NA,0,0
+aml1990jan5,1990-01-08,1990,1,NA,2.281,0.5160062303860478
+aml1990jan6,1990-01-08,1990,1,NA,0,0
+aml1990jan7,1990-01-08,1990,1,NA,0,0
+aml1990jan8,1990-01-08,1990,1,NA,0,0
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+aml1991jan1,1991-01-21,1991,1,day,50.593,1.7125907817125274
+aml1991jan10,1991-01-23,1991,1,day,4.465,0.7375901662857216
+aml1991jan11,1991-01-23,1991,1,day,0.645,0.2161659022859931
+aml1991jan12,1991-01-23,1991,1,day,0.32,0.1205739312058499
+aml1991jan13,1991-01-23,1991,1,day,1.601,0.41514035219587275
+aml1991jan14,1991-01-24,1991,1,night,0,0
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+aml1991jan16,1991-01-24,1991,1,day,4.159,0.7125655278733083
+aml1991jan17,1991-01-24,1991,1,day,2.271,0.5146805441249817
+aml1991jan18,1991-01-24,1991,1,night,4.047,0.7030333047336859
+aml1991jan19,1991-01-24,1991,1,night,0.143,0.05804623039528174
+aml1991jan2,1991-01-21,1991,1,day,27.08,1.4483971034577676
+aml1991jan20,1991-01-26,1991,1,day,0.327,0.1228709228644355
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+aml1991jan22,1991-01-26,1991,1,day,0.662,0.22063101944809219
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+aml1991jan27,1991-01-27,1991,1,day,7.873,0.9480704815189411
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+aml1991jan29,1991-01-28,1991,1,night,1.503,0.39846084960822326
+aml1991jan3,1991-01-21,1991,1,day,18.527,1.2906355262615457
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+aml1991jan31,1991-01-28,1991,1,day,0.678,0.22479195649268144
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+aml1991jan33,1991-01-28,1991,1,night,0.301,0.11427729656158624
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+aml1991jan38,1991-01-29,1991,1,night,1.064,0.31470969295517376
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+aml1991jan48,1991-02-01,1991,2,night,11.765,1.1060208191402692
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+aml1991jan7,1991-01-22,1991,1,day,1.917,0.4649364291217326
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+aml1992feb027,1992-03-14,1992,3,day,0.354,0.13161866434912553
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+aml1992xst015,1992-02-14,1992,2,day,1.868,0.4575791469957625
+aml1992xst016,1992-02-14,1992,2,day,2.38,0.5289167002776547
+aml1992xst029,1992-02-14,1992,2,night,1.794,0.4462264017781631
+aml1992xst030,1992-03-14,1992,3,day,0.73,0.23804610312879543
+aml1992xst031,1992-03-14,1992,3,day,2.862,0.58681226944337594
+aml1992xst032,1992-03-14,1992,3,day,5.237,0.7949757440511316
+aml1992xst033,1992-03-14,1992,3,day,20.586,1.3341721727246059
+aml1992xst034,1992-03-14,1992,3,night,107.296,2.034612415905013
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+aml1992xst036,1992-03-14,1992,3,night,1.627,0.4194600727860702
+aml1992xst037,1992-03-14,1992,3,day,49.796,1.7058295145216351
+aml1992xst038,1992-03-14,1992,3,day,11.261,1.088525892431256
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+aml1992xst040,1992-03-14,1992,3,night,3.575,0.6603910984024671
+aml1992xst041,1992-03-14,1992,3,night,15.048,1.205420915676343
+aml1992xst042,1992-03-14,1992,3,night,1.844,0.45392959205772876
+aml1992xst044,1992-03-14,1992,3,day,1.891,0.46104809167065786
+aml1993feb1,1993-03-06,1993,3,night,2.482,0.5418287667813124
+aml1993feb13,1993-03-04,1993,3,day,0.081,0.03382569395331033
+aml1993feb14,1993-03-04,1993,3,day,0,0
+aml1993feb17,1993-03-03,1993,3,day,0,0
+aml1993feb19,1993-03-03,1993,3,day,38.507,1.5966740523867795
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+aml1993feb20,1993-03-03,1993,3,day,0.306,0.11594317693905512
+aml1993feb21,1993-03-03,1993,3,day,49.556,1.7037727051744727
+aml1993feb24,1993-03-02,1993,3,night,5.503,0.8131137540078986
+aml1993feb25,1993-03-02,1993,3,day,0.658,0.2195845262142547
+aml1993feb26,1993-03-02,1993,3,day,0.867,0.2711443179490783
+aml1993feb27,1993-03-02,1993,3,day,10.272,1.0520009801013
+aml1993feb28,1993-03-02,1993,3,day,0.489,0.1728946977521761
+aml1993feb29,1993-03-02,1993,3,night,0.079,0.033021444682910656
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+aml1993feb30,1993-03-02,1993,3,night,2.807,0.5805828768143673
+aml1993feb31,1993-03-01,1993,3,day,0.138,0.05614226205905233
+aml1993feb32,1993-03-01,1993,3,day,0.27,0.10380372095595687
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+aml1993feb34,1993-03-01,1993,3,day,0.165,0.0663259253620378
+aml1993feb35,1993-03-01,1993,3,day,0.165,0.0663259253620378
+aml1993feb36,1993-03-01,1993,3,night,10.417,1.0575520010545327
+aml1993feb37,1993-02-28,1993,2,night,35.25,1.5593080109070125
+aml1993feb38,1993-02-28,1993,2,night,3.516,0.6547539332529303
+aml1993feb39,1993-02-28,1993,2,day,0.107,0.0441476208787228
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+aml1993feb40,1993-02-28,1993,2,day,0.229,0.08955188288645412
+aml1993feb41,1993-02-28,1993,2,day,0.485,0.17172645365323114
+aml1993feb42,1993-02-28,1993,2,day,4.088,0.7065471026403575
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+aml1993feb50,1993-02-27,1993,2,night,0.404,0.14736710779378645
+aml1993feb51,1993-02-27,1993,2,night,82.152,1.9198726994787843
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+aml1993feb56,1993-02-26,1993,2,day,1.256,0.35333909531130475
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+aml1993feb60,1993-02-26,1993,2,night,94.528,1.9801306853344047
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+aml1994feb72,1994-02-27,1994,2,day,0.18,0.07188200730612536
+aml1994feb73,1994-02-27,1994,2,day,1.652,0.42357351973273555
+aml1994feb74,1994-02-27,1994,2,day,0.147,0.059563417901267686
+aml1994feb75,1994-02-27,1994,2,day,0,0
+aml1994feb76,1994-02-27,1994,2,night,0,0
+aml1994feb77,1994-02-27,1994,2,night,0,0
+aml1994feb78,1994-02-26,1994,2,night,0.212,0.0835026198302674
+aml1994feb79,1994-02-26,1994,2,night,0,0
+aml1994feb8,1994-03-08,1994,3,night,2.197,0.504742636271688
+aml1994feb80,1994-02-26,1994,2,day,0,0
+aml1994feb81,1994-02-26,1994,2,day,2.475,0.5409548089261327
+aml1994feb82,1994-02-26,1994,2,day,0.83,0.26245108973042947
+aml1994feb83,1994-02-26,1994,2,day,0,0
+aml1994feb84,1994-02-26,1994,2,night,0.188,0.07481644064517472
+aml1994feb85,1994-02-26,1994,2,night,0.181,0.07224989761351482
+aml1994feb86,1994-02-25,1994,2,night,0.108,0.04453976039241098
+aml1994feb87,1994-02-25,1994,2,day,0.467,0.1664301138432827
+aml1994feb88,1994-02-25,1994,2,day,0,0
+aml1994feb89,1994-02-25,1994,2,day,0,0
+aml1994feb9,1994-03-07,1994,3,day,13.221,1.1529301364227256
+aml1994feb90,1994-02-25,1994,2,day,0.341,0.1274287778515989
+aml1994feb91,1994-02-25,1994,2,day,0.458,0.1637575239819558
+aml1994febsalp,1994-03-02,1994,3,NA,NA,NA
+aml1994jan1,1994-01-17,1994,1,night,0.04,0.01703333929878037
+aml1994jan10,1994-01-18,1994,1,day,0.951,0.29025726939451807
+aml1994jan11,1994-01-18,1994,1,day,2.524,0.5470358997400103
+aml1994jan12,1994-01-18,1994,1,day,1.894,0.4614985267830187
+aml1994jan13,1994-01-18,1994,1,day,0.109,0.044931546149160055
+aml1994jan14,1994-01-18,1994,1,night,0.043,0.018284308426530838
+aml1994jan15,1994-01-19,1994,1,night,0.051,0.02160271602824219
+aml1994jan16,1994-01-19,1994,1,day,0.073,0.030599721965951062
+aml1994jan17,1994-01-19,1994,1,day,0.235,0.09166695759568448
+aml1994jan18,1994-01-19,1994,1,day,1.26,0.35410843914740087
+aml1994jan19,1994-01-19,1994,1,day,1.133,0.32899085544942874
+aml1994jan20,1994-01-19,1994,1,night,3.173,0.6204483847117088
+aml1994jan21,1994-01-20,1994,1,night,0.058,0.024485667699166973
+aml1994jan22,1994-01-20,1994,1,day,0,0
+aml1994jan23,1994-01-20,1994,1,day,3.351,0.6385890832927169
+aml1994jan24,1994-01-20,1994,1,day,1.921,0.46553155697354975
+aml1994jan25,1994-01-20,1994,1,day,0.175,0.07003786660775509
+aml1994jan26,1994-01-21,1994,1,day,0.22,0.08635983067474821
+aml1994jan27,1994-01-21,1994,1,day,2.112,0.4930395883176514
+aml1994jan28,1994-01-21,1994,1,day,8.068,0.9575115114544798
+aml1994jan29,1994-01-21,1994,1,day,3.772,0.6787004349983043
+aml1994jan3,1994-01-17,1994,1,day,0,0
+aml1994jan30,1994-01-21,1994,1,day,0.707,0.23223352111473358
+aml1994jan31,1994-01-21,1994,1,night,0,0
+aml1994jan32,1994-01-22,1994,1,night,35.167,1.5583124861605593
+aml1994jan33,1994-01-22,1994,1,day,0.318,0.11991541025799107
+aml1994jan34,1994-01-22,1994,1,day,0,0
+aml1994jan35,1994-01-22,1994,1,day,0,0
+aml1994jan36,1994-01-22,1994,1,day,0,0
+aml1994jan37,1994-01-22,1994,1,day,83.001,1.9242844562035106
+aml1994jan38,1994-01-22,1994,1,day,15.531,1.2182991258851728
+aml1994jan39,1994-01-22,1994,1,night,6.028,0.8468317528232574
+aml1994jan4,1994-01-17,1994,1,day,0.163,0.06557971472844842
+aml1994jan40,1994-01-23,1994,1,night,24.127,1.400140639757697
+aml1994jan41,1994-01-23,1994,1,day,8.382,0.9722954286111395
+aml1994jan42,1994-01-23,1994,1,day,2.522,0.5467893516312581
+aml1994jan43,1994-01-23,1994,1,day,1.012,0.3036279763838898
+aml1994jan44,1994-01-23,1994,1,day,2.709,0.5692568333286101
+aml1994jan45,1994-01-23,1994,1,day,0.687,0.22711508258912524
+aml1994jan46,1994-01-23,1994,1,night,2.657,0.5631249603380444
+aml1994jan47,1994-01-24,1994,1,night,79.479,1.9056825713751215
+aml1994jan48,1994-01-24,1994,1,night,0.107,0.0441476208787228
+aml1994jan49,1994-01-24,1994,1,day,0.092,0.038222638368718455
+aml1994jan5,1994-01-17,1994,1,day,0,0
+aml1994jan50,1994-01-24,1994,1,day,0,0
+aml1994jan51,1994-01-24,1994,1,day,36.932,1.5790057418872046
+aml1994jan52,1994-01-24,1994,1,day,0,0
+aml1994jan53,1994-01-24,1994,1,day,0,0
+aml1994jan54,1994-01-24,1994,1,NA,0,0
+aml1994jan55,1994-01-24,1994,1,NA,0.823,0.2607866686549763
+aml1994jan56,1994-01-25,1994,1,night,0,0
+aml1994jan57,1994-01-25,1994,1,day,1.62,0.41830129131974547
+aml1994jan58,1994-01-25,1994,1,day,2.315,0.520483532740792
+aml1994jan59,1994-01-25,1994,1,day,1.389,0.37821614974987794
+aml1994jan6,1994-01-17,1994,1,day,0.111,0.04571405894086761
+aml1994jan60,1994-01-25,1994,1,day,1.567,0.40942586867144337
+aml1994jan61,1994-01-25,1994,1,day,0.531,0.18497519069826104
+aml1994jan62,1994-01-25,1994,1,day,4.937,0.7735670489260588
+aml1994jan63,1994-01-25,1994,1,night,5.559,0.8168376309020351
+aml1994jan64,1994-01-26,1994,1,night,77.494,1.8948364609939157
+aml1994jan65,1994-01-26,1994,1,day,1.142,0.3308194664958368
+aml1994jan66,1994-01-26,1994,1,day,0,0
+aml1994jan67,1994-01-26,1994,1,day,0.846,0.26623169668989327
+aml1994jan68,1994-01-26,1994,1,day,0.889,0.2762319579218336
+aml1994jan69,1994-01-26,1994,1,day,0.508,0.17840134153375525
+aml1994jan7,1994-01-17,1994,1,day,0.49,0.17318626841227402
+aml1994jan70,1994-01-26,1994,1,day,0,0
+aml1994jan72,1994-01-27,1994,1,night,0,0
+aml1994jan73,1994-01-27,1994,1,day,2.026,0.4808689236871679
+aml1994jan74,1994-01-27,1994,1,day,1.026,0.3066394410242616
+aml1994jan75,1994-01-27,1994,1,day,1.647,0.42275394130134825
+aml1994jan76,1994-01-27,1994,1,day,4.163,0.7129021250472227
+aml1994jan77,1994-01-27,1994,1,day,4.155,0.7122286696195353
+aml1994jan78,1994-01-27,1994,1,day,0,0
+aml1994jan79,1994-01-27,1994,1,night,40.121,1.61406366749503
+aml1994jan8,1994-01-17,1994,1,day,0,0
+aml1994jan80,1994-01-28,1994,1,night,0.069,0.028977705208777998
+aml1994jan81,1994-01-28,1994,1,day,0,0
+aml1994jan82,1994-01-28,1994,1,day,0.116,0.04766419460155998
+aml1994jan83,1994-01-28,1994,1,day,0.134,0.05461305455688774
+aml1994jan9,1994-01-18,1994,1,night,0.187,0.07445071895459124
+aml1994jansalp,1994-01-22,1994,1,NA,NA,NA
+aml1994xst10,1994-02-22,1994,2,day,0,0
+aml1994xst12,1994-02-22,1994,2,day,0,0
+aml1994xst13,1994-02-22,1994,2,day,0.166,0.06669855042299526
+aml1994xst14,1994-02-22,1994,2,night,0.166,0.06669855042299526
+aml1994xst15,1994-02-23,1994,2,night,10.63,1.0655797147284485
+aml1994xst16,1994-02-23,1994,2,night,36.543,1.574528973436864
+aml1994xst17,1994-02-23,1994,2,day,44.407,1.6571228093996466
+aml1994xst18,1994-02-23,1994,2,day,1.62,0.41830129131974547
+aml1994xst19,1994-02-23,1994,2,day,0.973,0.29512708525219117
+aml1994xst20,1994-02-23,1994,2,day,0,0
+aml1994xst21,1994-02-23,1994,2,day,0,0
+aml1994xst22,1994-02-24,1994,2,day,0.373,0.1376705372367551
+aml1994xst23,1994-02-24,1994,2,day,0.106,0.04375512696867949
+aml1994xst24,1994-02-24,1994,2,day,0.232,0.09061070782840663
+aml1994xst26,1994-02-24,1994,2,day,28.854,1.4750025284819432
+aml1994xst27,1994-02-24,1994,2,day,0.185,0.07371835034612269
+aml1994xst28,1994-02-24,1994,2,night,14.341,1.1858536699341418
+aml1994xst29,1994-02-24,1994,2,night,20.379,1.3299873872788033
+aml1994xst8,1994-02-22,1994,2,day,0.363,0.13449585583467355
+aml1994xst9,1994-02-22,1994,2,day,1.216,0.3455697560563922
+aml1995feb1,1995-02-26,1995,2,day,0,0
+aml1995feb10,1995-02-27,1995,2,day,8.284,0.9677351317838802
+aml1995feb11,1995-02-27,1995,2,day,9.32,1.0136796972911926
+aml1995feb12,1995-02-27,1995,2,day,2.43,0.5352941200427705
+aml1995feb13,1995-02-27,1995,2,day,3.159,0.6189889203649336
+aml1995feb14,1995-02-24,1995,2,night,3.754,0.6770591773921615
+aml1995feb16,1995-02-24,1995,2,day,1.604,0.41564097989615434
+aml1995feb17,1995-02-24,1995,2,day,6.72,0.8876173003357362
+aml1995feb18,1995-02-24,1995,2,day,0.287,0.10957854690438665
+aml1995feb19,1995-02-24,1995,2,day,0.533,0.18554215485437514
+aml1995feb2,1995-02-26,1995,2,day,1.174,0.3372595397502757
+aml1995feb20,1995-02-24,1995,2,night,0.139,0.05652372407910037
+aml1995feb21,1995-02-24,1995,2,night,0.088,0.03662889536216113
+aml1995feb22,1995-02-23,1995,2,night,0.387,0.14207646107328487
+aml1995feb23,1995-02-23,1995,2,day,0,0
+aml1995feb24,1995-02-23,1995,2,day,0,0
+aml1995feb25,1995-02-23,1995,2,day,0,0
+aml1995feb26,1995-02-23,1995,2,day,0,0
+aml1995feb27,1995-02-23,1995,2,day,0,0
+aml1995feb28,1995-02-23,1995,2,night,0,0
+aml1995feb29,1995-02-22,1995,2,night,0.817,0.2593549273080343
+aml1995feb3,1995-02-26,1995,2,night,2.216,0.5073160400764136
+aml1995feb30,1995-02-22,1995,2,night,0.198,0.07845681805329255
+aml1995feb31,1995-02-22,1995,2,day,0.317,0.1195857749617838
+aml1995feb32,1995-02-22,1995,2,day,0,0
+aml1995feb33,1995-02-22,1995,2,day,0.465,0.16583762469012828
+aml1995feb34,1995-02-22,1995,2,day,0.382,0.14050804303817965
+aml1995feb35,1995-02-22,1995,2,night,2.049,0.4841574243653807
+aml1995feb36,1995-02-22,1995,2,night,0.855,0.26834391395106466
+aml1995feb37,1995-02-21,1995,2,night,0,0
+aml1995feb38,1995-02-21,1995,2,night,1.176,0.3376588910261423
+aml1995feb39,1995-02-21,1995,2,day,0,0
+aml1995feb4,1995-02-26,1995,2,night,0.157,0.06333335895174956
+aml1995feb40,1995-02-21,1995,2,day,0,0
+aml1995feb41,1995-02-21,1995,2,day,0,0
+aml1995feb43,1995-02-21,1995,2,day,0,0
+aml1995feb44,1995-02-21,1995,2,night,0,0
+aml1995feb45,1995-02-21,1995,2,night,0,0
+aml1995feb46,1995-02-20,1995,2,night,0,0
+aml1995feb47,1995-02-20,1995,2,day,0,0
+aml1995feb48,1995-02-20,1995,2,day,2.058,0.48543748107630125
+aml1995feb49,1995-02-20,1995,2,day,1.235,0.3492775274679554
+aml1995feb5,1995-02-25,1995,2,night,0.891,0.2766915288450397
+aml1995feb50,1995-02-20,1995,2,day,1.991,0.47581641303131816
+aml1995feb51,1995-02-20,1995,2,day,0.771,0.2482185611900747
+aml1995feb52,1995-02-20,1995,2,night,0.286,0.10924096858820329
+aml1995feb53,1995-02-20,1995,2,night,0.465,0.16583762469012828
+aml1995feb54,1995-02-19,1995,2,night,0,0
+aml1995feb55,1995-02-19,1995,2,day,0.206,0.08134730780413249
+aml1995feb56,1995-02-19,1995,2,day,5.448,0.8094250287970343
+aml1995feb57,1995-02-19,1995,2,day,0.145,0.0588054866759068
+aml1995feb58,1995-02-19,1995,2,day,0.866,0.2709116394104812
+aml1995feb59,1995-02-19,1995,2,day,0,0
+aml1995feb6,1995-02-25,1995,2,day,1.368,0.37438169805088223
+aml1995feb60,1995-02-19,1995,2,night,1.508,0.39932753215867883
+aml1995feb61,1995-02-19,1995,2,night,0.231,0.09025805293131633
+aml1995feb62,1995-02-18,1995,2,night,1.022,0.3057811512549823
+aml1995feb63,1995-02-18,1995,2,day,3.006,0.6027109449575575
+aml1995feb64,1995-02-18,1995,2,day,0.542,0.1880843737149382
+aml1995feb65,1995-02-18,1995,2,day,3.86,0.6866362692622934
+aml1995feb66,1995-02-18,1995,2,day,2.536,0.5485122563410355
+aml1995feb67,1995-02-18,1995,2,day,4.557,0.7448403967853792
+aml1995feb68,1995-02-18,1995,2,night,0.271,0.10414555055400815
+aml1995feb69,1995-02-18,1995,2,night,1.238,0.3498600821923312
+aml1995feb7,1995-02-25,1995,2,day,0.251,0.0972573096934199
+aml1995feb70,1995-02-17,1995,2,night,1.875,0.4586378490256493
+aml1995feb71,1995-02-17,1995,2,day,23.592,1.3907938501556698
+aml1995feb72,1995-02-17,1995,2,day,1.708,0.43264866001310676
+aml1995feb73,1995-02-17,1995,2,day,0.577,0.19783169332890285
+aml1995feb74,1995-02-17,1995,2,day,1.68,0.42813479402878873
+aml1995feb75,1995-02-17,1995,2,day,0.211,0.08314414314305227
+aml1995feb76,1995-02-17,1995,2,night,0.203,0.08026562733984477
+aml1995feb77,1995-02-17,1995,2,night,5.235,0.7948364578145615
+aml1995feb78,1995-02-16,1995,2,night,0.949,0.28981183911762143
+aml1995feb79,1995-02-16,1995,2,day,1.967,0.47231754631684186
+aml1995feb8,1995-02-25,1995,2,day,1.925,0.4661258704181992
+aml1995feb80,1995-02-16,1995,2,day,44.406,1.6571132448112746
+aml1995feb81,1995-02-16,1995,2,day,9.827,1.0345081367791695
+aml1995feb82,1995-02-16,1995,2,day,1.298,0.3613500243522664
+aml1995feb83,1995-02-16,1995,2,day,4.449,0.7363168079041088
+aml1995feb84,1995-02-16,1995,2,night,5.667,0.823930455125564
+aml1995feb85,1995-02-16,1995,2,night,0.087,0.03622954408629452
+aml1995feb86,1995-02-15,1995,2,night,0.061,0.025715383901340642
+aml1995feb87,1995-02-15,1995,2,day,6.959,0.9008585047019916
+aml1995feb88,1995-02-15,1995,2,day,1.653,0.4237372499823292
+aml1995feb89,1995-02-15,1995,2,day,3.509,0.6540802353065707
+aml1995feb9,1995-02-27,1995,2,night,10.934,1.0767860335080792
+aml1995feb90,1995-02-15,1995,2,day,2.633,0.5602653978627147
+aml1995feb91,1995-02-15,1995,2,night,0.072,0.03019478535675124
+aml1995febsalp,1995-02-20,1995,2,NA,NA,NA
+aml1995jan1,1995-01-16,1995,1,day,0,0
+aml1995jan10,1995-01-17,1995,1,night,0.711,0.23325000954110026
+aml1995jan11,1995-01-18,1995,1,night,9.033,1.0014308122463982
+aml1995jan12,1995-01-18,1995,1,day,9.549,1.0232112923288885
+aml1995jan13,1995-01-18,1995,1,day,114.44,2.062356318085438
+aml1995jan14,1995-01-18,1995,1,day,0.403,0.1470576710283599
+aml1995jan15,1995-01-18,1995,1,day,1.497,0.39741854235134777
+aml1995jan16,1995-01-18,1995,1,day,2.819,0.5819496583733179
+aml1995jan17,1995-01-18,1995,1,night,0.057,0.024074987307426243
+aml1995jan18,1995-01-19,1995,1,night,0.041,0.017450729510536125
+aml1995jan19,1995-01-19,1995,1,day,1.992,0.4759615891924237
+aml1995jan2,1995-01-16,1995,1,day,0,0
+aml1995jan20,1995-01-19,1995,1,day,0.073,0.030599721965951062
+aml1995jan21,1995-01-19,1995,1,day,1.228,0.34791518650169123
+aml1995jan22,1995-01-19,1995,1,day,0.846,0.26623169668989327
+aml1995jan23,1995-01-19,1995,1,day,1.365,0.3738311450738304
+aml1995jan24,1995-01-19,1995,1,day,0.122,0.04999285692014256
+aml1995jan25,1995-01-19,1995,1,night,0.144,0.05842602445700535
+aml1995jan26,1995-01-20,1995,1,night,0,0
+aml1995jan27,1995-01-20,1995,1,day,1.694,0.43039759138696676
+aml1995jan28,1995-01-20,1995,1,day,1.331,0.36754227352057667
+aml1995jan29,1995-01-20,1995,1,day,2.76,0.5751878449276611
+aml1995jan3,1995-01-16,1995,1,day,0.486,0.1720188094245565
+aml1995jan30,1995-01-20,1995,1,day,2.683,0.5662017188549129
+aml1995jan31,1995-01-20,1995,1,day,11.33,1.0909630765957317
+aml1995jan32,1995-01-20,1995,1,night,0,0
+aml1995jan33,1995-01-21,1995,1,night,0.031,0.01325866528351651
+aml1995jan34,1995-01-21,1995,1,day,0,0
+aml1995jan35,1995-01-21,1995,1,day,0.271,0.10414555055400815
+aml1995jan36,1995-01-21,1995,1,day,6.922,0.898834838068274
+aml1995jan37,1995-01-21,1995,1,day,4.037,0.7021719508577112
+aml1995jan38,1995-01-21,1995,1,day,7.453,0.927010868975651
+aml1995jan39,1995-01-21,1995,1,night,1.044,0.3104808914626751
+aml1995jan4,1995-01-17,1995,1,night,0.044,0.01870049866624337
+aml1995jan40,1995-01-22,1995,1,night,0.797,0.25454807710897387
+aml1995jan41,1995-01-22,1995,1,night,0.079,0.033021444682910656
+aml1995jan42,1995-01-22,1995,1,day,0,0
+aml1995jan43,1995-01-22,1995,1,day,1.471,0.3928727454020794
+aml1995jan44,1995-01-22,1995,1,day,0.316,0.11925588927793669
+aml1995jan45,1995-01-22,1995,1,day,0.396,0.14488541828714224
+aml1995jan46,1995-01-22,1995,1,day,0.942,0.288249225571986
+aml1995jan47,1995-01-22,1995,1,night,2.063,0.4861469968065727
+aml1995jan48,1995-01-23,1995,1,night,0.33,0.12385164096708583
+aml1995jan49,1995-01-23,1995,1,day,4.382,0.7309436934277357
+aml1995jan5,1995-01-17,1995,1,day,0.161,0.06483221973857385
+aml1995jan50,1995-01-23,1995,1,day,0,0
+aml1995jan51,1995-01-23,1995,1,day,0.893,0.27715061396379675
+aml1995jan52,1995-01-23,1995,1,day,0.349,0.13001194967190424
+aml1995jan53,1995-01-23,1995,1,day,0,0
+aml1995jan54,1995-01-23,1995,1,night,0,0
+aml1995jan55,1995-01-24,1995,1,night,0.848,0.26670196688408787
+aml1995jan56,1995-01-24,1995,1,night,4.269,0.7217281985727882
+aml1995jan57,1995-01-24,1995,1,day,0.276,0.10585067438514352
+aml1995jan58,1995-01-24,1995,1,day,0.539,0.18723861983147874
+aml1995jan59,1995-01-24,1995,1,day,0.142,0.05766610390982921
+aml1995jan6,1995-01-17,1995,1,day,0.098,0.04060234011407314
+aml1995jan60,1995-01-24,1995,1,day,0,0
+aml1995jan61,1995-01-24,1995,1,day,0.868,0.2713768718940745
+aml1995jan62,1995-01-24,1995,1,night,1.138,0.3300077008727592
+aml1995jan63,1995-01-25,1995,1,night,0.464,0.16554107672237303
+aml1995jan64,1995-01-25,1995,1,day,1.145,0.33142729652074304
+aml1995jan65,1995-01-25,1995,1,day,2.866,0.5872618496925341
+aml1995jan66,1995-01-25,1995,1,day,0,0
+aml1995jan67,1995-01-25,1995,1,day,1.847,0.4543874671469551
+aml1995jan68,1995-01-25,1995,1,day,11.786,1.1067347000978456
+aml1995jan69,1995-01-25,1995,1,night,0.746,0.24204423936955088
+aml1995jan7,1995-01-17,1995,1,day,0,0
+aml1995jan70,1995-01-26,1995,1,night,1.27,0.35602585719312274
+aml1995jan71,1995-01-26,1995,1,day,3.35,0.6384892569546373
+aml1995jan72,1995-01-26,1995,1,day,1.387,0.37785241900675454
+aml1995jan73,1995-01-26,1995,1,day,3.422,0.6456187382426952
+aml1995jan75,1995-01-26,1995,1,night,0.704,0.2314695904306813
+aml1995jan76,1995-01-27,1995,1,night,3.275,0.6309361190641914
+aml1995jan77,1995-01-27,1995,1,night,0.843,0.26552533521907384
+aml1995jan78,1995-01-27,1995,1,day,3.836,0.6844862921887342
+aml1995jan79,1995-01-27,1995,1,day,5.719,0.827304641089735
+aml1995jan8,1995-01-17,1995,1,day,0.054,0.022840610876527823
+aml1995jan80,1995-01-27,1995,1,day,60.611,1.7896582578269657
+aml1995jan81,1995-01-27,1995,1,day,20.446,1.3313463016525666
+aml1995jan82,1995-01-27,1995,1,day,0.955,0.2911467617318856
+aml1995jan83,1995-01-27,1995,1,night,1.123,0.32694999416599885
+aml1995jan84,1995-01-28,1995,1,night,2.227,0.5087989654039049
+aml1995jan85,1995-01-28,1995,1,day,5.621,0.8209235878813175
+aml1995jan86,1995-01-28,1995,1,day,9.61,1.0257153839013406
+aml1995jan87,1995-01-28,1995,1,day,10.614,1.0649818216973055
+aml1995jan88,1995-01-28,1995,1,day,2.184,0.5029730590656314
+aml1995jan89,1995-01-28,1995,1,day,2.555,0.5508396050657851
+aml1995jan9,1995-01-17,1995,1,day,35.802,1.565871420993129
+aml1995jan90,1995-01-28,1995,1,night,0.377,0.13893394025692368
+aml1995jan91,1995-01-29,1995,1,night,6.696,0.8862650590297565
+aml1995jansalp,1995-01-22,1995,1,NA,NA,NA
+aml1996feb1,1996-03-08,1996,3,day,0.234,0.09131515969722287
+aml1996feb10,1996-03-07,1996,3,night,2.902,0.5912872650584993
+aml1996feb11,1996-03-07,1996,3,night,0.941,0.2880255353883628
+aml1996feb12,1996-03-06,1996,3,night,1.354,0.3718064585074159
+aml1996feb13,1996-03-06,1996,3,day,0,0
+aml1996feb14,1996-03-06,1996,3,day,1.108,0.32387060654050903
+aml1996feb15,1996-03-06,1996,3,day,0.136,0.05537833137500011
+aml1996feb16,1996-03-06,1996,3,day,8.867,0.9941851282023172
+aml1996feb17,1996-03-06,1996,3,night,2.818,0.5818359240576481
+aml1996feb18,1996-03-05,1996,3,night,14.617,1.1935976102980945
+aml1996feb19,1996-03-05,1996,3,day,0.221,0.0867156639448825
+aml1996feb2,1996-03-08,1996,3,night,0.177,0.0707764628434347
+aml1996feb20,1996-03-05,1996,3,day,0.144,0.05842602445700535
+aml1996feb21,1996-03-05,1996,3,day,0,0
+aml1996feb22,1996-03-05,1996,3,day,0,0
+aml1996feb23,1996-03-05,1996,3,night,2.754,0.5744942682853276
+aml1996feb24,1996-03-04,1996,3,night,4.217,0.7174208367223749
+aml1996feb25,1996-03-04,1996,3,day,0,0
+aml1996feb26,1996-03-04,1996,3,day,0.49,0.17318626841227402
+aml1996feb27,1996-03-04,1996,3,day,1.273,0.35659943572497077
+aml1996feb28,1996-03-04,1996,3,day,0,0
+aml1996feb29,1996-03-04,1996,3,night,0.129,0.05269394192496786
+aml1996feb3,1996-03-08,1996,3,night,1.238,0.3498600821923312
+aml1996feb30,1996-03-04,1996,3,night,3.815,0.6825962914605532
+aml1996feb31,1996-03-03,1996,3,night,54.974,1.747986343459441
+aml1996feb32,1996-03-03,1996,3,day,1.053,0.31238894937059186
+aml1996feb33,1996-03-03,1996,3,day,0.279,0.10687054447865389
+aml1996feb34,1996-03-03,1996,3,day,39.405,1.606435111101875
+aml1996feb35,1996-03-03,1996,3,day,0,0
+aml1996feb36,1996-03-03,1996,3,day,1.318,0.3651134316275772
+aml1996feb37,1996-03-03,1996,3,night,1.638,0.4212747912103465
+aml1996feb38,1996-03-03,1996,3,night,50.81,1.714413592287121
+aml1996feb39,1996-03-02,1996,3,night,0.216,0.08493357493671612
+aml1996feb4,1996-03-08,1996,3,night,2.126,0.4949889736831681
+aml1996feb40,1996-03-02,1996,3,day,0.149,0.06032002868828519
+aml1996feb41,1996-03-02,1996,3,day,2.337,0.5233562066547928
+aml1996feb42,1996-03-02,1996,3,day,0.167,0.06707085604537018
+aml1996feb43,1996-03-02,1996,3,day,0,0
+aml1996feb44,1996-03-02,1996,3,night,0.322,0.12123145514962146
+aml1996feb45,1996-03-02,1996,3,night,18.892,1.2986784505601874
+aml1996feb46,1996-03-01,1996,3,night,2.025,0.48072537898848766
+aml1996feb47,1996-03-01,1996,3,day,1.03,0.30749603791321295
+aml1996feb48,1996-03-01,1996,3,day,0.207,0.08170727009734924
+aml1996feb49,1996-03-01,1996,3,day,7.415,0.9250541203118426
+aml1996feb5,1996-03-07,1996,3,night,0.608,0.20628604441243248
+aml1996feb50,1996-03-01,1996,3,night,1.892,0.46119828862249324
+aml1996feb51,1996-02-29,1996,2,night,11.225,1.0872488677956578
+aml1996feb52,1996-02-29,1996,2,night,1.168,0.33605927786634937
+aml1996feb53,1996-02-29,1996,2,day,0.164,0.06595298031386966
+aml1996feb54,1996-02-29,1996,2,day,0,0
+aml1996feb55,1996-02-29,1996,2,day,108.535,2.039552912583547
+aml1996feb56,1996-02-29,1996,2,day,3.84,0.6848453616444125
+aml1996feb57,1996-02-29,1996,2,night,1.066,0.31513031718360174
+aml1996feb58,1996-02-29,1996,2,night,17.8,1.2741578492636798
+aml1996feb59,1996-02-28,1996,2,night,3.302,0.6336704060514438
+aml1996feb6,1996-03-07,1996,3,day,0.136,0.05537833137500011
+aml1996feb60,1996-02-28,1996,2,day,0.301,0.11427729656158624
+aml1996feb61,1996-02-28,1996,2,day,0.326,0.12254352406875435
+aml1996feb62,1996-02-28,1996,2,day,0.39,0.1430148002540951
+aml1996feb63,1996-02-28,1996,2,day,0.749,0.24278980947867654
+aml1996feb64,1996-02-28,1996,2,day,1.361,0.37309598707872693
+aml1996feb65,1996-02-28,1996,2,night,14.788,1.1983271177234778
+aml1996feb66,1996-02-27,1996,2,night,2.97,0.5987905067631151
+aml1996feb67,1996-02-27,1996,2,day,36.627,1.5754995934093206
+aml1996feb68,1996-02-27,1996,2,day,0,0
+aml1996feb69,1996-02-27,1996,2,day,0.15,0.06069784035361165
+aml1996feb7,1996-03-07,1996,3,day,0.863,0.27021285489624264
+aml1996feb70,1996-02-27,1996,2,day,10.943,1.0771134323037603
+aml1996feb71,1996-02-27,1996,2,day,54.462,1.7439955264986522
+aml1996feb72,1996-02-27,1996,2,night,2.78,0.5774917998372253
+aml1996feb73,1996-02-27,1996,2,night,30.207,1.4942520209540773
+aml1996feb74,1996-02-26,1996,2,night,5.548,0.8161086707399038
+aml1996feb75,1996-02-26,1996,2,day,0,0
+aml1996feb76,1996-02-26,1996,2,day,3.174,0.6205524447294352
+aml1996feb77,1996-02-26,1996,2,day,0.129,0.05269394192496786
+aml1996feb78,1996-02-26,1996,2,day,0.777,0.24968742780530156
+aml1996feb79,1996-02-26,1996,2,night,0.323,0.12155984418750096
+aml1996feb8,1996-03-07,1996,3,day,0,0
+aml1996feb80,1996-02-26,1996,2,night,1825.162,3.2615393014487326
+aml1996feb81,1996-02-25,1996,2,night,1.063,0.3144992279731515
+aml1996feb82,1996-02-25,1996,2,day,11.399,1.0933866600013706
+aml1996feb83,1996-02-25,1996,2,day,0,0
+aml1996feb84,1996-02-25,1996,2,night,7.073,0.907034952483417
+aml1996feb85,1996-02-24,1996,2,night,0.967,0.29380435991933673
+aml1996feb86,1996-02-24,1996,2,day,9.518,1.0219331664774387
+aml1996feb87,1996-02-24,1996,2,day,13.973,1.1753088245857848
+aml1996feb88,1996-02-24,1996,2,day,1.667,0.4260230156898761
+aml1996feb89,1996-02-24,1996,2,day,0.418,0.1516762308470477
+aml1996feb9,1996-03-07,1996,3,day,2.973,0.5991185650553628
+aml1996feb90,1996-02-24,1996,2,night,1.177,0.33785842904109437
+aml1996feb91,1996-02-23,1996,2,night,5.98,0.8438554226231612
+aml1996febsalp,1996-03-01,1996,3,NA,NA,NA
+aml1996jan1,1996-01-23,1996,1,night,0.061,0.025715383901340642
+aml1996jan10,1996-01-24,1996,1,day,79.506,1.90582824893665
+aml1996jan11,1996-01-24,1996,1,day,3.386,0.642068627341504
+aml1996jan12,1996-01-24,1996,1,night,2.786,0.578180609627778
+aml1996jan13,1996-01-25,1996,1,night,63.424,1.8090476860843707
+aml1996jan14,1996-01-25,1996,1,day,0.309,0.11693964655075577
+aml1996jan15,1996-01-25,1996,1,day,17.636,1.2703527017761833
+aml1996jan16,1996-01-25,1996,1,day,34.358,1.5484876914787928
+aml1996jan17,1996-01-25,1996,1,day,173.494,2.241780498276914
+aml1996jan18,1996-01-25,1996,1,night,5.52,0.8142475957319202
+aml1996jan19,1996-01-26,1996,1,night,38.634,1.598067905043341
+aml1996jan2,1996-01-23,1996,1,day,0.107,0.0441476208787228
+aml1996jan20,1996-01-26,1996,1,day,0.093,0.03862016194970278
+aml1996jan21,1996-01-26,1996,1,day,0.157,0.06333335895174956
+aml1996jan22,1996-01-26,1996,1,day,5.288,0.7985125330313515
+aml1996jan23,1996-01-26,1996,1,day,0.519,0.18155777386278638
+aml1996jan24,1996-01-26,1996,1,day,1.783,0.44451320633404295
+aml1996jan25,1996-01-26,1996,1,night,0.161,0.06483221973857385
+aml1996jan26,1996-01-27,1996,1,night,0.937,0.28712962071911086
+aml1996jan27,1996-01-27,1996,1,day,6.764,0.8900855267163252
+aml1996jan28,1996-01-27,1996,1,day,6.534,0.8770256158672488
+aml1996jan29,1996-01-27,1996,1,day,4.249,0.7200765727681406
+aml1996jan3,1996-01-23,1996,1,day,4.967,0.775756037844098
+aml1996jan30,1996-01-27,1996,1,day,3.4,0.6434526764861874
+aml1996jan31,1996-01-27,1996,1,day,4.772,0.7613263224214566
+aml1996jan32,1996-01-27,1996,1,night,7.579,0.9334366678262805
+aml1996jan33,1996-01-28,1996,1,night,13.446,1.1597476105242728
+aml1996jan34,1996-01-28,1996,1,night,1.41,0.3820170425748684
+aml1996jan35,1996-01-28,1996,1,day,6.474,0.8735530935136189
+aml1996jan36,1996-01-28,1996,1,day,6.843,0.8944822150100906
+aml1996jan37,1996-01-28,1996,1,day,0.199,0.0788191830988487
+aml1996jan38,1996-01-28,1996,1,day,66.741,1.8308516034582474
+aml1996jan39,1996-01-28,1996,1,day,1.229,0.34811006848023773
+aml1996jan4,1996-01-23,1996,1,day,14.137,1.1800398109271761
+aml1996jan40,1996-01-28,1996,1,night,7.982,0.953373050726696
+aml1996jan41,1996-01-29,1996,1,night,10.346,1.0548427792286834
+aml1996jan42,1996-01-29,1996,1,day,0.814,0.25863728272407643
+aml1996jan43,1996-01-29,1996,1,day,11.64,1.1017470739463662
+aml1996jan44,1996-01-29,1996,1,day,5.807,0.8329557506045984
+aml1996jan45,1996-01-29,1996,1,day,11.227,1.0873199122064008
+aml1996jan46,1996-01-29,1996,1,day,4.88,0.7693773260761385
+aml1996jan47,1996-01-29,1996,1,night,0,0
+aml1996jan48,1996-01-30,1996,1,night,284.279,2.4552698034839606
+aml1996jan49,1996-01-30,1996,1,night,0.857,0.2688119037397804
+aml1996jan5,1996-01-23,1996,1,day,0.439,0.15806079393660522
+aml1996jan50,1996-01-30,1996,1,day,0,0
+aml1996jan51,1996-01-30,1996,1,day,1.534,0.4038066105474225
+aml1996jan52,1996-01-30,1996,1,day,0.71,0.23299611039215382
+aml1996jan53,1996-01-30,1996,1,day,0.183,0.07298474462793038
+aml1996jan54,1996-01-30,1996,1,day,0,0
+aml1996jan55,1996-01-30,1996,1,day,32.925,1.5305198563317746
+aml1996jan56,1996-01-30,1996,1,night,57.555,1.7675639853126701
+aml1996jan57,1996-01-31,1996,1,night,1.21,0.3443922736851107
+aml1996jan58,1996-01-31,1996,1,day,4.691,0.755188585608325
+aml1996jan59,1996-01-31,1996,1,day,1.329,0.3671694885346807
+aml1996jan6,1996-01-23,1996,1,night,1.433,0.38614210893081846
+aml1996jan60,1996-01-31,1996,1,day,0.244,0.0948203803547999
+aml1996jan61,1996-01-31,1996,1,day,76.171,1.8874541279766357
+aml1996jan62,1996-01-31,1996,1,day,21.872,1.3593041423499324
+aml1996jan63,1996-01-31,1996,1,night,2.505,0.5446880223026774
+aml1996jan64,1996-02-01,1996,2,night,29.337,1.4819726315639583
+aml1996jan65,1996-02-01,1996,2,day,1.229,0.34811006848023773
+aml1996jan66,1996-02-01,1996,2,day,0.24,0.09342168516223506
+aml1996jan67,1996-02-01,1996,2,day,0.45,0.16136800223497488
+aml1996jan68,1996-02-01,1996,2,day,0.129,0.05269394192496786
+aml1996jan69,1996-02-01,1996,2,night,11.457,1.0954134644484463
+aml1996jan7,1996-01-24,1996,1,night,606.519,2.7835598649306323
+aml1996jan70,1996-02-02,1996,2,night,261.854,2.419714590312822
+aml1996jan71,1996-02-02,1996,2,day,3.285,0.631950826259217
+aml1996jan72,1996-02-02,1996,2,day,7.579,0.9334366678262805
+aml1996jan73,1996-02-02,1996,2,day,26.263,1.4355736435661135
+aml1996jan74,1996-02-02,1996,2,day,1.725,0.4353665066126613
+aml1996jan75,1996-02-02,1996,2,day,8.172,0.9624640460579013
+aml1996jan76,1996-02-02,1996,2,day,2.109,0.49262072204319185
+aml1996jan77,1996-02-02,1996,2,night,1.542,0.40517554621798935
+aml1996jan78,1996-02-03,1996,2,night,14.225,1.182557301304913
+aml1996jan79,1996-02-03,1996,2,day,6.244,0.8599784416420208
+aml1996jan8,1996-01-24,1996,1,night,1.09,0.320146286111054
+aml1996jan80,1996-02-03,1996,2,day,18.948,1.2998993595463255
+aml1996jan81,1996-02-03,1996,2,day,196.825,2.296281174399547
+aml1996jan82,1996-02-03,1996,2,day,4.636,0.7509709844373188
+aml1996jan83,1996-02-03,1996,2,day,6.557,0.8783494222177755
+aml1996jan84,1996-02-03,1996,2,night,0.15,0.06069784035361165
+aml1996jan85,1996-02-04,1996,2,night,3.305,0.6339731557896735
+aml1996jan86,1996-02-04,1996,2,night,1.4,0.38021124171160603
+aml1996jan87,1996-02-04,1996,2,day,1.57,0.4099331233312946
+aml1996jan88,1996-02-04,1996,2,day,2.255,0.5125509929042107
+aml1996jan89,1996-02-04,1996,2,day,11.225,1.0872488677956578
+aml1996jan9,1996-01-24,1996,1,day,16.315,1.2384224958854797
+aml1996jan90,1996-02-04,1996,2,day,5.387,0.8052969161579849
+aml1996jan91,1996-02-04,1996,2,day,3.207,0.6239725120169963
+aml1996jansalp,1996-01-29,1996,1,NA,NA,NA
+aml1997feb10,1997-01-31,1997,1,night,1.961,0.4714384073892992
+aml1997feb107,1997-01-30,1997,1,day,3.59,0.6618126855372612
+aml1997feb108,1997-01-29,1997,1,day,73.989,1.8749975621961523
+aml1997feb11,1997-01-31,1997,1,night,8.789,0.9907383285075373
+aml1997feb113,1997-01-30,1997,1,day,0.772,0.24846371755103194
+aml1997feb115,1997-01-30,1997,1,day,0.566,0.19479175772192453
+aml1997feb12,1997-01-30,1997,1,day,5.632,0.8216445175422171
+aml1997feb128,1997-01-28,1997,1,day,0,0
+aml1997feb129,1997-01-28,1997,1,day,22.579,1.3725253824515589
+aml1997feb130,1997-01-28,1997,1,day,0.114,0.04688519083771013
+aml1997feb131,1997-01-29,1997,1,day,0.159,0.064083435963596
+aml1997feb143,1997-01-27,1997,1,day,4.115,0.708845638048179
+aml1997feb144,1997-01-27,1997,1,night,1.676,0.42748610909578555
+aml1997feb145,1997-01-28,1997,1,night,24.09,1.3995006613146106
+aml1997feb15,1997-01-30,1997,1,day,11.81,1.1075491297446862
+aml1997feb158,1997-01-27,1997,1,day,0,0
+aml1997feb159,1997-01-27,1997,1,day,0.082,0.03422726077055066
+aml1997feb160,1997-01-27,1997,1,day,0.953,0.2907022432878543
+aml1997feb169,1997-01-26,1997,1,night,0,0
+aml1997feb17,1997-01-31,1997,1,day,6.522,0.8763333290863804
+aml1997feb170,1997-01-27,1997,1,night,0.569,0.19562294358693666
+aml1997feb171,1997-01-27,1997,1,day,0.338,0.12645611343180432
+aml1997feb18,1997-01-31,1997,1,day,3.606,0.6633239336282123
+aml1997feb19,1997-01-31,1997,1,day,1.501,0.3981136917305025
+aml1997feb2,1997-01-29,1997,1,night,0.438,0.15775888604686378
+aml1997feb20,1997-01-31,1997,1,day,1.116,0.3255156633631482
+aml1997feb21,1997-01-31,1997,1,day,1.218,0.34596154181314126
+aml1997feb22,1997-01-31,1997,1,day,0,0
+aml1997feb23,1997-02-01,1997,2,night,0.075,0.031408464251624114
+aml1997feb24,1997-02-01,1997,2,night,1.082,0.3184807251745173
+aml1997feb25,1997-02-01,1997,2,day,5.838,0.8349290964605758
+aml1997feb26,1997-02-01,1997,2,day,1.138,0.3300077008727592
+aml1997feb27,1997-02-01,1997,2,day,2.528,0.5475285764597821
+aml1997feb28,1997-02-01,1997,2,day,0.976,0.2957869402516093
+aml1997feb29,1997-02-01,1997,2,day,0.469,0.16702179579025647
+aml1997feb30,1997-02-01,1997,2,day,0.614,0.20790353038605158
+aml1997feb31,1997-02-01,1997,2,night,7.856,0.9472376078706664
+aml1997feb32,1997-02-02,1997,2,night,0.396,0.14488541828714224
+aml1997feb33,1997-02-02,1997,2,day,1.73,0.436162647040756
+aml1997feb34,1997-01-29,1997,1,day,130.052,2.1174436531524092
+aml1997feb35,1997-02-02,1997,2,day,0.868,0.2713768718940745
+aml1997feb36,1997-02-02,1997,2,day,0.284,0.10856502373283447
+aml1997feb37,1997-02-02,1997,2,day,13.214,1.1527163111064087
+aml1997feb38,1997-02-02,1997,2,day,17.04,1.2562365332059229
+aml1997feb39,1997-02-02,1997,2,day,1.367,0.37419825792908273
+aml1997feb4,1997-01-29,1997,1,day,2.443,0.5369370227046735
+aml1997feb40,1997-02-02,1997,2,night,3.948,0.6944296909570831
+aml1997feb41,1997-02-02,1997,2,night,0.498,0.17551181336344768
+aml1997feb42,1997-02-03,1997,2,night,2.538,0.5487578285737043
+aml1997feb43,1997-02-03,1997,2,day,0.334,0.12515582958053018
+aml1997feb44,1997-02-03,1997,2,day,0.156,0.06295783408451021
+aml1997feb45,1997-02-03,1997,2,day,2.532,0.548020694905531
+aml1997feb46,1997-02-03,1997,2,day,13.215,1.1527468640264609
+aml1997feb47,1997-02-03,1997,2,day,0.1,0.04139268515822507
+aml1997feb48,1997-02-03,1997,2,night,2.486,0.5423273827739744
+aml1997feb49,1997-02-04,1997,2,night,85.018,1.9345893405772745
+aml1997feb50,1997-02-04,1997,2,night,9.458,1.019448637493637
+aml1997feb51,1997-02-04,1997,2,day,0.114,0.04688519083771013
+aml1997feb52,1997-02-09,1997,2,night,0.454,0.162564406523019
+aml1997feb53,1997-02-09,1997,2,day,0.138,0.05614226205905233
+aml1997feb54,1997-02-09,1997,2,day,0.671,0.22297644989339133
+aml1997feb55,1997-02-09,1997,2,day,0.957,0.2915908256580011
+aml1997feb56,1997-02-09,1997,2,day,53.054,1.7328278373022872
+aml1997feb57,1997-02-09,1997,2,day,4.363,0.7294077969630681
+aml1997feb58,1997-02-09,1997,2,day,0.165,0.0663259253620378
+aml1997feb59,1997-02-09,1997,2,night,19.865,1.3194183890477276
+aml1997feb6,1997-01-29,1997,1,day,38.008,1.591153683938288
+aml1997feb60,1997-02-08,1997,2,night,4.359,0.7290837570436118
+aml1997feb61,1997-02-08,1997,2,day,1.539,0.4046627008737222
+aml1997feb62,1997-02-08,1997,2,day,0.549,0.19005141775920598
+aml1997feb63,1997-02-08,1997,2,day,2.654,0.562768543016519
+aml1997feb64,1997-02-08,1997,2,day,0.626,0.21112054125804933
+aml1997feb65,1997-02-08,1997,2,day,15.252,1.2109068136543741
+aml1997feb66,1997-02-08,1997,2,night,0.132,0.05384642685225267
+aml1997feb67,1997-02-09,1997,2,night,13.432,1.1593265201978666
+aml1997feb68,1997-02-07,1997,2,night,95.533,1.9846758031672158
+aml1997feb69,1997-02-07,1997,2,day,29.051,1.4778589284936956
+aml1997feb70,1997-02-07,1997,2,day,0,0
+aml1997feb71,1997-02-07,1997,2,day,5.188,0.7915503050273299
+aml1997feb72,1997-02-07,1997,2,day,27.694,1.4577910939706054
+aml1997feb73,1997-02-07,1997,2,day,8.659,0.9849321660674121
+aml1997feb75,1997-02-07,1997,2,night,5.781,0.8312937443770096
+aml1997feb76,1997-02-06,1997,2,night,19.98,1.321805483857539
+aml1997feb77,1997-02-06,1997,2,night,17.868,1.2757258675736642
+aml1997feb78,1997-02-06,1997,2,day,0.443,0.15926633109349422
+aml1997feb79,1997-02-06,1997,2,day,11.896,1.1104550244610154
+aml1997feb8,1997-01-29,1997,1,night,0.192,0.07627625540421759
+aml1997feb80,1997-02-06,1997,2,day,1.238,0.3498600821923312
+aml1997feb81,1997-02-06,1997,2,day,0.134,0.05461305455688774
+aml1997feb82,1997-02-06,1997,2,day,0,0
+aml1997feb83,1997-02-06,1997,2,day,0,0
+aml1997feb84,1997-02-05,1997,2,night,4.448,0.7362370989047288
+aml1997feb85,1997-02-05,1997,2,day,0.309,0.11693964655075577
+aml1997feb86,1997-02-05,1997,2,day,0.984,0.2975416678181598
+aml1997feb87,1997-02-05,1997,2,day,1.024,0.3062105081677615
+aml1997feb88,1997-02-05,1997,2,day,2.689,0.5669086552268034
+aml1997feb89,1997-02-05,1997,2,day,0.905,0.2798949800116381
+aml1997feb9,1997-01-30,1997,1,night,11.972,1.1130069400009353
+aml1997feb90,1997-02-05,1997,2,day,3.257,0.6291036501771364
+aml1997feb91,1997-02-05,1997,2,night,0.068,0.028571252692537637
+aml1997feb92,1997-02-04,1997,2,night,47.266,1.683641308557471
+aml1997feb93,1997-02-04,1997,2,day,63.654,1.8105953988867736
+aml1997feb97,1997-02-04,1997,2,day,248.86,2.3976967356393635
+aml1997feb98,1997-02-06,1997,2,night,0.068,0.028571252692537637
+aml1997mar29,1997-03-22,1997,3,day,0,0
+aml1997mar36,1997-03-17,1997,3,night,44.78,1.6606757883385241
+aml1997mar37,1997-03-17,1997,3,night,7.555,0.9322200138771191
+aml1997mar39,1997-03-22,1997,3,night,0.259,0.10002573010786256
+aml1997mar48,1997-03-22,1997,3,night,3.871,0.6876181295717699
+aml1997mar49,1997-03-17,1997,3,day,0,0
+aml1997mar50,1997-03-17,1997,3,day,0,0
+aml1997mar55,1997-03-17,1997,3,day,0,0
+aml1997mar60,1997-03-24,1997,3,night,2.85,0.5854607295085006
+aml1997mar61,1997-03-23,1997,3,night,2.848,0.5852350633657754
+aml1997mar62,1997-03-16,1997,3,night,20.956,1.3415532221095774
+aml1997mar63,1997-03-17,1997,3,night,66.749,1.8309028892484793
+aml1997mar64,1997-03-17,1997,3,night,10.048,1.0432836655705748
+aml1997mar65,1997-03-22,1997,3,night,0.399,0.14581771449182762
+aml1997mar66,1997-03-22,1997,3,night,0.562,0.19368102954128152
+aml1997mar67,1997-03-23,1997,3,night,0,0
+aml1998feb1,1998-02-14,1998,2,night,0.21,0.08278537031645007
+aml1998feb10,1998-02-08,1998,2,day,10.78,1.0711452904510828
+aml1998feb104,1998-02-08,1998,2,day,11.202,1.0864310206563688
+aml1998feb105,1998-02-08,1998,2,day,0,0
+aml1998feb108,1998-02-15,1998,2,day,205.635,2.3152038845576284
+aml1998feb109,1998-02-15,1998,2,day,0,0
+aml1998feb110,1998-02-09,1998,2,night,4.389,0.7315081835960254
+aml1998feb111,1998-02-09,1998,2,night,0.254,0.09829753649469763
+aml1998feb116,1998-02-09,1998,2,day,7.892,0.948999454026953
+aml1998feb118,1998-02-15,1998,2,night,337.49,2.529545842861724
+aml1998feb121,1998-02-14,1998,2,night,149.432,2.1773402295081583
+aml1998feb123,1998-02-09,1998,2,day,0,0
+aml1998feb124,1998-02-09,1998,2,day,0.435,0.15685190107001115
+aml1998feb13,1998-02-08,1998,2,day,2.101,0.4915017662373264
+aml1998feb131,1998-02-14,1998,2,day,3.549,0.6579159368299551
+aml1998feb134,1998-02-14,1998,2,day,172.582,2.2395046879719414
+aml1998feb135,1998-02-09,1998,2,day,45.24,1.6650178254124726
+aml1998feb136,1998-02-10,1998,2,night,0.226,0.08849047018239622
+aml1998feb14,1998-02-09,1998,2,day,0.577,0.19783169332890285
+aml1998feb142,1998-02-10,1998,2,day,0.135,0.05499586152914153
+aml1998feb143,1998-02-10,1998,2,day,17.647,1.270608970823178
+aml1998feb144,1998-02-10,1998,2,day,14.09,1.17868923977559
+aml1998feb145,1998-02-10,1998,2,day,0.111,0.04571405894086761
+aml1998feb158,1998-02-11,1998,2,night,0.986,0.29797924415936233
+aml1998feb159,1998-02-12,1998,2,night,23.539,1.3898568606187423
+aml1998feb16,1998-02-08,1998,2,night,0.085,0.0354297381845483
+aml1998feb160,1998-02-12,1998,2,night,42.641,1.6398946935548493
+aml1998feb166,1998-02-12,1998,2,day,1.511,0.39984671271292244
+aml1998feb169,1998-02-12,1998,2,day,0,0
+aml1998feb170,1998-02-12,1998,2,day,6.478,0.8737854608182007
+aml1998feb171,1998-02-12,1998,2,night,0.401,0.1464381352857746
+aml1998feb176,1998-02-13,1998,2,day,0,0
+aml1998feb177,1998-02-13,1998,2,day,0,0
+aml1998feb178,1998-02-13,1998,2,day,1.05,0.31175386105575426
+aml1998feb179,1998-02-13,1998,2,day,9.333,1.0142264293892296
+aml1998feb18,1998-02-16,1998,2,day,2.267,0.5141491344754374
+aml1998feb19,1998-02-16,1998,2,day,3.062,0.6087399190687879
+aml1998feb2,1998-02-14,1998,2,day,2.671,0.5647843845039868
+aml1998feb20,1998-02-16,1998,2,day,1.412,0.3823773034681137
+aml1998feb21,1998-02-16,1998,2,day,0.635,0.21351775699630487
+aml1998feb22,1998-02-16,1998,2,day,2.099,0.49122157623928303
+aml1998feb23,1998-02-16,1998,2,night,0.152,0.06145247908719321
+aml1998feb24,1998-02-17,1998,2,night,22.295,1.3672627147842398
+aml1998feb26,1998-02-17,1998,2,night,0.151,0.06107532362979181
+aml1998feb27,1998-02-17,1998,2,day,1.965,0.4720246977002814
+aml1998feb28,1998-02-17,1998,2,day,1.851,0.45499721730945997
+aml1998feb29,1998-02-17,1998,2,day,3.218,0.6251065754034676
+aml1998feb3,1998-02-14,1998,2,day,0,0
+aml1998feb30,1998-02-17,1998,2,day,7.483,0.9285494670016639
+aml1998feb31,1998-02-17,1998,2,night,1.577,0.41111441855090475
+aml1998feb32,1998-02-18,1998,2,night,48.338,1.6931815406428044
+aml1998feb34,1998-02-15,1998,2,day,0.254,0.09829753649469763
+aml1998feb37,1998-02-18,1998,2,day,16.639,1.2464739602293438
+aml1998feb38,1998-02-18,1998,2,day,0.257,0.09933527768595779
+aml1998feb39,1998-02-18,1998,2,day,3.362,0.6396856612426814
+aml1998feb4,1998-02-15,1998,2,day,0.112,0.0461047872460387
+aml1998feb40,1998-02-18,1998,2,day,1.105,0.3232521001716871
+aml1998feb41,1998-02-18,1998,2,day,4.935,0.7734207232906101
+aml1998feb42,1998-02-18,1998,2,day,0.288,0.1099158630237933
+aml1998feb43,1998-02-18,1998,2,night,1307.418,3.1167465101142877
+aml1998feb45,1998-02-19,1998,2,night,5.221,0.7938602013426697
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+aml1998feb47,1998-02-19,1998,2,day,0.107,0.0441476208787228
+aml1998feb48,1998-02-19,1998,2,day,1.067,0.3153404766272883
+aml1998feb49,1998-02-20,1998,2,day,0,0
+aml1998feb5,1998-02-14,1998,2,day,0.302,0.11461098423217315
+aml1998feb50,1998-02-20,1998,2,day,0.795,0.25406445291433793
+aml1998feb51,1998-02-20,1998,2,day,0.634,0.21325205219639665
+aml1998feb53,1998-02-19,1998,2,night,1.634,0.420615770625765
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+aml1998feb56,1998-02-20,1998,2,night,23.262,1.384926598392155
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+aml1998feb58,1998-02-21,1998,2,day,8.366,0.9715541534460611
+aml1998feb59,1998-02-21,1998,2,day,8.378,0.9721102284028306
+aml1998feb6,1998-02-15,1998,2,night,0.71,0.23299611039215382
+aml1998feb61,1998-02-21,1998,2,day,3.891,0.6893976628212823
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+aml1998feb64,1998-02-21,1998,2,night,157.155,2.1990829264957132
+aml1998feb65,1998-02-22,1998,2,day,0.099,0.04099769242349056
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+aml1998feb69,1998-02-22,1998,2,day,29.197,1.4799637989782517
+aml1998feb7,1998-02-15,1998,2,day,0.136,0.05537833137500011
+aml1998feb70,1998-02-22,1998,2,day,0.623,0.21031851982623187
+aml1998feb71,1998-02-22,1998,2,night,522.538,2.718948209589592
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+aml1998feb79,1998-02-24,1998,2,day,5.654,0.8230827965328037
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+aml1998feb80,1998-02-24,1998,2,day,3.021,0.6043340731029111
+aml1998feb81,1998-02-24,1998,2,day,0.3,0.11394335230683678
+aml1998feb82,1998-02-24,1998,2,day,56.979,1.763270720535585
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+aml1998feb85,1998-02-24,1998,2,night,43.659,1.6499089941643441
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+aml1998feb87,1998-02-25,1998,2,night,0.107,0.0441476208787228
+aml1998feb88,1998-02-25,1998,2,day,4.406,0.7328760413627066
+aml1998feb89,1998-02-25,1998,2,day,1.894,0.4614985267830187
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+aml1998feb91,1998-02-25,1998,2,day,0.363,0.13449585583467355
+aml1998jan1,1998-01-14,1998,1,night,0.084,0.03502928220236815
+aml1998jan10,1998-01-09,1998,1,night,35.573,1.5631605859877016
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+aml1998jan105,1998-01-08,1998,1,day,1.949,0.469674772551798
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+aml1998jan110,1998-01-09,1998,1,day,4.163,0.7129021250472227
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+aml1998jan123,1998-01-10,1998,1,night,6.497,0.8748875108461125
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+aml1998jan143,1998-01-11,1998,1,night,11.004,1.0793259867528149
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+aml1998jan145,1998-01-11,1998,1,day,0.331,0.1241780554746751
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+aml1998jan160,1998-01-11,1998,1,day,67.77,1.8373990243420224
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+aml1998jan176,1998-01-12,1998,1,night,0.098,0.04060234011407314
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+aml1998jan178,1998-01-13,1998,1,day,0.39,0.1430148002540951
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+aml1998jan26,1998-01-17,1998,1,day,14.633,1.1940423278864234
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+aml1998jan3,1998-01-14,1998,1,day,1.361,0.37309598707872693
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+aml1998jan37,1998-01-18,1998,1,day,2.879,0.5887197796000565
+aml1998jan38,1998-01-18,1998,1,day,16.108,1.2331992415847735
+aml1998jan39,1998-01-18,1998,1,day,8.266,0.9668922958671364
+aml1998jan4,1998-01-15,1998,1,day,0.532,0.18525876529658514
+aml1998jan40,1998-01-18,1998,1,day,17.382,1.2643927617677173
+aml1998jan41,1998-01-18,1998,1,day,1.159,0.33425264233423074
+aml1998jan42,1998-01-18,1998,1,night,28.538,1.4703810861563993
+aml1998jan43,1998-01-19,1998,1,night,1.12,0.32633586092875144
+aml1998jan45,1998-01-19,1998,1,day,8.626,0.9834458573413396
+aml1998jan46,1998-01-19,1998,1,day,1.088,0.31973049433022455
+aml1998jan47,1998-01-19,1998,1,day,6.372,0.8675853265470362
+aml1998jan48,1998-01-19,1998,1,day,4.041,0.7025166974381506
+aml1998jan49,1998-01-19,1998,1,night,1.107,0.3236645356081002
+aml1998jan5,1998-01-14,1998,1,day,0,0
+aml1998jan50,1998-01-20,1998,1,night,0.052,0.02201573981772028
+aml1998jan51,1998-01-20,1998,1,day,0,0
+aml1998jan53,1998-01-20,1998,1,day,0.235,0.09166695759568448
+aml1998jan55,1998-01-20,1998,1,NA,0.076,0.0318122713303704
+aml1998jan56,1998-01-20,1998,1,day,1.67,0.4265112613645752
+aml1998jan57,1998-01-20,1998,1,night,0.205,0.08098704691088722
+aml1998jan58,1998-01-21,1998,1,night,25.447,1.422376415224179
+aml1998jan6,1998-01-15,1998,1,day,0,0
+aml1998jan62,1998-01-21,1998,1,day,0.393,0.1439511164239635
+aml1998jan63,1998-01-22,1998,1,night,0.65,0.21748394421390624
+aml1998jan64,1998-01-22,1998,1,night,0.035,0.014940349792936524
+aml1998jan65,1998-01-22,1998,1,day,0,0
+aml1998jan66,1998-01-22,1998,1,day,5.744,0.828917561616686
+aml1998jan67,1998-01-22,1998,1,day,10.544,1.0623563180854378
+aml1998jan69,1998-01-22,1998,1,day,31.076,1.5061802048041621
+aml1998jan7,1998-01-15,1998,1,day,0.114,0.04688519083771013
+aml1998jan70,1998-01-22,1998,1,night,0.123,0.050379756261457784
+aml1998jan71,1998-01-23,1998,1,night,27.779,1.4590756991896359
+aml1998jan72,1998-01-23,1998,1,day,5.937,0.8411716944995322
+aml1998jan73,1998-01-23,1998,1,day,9.262,1.011232010319366
+aml1998jan74,1998-01-23,1998,1,day,36.988,1.5796464293318135
+aml1998jan75,1998-01-23,1998,1,day,13.469,1.1604385166415452
+aml1998jan77,1998-01-23,1998,1,day,8.95,0.9978230807457255
+aml1998jan78,1998-01-23,1998,1,night,0.141,0.05728564441821465
+aml1998jan79,1998-01-24,1998,1,night,4.11,0.7084209001347128
+aml1998jan8,1998-01-16,1998,1,night,5.079,0.7838321433844411
+aml1998jan80,1998-01-24,1998,1,night,0.125,0.05115252244738129
+aml1998jan81,1998-01-24,1998,1,day,0,0
+aml1998jan82,1998-01-24,1998,1,day,5.443,0.8090881313463463
+aml1998jan83,1998-01-24,1998,1,day,0.12,0.04921802267018165
+aml1998jan85,1998-01-24,1998,1,day,45.246,1.6650741748468751
+aml1998jan86,1998-01-25,1998,1,day,2.545,0.5496162395190853
+aml1998jan87,1998-01-25,1998,1,night,3.87,0.6875289612146344
+aml1998jan88,1998-01-25,1998,1,night,0.055,0.023252459633711444
+aml1998jan89,1998-01-25,1998,1,day,2.182,0.5027001753105627
+aml1998jan90,1998-01-25,1998,1,day,6.747,0.8891335559667239
+aml1998jan91,1998-01-25,1998,1,day,3.313,0.6347794581459517
+aml1999feb1,1999-02-25,1999,2,night,2.553,0.5505952074893279
+aml1999feb10,1999-02-15,1999,2,night,0.216,0.08493357493671612
+aml1999feb104,1999-02-15,1999,2,day,0,0
+aml1999feb105,1999-02-15,1999,2,day,0,0
+aml1999feb110,1999-02-14,1999,2,day,0,0
+aml1999feb111,1999-02-14,1999,2,day,0,0
+aml1999feb116,1999-02-13,1999,2,day,0.716,0.2345172835126866
+aml1999feb125,1999-02-14,1999,2,day,0,0
+aml1999feb13,1999-02-14,1999,2,day,112.479,2.0549155000494803
+aml1999feb135,1999-02-13,1999,2,day,0.095,0.03941411917613713
+aml1999feb136,1999-02-13,1999,2,day,0,0
+aml1999feb137,1999-02-13,1999,2,night,0.066,0.027757204690553483
+aml1999feb138,1999-02-12,1999,2,night,0,0
+aml1999feb14,1999-02-13,1999,2,day,0.083,0.03462845662532034
+aml1999feb150,1999-02-12,1999,2,night,0.273,0.10482840365365544
+aml1999feb153,1999-02-12,1999,2,day,0,0
+aml1999feb16,1999-02-14,1999,2,night,0,0
+aml1999feb164,1999-02-11,1999,2,day,0,0
+aml1999feb165,1999-02-11,1999,2,day,0,0
+aml1999feb166,1999-02-11,1999,2,day,0,0
+aml1999feb17,1999-02-18,1999,2,day,0.099,0.04099769242349056
+aml1999feb174,1999-02-10,1999,2,day,0,0
+aml1999feb175,1999-02-10,1999,2,night,0.062,0.026124516745450282
+aml1999feb176,1999-02-11,1999,2,night,0.083,0.03462845662532034
+aml1999feb18,1999-02-18,1999,2,day,0.404,0.14736710779378645
+aml1999feb186,1999-02-15,1999,2,day,0,0
+aml1999feb187,1999-02-15,1999,2,day,0,0
+aml1999feb188,1999-02-14,1999,2,day,0,0
+aml1999feb189,1999-02-24,1999,2,night,0.409,0.14891099310935643
+aml1999feb19,1999-02-16,1999,2,night,0,0
+aml1999feb20,1999-02-16,1999,2,day,0,0
+aml1999feb21,1999-02-16,1999,2,day,0,0
+aml1999feb22,1999-02-16,1999,2,day,0,0
+aml1999feb23,1999-02-16,1999,2,day,0,0
+aml1999feb24,1999-02-16,1999,2,day,0.882,0.2746196190912381
+aml1999feb36,1999-02-18,1999,2,day,0.358,0.1328997699444829
+aml1999feb37,1999-02-18,1999,2,night,1.423,0.38435341413750623
+aml1999feb38,1999-02-18,1999,2,night,35.536,1.5627209985989443
+aml1999feb39,1999-02-19,1999,2,night,227.053,2.358035789687573
+aml1999feb40,1999-02-19,1999,2,day,0.255,0.0986437258170569
+aml1999feb41,1999-02-19,1999,2,day,0,0
+aml1999feb43,1999-02-19,1999,2,night,0,0
+aml1999feb52,1999-02-21,1999,2,night,0.974,0.2953471483336179
+aml1999feb53,1999-02-20,1999,2,night,9.967,1.0400878434698808
+aml1999feb54,1999-02-20,1999,2,night,0.184,0.07335170238690095
+aml1999feb55,1999-02-20,1999,2,day,0,0
+aml1999feb56,1999-02-20,1999,2,day,0.672,0.22323627310299762
+aml1999feb57,1999-02-20,1999,2,day,0,0
+aml1999feb58,1999-02-20,1999,2,day,0.207,0.08170727009734924
+aml1999feb59,1999-02-19,1999,2,night,6.756,0.8896378004066678
+aml1999feb68,1999-02-21,1999,2,day,0.314,0.11859536522376199
+aml1999feb69,1999-02-21,1999,2,day,0.159,0.064083435963596
+aml1999feb7,1999-02-24,1999,2,day,0.55,0.1903316981702915
+aml1999feb70,1999-02-21,1999,2,day,17.205,1.2601906832699625
+aml1999feb71,1999-02-21,1999,2,night,0.742,0.2410481506716444
+aml1999feb72,1999-02-22,1999,2,night,1.503,0.39846084960822326
+aml1999feb73,1999-02-22,1999,2,day,1.324,0.36623612371829306
+aml1999feb74,1999-02-22,1999,2,day,0.424,0.15350998930083753
+aml1999feb75,1999-02-22,1999,2,day,0,0
+aml1999feb84,1999-02-23,1999,2,day,0.162,0.06520612805431189
+aml1999feb85,1999-02-23,1999,2,day,0.485,0.17172645365323114
+aml1999feb86,1999-02-23,1999,2,day,2.412,0.5330090224954853
+aml1999feb87,1999-02-23,1999,2,day,5.254,0.7961578769069144
+aml1999feb88,1999-02-23,1999,2,night,0.19,0.07554696139253074
+aml1999feb89,1999-02-23,1999,2,night,20.635,1.3351568995347431
+aml1999feb90,1999-02-22,1999,2,night,1.432,0.3859635706006973
+aml1999feb91,1999-02-22,1999,2,day,0.265,0.10209052551183675
+aml1999jan1,1999-01-21,1999,1,day,31.585,1.5130177253316184
+aml1999jan10,1999-01-15,1999,1,day,8.245,0.9659069154951918
+aml1999jan104,1999-01-15,1999,1,day,1.178,0.3380578754197561
+aml1999jan105,1999-01-15,1999,1,night,0,0
+aml1999jan109,1999-01-22,1999,1,day,2.019,0.47986311302309775
+aml1999jan110,1999-01-16,1999,1,day,0,0
+aml1999jan116,1999-01-17,1999,1,day,0,0
+aml1999jan121,1999-01-22,1999,1,night,0.176,0.07040732174011965
+aml1999jan123,1999-01-17,1999,1,day,0.461,0.16465021593429677
+aml1999jan124,1999-01-17,1999,1,day,0,0
+aml1999jan125,1999-01-17,1999,1,day,0.618,0.20897851727625347
+aml1999jan13,1999-01-17,1999,1,night,0.358,0.1328997699444829
+aml1999jan133,1999-01-21,1999,1,day,0.236,0.09201847075279702
+aml1999jan134,1999-01-21,1999,1,day,2.085,0.4892551683692605
+aml1999jan135,1999-01-18,1999,1,day,15.412,1.215161508294875
+aml1999jan136,1999-01-18,1999,1,day,1.12,0.32633586092875144
+aml1999jan137,1999-01-18,1999,1,night,0,0
+aml1999jan138,1999-01-17,1999,1,night,0,0
+aml1999jan14,1999-01-17,1999,1,day,4.894,0.7704101315139064
+aml1999jan150,1999-01-19,1999,1,night,2.763,0.5755342183198644
+aml1999jan151,1999-01-19,1999,1,day,0,0
+aml1999jan152,1999-01-19,1999,1,day,0,0
+aml1999jan153,1999-01-19,1999,1,day,0,0
+aml1999jan16,1999-01-16,1999,1,night,0.463,0.16524432612531084
+aml1999jan164,1999-01-19,1999,1,day,1.431,0.38578495884333575
+aml1999jan165,1999-01-20,1999,1,night,0,0
+aml1999jan166,1999-01-20,1999,1,night,0,0
+aml1999jan17,1999-01-22,1999,1,night,0.644,0.21590181320403162
+aml1999jan174,1999-01-20,1999,1,day,5.981,0.8439176380063924
+aml1999jan175,1999-01-20,1999,1,day,0,0
+aml1999jan176,1999-01-20,1999,1,day,0,0
+aml1999jan18,1999-01-23,1999,1,night,1.031,0.3077099234048067
+aml1999jan186,1999-01-16,1999,1,night,0,0
+aml1999jan187,1999-01-16,1999,1,day,0,0
+aml1999jan19,1999-01-23,1999,1,day,0.079,0.033021444682910656
+aml1999jan20,1999-01-23,1999,1,day,0,0
+aml1999jan21,1999-01-23,1999,1,day,0.159,0.064083435963596
+aml1999jan22,1999-01-23,1999,1,day,0.239,0.09307130637606341
+aml1999jan23,1999-01-23,1999,1,day,0.398,0.14550717140966266
+aml1999jan24,1999-01-23,1999,1,night,0,0
+aml1999jan3,1999-01-22,1999,1,day,0,0
+aml1999jan36,1999-01-25,1999,1,night,1.567,0.40942586867144337
+aml1999jan37,1999-01-24,1999,1,night,2.153,0.49872397074790464
+aml1999jan38,1999-01-24,1999,1,day,0.522,0.18241465243455401
+aml1999jan39,1999-01-24,1999,1,day,0,0
+aml1999jan40,1999-01-24,1999,1,day,0,0
+aml1999jan41,1999-01-24,1999,1,day,0,0
+aml1999jan42,1999-01-24,1999,1,day,0,0
+aml1999jan43,1999-01-24,1999,1,night,0.088,0.03662889536216113
+aml1999jan5,1999-01-22,1999,1,day,0.544,0.18864729599971736
+aml1999jan52,1999-01-25,1999,1,day,0.7,0.2304489213782739
+aml1999jan53,1999-01-25,1999,1,day,0,0
+aml1999jan54,1999-01-25,1999,1,day,0,0
+aml1999jan55,1999-01-25,1999,1,day,2.247,0.5114822886260014
+aml1999jan56,1999-01-25,1999,1,day,1.252,0.3525683861793086
+aml1999jan57,1999-01-25,1999,1,night,0,0
+aml1999jan58,1999-01-26,1999,1,night,1.498,0.3975924340381167
+aml1999jan59,1999-01-26,1999,1,night,0,0
+aml1999jan68,1999-01-27,1999,1,day,14.297,1.184606266687136
+aml1999jan69,1999-01-27,1999,1,night,3.326,0.6360865151030727
+aml1999jan7,1999-01-22,1999,1,day,5.146,0.7885925559203594
+aml1999jan70,1999-01-27,1999,1,night,1.836,0.452706226511029
+aml1999jan71,1999-01-26,1999,1,night,0,0
+aml1999jan72,1999-01-26,1999,1,day,2.925,0.5938396610812713
+aml1999jan73,1999-01-26,1999,1,day,1.75,0.43933269383026263
+aml1999jan74,1999-01-26,1999,1,day,0,0
+aml1999jan75,1999-01-26,1999,1,day,13.874,1.172427777151465
+aml1999jan84,1999-01-27,1999,1,day,4.426,0.7344797894255773
+aml1999jan85,1999-01-27,1999,1,day,4.344,0.7278664494674894
+aml1999jan86,1999-01-27,1999,1,day,4.099,0.7074850119674736
+aml1999jan87,1999-01-27,1999,1,day,1.955,0.47055748521727414
+aml1999jan88,1999-01-27,1999,1,night,1.384,0.3773062510681988
+aml1999jan89,1999-01-28,1999,1,night,0.284,0.10856502373283447
+aml1999jan90,1999-01-28,1999,1,day,1.899,0.46224821535499744
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+aml2000feb1,2000-03-07,2000,3,night,0.13,0.05307844348341968
+aml2000feb10,2000-02-25,2000,2,day,0.62,0.20951501454263097
+aml2000feb104,2000-02-25,2000,2,day,0,0
+aml2000feb105,2000-02-25,2000,2,night,1.509,0.3995006613146105
+aml2000feb109,2000-03-05,2000,3,night,6.754,0.8895257966711911
+aml2000feb110,2000-02-25,2000,2,night,0.284,0.10856502373283447
+aml2000feb111,2000-02-25,2000,2,night,0.611,0.20709554041921804
+aml2000feb116,2000-02-24,2000,2,day,2.18,0.5024271199844327
+aml2000feb121,2000-03-06,2000,3,day,1.339,0.369030221809153
+aml2000feb123,2000-02-24,2000,2,day,0,0
+aml2000feb124,2000-02-24,2000,2,day,0,0
+aml2000feb125,2000-02-24,2000,2,day,1.94,0.46834733041215726
+aml2000feb13,2000-02-25,2000,2,day,0,0
+aml2000feb133,2000-03-06,2000,3,day,3.598,0.6625689669332602
+aml2000feb134,2000-03-06,2000,3,night,0.055,0.023252459633711444
+aml2000feb135,2000-02-24,2000,2,night,94.42,1.9796394122229073
+aml2000feb136,2000-02-23,2000,2,night,46.576,1.677387925491221
+aml2000feb137,2000-02-23,2000,2,day,0,0
+aml2000feb138,2000-02-23,2000,2,day,2.671,0.5647843845039868
+aml2000feb14,2000-02-24,2000,2,night,1.986,0.4750898033890065
+aml2000feb150,2000-02-23,2000,2,night,2.666,0.5641924606261979
+aml2000feb151,2000-02-23,2000,2,night,0.338,0.12645611343180432
+aml2000feb152,2000-02-23,2000,2,day,0,0
+aml2000feb153,2000-02-23,2000,2,day,0,0
+aml2000feb16,2000-02-25,2000,2,day,3.34,0.6374897295125107
+aml2000feb164,2000-02-23,2000,2,night,2.326,0.5219222448835005
+aml2000feb165,2000-02-22,2000,2,day,3.218,0.6251065754034676
+aml2000feb166,2000-02-22,2000,2,day,0,0
+aml2000feb17,2000-02-27,2000,2,day,0.334,0.12515582958053018
+aml2000feb174,2000-02-22,2000,2,night,9.174,1.007491733295336
+aml2000feb175,2000-02-22,2000,2,day,4.687,0.7548832282521676
+aml2000feb176,2000-02-22,2000,2,day,0,0
+aml2000feb18,2000-02-27,2000,2,day,1.297,0.36116099519502604
+aml2000feb186,2000-02-26,2000,2,night,4.214,0.7171710268323102
+aml2000feb187,2000-02-26,2000,2,night,0,0
+aml2000feb188,2000-02-24,2000,2,night,0,0
+aml2000feb19,2000-02-27,2000,2,night,0.138,0.05614226205905233
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+aml2000feb21,2000-02-26,2000,2,night,0.061,0.025715383901340642
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+aml2000feb24,2000-02-26,2000,2,day,0,0
+aml2000feb25,2000-02-27,2000,2,night,2.91,0.5921767573958667
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+aml2000feb36,2000-02-29,2000,2,night,5.749,0.829239428141389
+aml2000feb37,2000-02-29,2000,2,night,1.499,0.39776625612645006
+aml2000feb38,2000-02-28,2000,2,night,7.233,0.9155581154115204
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+aml2000feb40,2000-02-28,2000,2,day,15.577,1.2195059374728567
+aml2000feb41,2000-02-28,2000,2,day,0.163,0.06557971472844842
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+aml2000feb43,2000-02-28,2000,2,day,1.099,0.3220124385824005
+aml2000feb44,2000-02-29,2000,2,night,0.682,0.22582599146189336
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+aml2000feb47,2000-02-29,2000,2,day,0.142,0.05766610390982921
+aml2000feb49,2000-02-29,2000,2,day,0.31,0.11727129565576427
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+aml2000feb52,2000-03-01,2000,3,night,0.073,0.030599721965951062
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+aml2000feb54,2000-03-01,2000,3,day,0,0
+aml2000feb55,2000-03-01,2000,3,day,0,0
+aml2000feb56,2000-03-01,2000,3,day,2.62,0.5587085705331657
+aml2000feb57,2000-03-01,2000,3,day,3.817,0.682776646314434
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+aml2000feb7,2000-03-06,2000,3,night,0.292,0.11126251365906528
+aml2000feb70,2000-03-03,2000,3,day,0.158,0.06370855939141737
+aml2000feb71,2000-03-03,2000,3,day,26.95,1.446381812222442
+aml2000feb72,2000-03-03,2000,3,night,2.454,0.53832233323144
+aml2000feb73,2000-03-03,2000,3,night,2.339,0.5236164190543707
+aml2000feb74,2000-03-02,2000,3,night,3.626,0.6652056284346005
+aml2000feb75,2000-03-02,2000,3,day,2.695,0.5676144427308445
+aml2000feb76,2000-03-04,2000,3,day,4.955,0.7748817658187963
+aml2000feb78,2000-03-04,2000,3,day,9.868,1.0361496297458532
+aml2000feb79,2000-03-04,2000,3,night,2.916,0.5928426831311002
+aml2000feb81,2000-03-04,2000,3,night,0.179,0.07151380509508916
+aml2000feb83,2000-03-03,2000,3,day,0,0
+aml2000feb84,2000-03-05,2000,3,day,0.521,0.18212921405299837
+aml2000feb85,2000-03-05,2000,3,day,0.608,0.20628604441243248
+aml2000feb86,2000-03-05,2000,3,night,0.704,0.2314695904306813
+aml2000feb87,2000-03-05,2000,3,night,9.218,1.0093658983462446
+aml2000feb88,2000-03-04,2000,3,night,44.447,1.6575052203094263
+aml2000feb89,2000-03-04,2000,3,night,0.72,0.23552844690754893
+aml2000feb90,2000-03-04,2000,3,day,9.684,1.0287338793493352
+aml2000feb91,2000-03-06,2000,3,day,6.262,0.8610562445768735
+aml2001feb1,2001-02-28,2001,2,night,0.342,0.12775251583297328
+aml2001feb10,2001-02-16,2001,2,day,0.232,0.09061070782840663
+aml2001feb104,2001-02-16,2001,2,day,4.745,0.759290033024304
+aml2001feb105,2001-02-16,2001,2,day,1.564,0.4089180208467798
+aml2001feb109,2001-02-26,2001,2,day,0,0
+aml2001feb110,2001-02-15,2001,2,day,0.247,0.09586645347854257
+aml2001feb111,2001-02-15,2001,2,day,0.891,0.2766915288450397
+aml2001feb116,2001-02-14,2001,2,day,1.741,0.4379090355394984
+aml2001feb121,2001-02-27,2001,2,day,0.244,0.0948203803547999
+aml2001feb123,2001-02-14,2001,2,day,0.548,0.1897709563468738
+aml2001feb124,2001-02-14,2001,2,night,0.264,0.10174707394636621
+aml2001feb125,2001-02-15,2001,2,night,0.374,0.13798673272353165
+aml2001feb13,2001-02-16,2001,2,night,16.624,1.2461044841427111
+aml2001feb133,2001-02-27,2001,2,day,0,0
+aml2001feb134,2001-02-27,2001,2,day,0,0
+aml2001feb135,2001-02-14,2001,2,day,25.061,1.415991076232649
+aml2001feb136,2001-02-14,2001,2,night,0.992,0.29928933408767994
+aml2001feb137,2001-02-13,2001,2,night,1.498,0.3975924340381167
+aml2001feb138,2001-02-13,2001,2,day,18.472,1.2894105608859898
+aml2001feb14,2001-02-14,2001,2,day,0,0
+aml2001feb150,2001-02-13,2001,2,night,39.451,1.6069292623911515
+aml2001feb151,2001-02-13,2001,2,day,3.573,0.660201201380682
+aml2001feb152,2001-02-13,2001,2,day,1.741,0.4379090355394984
+aml2001feb153,2001-02-13,2001,2,day,0.79,0.2528530309798932
+aml2001feb16,2001-02-15,2001,2,night,28.908,1.475787371974642
+aml2001feb164,2001-02-12,2001,2,night,5.408,0.8067225030761813
+aml2001feb165,2001-02-12,2001,2,day,1.591,0.41346741298582484
+aml2001feb166,2001-02-12,2001,2,day,0.395,0.14457420760961637
+aml2001feb17,2001-02-18,2001,2,night,7.423,0.9254668006915379
+aml2001feb174,2001-02-12,2001,2,day,1.709,0.4328090050331682
+aml2001feb175,2001-02-12,2001,2,day,4.203,0.7162538258960366
+aml2001feb176,2001-02-12,2001,2,day,0.846,0.26623169668989327
+aml2001feb18,2001-02-18,2001,2,night,19.259,1.3066180044398448
+aml2001feb186,2001-02-16,2001,2,day,0,0
+aml2001feb187,2001-02-16,2001,2,day,0,0
+aml2001feb188,2001-02-15,2001,2,day,0.107,0.0441476208787228
+aml2001feb19,2001-02-17,2001,2,day,0.579,0.19838213000829422
+aml2001feb190,2001-03-02,2001,3,night,5.802,0.8326366275967033
+aml2001feb191,2001-03-02,2001,3,day,0.292,0.11126251365906528
+aml2001feb20,2001-02-17,2001,2,day,1.118,0.32592595577146627
+aml2001feb21,2001-02-17,2001,2,day,0,0
+aml2001feb22,2001-02-17,2001,2,day,0.776,0.2494429614425822
+aml2001feb23,2001-02-17,2001,2,day,2.685,0.5664374921950703
+aml2001feb24,2001-02-17,2001,2,day,93.458,1.9752387458335325
+aml2001feb25,2001-02-18,2001,2,day,0.529,0.18440748541232008
+aml2001feb27,2001-02-18,2001,2,day,0,0
+aml2001feb29,2001-02-18,2001,2,day,0,0
+aml2001feb3,2001-02-26,2001,2,day,0.444,0.15956719323362029
+aml2001feb30,2001-02-18,2001,2,day,0.136,0.05537833137500011
+aml2001feb32,2001-02-18,2001,2,night,0.673,0.22349594096239453
+aml2001feb36,2001-02-19,2001,2,day,1.321,0.36567514045591765
+aml2001feb37,2001-02-19,2001,2,day,3.82,0.6830470382388496
+aml2001feb38,2001-02-19,2001,2,day,1.595,0.4141373621844766
+aml2001feb39,2001-02-19,2001,2,day,0.514,0.18012587516405396
+aml2001feb40,2001-02-19,2001,2,day,0,0
+aml2001feb41,2001-02-19,2001,2,night,2.112,0.4930395883176514
+aml2001feb42,2001-02-19,2001,2,night,3.88,0.6884198220027106
+aml2001feb43,2001-02-18,2001,2,night,3.395,0.6429588794097907
+aml2001feb44,2001-02-20,2001,2,day,0.78,0.250420002308894
+aml2001feb46,2001-02-20,2001,2,night,3.459,0.6492374723496073
+aml2001feb47,2001-02-20,2001,2,night,415.162,2.6192624219982714
+aml2001feb49,2001-02-19,2001,2,night,0.683,0.22608411597582387
+aml2001feb5,2001-02-26,2001,2,night,0,0
+aml2001feb51,2001-02-19,2001,2,day,0,0
+aml2001feb52,2001-02-22,2001,2,night,3.277,0.6311392502568111
+aml2001feb53,2001-02-21,2001,2,night,0.368,0.13608609738409738
+aml2001feb57,2001-02-20,2001,2,day,5.235,0.7948364578145615
+aml2001feb58,2001-02-20,2001,2,day,2.101,0.4915017662373264
+aml2001feb59,2001-02-20,2001,2,day,0.393,0.1439511164239635
+aml2001feb60,2001-02-23,2001,2,night,0.48,0.17026171539495738
+aml2001feb63,2001-02-22,2001,2,day,8.523,0.9787737843301224
+aml2001feb64,2001-02-22,2001,2,day,1.187,0.3398487830376371
+aml2001feb65,2001-02-22,2001,2,day,0,0
+aml2001feb67,2001-02-22,2001,2,day,0.295,0.1122697684172706
+aml2001feb68,2001-02-24,2001,2,night,63.504,1.8095866467912964
+aml2001feb69,2001-02-23,2001,2,night,0.746,0.24204423936955088
+aml2001feb7,2001-02-26,2001,2,day,0,0
+aml2001feb70,2001-02-23,2001,2,night,0,0
+aml2001feb71,2001-02-23,2001,2,day,7.924,0.9505595616118001
+aml2001feb72,2001-02-23,2001,2,day,1.682,0.4284587735155801
+aml2001feb73,2001-02-23,2001,2,day,13.424,1.1590857137143455
+aml2001feb74,2001-02-23,2001,2,day,0,0
+aml2001feb75,2001-02-23,2001,2,day,2.68,0.5658478186735176
+aml2001feb76,2001-02-24,2001,2,night,0.117,0.048053173115609055
+aml2001feb78,2001-02-24,2001,2,day,1.576,0.4109458586877745
+aml2001feb79,2001-02-24,2001,2,day,5.696,0.8258154449852035
+aml2001feb81,2001-02-24,2001,2,day,12.784,1.1393752644399722
+aml2001feb83,2001-02-24,2001,2,night,1.899,0.46224821535499744
+aml2001feb84,2001-02-25,2001,2,night,0,0
+aml2001feb85,2001-02-25,2001,2,day,0,0
+aml2001feb86,2001-02-25,2001,2,day,0,0
+aml2001feb87,2001-02-25,2001,2,day,0.408,0.14860265480609336
+aml2001feb88,2001-02-25,2001,2,day,1.419,0.3836358683618797
+aml2001feb89,2001-02-25,2001,2,night,4.122,0.7094395741324109
+aml2001feb90,2001-02-25,2001,2,night,28.231,1.465843672937086
+aml2001feb91,2001-02-24,2001,2,night,45.979,1.6719037681013553
+aml2001jan1,2001-01-15,2001,1,night,0.23,0.08990511143939793
+aml2001jan10,2001-01-26,2001,1,day,7.692,0.9391197176484869
+aml2001jan104,2001-01-26,2001,1,night,0.038,0.01619735351243906
+aml2001jan105,2001-01-25,2001,1,night,0.047,0.019946681678842303
+aml2001jan109,2001-01-16,2001,1,night,0.135,0.05499586152914153
+aml2001jan110,2001-01-26,2001,1,day,3.405,0.6439459127480667
+aml2001jan111,2001-01-26,2001,1,day,0,0
+aml2001jan116,2001-01-27,2001,1,day,15.965,1.2295538639811365
+aml2001jan121,2001-01-16,2001,1,day,133.509,2.1287513439634846
+aml2001jan123,2001-01-27,2001,1,day,3.723,0.6742179455766999
+aml2001jan124,2001-01-27,2001,1,day,0.771,0.2482185611900747
+aml2001jan125,2001-01-27,2001,1,day,0.451,0.1616674124377359
+aml2001jan13,2001-01-26,2001,1,day,0.105,0.04336227802112949
+aml2001jan133,2001-01-16,2001,1,day,101.123,2.009123564302097
+aml2001jan134,2001-01-16,2001,1,night,4.998,0.7780064614235084
+aml2001jan135,2001-01-27,2001,1,night,2.708,0.5691397254724595
+aml2001jan136,2001-01-28,2001,1,night,0,0
+aml2001jan137,2001-01-28,2001,1,day,1.231,0.3484995702838377
+aml2001jan138,2001-01-28,2001,1,day,10.491,1.0603578246486156
+aml2001jan14,2001-01-27,2001,1,night,0,0
+aml2001jan150,2001-01-28,2001,1,night,0.062,0.026124516745450282
+aml2001jan151,2001-01-28,2001,1,day,2.871,0.587823171318955
+aml2001jan152,2001-01-28,2001,1,day,1.854,0.45545396877862815
+aml2001jan153,2001-01-28,2001,1,day,3.803,0.6815125866389622
+aml2001jan16,2001-01-26,2001,1,day,18.419,1.288226861737593
+aml2001jan164,2001-01-29,2001,1,night,3.969,0.6962689967455328
+aml2001jan165,2001-01-29,2001,1,day,0.398,0.14550717140966266
+aml2001jan166,2001-01-29,2001,1,day,0.293,0.11159852488039401
+aml2001jan17,2001-01-24,2001,1,day,7.138,0.9105176855172664
+aml2001jan174,2001-01-29,2001,1,day,11.919,1.1112288982341556
+aml2001jan175,2001-01-29,2001,1,day,2.435,0.5359267413955693
+aml2001jan176,2001-01-29,2001,1,day,0.304,0.11527759139590141
+aml2001jan179,2001-01-29,2001,1,night,0.138,0.05614226205905233
+aml2001jan18,2001-01-24,2001,1,day,5.466,0.8106357002755373
+aml2001jan180,2001-01-30,2001,1,night,24.319,1.4034465487758765
+aml2001jan186,2001-01-25,2001,1,day,0,0
+aml2001jan187,2001-01-25,2001,1,day,0.189,0.0751818546186916
+aml2001jan188,2001-01-27,2001,1,night,0,0
+aml2001jan19,2001-01-24,2001,1,day,1.328,0.3669829759778509
+aml2001jan190,2001-01-30,2001,1,day,8.098,0.9589459324939364
+aml2001jan191,2001-01-30,2001,1,day,2.169,0.5009222391903005
+aml2001jan20,2001-01-24,2001,1,day,0.899,0.27852496473701754
+aml2001jan21,2001-01-24,2001,1,night,0.595,0.20276068739319994
+aml2001jan22,2001-01-25,2001,1,night,0.279,0.10687054447865389
+aml2001jan23,2001-01-25,2001,1,night,0.964,0.29314148345093083
+aml2001jan24,2001-01-25,2001,1,day,75.684,1.884704758517702
+aml2001jan25,2001-01-23,2001,1,day,0,0
+aml2001jan27,2001-01-23,2001,1,day,2.541,0.549125926758111
+aml2001jan29,2001-01-24,2001,1,night,0.041,0.017450729510536125
+aml2001jan3,2001-01-16,2001,1,day,3.586,0.6614340503939201
+aml2001jan30,2001-01-24,2001,1,night,0.604,0.20520436394814473
+aml2001jan32,2001-01-24,2001,1,day,4.116,0.7089305358066162
+aml2001jan36,2001-01-22,2001,1,day,4.732,0.7583061817253068
+aml2001jan37,2001-01-22,2001,1,day,1.95,0.46982201597816303
+aml2001jan38,2001-01-22,2001,1,night,0.683,0.22608411597582387
+aml2001jan39,2001-01-23,2001,1,night,0,0
+aml2001jan40,2001-01-23,2001,1,day,0,0
+aml2001jan41,2001-01-23,2001,1,day,1.511,0.39984671271292244
+aml2001jan42,2001-01-23,2001,1,day,2.064,0.48628876096056634
+aml2001jan43,2001-01-23,2001,1,day,0.654,0.21853550521652784
+aml2001jan44,2001-01-21,2001,1,night,0.198,0.07845681805329255
+aml2001jan46,2001-01-22,2001,1,night,9.164,1.0070646563783316
+aml2001jan47,2001-01-22,2001,1,day,8.265,0.9668454236549164
+aml2001jan49,2001-01-22,2001,1,day,0.364,0.13481437032046006
+aml2001jan5,2001-01-16,2001,1,day,3.241,0.6274682724597096
+aml2001jan51,2001-01-22,2001,1,day,10.605,1.0646451447919365
+aml2001jan52,2001-01-21,2001,1,night,1.226,0.3475251599986895
+aml2001jan53,2001-01-21,2001,1,day,0.196,0.07773117965239201
+aml2001jan54,2001-01-21,2001,1,day,0.064,0.0269416279590294
+aml2001jan55,2001-01-21,2001,1,day,0,0
+aml2001jan56,2001-01-21,2001,1,day,54.745,1.7462059197956998
+aml2001jan57,2001-01-21,2001,1,day,2.189,0.5036545192429592
+aml2001jan58,2001-01-21,2001,1,day,3.054,0.6078837443569899
+aml2001jan59,2001-01-21,2001,1,day,0.915,0.28216877830464154
+aml2001jan60,2001-01-20,2001,1,night,0,0
+aml2001jan62,2001-01-20,2001,1,day,15.8,1.2253092817258628
+aml2001jan64,2001-01-20,2001,1,day,0.759,0.24526583945746122
+aml2001jan65,2001-01-20,2001,1,day,0.221,0.0867156639448825
+aml2001jan67,2001-01-20,2001,1,night,0,0
+aml2001jan68,2001-01-19,2001,1,day,1.025,0.30642502755068735
+aml2001jan69,2001-01-19,2001,1,day,1.11,0.32428245529769273
+aml2001jan7,2001-01-16,2001,1,day,1.114,0.32510498297140744
+aml2001jan70,2001-01-19,2001,1,day,0.276,0.10585067438514352
+aml2001jan71,2001-01-19,2001,1,day,38.011,1.591187083070457
+aml2001jan72,2001-01-19,2001,1,day,12.905,1.1431709932001783
+aml2001jan73,2001-01-19,2001,1,day,1.595,0.4141373621844766
+aml2001jan74,2001-01-19,2001,1,night,0.18,0.07188200730612536
+aml2001jan75,2001-01-20,2001,1,night,3.567,0.6596310116070007
+aml2001jan76,2001-01-18,2001,1,day,13.66,1.166133970305109
+aml2001jan78,2001-01-18,2001,1,day,2.927,0.5940609012704183
+aml2001jan79,2001-01-18,2001,1,day,11.251,1.0881715398643523
+aml2001jan81,2001-01-18,2001,1,night,0.137,0.05576046468773478
+aml2001jan83,2001-01-19,2001,1,night,0.226,0.08849047018239622
+aml2001jan84,2001-01-17,2001,1,day,1.47,0.3926969532596657
+aml2001jan85,2001-01-17,2001,1,day,12.487,1.1299153575044223
+aml2001jan86,2001-01-17,2001,1,day,8.49,0.9772662124272926
+aml2001jan87,2001-01-17,2001,1,day,4.517,0.7417029839577399
+aml2001jan88,2001-01-17,2001,1,night,2.157,0.4992745818922174
+aml2001jan89,2001-01-18,2001,1,night,0.351,0.13065534902203058
+aml2001jan90,2001-01-18,2001,1,day,5.316,0.8004421213362566
+aml2001jan91,2001-01-18,2001,1,day,20.6,1.334453751150931
+aml2002feb0201,2002-03-05,2002,3,night,0.708,0.23248786635298624
+aml2002feb0202,2002-03-05,2002,3,day,0,0
+aml2002feb0203,2002-03-05,2002,3,day,0,0
+aml2002feb0204,2002-03-05,2002,3,day,30.173,1.4937785995418644
+aml2002feb0205,2002-03-05,2002,3,day,0.227,0.08884456272700426
+aml2002feb0206,2002-03-05,2002,3,day,3.584,0.6612446089593336
+aml2002feb0207,2002-03-05,2002,3,night,0.075,0.031408464251624114
+aml2002feb0208,2002-03-06,2002,3,night,1.174,0.3372595397502757
+aml2002feb0209,2002-03-06,2002,3,night,1.633,0.42045085910606816
+aml2002feb0211,2002-03-06,2002,3,day,0.7,0.2304489213782739
+aml2002feb0213,2002-03-06,2002,3,day,1.465,0.3918169236132488
+aml2002feb0302,2002-03-04,2002,3,night,0.446,0.160168292958512
+aml2002feb0304,2002-03-04,2002,3,day,0.168,0.06744284277638066
+aml2002feb0306,2002-03-04,2002,3,day,0.651,0.21774707326279366
+aml2002feb0308,2002-03-04,2002,3,day,0,0
+aml2002feb0401,2002-03-03,2002,3,day,0,0
+aml2002feb0402,2002-03-03,2002,3,day,0,0
+aml2002feb0403,2002-03-03,2002,3,day,0.507,0.17811325231463185
+aml2002feb0404,2002-03-03,2002,3,day,0,0
+aml2002feb0405,2002-03-03,2002,3,night,19.1,1.3031960574204888
+aml2002feb0406,2002-03-04,2002,3,night,13.081,1.1486334984893494
+aml2002feb0407,2002-03-04,2002,3,night,0.31,0.11727129565576427
+aml2002feb0408,2002-03-04,2002,3,day,0,0
+aml2002feb0409,2002-03-06,2002,3,night,3.133,0.6162654052817081
+aml2002feb0411,2002-03-06,2002,3,day,0,0
+aml2002feb0413,2002-03-06,2002,3,day,0.309,0.11693964655075577
+aml2002feb0502,2002-03-03,2002,3,night,0,0
+aml2002feb0504,2002-03-03,2002,3,night,3.353,0.6387886671573982
+aml2002feb0606,2002-03-02,2002,3,night,10.057,1.0436373095925706
+aml2002feb0608,2002-03-02,2002,3,day,0,0
+aml2002feb0609,2002-03-07,2002,3,night,0.681,0.22556771343947096
+aml2002feb0611,2002-03-07,2002,3,day,0.168,0.06744284277638066
+aml2002feb0612,2002-03-07,2002,3,day,0.543,0.1883659260631483
+aml2002feb0701,2002-03-01,2002,3,day,0,0
+aml2002feb0702,2002-03-01,2002,3,night,0.294,0.11193427633268159
+aml2002feb0703,2002-03-02,2002,3,night,0.176,0.07040732174011965
+aml2002feb0704,2002-03-02,2002,3,night,0.118,0.04844180355040444
+aml2002feb0705,2002-03-02,2002,3,day,0.176,0.07040732174011965
+aml2002feb0706,2002-03-02,2002,3,day,0.345,0.12872228433842678
+aml2002feb0707,2002-03-02,2002,3,day,0,0
+aml2002feb0708,2002-03-02,2002,3,day,0,0
+aml2002feb0711,2002-03-07,2002,3,day,0,0
+aml2002feb0802,2002-03-01,2002,3,day,0,0
+aml2002feb0804,2002-03-01,2002,3,day,0,0
+aml2002feb0806,2002-03-01,2002,3,day,1.254,0.3529539117100877
+aml2002feb0808,2002-03-01,2002,3,night,0.962,0.2926990030439297
+aml2002feb0810,2002-03-07,2002,3,day,0.183,0.07298474462793038
+aml2002feb0812,2002-03-08,2002,3,night,50.095,1.7083784034878855
+aml2002feb0901,2002-02-28,2002,2,day,0,0
+aml2002feb0902,2002-02-28,2002,2,day,0,0
+aml2002feb0903,2002-02-28,2002,2,day,0,0
+aml2002feb0904,2002-02-28,2002,2,day,0,0
+aml2002feb0906,2002-02-28,2002,2,night,5.276,0.79768293491489906
+aml2002feb0907,2002-02-28,2002,2,night,0.208,0.082066934285113
+aml2002feb0908,2002-03-01,2002,3,night,0.069,0.028977705208777998
+aml2002feb0909,2002-03-07,2002,3,day,0,0
+aml2002feb0911,2002-03-08,2002,3,night,1.179,0.3382572302462557
+aml2002feb0913,2002-03-08,2002,3,day,57.958,1.7705427427665992
+aml2002feb1010,2002-03-07,2002,3,night,1.506,0.39898106665813127
+aml2002feb1012,2002-03-08,2002,3,day,0.837,0.26410915630580833
+aml2002feb1101,2002-02-28,2002,2,night,0.076,0.0318122713303704
+aml2002feb1103,2002-02-27,2002,2,night,0,0
+aml2002feb1105,2002-02-27,2002,2,day,0,0
+aml2002feb1107,2002-02-27,2002,2,day,1.227,0.34772021703403827
+aml2002feb1111,2002-03-08,2002,3,day,0,0
+aml2002feb1113,2002-03-08,2002,3,day,0,0
+aml2002feb1208,2002-02-27,2002,2,day,0,0
+aml2002feb1212,2002-03-08,2002,3,day,0,0
+aml2002feb1214,2002-03-08,2002,3,night,83.556,1.9271444300820524
+aml2002feb1307,2002-02-27,2002,2,day,0,0
+aml2002feb1309,2002-02-26,2002,2,day,3237.016,3.5102789904008453
+aml2002feb1313,2002-03-09,2002,3,night,99.497,2.002153097548626
+aml2002feb1406,2002-02-27,2002,2,night,0.063,0.026533264523296733
+aml2002feb1408,2002-02-26,2002,2,day,0.877,0.2734642726213463
+aml2002feb1410,2002-02-26,2002,2,day,1181.893,3.0729454617782026
+aml2002feb1412,2002-03-09,2002,3,night,2.427,0.5349141044298669
+aml2002feb1505,2002-02-26,2002,2,night,0,0
+aml2002feb1507,2002-02-26,2002,2,day,0,0
+aml2002feb1509,2002-02-26,2002,2,night,1.594,0.4139699717480613
+aml2002feb1515,2002-02-24,2002,2,night,1179.106,3.0719210184480934
+aml2002feb1606,2002-02-26,2002,2,day,0,0
+aml2002feb1608,2002-02-26,2002,2,night,0.11,0.04532297878665747
+aml2002feb1610,2002-02-25,2002,2,day,0,0
+aml2002feb1614,2002-02-24,2002,2,night,4.227,0.7182525000977507
+aml2002feb1707,2002-02-25,2002,2,night,0,0
+aml2002feb1709,2002-02-25,2002,2,day,0,0
+aml2002feb1711,2002-02-25,2002,2,day,0.924,0.28420506770179416
+aml2002feb1713,2002-02-24,2002,2,day,0.279,0.10687054447865389
+aml2002feb1808,2002-02-25,2002,2,day,0,0
+aml2002feb1810,2002-02-25,2002,2,night,0.196,0.07773117965239201
+aml2002feb1812,2002-02-24,2002,2,day,0,0
+aml2002feb1911,2002-02-24,2002,2,day,0.292,0.11126251365906528
+aml2002feb2010,2002-02-24,2002,2,day,0,0
+aml2002jan0201,2002-01-25,2002,1,day,3.398,0.6432552250247715
+aml2002jan0202,2002-01-25,2002,1,day,2.97,0.5987905067631151
+aml2002jan0203,2002-01-25,2002,1,night,13.289,1.1550018362312529
+aml2002jan0204,2002-01-26,2002,1,night,11.771,1.106224904896799
+aml2002jan0205,2002-01-26,2002,1,night,53.221,1.7341675230282554
+aml2002jan0206,2002-01-26,2002,1,day,3.985,0.6976651626476745
+aml2002jan0207,2002-01-26,2002,1,day,9.485,1.0205684348013626
+aml2002jan0208,2002-01-26,2002,1,day,4.873,0.7688600008429571
+aml2002jan0209,2002-01-26,2002,1,day,0,0
+aml2002jan0211,2002-01-26,2002,1,day,33.999,1.5440556357592405
+aml2002jan0213,2002-01-26,2002,1,night,1.644,0.42226145081360267
+aml2002jan0302,2002-01-25,2002,1,day,7.691,0.9390697499234242
+aml2002jan0304,2002-01-25,2002,1,day,0.242,0.09412159584056139
+aml2002jan0306,2002-01-25,2002,1,day,0.212,0.0835026198302674
+aml2002jan0308,2002-01-25,2002,1,night,0.092,0.038222638368718455
+aml2002jan0401,2002-01-23,2002,1,day,0.527,0.1838390370564212
+aml2002jan0402,2002-01-23,2002,1,day,5.944,0.8416097121684355
+aml2002jan0403,2002-01-23,2002,1,night,42.999,1.6434428060448887
+aml2002jan0404,2002-01-24,2002,1,night,9.635,1.0267374942387477
+aml2002jan0405,2002-01-24,2002,1,night,27.621,1.4566848036688598
+aml2002jan0406,2002-01-24,2002,1,day,1.56,0.4082399653118496
+aml2002jan0407,2002-01-24,2002,1,day,0.368,0.13608609738409738
+aml2002jan0408,2002-01-24,2002,1,night,0.05,0.021189299069938092
+aml2002jan0409,2002-01-27,2002,1,day,0,0
+aml2002jan0411,2002-01-27,2002,1,day,2.723,0.5708930362183922
+aml2002jan0413,2002-01-27,2002,1,night,0.233,0.09096307659573168
+aml2002jan0502,2002-01-23,2002,1,day,2.145,0.4976206497812877
+aml2002jan0504,2002-01-23,2002,1,day,5.305,0.7996850909091004
+aml2002jan0606,2002-01-23,2002,1,night,0.865,0.2706788361447064
+aml2002jan0608,2002-01-22,2002,1,night,0,0
+aml2002jan0609,2002-01-27,2002,1,day,0,0
+aml2002jan0611,2002-01-27,2002,1,day,13.566,1.1633403056182516
+aml2002jan0612,2002-01-27,2002,1,night,58.112,1.7716756535507092
+aml2002jan0701,2002-01-21,2002,1,night,1.112,0.32469391386177465
+aml2002jan0702,2002-01-21,2002,1,night,0.326,0.12254352406875435
+aml2002jan0703,2002-01-22,2002,1,day,0,0
+aml2002jan0704,2002-01-22,2002,1,day,0.339,0.12678057701200898
+aml2002jan0705,2002-01-22,2002,1,day,0.572,0.1964525417033891
+aml2002jan0706,2002-01-22,2002,1,day,5.186,0.7914099156671601
+aml2002jan0707,2002-01-22,2002,1,day,8.139,0.9608986773266607
+aml2002jan0708,2002-01-22,2002,1,day,0.109,0.044931546149160055
+aml2002jan0711,2002-01-28,2002,1,night,95.21,1.9832202146481028
+aml2002jan0802,2002-01-21,2002,1,day,0.114,0.04688519083771013
+aml2002jan0804,2002-01-21,2002,1,day,0,0
+aml2002jan0806,2002-01-21,2002,1,day,0.293,0.11159852488039401
+aml2002jan0808,2002-01-21,2002,1,night,0.112,0.0461047872460387
+aml2002jan0810,2002-01-28,2002,1,day,0.324,0.12188798510368115
+aml2002jan0812,2002-01-28,2002,1,day,111.168,2.0498689762997766
+aml2002jan0901,2002-01-20,2002,1,night,0,0
+aml2002jan0902,2002-01-20,2002,1,day,0.319,0.12024479554636529
+aml2002jan0903,2002-01-20,2002,1,day,0.101,0.041787318971751766
+aml2002jan0904,2002-01-20,2002,1,day,0.981,0.296884475538547
+aml2002jan0905,2002-01-20,2002,1,day,2.218,0.5075860397630108
+aml2002jan0906,2002-01-20,2002,1,day,0,0
+aml2002jan0907,2002-01-20,2002,1,day,4.298,0.7241119539612122
+aml2002jan0908,2002-01-20,2002,1,night,3.937,0.6934631272195311
+aml2002jan0909,2002-01-28,2002,1,day,0,0
+aml2002jan0911,2002-01-28,2002,1,day,392.281,2.5947029654932035
+aml2002jan0913,2002-01-28,2002,1,day,0.246,0.09551804232315081
+aml2002jan1010,2002-01-28,2002,1,day,0,0
+aml2002jan1012,2002-01-28,2002,1,night,0,0
+aml2002jan1101,2002-01-19,2002,1,night,0,0
+aml2002jan1103,2002-01-19,2002,1,day,0,0
+aml2002jan1105,2002-01-19,2002,1,day,0,0
+aml2002jan1107,2002-01-19,2002,1,day,38.778,1.5996429434239283
+aml2002jan1111,2002-01-29,2002,1,night,0,0
+aml2002jan1113,2002-01-29,2002,1,day,0.228,0.08919836680514887
+aml2002jan1208,2002-01-19,2002,1,day,4.599,0.7481104674949838
+aml2002jan1212,2002-01-29,2002,1,day,0.316,0.11925588927793669
+aml2002jan1214,2002-01-29,2002,1,day,0,0
+aml2002jan1307,2002-01-19,2002,1,night,0.041,0.017450729510536125
+aml2002jan1309,2002-01-18,2002,1,day,0.651,0.21774707326279366
+aml2002jan1313,2002-01-29,2002,1,day,0.196,0.07773117965239201
+aml2002jan1406,2002-01-18,2002,1,night,0,0
+aml2002jan1408,2002-01-18,2002,1,day,1.642,0.4219328132785084
+aml2002jan1410,2002-01-18,2002,1,night,1.356,0.372175286115064
+aml2002jan1412,2002-01-29,2002,1,day,0.388,0.14238946611883607
+aml2002jan1505,2002-01-18,2002,1,day,0,0
+aml2002jan1507,2002-01-18,2002,1,day,0.2,0.07918124604762482
+aml2002jan1509,2002-01-17,2002,1,night,2.494,0.5433229006469121
+aml2002jan1515,2002-01-15,2002,1,night,0,0
+aml2002jan1606,2002-01-18,2002,1,day,0,0
+aml2002jan1608,2002-01-17,2002,1,day,0.097,0.04020662757471112
+aml2002jan1610,2002-01-17,2002,1,day,12.333,1.124927879110533
+aml2002jan1614,2002-01-16,2002,1,night,0,0
+aml2002jan1707,2002-01-17,2002,1,day,0,0
+aml2002jan1709,2002-01-17,2002,1,day,3.421,0.6455205149058739
+aml2002jan1711,2002-01-17,2002,1,night,51.764,1.722337711667331
+aml2002jan1713,2002-01-16,2002,1,day,0.192,0.07627625540421759
+aml2002jan1808,2002-01-17,2002,1,day,0,0
+aml2002jan1810,2002-01-16,2002,1,night,0,0
+aml2002jan1812,2002-01-16,2002,1,day,4.285,0.723044991643445
+aml2002jan1909,2002-01-16,2002,1,day,0.1,0.04139268515822507
+aml2002jan1911,2002-01-16,2002,1,day,1.417,0.38327665040765035
+aml2002jan2010,2002-01-16,2002,1,day,0,0
+aml2003feb0201,2003-02-21,2003,2,night,1.391,0.37857957611577475
+aml2003feb0202,2003-02-21,2003,2,night,0.066,0.027757204690553483
+aml2003feb0203,2003-02-21,2003,2,day,0.416,0.1510632533537502
+aml2003feb0204,2003-02-21,2003,2,day,0.775,0.24919835739111287
+aml2003feb0205,2003-02-21,2003,2,day,29.899,1.4899444243598032
+aml2003feb0206,2003-02-21,2003,2,day,0.164,0.06595298031386966
+aml2003feb0207,2003-02-21,2003,2,night,0.19,0.07554696139253074
+aml2003feb0208,2003-02-21,2003,2,night,77.284,1.893673008267842
+aml2003feb0209,2003-02-21,2003,2,night,3.748,0.6765107102825536
+aml2003feb0302,2003-02-20,2003,2,night,44.434,1.6573809736945506
+aml2003feb0304,2003-02-20,2003,2,day,0.292,0.11126251365906528
+aml2003feb0306,2003-02-20,2003,2,day,323.27,2.5109067715618933
+aml2003feb0308,2003-02-20,2003,2,day,0,0
+aml2003feb0401,2003-02-19,2003,2,day,0,0
+aml2003feb0402,2003-02-19,2003,2,day,34.08,1.545059584694003
+aml2003feb0403,2003-02-19,2003,2,day,4.755,0.7600453279658106
+aml2003feb0404,2003-02-19,2003,2,day,0.158,0.06370855939141737
+aml2003feb0405,2003-02-20,2003,2,day,8.083,0.9582293141883822
+aml2003feb0406,2003-02-20,2003,2,night,2.933,0.5947239464097467
+aml2003feb0407,2003-02-20,2003,2,night,1.106,0.32345836684946766
+aml2003feb0408,2003-02-20,2003,2,day,0.132,0.05384642685225267
+aml2003feb0409,2003-02-22,2003,2,day,6.588,0.8801273222166249
+aml2003feb0609,2003-02-22,2003,2,day,3.313,0.6347794581459517
+aml2003feb0611,2003-02-22,2003,2,day,18.574,1.2916795839828357
+aml2003feb0701,2003-02-17,2003,2,day,2.706,0.5689054149828787
+aml2003feb0702,2003-02-17,2003,2,day,1.215,0.3453737305590883
+aml2003feb0703,2003-02-17,2003,2,day,0,0
+aml2003feb0704,2003-02-17,2003,2,day,0,0
+aml2003feb0705,2003-02-17,2003,2,night,0,0
+aml2003feb0706,2003-02-18,2003,2,night,2.612,0.5577477416414682
+aml2003feb0707,2003-02-18,2003,2,night,20.98,1.3420276880874717
+aml2003feb0708,2003-02-18,2003,2,day,2.946,0.5961570809161724
+aml2003feb0711,2003-02-22,2003,2,day,161.829,2.2117317557028064
+aml2003feb0802,2003-02-17,2003,2,night,0.169,0.06781451116184012
+aml2003feb0804,2003-02-17,2003,2,night,0.399,0.14581771449182762
+aml2003feb0806,2003-02-16,2003,2,day,0.351,0.13065534902203058
+aml2003feb0808,2003-02-23,2003,2,night,23.658,1.391957848245589
+aml2003feb0810,2003-02-22,2003,2,night,186.272,2.272472848627141
+aml2003feb0812,2003-02-23,2003,2,day,508.135,2.7068329532235285
+aml2003feb0901,2003-02-14,2003,2,night,8.04,0.9561684304753633
+aml2003feb0902,2003-02-15,2003,2,night,29.291,1.4813136109793767
+aml2003feb0903,2003-02-15,2003,2,day,0.101,0.041787318971751766
+aml2003feb0904,2003-02-15,2003,2,day,11.636,1.1016096172331113
+aml2003feb0905,2003-02-15,2003,2,day,1.041,0.3098430047160705
+aml2003feb0906,2003-02-15,2003,2,day,2.959,0.5975855017522047
+aml2003feb0907,2003-02-15,2003,2,day,30.91,1.5039268041935103
+aml2003feb0908,2003-02-23,2003,2,day,0.265,0.10209052551183675
+aml2003feb0909,2003-02-23,2003,2,day,0.292,0.11126251365906528
+aml2003feb0911,2003-02-23,2003,2,day,57.828,1.7695840837481718
+aml2003feb1010,2003-02-23,2003,2,day,0.796,0.2543063323312856
+aml2003feb1012,2003-02-23,2003,2,night,342.803,2.5363096619896646
+aml2003feb1101,2003-02-14,2003,2,day,0.116,0.04766419460155998
+aml2003feb1103,2003-02-14,2003,2,day,6.583,0.8798410559865626
+aml2003feb1105,2003-02-14,2003,2,day,18.031,1.279461609257673
+aml2003feb1107,2003-02-14,2003,2,night,19.653,1.3149831450391234
+aml2003feb1111,2003-02-24,2003,2,night,4.094,0.7070589406275963
+aml2003feb1113,2003-02-24,2003,2,day,142.948,2.1582056352092533
+aml2003feb1208,2003-02-13,2003,2,day,13.102,1.1492807103410236
+aml2003feb1212,2003-02-24,2003,2,day,0.29,0.11058971029924897
+aml2003feb1307,2003-02-13,2003,2,day,33.088,1.532601521192397
+aml2003feb1309,2003-02-12,2003,2,day,2.154,0.4988616889928841
+aml2003feb1311,2003-02-24,2003,2,day,2.005,0.4778444763387583
+aml2003feb1313,2003-02-24,2003,2,day,1.676,0.42748610909578555
+aml2003feb1406,2003-02-13,2003,2,day,1.415,0.38291713508753095
+aml2003feb1408,2003-02-12,2003,2,night,5.141,0.7882390973821681
+aml2003feb1410,2003-02-12,2003,2,day,0,0
+aml2003feb1412,2003-02-24,2003,2,night,1.221,0.346548558548474
+aml2003feb1414,2003-02-25,2003,2,day,364.004,2.5622976238219786
+aml2003feb1505,2003-02-13,2003,2,day,0.48,0.17026171539495738
+aml2003feb1507,2003-02-13,2003,2,night,8.57,0.9809119377768436
+aml2003feb1509,2003-02-12,2003,2,day,39.377,1.6061340478456976
+aml2003feb1513,2003-02-25,2003,2,day,2.624,0.5591881890047754
+aml2003feb1515,2003-02-25,2003,2,day,105.123,2.0258095185910103
+aml2003feb1606,2003-02-13,2003,2,night,0.051,0.02160271602824219
+aml2003feb1608,2003-02-12,2003,2,night,0.169,0.06781451116184012
+aml2003feb1610,2003-02-11,2003,2,day,13.069,1.1482632296368789
+aml2003feb1612,2003-02-25,2003,2,night,6.677,0.8851915406068479
+aml2003feb1707,2003-02-12,2003,2,night,43.099,1.6444287414087466
+aml2003feb1709,2003-02-11,2003,2,day,5.078,0.783760695743924
+aml2003feb1711,2003-02-11,2003,2,day,106.611,2.031856667195871
+aml2003feb1713,2003-02-25,2003,2,day,0.838,0.26434550705009247
+aml2003feb1808,2003-02-11,2003,2,night,0,0
+aml2003feb1810,2003-02-11,2003,2,day,2.026,0.4808689236871679
+aml2003feb1812,2003-02-10,2003,2,day,24.803,1.4116702023858094
+aml2003feb1909,2003-02-11,2003,2,night,0.421,0.15259407792746976
+aml2003feb1911,2003-02-10,2003,2,day,1.39,0.37839790094813763
+aml2003feb2010,2003-02-11,2003,2,night,100.612,2.0069449995427755
+aml2003feb5501,2003-02-19,2003,2,night,0,0
+aml2003feb5502,2003-02-19,2003,2,night,1.919,0.4652340949880144
+aml2003feb5503,2003-02-18,2003,2,night,190.77,2.2827806682491265
+aml2003feb5504,2003-02-18,2003,2,day,62.717,1.8042553196382403
+aml2003feb5505,2003-02-18,2003,2,day,50.833,1.7146063457436547
+aml2003feb5506,2003-02-18,2003,2,day,10.444,1.0585778491332252
+aml2003feb5507,2003-02-18,2003,2,day,0.71,0.23299611039215382
+aml2003feb5508,2003-02-18,2003,2,day,2.931,0.5945030438200891
+aml2003jan0201,2003-01-25,2003,1,night,0.046,0.01953168453125545
+aml2003jan0302,2003-01-25,2003,1,day,5.901,0.8389120274059986
+aml2003jan0306,2003-01-26,2003,1,day,0,0
+aml2003jan0307,2003-01-26,2003,1,day,22.512,1.3712895730645562
+aml2003jan0308,2003-01-26,2003,1,day,62.142,1.8003183338946138
+aml2003jan0401,2003-01-24,2003,1,day,15.469,1.2166672295482093
+aml2003jan0402,2003-01-24,2003,1,day,5.944,0.8416097121684355
+aml2003jan0406,2003-01-26,2003,1,day,0.956,0.2913688504515826
+aml2003jan0407,2003-01-26,2003,1,day,216.88,2.3382173666010946
+aml2003jan0408,2003-01-26,2003,1,day,184.653,2.2687019714610135
+aml2003jan0409,2003-01-26,2003,1,night,0.263,0.10140335055533072
+aml2003jan0609,2003-01-27,2003,1,day,383.056,2.5843945543617752
+aml2003jan0610,2003-01-27,2003,1,day,15.909,1.2281179241368452
+aml2003jan0701,2003-01-22,2003,1,day,2.695,0.5676144427308445
+aml2003jan0702,2003-01-22,2003,1,night,980.839,2.992040278882612
+aml2003jan0703,2003-01-23,2003,1,night,6.289,0.8626679502285879
+aml2003jan0704,2003-01-23,2003,1,night,2.798,0.5795549604009987
+aml2003jan0705,2003-01-23,2003,1,day,1.734,0.4367985102318035
+aml2003jan0706,2003-01-23,2003,1,day,2.238,0.5102768444173549
+aml2003jan0707,2003-01-23,2003,1,day,24.209,1.401555618262893
+aml2003jan0708,2003-01-23,2003,1,day,0.095,0.03941411917613713
+aml2003jan0711,2003-01-14,2003,1,day,0.267,0.10277661488344131
+aml2003jan0802,2003-01-22,2003,1,day,9.293,1.0125419727758358
+aml2003jan0804,2003-01-22,2003,1,day,0.092,0.038222638368718455
+aml2003jan0806,2003-01-22,2003,1,day,1.922,0.46568021159827794
+aml2003jan0808,2003-01-22,2003,1,night,1.358,0.37254380075907034
+aml2003jan0810,2003-01-14,2003,1,day,0,0
+aml2003jan0812,2003-01-14,2003,1,day,1.083,0.3186892699477459
+aml2003jan0901,2003-01-21,2003,1,night,4.163,0.7129021250472227
+aml2003jan0902,2003-01-21,2003,1,day,6.473,0.873494982256169
+aml2003jan0903,2003-01-21,2003,1,day,11.504,1.0970489650111306
+aml2003jan0904,2003-01-21,2003,1,day,9.821,1.0342673970380256
+aml2003jan0905,2003-01-21,2003,1,day,1.437,0.3868555291847244
+aml2003jan0906,2003-01-21,2003,1,day,4.226,0.7181694053913068
+aml2003jan0907,2003-01-21,2003,1,day,40.159,1.6144648145899658
+aml2003jan0908,2003-01-21,2003,1,night,30.068,1.492313296482497
+aml2003jan0909,2003-01-14,2003,1,day,0.605,0.20547503674089088
+aml2003jan0911,2003-01-14,2003,1,night,0,0
+aml2003jan0913,2003-01-15,2003,1,day,150.344,2.179965208123492
+aml2003jan1010,2003-01-15,2003,1,night,0,0
+aml2003jan1012,2003-01-15,2003,1,day,0.266,0.1024337056813363
+aml2003jan1101,2003-01-20,2003,1,day,3.441,0.6474807731736759
+aml2003jan1103,2003-01-20,2003,1,day,0.583,0.19948091486235592
+aml2003jan1105,2003-01-20,2003,1,day,14.332,1.1855988105823145
+aml2003jan1107,2003-01-20,2003,1,day,0.356,0.1322596895310445
+aml2003jan1111,2003-01-15,2003,1,day,0.129,0.05269394192496786
+aml2003jan1113,2003-01-15,2003,1,day,0,0
+aml2003jan1208,2003-01-20,2003,1,night,5.616,0.8205954965444903
+aml2003jan1212,2003-01-15,2003,1,day,142.916,2.1581090797283444
+aml2003jan1214,2003-01-16,2003,1,day,25.436,1.4221957431983925
+aml2003jan1307,2003-01-19,2003,1,night,47.68,1.6873505695580273
+aml2003jan1309,2003-01-19,2003,1,night,0.783,0.2511513431753546
+aml2003jan1313,2003-01-16,2003,1,day,37.474,1.5851673406509925
+aml2003jan1406,2003-01-19,2003,1,day,14.74,1.1970047280230458
+aml2003jan1408,2003-01-19,2003,1,day,13.528,1.1622058311770098
+aml2003jan1410,2003-01-18,2003,1,night,0.041,0.017450729510536125
+aml2003jan1412,2003-01-16,2003,1,day,1.624,0.4189638307036227
+aml2003jan1505,2003-01-19,2003,1,day,2.564,0.5519376953648372
+aml2003jan1507,2003-01-19,2003,1,day,0.093,0.03862016194970278
+aml2003jan1509,2003-01-18,2003,1,day,8.608,0.9826329943948496
+aml2003jan1515,2003-01-16,2003,1,day,3.424,0.6458151182966418
+aml2003jan1606,2003-01-19,2003,1,day,0,0
+aml2003jan1608,2003-01-18,2003,1,day,4.844,0.7667102072622591
+aml2003jan1610,2003-01-18,2003,1,night,0.037,0.01577875638904093
+aml2003jan1614,2003-01-16,2003,1,night,0.071,0.029789470831855614
+aml2003jan1707,2003-01-18,2003,1,day,1.984,0.47479881880063124
+aml2003jan1709,2003-01-18,2003,1,day,0.488,0.1726029312098599
+aml2003jan1711,2003-01-17,2003,1,night,0.194,0.07700432679335026
+aml2003jan1713,2003-01-17,2003,1,night,0.038,0.01619735351243906
+aml2003jan1808,2003-01-18,2003,1,day,4.11,0.7084209001347128
+aml2003jan1810,2003-01-17,2003,1,day,1.496,0.3972445810103864
+aml2003jan1812,2003-01-17,2003,1,day,7.102,0.9085922388475695
+aml2003jan1909,2003-01-17,2003,1,day,1.607,0.41614103116832896
+aml2003jan1911,2003-01-17,2003,1,day,35.443,1.561614121040185
+aml2003jan2010,2003-01-17,2003,1,day,1.087,0.31952244906545396
+aml2003jan5501,2003-01-24,2003,1,day,11.213,1.0868223570990618
+aml2003jan5502,2003-01-24,2003,1,day,25.373,1.4211595347740806
+aml2003jan5503,2003-01-24,2003,1,day,41.069,1.6239621887547298
+aml2003jan5504,2003-01-24,2003,1,day,53.927,1.739785879395918
+aml2003jan5505,2003-01-24,2003,1,night,1.331,0.36754227352057667
+aml2003jan5506,2003-01-23,2003,1,night,2.617,0.5583485087616198
+aml2003jan5507,2003-01-23,2003,1,day,1.516,0.4007106367732313
+aml2003jan5508,2003-01-23,2003,1,day,0.542,0.1880843737149382
+aml2004feb0201,2004-02-29,2004,2,night,64.209,1.814307540219545
+aml2004feb0202,2004-03-01,2004,3,night,10.134,1.0466512169709197
+aml2004feb0203,2004-03-01,2004,3,night,71.387,1.859660578141867
+aml2004feb0204,2004-03-01,2004,3,night,2.427,0.5349141044298669
+aml2004feb0205,2004-03-01,2004,3,day,11.203,1.0864666112715824
+aml2004feb0206,2004-03-01,2004,3,day,6.223,0.8587176148602916
+aml2004feb0207,2004-03-01,2004,3,day,1.625,0.41912930774197565
+aml2004feb0208,2004-03-01,2004,3,day,0.342,0.12775251583297328
+aml2004feb0209,2004-03-01,2004,3,night,28.387,1.4681552529679343
+aml2004feb0302,2004-03-02,2004,3,day,5.727,0.8278214276828022
+aml2004feb0304,2004-02-29,2004,2,day,1.463,0.39146441183910324
+aml2004feb0306,2004-02-29,2004,2,day,0.29,0.11058971029924897
+aml2004feb0308,2004-02-29,2004,2,night,34.297,1.5477377949398858
+aml2004feb0309,2004-02-29,2004,2,night,2.965,0.5982431916536226
+aml2004feb0401,2004-02-28,2004,2,night,12.591,1.1332514124723199
+aml2004feb0402,2004-02-28,2004,2,night,1.371,0.37493155397818817
+aml2004feb0403,2004-02-28,2004,2,day,0.432,0.1559430179718367
+aml2004feb0404,2004-02-28,2004,2,day,11.251,1.0881715398643523
+aml2004feb0405,2004-02-28,2004,2,day,11.841,1.1085988459735667
+aml2004feb0406,2004-02-28,2004,2,day,0.098,0.04060234011407314
+aml2004feb0407,2004-02-28,2004,2,night,14.322,1.1853154580036565
+aml2004feb0408,2004-02-28,2004,2,night,0.201,0.07954300740290607
+aml2004feb0409,2004-02-28,2004,2,day,9.793,1.0331421770606246
+aml2004feb0410,2004-02-27,2004,2,day,0.412,0.14983469671578495
+aml2004feb0509,2004-03-02,2004,3,day,0.142,0.05766610390982921
+aml2004feb0510,2004-03-02,2004,3,day,137.414,2.1411800194216544
+aml2004feb05501,2004-02-27,2004,2,night,13.974,1.1753378287920202
+aml2004feb05502,2004-02-27,2004,2,day,6.676,0.885134966063434
+aml2004feb05503,2004-02-27,2004,2,day,0.364,0.13481437032046006
+aml2004feb05504,2004-02-27,2004,2,day,0.817,0.2593549273080343
+aml2004feb05505,2004-02-27,2004,2,day,0,0
+aml2004feb05506,2004-02-27,2004,2,day,0.216,0.08493357493671612
+aml2004feb05507,2004-02-27,2004,2,night,0.131,0.05346260492545529
+aml2004feb05508,2004-02-27,2004,2,night,24.339,1.4037894715161883
+aml2004feb0609,2004-03-02,2004,3,day,0.303,0.11494441571258467
+aml2004feb0610,2004-03-02,2004,3,night,0,0
+aml2004feb0701,2004-02-26,2004,2,night,0.499,0.1758016328482795
+aml2004feb0702,2004-02-26,2004,2,night,0.642,0.21537315278342195
+aml2004feb0703,2004-02-26,2004,2,day,3.719,0.6738499773429493
+aml2004feb0704,2004-02-26,2004,2,day,0.74,0.24054924828259971
+aml2004feb0705,2004-02-26,2004,2,day,40.712,1.6202610138242537
+aml2004feb0706,2004-02-26,2004,2,day,0,0
+aml2004feb0707,2004-02-26,2004,2,day,1.248,0.3517963068970235
+aml2004feb0708,2004-02-26,2004,2,night,24.349,1.403960831399213
+aml2004feb0711,2004-03-03,2004,3,day,0,0
+aml2004feb0802,2004-02-25,2004,2,night,15.043,1.205285583500525
+aml2004feb0804,2004-02-25,2004,2,day,4.239,0.7192483984479462
+aml2004feb0808,2004-02-25,2004,2,day,0,0
+aml2004feb0810,2004-03-03,2004,3,day,0.459,0.16405529189345164
+aml2004feb0901,2004-02-24,2004,2,night,0,0
+aml2004feb0902,2004-02-24,2004,2,day,51.061,1.7165125058844513
+aml2004feb0903,2004-02-24,2004,2,day,6.664,0.884455496070488
+aml2004feb0904,2004-02-24,2004,2,day,0.417,0.1513698502474604
+aml2004feb0905,2004-02-24,2004,2,day,0,0
+aml2004feb0906,2004-02-24,2004,2,day,11.126,1.0837175638989476
+aml2004feb0907,2004-02-25,2004,2,night,25.026,1.4154074254501365
+aml2004feb0908,2004-02-25,2004,2,night,1.652,0.42357351973273555
+aml2004feb0909,2004-03-03,2004,3,day,0,0
+aml2004feb0911,2004-03-03,2004,3,day,0,0
+aml2004feb1010,2004-03-03,2004,3,day,0,0
+aml2004feb1012,2004-03-04,2004,3,night,0.988,0.2984163800612945
+aml2004feb1101,2004-02-24,2004,2,night,0.448,0.1607685618611281
+aml2004feb1103,2004-02-23,2004,2,day,0,0
+aml2004feb1105,2004-02-23,2004,2,day,0,0
+aml2004feb1107,2004-02-23,2004,2,day,0.605,0.20547503674089088
+aml2004feb1111,2004-03-04,2004,3,night,77.708,1.89601887694966
+aml2004feb1113,2004-03-05,2004,3,day,0.156,0.06295783408451021
+aml2004feb1208,2004-02-23,2004,2,night,186.216,2.2723429620023743
+aml2004feb1212,2004-03-05,2004,3,day,0,0
+aml2004feb1307,2004-02-23,2004,2,night,0.127,0.05192391604610654
+aml2004feb1309,2004-02-22,2004,2,night,1.318,0.3651134316275772
+aml2004feb1311,2004-03-04,2004,3,night,2.746,0.5735677730392186
+aml2004feb1313,2004-03-05,2004,3,day,0,0
+aml2004feb1406,2004-02-22,2004,2,day,0.137,0.05576046468773478
+aml2004feb1408,2004-02-22,2004,2,night,0.123,0.050379756261457784
+aml2004feb1410,2004-02-21,2004,2,night,0.362,0.13417710757676637
+aml2004feb1412,2004-03-05,2004,3,day,1.023,0.30599588277080464
+aml2004feb1414,2004-03-06,2004,3,night,414.234,2.618292906998003
+aml2004feb1505,2004-02-22,2004,2,day,0,0
+aml2004feb1507,2004-02-22,2004,2,day,0,0
+aml2004feb1509,2004-02-21,2004,2,day,0,0
+aml2004feb1513,2004-03-05,2004,3,night,0.145,0.0588054866759068
+aml2004feb1515,2004-03-06,2004,3,night,0.277,0.10619089726341532
+aml2004feb1606,2004-02-22,2004,2,day,0,0
+aml2004feb1608,2004-02-21,2004,2,day,0.141,0.05728564441821465
+aml2004feb1610,2004-02-21,2004,2,night,2.21,0.5065050324048721
+aml2004feb1614,2004-03-06,2004,3,day,0,0
+aml2004feb1707,2004-02-21,2004,2,day,7.754,0.9422065422770952
+aml2004feb1709,2004-02-21,2004,2,night,2.337,0.5233562066547928
+aml2004feb1711,2004-02-20,2004,2,day,0,0
+aml2004feb1713,2004-03-06,2004,3,day,36.885,1.5784672915854474
+aml2004feb1808,2004-02-21,2004,2,day,0,0
+aml2004feb1810,2004-02-20,2004,2,day,0.679,0.2250506961380488
+aml2004feb1812,2004-02-20,2004,2,night,3.176,0.6207604899942057
+aml2004feb1909,2004-02-20,2004,2,day,0.126,0.05153839051532738
+aml2004feb1911,2004-02-20,2004,2,day,0,0
+aml2004feb2010,2004-02-20,2004,2,day,0,0
+aml2004jan0201,2004-01-27,2004,1,night,1.91,0.4638929889859073
+aml2004jan0202,2004-01-27,2004,1,day,1.872,0.4581844355702627
+aml2004jan0203,2004-01-27,2004,1,day,0,0
+aml2004jan0204,2004-01-27,2004,1,day,0.119,0.04883008652835004
+aml2004jan0205,2004-01-27,2004,1,day,0,0
+aml2004jan0207,2004-01-27,2004,1,day,0,0
+aml2004jan0302,2004-01-26,2004,1,day,0.801,0.2555137128195334
+aml2004jan0304,2004-01-26,2004,1,day,0,0
+aml2004jan0306,2004-01-26,2004,1,day,0.096,0.039810554148350386
+aml2004jan0308,2004-01-26,2004,1,day,0,0
+aml2004jan0309,2004-01-27,2004,1,day,0.204,0.08062648692180573
+aml2004jan0401,2004-01-25,2004,1,night,90.147,1.959742378916536
+aml2004jan0402,2004-01-25,2004,1,night,0.862,0.26997967664532385
+aml2004jan0403,2004-01-25,2004,1,day,0.19,0.07554696139253074
+aml2004jan0404,2004-01-25,2004,1,day,0.635,0.21351775699630487
+aml2004jan0405,2004-01-25,2004,1,day,2.006,0.4779889762508893
+aml2004jan0406,2004-01-25,2004,1,day,0.183,0.07298474462793038
+aml2004jan0407,2004-01-25,2004,1,day,0.106,0.04375512696867949
+aml2004jan0408,2004-01-26,2004,1,night,98.905,1.9995872241426007
+aml2004jan0509,2004-01-28,2004,1,night,0.109,0.044931546149160055
+aml2004jan05501,2004-01-24,2004,1,day,0.107,0.0441476208787228
+aml2004jan05502,2004-01-24,2004,1,day,2.254,0.51241754860084
+aml2004jan05503,2004-01-24,2004,1,day,9.389,1.0165737462691233
+aml2004jan05504,2004-01-24,2004,1,day,22.555,1.3720831085742524
+aml2004jan05505,2004-01-24,2004,1,day,1.705,0.43216726944258826
+aml2004jan05506,2004-01-24,2004,1,day,12.005,1.1141103565318915
+aml2004jan05507,2004-01-24,2004,1,night,0.091,0.037824750588341866
+aml2004jan05508,2004-01-24,2004,1,night,0.16,0.06445798922691845
+aml2004jan0609,2004-01-28,2004,1,day,0,0
+aml2004jan0610,2004-01-28,2004,1,day,0,0
+aml2004jan0611,2004-01-28,2004,1,day,0,0
+aml2004jan0701,2004-01-22,2004,1,day,0.739,0.24029958200271245
+aml2004jan0702,2004-01-22,2004,1,day,0,0
+aml2004jan0703,2004-01-23,2004,1,night,18.145,1.2820553706837063
+aml2004jan0704,2004-01-23,2004,1,day,2.221,0.5079907248196914
+aml2004jan0705,2004-01-23,2004,1,day,1.418,0.3834562965247531
+aml2004jan0706,2004-01-23,2004,1,day,8.008,0.9546283775072709
+aml2004jan0707,2004-01-23,2004,1,day,0.27,0.10380372095595687
+aml2004jan0708,2004-01-23,2004,1,day,68.371,1.8411779550062592
+aml2004jan0711,2004-01-28,2004,1,day,0.11,0.04532297878665747
+aml2004jan0802,2004-01-22,2004,1,day,0.778,0.24993175663419492
+aml2004jan0804,2004-01-22,2004,1,day,6.211,0.857995495560924
+aml2004jan0806,2004-01-22,2004,1,day,9.74,1.0310042813635367
+aml2004jan0808,2004-01-22,2004,1,night,0.078,0.03261876085071993
+aml2004jan0810,2004-01-28,2004,1,day,56.321,1.7583137583249862
+aml2004jan0901,2004-01-21,2004,1,night,23.424,1.3878167912906054
+aml2004jan0902,2004-01-21,2004,1,night,0.194,0.07700432679335026
+aml2004jan0903,2004-01-21,2004,1,day,0.288,0.1099158630237933
+aml2004jan0904,2004-01-21,2004,1,day,0.793,0.2535802895621829
+aml2004jan0905,2004-01-21,2004,1,day,16.973,1.2546205743100274
+aml2004jan0906,2004-01-21,2004,1,day,3.575,0.6603910984024671
+aml2004jan0907,2004-01-21,2004,1,day,3.684,0.6706168864003256
+aml2004jan0908,2004-01-21,2004,1,day,0.095,0.03941411917613713
+aml2004jan0909,2004-01-28,2004,1,day,0.72,0.23552844690754893
+aml2004jan0911,2004-01-29,2004,1,day,0.306,0.11594317693905512
+aml2004jan1010,2004-01-29,2004,1,night,8.074,0.9577987749299979
+aml2004jan1101,2004-01-20,2004,1,day,0,0
+aml2004jan1103,2004-01-20,2004,1,day,0.926,0.28465628278851574
+aml2004jan1105,2004-01-20,2004,1,night,0.437,0.15745676813422566
+aml2004jan1107,2004-01-19,2004,1,day,16.597,1.2454386340357673
+aml2004jan1111,2004-01-30,2004,1,night,0.047,0.019946681678842303
+aml2004jan1208,2004-01-19,2004,1,day,1.341,0.3694014136966244
+aml2004jan1210,2004-01-30,2004,1,night,0.053,0.02242837118548649
+aml2004jan1212,2004-01-30,2004,1,day,0.585,0.2000292665537703
+aml2004jan1307,2004-01-19,2004,1,day,0.429,0.15503222879097023
+aml2004jan1309,2004-01-18,2004,1,day,0.588,0.20085049809107747
+aml2004jan1311,2004-01-30,2004,1,day,0,0
+aml2004jan1313,2004-01-30,2004,1,day,34.231,1.5469249704113963
+aml2004jan1406,2004-01-19,2004,1,day,0.221,0.0867156639448825
+aml2004jan1408,2004-01-18,2004,1,day,4.865,0.7682680164515481
+aml2004jan1410,2004-01-18,2004,1,day,0.098,0.04060234011407314
+aml2004jan1412,2004-01-30,2004,1,day,0.1,0.04139268515822507
+aml2004jan1414,2004-01-31,2004,1,night,0,0
+aml2004jan1505,2004-01-19,2004,1,day,0,0
+aml2004jan1507,2004-01-19,2004,1,day,0.336,0.1258064581395269
+aml2004jan1509,2004-01-18,2004,1,day,0.203,0.08026562733984477
+aml2004jan1513,2004-01-30,2004,1,day,0.361,0.1338581252033347
+aml2004jan1515,2004-01-31,2004,1,day,102.032,2.0129721301910446
+aml2004jan1606,2004-01-19,2004,1,night,0,0
+aml2004jan1608,2004-01-18,2004,1,day,8.51,0.9781805169374139
+aml2004jan1610,2004-01-17,2004,1,day,0.201,0.07954300740290607
+aml2004jan1614,2004-01-31,2004,1,day,67.978,1.838710597953147
+aml2004jan1707,2004-01-18,2004,1,night,0.048,0.02036128264770786
+aml2004jan1709,2004-01-17,2004,1,day,2.22,0.5078558716958309
+aml2004jan1711,2004-01-17,2004,1,day,1.267,0.3554515201265174
+aml2004jan1713,2004-01-31,2004,1,day,0.295,0.1122697684172706
+aml2004jan1808,2004-01-18,2004,1,night,1.274,0.35679046035171597
+aml2004jan1810,2004-01-17,2004,1,day,0,0
+aml2004jan1812,2004-01-16,2004,1,day,21.045,1.3433101031623413
+aml2004jan1909,2004-01-17,2004,1,day,0.472,0.16790781000148003
+aml2004jan1911,2004-01-16,2004,1,day,4.098,0.7073998311332487
+aml2005feb0201,2005-03-04,2005,3,day,1.461,0.39111161370280256
+aml2005feb0202,2005-03-04,2005,3,day,2.036,0.48230176722344276
+aml2005feb0203,2005-03-04,2005,3,day,0.367,0.13576851456782227
+aml2005feb0204,2005-03-04,2005,3,day,0.697,0.22968184231767583
+aml2005feb0205,2005-03-04,2005,3,night,1.808,0.4483971034577676
+aml2005feb0206,2005-03-05,2005,3,night,0.404,0.14736710779378645
+aml2005feb0207,2005-03-05,2005,3,night,0.384,0.14113609012073897
+aml2005feb0208,2005-03-05,2005,3,night,0,0
+aml2005feb0209,2005-03-05,2005,3,day,0.169,0.06781451116184012
+aml2005feb0302,2005-03-04,2005,3,night,0.299,0.11360915107302785
+aml2005feb0304,2005-03-04,2005,3,night,9.899,1.0373866525823772
+aml2005feb0306,2005-03-03,2005,3,night,0,0
+aml2005feb0308,2005-03-03,2005,3,day,0.153,0.06182930729469903
+aml2005feb0310,2005-03-05,2005,3,night,30.08,1.4924810101288766
+aml2005feb0401,2005-03-02,2005,3,day,0.152,0.06145247908719321
+aml2005feb0402,2005-03-02,2005,3,day,5.354,0.8030472104911286
+aml2005feb0403,2005-03-02,2005,3,day,2.266,0.5140161804006493
+aml2005feb0404,2005-03-02,2005,3,night,186.742,2.27356144005768
+aml2005feb0405,2005-03-03,2005,3,night,1.249,0.3519894554356322
+aml2005feb0406,2005-03-03,2005,3,day,1.168,0.33605927786634937
+aml2005feb0407,2005-03-03,2005,3,day,0,0
+aml2005feb0408,2005-03-03,2005,3,day,0,0
+aml2005feb0409,2005-03-06,2005,3,night,0.383,0.14082218010931058
+aml2005feb0410,2005-03-06,2005,3,night,0.659,0.2198463860243607
+aml2005feb05501,2005-03-02,2005,3,day,8.831,0.9925976961020382
+aml2005feb05502,2005-03-02,2005,3,day,0.166,0.06669855042299526
+aml2005feb05503,2005-03-02,2005,3,night,0,0
+aml2005feb05504,2005-03-02,2005,3,night,96.249,1.9878851442217578
+aml2005feb05505,2005-03-01,2005,3,night,37.774,1.5885406059677267
+aml2005feb05506,2005-03-01,2005,3,day,0.478,0.16967443405880692
+aml2005feb05507,2005-03-01,2005,3,day,2.311,0.5199591807520684
+aml2005feb05508,2005-03-01,2005,3,day,0.188,0.07481644064517472
+aml2005feb0609,2005-03-06,2005,3,day,0.184,0.07335170238690095
+aml2005feb0611,2005-03-06,2005,3,day,0,0
+aml2005feb0701,2005-02-28,2005,2,day,4.739,0.7588362247469582
+aml2005feb0702,2005-02-28,2005,2,day,0.17,0.06818586174616162
+aml2005feb0703,2005-02-28,2005,2,day,0.177,0.0707764628434347
+aml2005feb0704,2005-02-28,2005,2,night,3.264,0.6298171960185158
+aml2005feb0705,2005-03-01,2005,3,night,3.068,0.609380944250707
+aml2005feb0706,2005-03-01,2005,3,night,0.588,0.20085049809107747
+aml2005feb0707,2005-03-01,2005,3,day,0.175,0.07003786660775509
+aml2005feb0708,2005-03-01,2005,3,day,4.414,0.7335182514344876
+aml2005feb0711,2005-03-06,2005,3,day,0,0
+aml2005feb0802,2005-02-28,2005,2,day,0,0
+aml2005feb0804,2005-02-28,2005,2,day,0,0
+aml2005feb0806,2005-02-28,2005,2,night,5.214,0.7933712489189557
+aml2005feb0808,2005-02-27,2005,2,night,0.083,0.03462845662532034
+aml2005feb0810,2005-03-06,2005,3,day,2.465,0.5397032389478256
+aml2005feb0812,2005-03-07,2005,3,day,1.391,0.37857957611577475
+aml2005feb0901,2005-02-27,2005,2,night,1.526,0.4024333462193119
+aml2005feb0902,2005-02-27,2005,2,night,1.435,0.38649896555065316
+aml2005feb0903,2005-02-27,2005,2,night,0.113,0.04649516433470831
+aml2005feb0904,2005-02-27,2005,2,day,2.748,0.5737995822157407
+aml2005feb0905,2005-02-27,2005,2,day,3.599,0.6626634095740376
+aml2005feb0906,2005-02-27,2005,2,day,10.998,1.0791088576014363
+aml2005feb0907,2005-02-27,2005,2,day,0.173,0.06929801211552926
+aml2005feb0908,2005-02-27,2005,2,day,0.335,0.125481265700594
+aml2005feb0909,2005-03-06,2005,3,night,0.137,0.05576046468773478
+aml2005feb0911,2005-03-07,2005,3,day,0.527,0.1838390370564212
+aml2005feb0913,2005-03-07,2005,3,day,1.337,0.36865871239222686
+aml2005feb1010,2005-03-07,2005,3,night,209.264,2.322764922045816
+aml2005feb1012,2005-03-07,2005,3,day,0.19,0.07554696139253074
+aml2005feb1101,2005-02-26,2005,2,day,0,0
+aml2005feb1103,2005-02-26,2005,2,day,0.178,0.07114529045108281
+aml2005feb1105,2005-02-26,2005,2,day,0,0
+aml2005feb1107,2005-02-26,2005,2,night,0.96,0.292256071356476
+aml2005feb1111,2005-03-07,2005,3,day,0,0
+aml2005feb1113,2005-03-08,2005,3,day,0.147,0.059563417901267686
+aml2005feb1208,2005-02-26,2005,2,night,0.257,0.09933527768595779
+aml2005feb1212,2005-03-08,2005,3,night,19.065,1.3024391640698576
+aml2005feb1214,2005-03-08,2005,3,day,145.069,2.1645580560611446
+aml2005feb1307,2005-02-25,2005,2,night,0.866,0.2709116394104812
+aml2005feb1309,2005-02-24,2005,2,night,9.708,1.029708362514895
+aml2005feb1311,2005-03-07,2005,3,night,0.182,0.07261747654523654
+aml2005feb1313,2005-03-08,2005,3,day,0.751,0.24328614608344612
+aml2005feb1406,2005-02-25,2005,2,day,0.964,0.29314148345093083
+aml2005feb1408,2005-02-25,2005,2,night,6.217,0.8583567052977734
+aml2005feb1410,2005-02-24,2005,2,day,0,0
+aml2005feb1412,2005-03-08,2005,3,day,40.251,1.615434481109406
+aml2005feb1414,2005-03-08,2005,3,night,3.137,0.6166855208955122
+aml2005feb1505,2005-02-25,2005,2,day,0.481,0.17055505852120845
+aml2005feb1507,2005-02-25,2005,2,day,0.155,0.06258198422816312
+aml2005feb1509,2005-02-24,2005,2,day,2.56,0.5514499979728752
+aml2005feb1513,2005-03-08,2005,3,night,0.167,0.06707085604537018
+aml2005feb1515,2005-03-08,2005,3,day,0.459,0.16405529189345164
+aml2005feb1606,2005-02-25,2005,2,day,0,0
+aml2005feb1608,2005-02-24,2005,2,day,8.095,0.9588027033995024
+aml2005feb1610,2005-02-24,2005,2,night,2.594,0.555578072772955
+aml2005feb1707,2005-02-24,2005,2,day,0,0
+aml2005feb1709,2005-02-24,2005,2,night,0.32,0.1205739312058499
+aml2005feb1711,2005-02-23,2005,2,day,0.973,0.29512708525219117
+aml2005feb1808,2005-02-24,2005,2,day,0.313,0.11826472608947933
+aml2005feb1810,2005-02-23,2005,2,day,0.305,0.11561051167429974
+aml2005feb1812,2005-02-22,2005,2,night,1.545,0.40568778667277755
+aml2005feb1909,2005-02-23,2005,2,day,0,0
+aml2005feb1911,2005-02-23,2005,2,day,0.295,0.1122697684172706
+aml2005feb2010,2005-02-23,2005,2,day,0,0
+aml2005jan0201,2005-01-26,2005,1,night,14.901,1.2014244375799117
+aml2005jan0202,2005-01-26,2005,1,day,4.376,0.7304592600457689
+aml2005jan0203,2005-01-26,2005,1,day,5.578,0.818093869146636
+aml2005jan0204,2005-01-26,2005,1,day,32.823,1.5292121256053968
+aml2005jan0205,2005-01-26,2005,1,day,2.705,0.5687882123153469
+aml2005jan0206,2005-01-26,2005,1,day,2.312,0.5200903281128425
+aml2005jan0207,2005-01-26,2005,1,day,23.032,1.3807899153240744
+aml2005jan0208,2005-01-26,2005,1,night,0.239,0.09307130637606341
+aml2005jan0209,2005-01-27,2005,1,night,0.091,0.037824750588341866
+aml2005jan0302,2005-01-25,2005,1,day,1.948,0.4695274791870139
+aml2005jan0304,2005-01-25,2005,1,day,17.673,1.271214097208833
+aml2005jan0306,2005-01-25,2005,1,day,1.937,0.4679039465228003
+aml2005jan0308,2005-01-25,2005,1,day,9.252,1.0108085975122063
+aml2005jan0401,2005-01-24,2005,1,day,9.003,1.0001302688052272
+aml2005jan0402,2005-01-24,2005,1,day,26.689,1.4423072712990532
+aml2005jan0403,2005-01-24,2005,1,day,15.447,1.216086692421913
+aml2005jan0404,2005-01-24,2005,1,day,9.32,1.0136796972911926
+aml2005jan0405,2005-01-24,2005,1,day,0,0
+aml2005jan0406,2005-01-25,2005,1,night,8.247,0.9660008576287841
+aml2005jan0407,2005-01-25,2005,1,day,1.305,0.3626709297256669
+aml2005jan0408,2005-01-25,2005,1,day,7.095,0.9082168530893925
+aml2005jan0409,2005-01-27,2005,1,day,2.387,0.5298151966446303
+aml2005jan04095,2005-01-27,2005,1,day,0.719,0.23527587668705235
+aml2005jan05501,2005-01-24,2005,1,day,0,0
+aml2005jan05502,2005-01-24,2005,1,night,0.298,0.11327469246435044
+aml2005jan05503,2005-01-23,2005,1,night,1.604,0.41564097989615434
+aml2005jan05504,2005-01-23,2005,1,day,14.593,1.1929296788902
+aml2005jan05505,2005-01-23,2005,1,day,5.825,0.8341026557127936
+aml2005jan05506,2005-01-23,2005,1,day,6.723,0.8877860348383715
+aml2005jan05507,2005-01-23,2005,1,day,0.225,0.0881360887005513
+aml2005jan05508,2005-01-23,2005,1,day,15.393,1.2146584388799184
+aml2005jan0609,2005-01-27,2005,1,day,40.461,1.6176397726552003
+aml2005jan0611,2005-01-27,2005,1,day,0,0
+aml2005jan0612,2005-01-27,2005,1,day,0,0
+aml2005jan0701,2005-01-22,2005,1,day,0.308,0.11660774398824847
+aml2005jan0702,2005-01-22,2005,1,day,0.105,0.04336227802112949
+aml2005jan0703,2005-01-22,2005,1,day,0.35,0.13033376849500614
+aml2005jan0704,2005-01-22,2005,1,day,1.028,0.3070679506612984
+aml2005jan0705,2005-01-22,2005,1,day,5.995,0.8447877188278464
+aml2005jan0706,2005-01-23,2005,1,night,1.186,0.339650157613684
+aml2005jan0707,2005-01-23,2005,1,day,7.351,0.9217384836845987
+aml2005jan0708,2005-01-23,2005,1,day,0.961,0.29247759366778403
+aml2005jan0711,2005-01-27,2005,1,night,0.121,0.049605612594973154
+aml2005jan0802,2005-01-22,2005,1,day,0,0
+aml2005jan0804,2005-01-22,2005,1,night,0,0
+aml2005jan0806,2005-01-21,2005,1,day,4.912,0.7717344253867693
+aml2005jan0808,2005-01-21,2005,1,day,5.457,0.8100307864058395
+aml2005jan0810,2005-01-28,2005,1,night,7.751,0.9420576838413952
+aml2005jan0812,2005-01-28,2005,1,day,0,0
+aml2005jan0901,2005-01-20,2005,1,day,0.609,0.20655604409902956
+aml2005jan0902,2005-01-20,2005,1,day,0.262,0.10105935490811552
+aml2005jan0903,2005-01-20,2005,1,night,0.111,0.04571405894086761
+aml2005jan0904,2005-01-21,2005,1,night,2.442,0.5368108659915415
+aml2005jan0905,2005-01-21,2005,1,day,0.534,0.18582535961296215
+aml2005jan0906,2005-01-21,2005,1,day,2.038,0.4825877695267675
+aml2005jan0907,2005-01-21,2005,1,day,9.109,1.0047081965443363
+aml2005jan0908,2005-01-21,2005,1,day,8.323,0.9695556842208435
+aml2005jan0909,2005-01-28,2005,1,day,43.695,1.650258941613691
+aml2005jan0911,2005-01-28,2005,1,day,1.483,0.3949767195545643
+aml2005jan0913,2005-01-28,2005,1,day,0,0
+aml2005jan1010,2005-01-28,2005,1,day,0.253,0.09795107099415004
+aml2005jan1012,2005-01-28,2005,1,day,0,0
+aml2005jan1101,2005-01-20,2005,1,day,1.047,0.3111178426625056
+aml2005jan1103,2005-01-20,2005,1,day,1.692,0.43007505555193926
+aml2005jan1105,2005-01-20,2005,1,day,0.955,0.2911467617318856
+aml2005jan1107,2005-01-20,2005,1,night,1.062,0.3142886609474978
+aml2005jan1111,2005-01-28,2005,1,night,0.14,0.056904851336472634
+aml2005jan1113,2005-01-29,2005,1,day,0.961,0.29247759366778403
+aml2005jan1208,2005-01-19,2005,1,day,0.865,0.2706788361447064
+aml2005jan1212,2005-01-29,2005,1,day,0.755,0.24427712080184286
+aml2005jan1214,2005-01-29,2005,1,day,5.697,0.8258802989361795
+aml2005jan1307,2005-01-19,2005,1,day,0,0
+aml2005jan1309,2005-01-18,2005,1,day,0,0
+aml2005jan1311,2005-01-29,2005,1,night,0.398,0.14550717140966266
+aml2005jan1313,2005-01-29,2005,1,day,0.113,0.04649516433470831
+aml2005jan1406,2005-01-19,2005,1,day,0,0
+aml2005jan1408,2005-01-19,2005,1,night,0,0
+aml2005jan1410,2005-01-18,2005,1,day,0.188,0.07481644064517472
+aml2005jan1412,2005-01-29,2005,1,day,0,0
+aml2005jan1414,2005-01-30,2005,1,day,0,0
+aml2005jan1505,2005-01-19,2005,1,day,0,0
+aml2005jan1507,2005-01-19,2005,1,day,0.575,0.19728055812561932
+aml2005jan1509,2005-01-18,2005,1,day,11.436,1.0946807133711542
+aml2005jan1513,2005-01-30,2005,1,night,0.094,0.039017321997412
+aml2005jan1515,2005-01-30,2005,1,day,0.446,0.160168292958512
+aml2005jan1606,2005-01-19,2005,1,day,0.554,0.19145101446489546
+aml2005jan1608,2005-01-18,2005,1,day,9.822,1.034307529596563
+aml2005jan1610,2005-01-17,2005,1,day,3.285,0.631950826259217
+aml2005jan1614,2005-01-30,2005,1,day,0.126,0.05153839051532738
+aml2005jan1707,2005-01-18,2005,1,day,0.212,0.0835026198302674
+aml2005jan1709,2005-01-18,2005,1,night,0,0
+aml2005jan1711,2005-01-17,2005,1,day,0,0
+aml2005jan1713,2005-01-30,2005,1,night,0.058,0.024485667699166973
+aml2005jan1808,2005-01-18,2005,1,day,0.236,0.09201847075279702
+aml2005jan1810,2005-01-17,2005,1,day,1.233,0.348888723071438
+aml2005jan1812,2005-01-17,2005,1,day,0,0
+aml2005jan1909,2005-01-17,2005,1,day,6.987,0.9023836844324715
+aml2005jan1911,2005-01-17,2005,1,day,2.91,0.5921767573958667
+aml2005jan2010,2005-01-17,2005,1,day,7.616,0.9353056902899252
+aml2006jan0201,2006-01-26,2006,1,day,2.902,0.5912872650584993
+aml2006jan0202,2006-01-26,2006,1,day,2.583,0.5542468081661106
+aml2006jan0203,2006-01-27,2006,1,night,35.232,1.5590923076749008
+aml2006jan0204,2006-01-27,2006,1,night,12.422,1.1278172344802733
+aml2006jan0205,2006-01-27,2006,1,day,34.064,1.5448614576459028
+aml2006jan0206,2006-01-27,2006,1,day,0.46,0.1643528557844371
+aml2006jan0207,2006-01-27,2006,1,day,5.148,0.7887338588277075
+aml2006jan0208,2006-01-27,2006,1,day,3.422,0.6456187382426952
+aml2006jan0209,2006-01-27,2006,1,day,7.62,0.9355072658247129
+aml2006jan0211,2006-01-28,2006,1,day,0.101,0.041787318971751766
+aml2006jan0302,2006-01-26,2006,1,day,2.911,0.5922878159521308
+aml2006jan0304,2006-01-26,2006,1,day,14.244,1.1830989401001295
+aml2006jan0306,2006-01-26,2006,1,day,14.041,1.1772767112585862
+aml2006jan0308,2006-01-25,2006,1,day,0,0
+aml2006jan0401,2006-01-24,2006,1,day,1.805,0.44793286559218015
+aml2006jan0402,2006-01-24,2006,1,day,1.671,0.4266738880213729
+aml2006jan0403,2006-01-24,2006,1,night,2.238,0.5102768444173549
+aml2006jan0404,2006-01-25,2006,1,night,1.452,0.38952046584637745
+aml2006jan0405,2006-01-25,2006,1,day,6.11,0.8518696007297664
+aml2006jan0406,2006-01-25,2006,1,day,0,0
+aml2006jan0407,2006-01-25,2006,1,day,8.46,0.9758911364017928
+aml2006jan0408,2006-01-25,2006,1,day,7.04,0.9052560487484512
+aml2006jan0409,2006-01-28,2006,1,day,3.058,0.6083120426973272
+aml2006jan0410,2006-01-28,2006,1,day,0,0
+aml2006jan05501,2006-01-24,2006,1,day,4.419,0.7339191510123908
+aml2006jan05502,2006-01-24,2006,1,day,3.501,0.6533090129384789
+aml2006jan05503,2006-01-24,2006,1,day,0.324,0.12188798510368115
+aml2006jan05504,2006-01-24,2006,1,night,4.641,0.7513560997253936
+aml2006jan05505,2006-01-23,2006,1,night,0.461,0.16465021593429677
+aml2006jan05506,2006-01-23,2006,1,day,1.344,0.3699576073460531
+aml2006jan05507,2006-01-23,2006,1,day,0.69,0.22788670461367355
+aml2006jan05508,2006-01-23,2006,1,day,0.934,0.2864564697469829
+aml2006jan0609,2006-01-28,2006,1,day,5.382,0.8049567998574916
+aml2006jan0611,2006-01-29,2006,1,night,60.798,1.7909744200237845
+aml2006jan0701,2006-01-22,2006,1,day,2.597,0.5559404378185111
+aml2006jan0702,2006-01-22,2006,1,day,0.193,0.0766404436703419
+aml2006jan0703,2006-01-22,2006,1,day,0.778,0.24993175663419492
+aml2006jan0704,2006-01-22,2006,1,day,0,0
+aml2006jan0705,2006-01-23,2006,1,night,1.669,0.4263485737875077
+aml2006jan0706,2006-01-23,2006,1,day,3.899,0.6901074394563306
+aml2006jan0707,2006-01-23,2006,1,day,6.971,0.9015128091299399
+aml2006jan0708,2006-01-23,2006,1,day,3.674,0.6696887080562081
+aml2006jan0711,2006-01-29,2006,1,night,1.43,0.3856062735983121
+aml2006jan0802,2006-01-22,2006,1,day,0,0
+aml2006jan0804,2006-01-22,2006,1,night,0,0
+aml2006jan0806,2006-01-21,2006,1,day,5.343,0.8022947113974638
+aml2006jan0808,2006-01-21,2006,1,day,10.387,1.056409320649918
+aml2006jan0810,2006-01-29,2006,1,day,0.765,0.24674470972384138
+aml2006jan0812,2006-01-30,2006,1,night,33.765,1.5411422337216345
+aml2006jan0901,2006-01-20,2006,1,day,1.172,0.33685982091680927
+aml2006jan0902,2006-01-20,2006,1,day,0,0
+aml2006jan0903,2006-01-20,2006,1,day,0,0
+aml2006jan0904,2006-01-20,2006,1,night,0,0
+aml2006jan0905,2006-01-21,2006,1,night,0,0
+aml2006jan0906,2006-01-21,2006,1,night,0.843,0.26552533521907384
+aml2006jan0907,2006-01-21,2006,1,day,2.932,0.5946135091600979
+aml2006jan0908,2006-01-21,2006,1,day,9.516,1.0218505774343252
+aml2006jan0909,2006-01-29,2006,1,day,1.975,0.4734869700645684
+aml2006jan0911,2006-01-29,2006,1,day,3.954,0.6949560022498182
+aml2006jan0913,2006-01-30,2006,1,night,1.147,0.33183204443624875
+aml2006jan1010,2006-01-29,2006,1,day,0.37,0.1367205671564068
+aml2006jan1012,2006-01-30,2006,1,day,2.172,0.5013331786455661
+aml2006jan1101,2006-01-20,2006,1,day,0.096,0.039810554148350386
+aml2006jan1103,2006-01-20,2006,1,day,0,0
+aml2006jan1105,2006-01-19,2006,1,day,0,0
+aml2006jan1107,2006-01-19,2006,1,day,3.517,0.6548500905613941
+aml2006jan1111,2006-01-30,2006,1,day,0.923,0.2839792842384799
+aml2006jan1113,2006-01-30,2006,1,day,0.132,0.05384642685225267
+aml2006jan1208,2006-01-19,2006,1,day,0.559,0.19284611518884173
+aml2006jan1212,2006-01-30,2006,1,day,4.691,0.755188585608325
+aml2006jan1214,2006-01-31,2006,1,night,5.709,0.8266577918758693
+aml2006jan1307,2006-01-19,2006,1,day,0.877,0.2734642726213463
+aml2006jan1309,2006-01-18,2006,1,day,4.924,0.7726150498491708
+aml2006jan1311,2006-01-30,2006,1,day,0.58,0.19865708695442263
+aml2006jan1313,2006-01-31,2006,1,night,0.584,0.19975517725347472
+aml2006jan1406,2006-01-19,2006,1,day,0,0
+aml2006jan1408,2006-01-18,2006,1,day,2.322,0.5213996281153757
+aml2006jan1410,2006-01-18,2006,1,night,0.499,0.1758016328482795
+aml2006jan1412,2006-01-31,2006,1,day,1.354,0.3718064585074159
+aml2006jan1414,2006-02-01,2006,2,night,0.844,0.2657609167176105
+aml2006jan1505,2006-01-19,2006,1,night,0.1,0.04139268515822507
+aml2006jan1507,2006-01-18,2006,1,day,0.376,0.13861843389949247
+aml2006jan1509,2006-01-18,2006,1,night,1.157,0.33385014510254507
+aml2006jan1513,2006-01-31,2006,1,day,3.137,0.6166855208955122
+aml2006jan1515,2006-02-01,2006,2,day,1.893,0.4613484336479829
+aml2006jan1606,2006-01-18,2006,1,day,0,0
+aml2006jan1608,2006-01-17,2006,1,day,1.974,0.4733409641859354
+aml2006jan1610,2006-01-17,2006,1,day,13.183,1.1517681028951787
+aml2006jan1614,2006-02-01,2006,2,day,3.76,0.6776069527204931
+aml2006jan1707,2006-01-17,2006,1,day,2.88,0.5888317255942073
+aml2006jan1709,2006-01-17,2006,1,day,1.978,0.4739246934161573
+aml2006jan1711,2006-01-17,2006,1,day,6.258,0.8608169638645377
+aml2006jan1713,2006-02-01,2006,2,day,18.588,1.2919900953461805
+aml2006jan1808,2006-01-17,2006,1,day,1.733,0.4366396316926607
+aml2006jan1810,2006-01-16,2006,1,day,0.495,0.17464119266044847
+aml2006jan1812,2006-01-16,2006,1,day,1.886,0.46029632675747545
+aml2006jan1909,2006-01-16,2006,1,day,0.601,0.2043913319192997
+aml2006jan1911,2006-01-16,2006,1,day,2.656,0.5630061870617938
+aml2006jan2010,2006-01-16,2006,1,day,4.927,0.7728349272390181
+aml2007jan0201,2007-01-24,2007,1,night,13.578,1.1636979458925691
+aml2007jan0202,2007-01-25,2007,1,night,5.613,0.8203985227039817
+aml2007jan0203,2007-01-25,2007,1,day,1.352,0.3714373174041009
+aml2007jan0204,2007-01-25,2007,1,day,19.348,1.3085217289593172
+aml2007jan0205,2007-01-25,2007,1,day,42.857,1.6420389208206194
+aml2007jan0206,2007-01-25,2007,1,day,55.455,1.7517024117392372
+aml2007jan0207,2007-01-25,2007,1,day,0.408,0.14860265480609336
+aml2007jan0208,2007-01-25,2007,1,day,0.226,0.08849047018239622
+aml2007jan0209,2007-01-26,2007,1,night,0.369,0.13640344813399
+aml2007jan0210,2007-01-26,2007,1,night,0.301,0.11427729656158624
+aml2007jan0302,2007-01-24,2007,1,day,3.169,0.6200318951262974
+aml2007jan0304,2007-01-24,2007,1,day,11.465,1.0956922828397921
+aml2007jan0306,2007-01-24,2007,1,day,13.225,1.1530522750671088
+aml2007jan0308,2007-01-24,2007,1,night,0.095,0.03941411917613713
+aml2007jan0401,2007-01-23,2007,1,night,3.907,0.6908160579809155
+aml2007jan0402,2007-01-23,2007,1,day,5.779,0.8311656339094424
+aml2007jan0403,2007-01-23,2007,1,day,2.57,0.5526682161121932
+aml2007jan0404,2007-01-23,2007,1,day,15.204,1.2096222345115506
+aml2007jan0405,2007-01-23,2007,1,day,0.202,0.0799044676667207
+aml2007jan0406,2007-01-23,2007,1,day,0,0
+aml2007jan0407,2007-01-23,2007,1,day,101.901,2.012419595290826
+aml2007jan0408,2007-01-24,2007,1,night,0.234,0.09131515969722287
+aml2007jan0409,2007-01-26,2007,1,day,539.937,2.733146688127705
+aml2007jan0509,2007-01-26,2007,1,day,20.668,1.3358188269151652
+aml2007jan05501,2007-01-22,2007,1,day,0.444,0.15956719323362029
+aml2007jan05502,2007-01-22,2007,1,day,4.232,0.7186677353162109
+aml2007jan05503,2007-01-22,2007,1,day,6.43,0.8709888137605752
+aml2007jan05504,2007-01-22,2007,1,day,91.934,1.9681746301505287
+aml2007jan05505,2007-01-22,2007,1,day,1.829,0.4516329474569908
+aml2007jan05506,2007-01-22,2007,1,day,57.988,1.7707636715770392
+aml2007jan05507,2007-01-22,2007,1,night,3.65,0.667452952889954
+aml2007jan05508,2007-01-22,2007,1,night,0,0
+aml2007jan0609,2007-01-26,2007,1,day,110.123,2.0458039575842975
+aml2007jan0610,2007-01-26,2007,1,day,0.569,0.19562294358693666
+aml2007jan0611,2007-01-26,2007,1,day,0.214,0.08421868673923877
+aml2007jan0701,2007-01-21,2007,1,night,9.279,1.0119508660593983
+aml2007jan0702,2007-01-21,2007,1,night,2.411,0.5328817194073973
+aml2007jan0703,2007-01-21,2007,1,day,32.421,1.5240194404102902
+aml2007jan0704,2007-01-21,2007,1,day,18.686,1.2941574807696914
+aml2007jan0705,2007-01-21,2007,1,day,2.875,0.5882717068423291
+aml2007jan0706,2007-01-21,2007,1,day,13.917,1.1736814897284589
+aml2007jan0707,2007-01-21,2007,1,day,14.472,1.1895464567389264
+aml2007jan0708,2007-01-21,2007,1,day,11.249,1.0881006346426596
+aml2007jan0711,2007-01-11,2007,1,day,5.846,0.8354368948018582
+aml2007jan0802,2007-01-20,2007,1,day,4.739,0.7588362247469582
+aml2007jan0804,2007-01-20,2007,1,day,2.907,0.5918434112247845
+aml2007jan0806,2007-01-20,2007,1,day,16.987,1.2549587346578492
+aml2007jan0808,2007-01-20,2007,1,day,2.275,0.5152113043278018
+aml2007jan0810,2007-01-11,2007,1,day,1.542,0.40517554621798935
+aml2007jan0812,2007-01-11,2007,1,day,7.126,0.9098768179903929
+aml2007jan0901,2007-01-19,2007,1,night,0.21,0.08278537031645007
+aml2007jan0902,2007-01-19,2007,1,day,19.308,1.3076671547325673
+aml2007jan0903,2007-01-19,2007,1,day,9.877,1.03650912853273
+aml2007jan0904,2007-01-19,2007,1,day,2.157,0.4992745818922174
+aml2007jan0905,2007-01-19,2007,1,day,6.816,0.8929845507107166
+aml2007jan0906,2007-01-19,2007,1,day,8.557,0.9803215860087564
+aml2007jan0907,2007-01-20,2007,1,night,8.142,0.9610412169533119
+aml2007jan0908,2007-01-20,2007,1,night,30.519,1.4985724302207144
+aml2007jan0909,2007-01-11,2007,1,day,6.862,0.8955330394840704
+aml2007jan0911,2007-01-11,2007,1,day,46.504,1.6767301802519694
+aml2007jan0913,2007-01-12,2007,1,day,1.913,0.4643404846276673
+aml2007jan1010,2007-01-11,2007,1,day,0.556,0.19200959265367015
+aml2007jan1012,2007-01-12,2007,1,night,122.148,2.0904273630202947
+aml2007jan1101,2007-01-18,2007,1,night,6.492,0.8745977687031997
+aml2007jan1107,2007-01-17,2007,1,day,27.03,1.4476230977602862
+aml2007jan1111,2007-01-12,2007,1,night,0,0
+aml2007jan1113,2007-01-12,2007,1,day,239.513,2.3811385554528037
+aml2007jan1208,2007-01-17,2007,1,day,72.122,1.8640480615152317
+aml2007jan1212,2007-01-12,2007,1,day,9.089,1.003848122034298
+aml2007jan1214,2007-01-13,2007,1,day,3.916,0.6916118742144165
+aml2007jan1307,2007-01-17,2007,1,day,4.349,0.7282725978950171
+aml2007jan1309,2007-01-16,2007,1,day,16.437,1.2414717675507634
+aml2007jan1311,2007-01-12,2007,1,day,0,0
+aml2007jan1313,2007-01-13,2007,1,day,3.84,0.6848453616444125
+aml2007jan1406,2007-01-17,2007,1,day,0.468,0.1667260555800517
+aml2007jan1408,2007-01-16,2007,1,day,3.179,0.6210723711436259
+aml2007jan1410,2007-01-16,2007,1,day,18.519,1.2904575640680382
+aml2007jan1412,2007-01-13,2007,1,night,0,0
+aml2007jan1414,2007-01-13,2007,1,day,4.241,0.7194141597025934
+aml2007jan1505,2007-01-17,2007,1,night,0,0
+aml2007jan1507,2007-01-16,2007,1,day,9.455,1.0193240371536907
+aml2007jan1509,2007-01-16,2007,1,day,2.938,0.5952757118020995
+aml2007jan1513,2007-01-13,2007,1,day,70.283,1.8529859690314685
+aml2007jan1515,2007-01-13,2007,1,day,80.808,1.9127957753824618
+aml2007jan1606,2007-01-16,2007,1,day,1.63,0.41995574848975786
+aml2007jan1608,2007-01-15,2007,1,day,6.508,0.8755242639493087
+aml2007jan1610,2007-01-15,2007,1,day,1.136,0.32960124835651883
+aml2007jan1614,2007-01-13,2007,1,night,2.182,0.5027001753105627
+aml2007jan1707,2007-01-15,2007,1,day,11.87,1.1095785469043866
+aml2007jan1709,2007-01-15,2007,1,day,0.326,0.12254352406875435
+aml2007jan1711,2007-01-15,2007,1,night,0.452,0.1619666163640749
+aml2007jan1713,2007-01-14,2007,1,night,0.131,0.05346260492545529
+aml2007jan1808,2007-01-15,2007,1,day,5.992,0.8446014196263466
+aml2007jan1810,2007-01-14,2007,1,day,0,0
+aml2007jan1812,2007-01-14,2007,1,day,4.042,0.702602841340427
+aml2007jan1909,2007-01-14,2007,1,day,6.795,0.8918161195248604
+aml2007jan1911,2007-01-14,2007,1,day,6.329,0.8650447216930992
+aml2007jan2010,2007-01-14,2007,1,day,0.918,0.2828486028346448
+aml2008dso004,2008-02-17,2008,2,day,1.464,0.39164070349238783
+aml2008dso005,2008-02-17,2008,2,night,26.695,1.4424013694827025
+aml2008dso006,2008-02-18,2008,2,night,0.466,0.16613397030510912
+aml2008dso007,2008-02-18,2008,2,night,109.335,2.0427132993461132
+aml2008dso008,2008-02-19,2008,2,night,17.367,1.2640382259931204
+aml2008dso009,2008-02-20,2008,2,day,0,0
+aml2008dso011,2008-02-20,2008,2,day,0.328,0.1231980750319987
+aml2008dso012,2008-02-20,2008,2,night,1.157,0.33385014510254507
+aml2008dso013,2008-02-21,2008,2,night,200.357,2.303966732073722
+aml2008dso014,2008-02-21,2008,2,day,2.303,0.5189085736914142
+aml2008dso015,2008-02-21,2008,2,day,0,0
+aml2008dso016,2008-02-21,2008,2,day,799.194,2.9031952906362544
+aml2008dso017,2008-02-21,2008,2,day,0.144,0.05842602445700535
+aml2008dso018,2008-02-21,2008,2,night,146.233,2.168005161303561
+aml2008dso019,2008-02-22,2008,2,night,6.423,0.8705794605526849
+aml2008dso020,2008-02-19,2008,2,day,0.122,0.04999285692014256
+aml2008dso021,2008-02-19,2008,2,day,0.736,0.2395497208404731
+aml2008dso022,2008-02-19,2008,2,day,0,0
+aml2008dso023,2008-02-22,2008,2,day,0,0
+aml2008dso024,2008-02-22,2008,2,day,92.989,1.973077028931166
+aml2008dso025,2008-02-22,2008,2,day,0,0
+aml2008dso026,2008-02-22,2008,2,day,0.134,0.05461305455688774
+aml2008dso027,2008-02-23,2008,2,night,6.146,0.8540630118664212
+aml2008dso028,2008-02-23,2008,2,night,0.918,0.2828486028346448
+aml2008dso029,2008-02-18,2008,2,day,445.032,2.649366017736542
+aml2008dso030,2008-02-18,2008,2,day,2.756,0.5747255835940732
+aml2008dso031,2008-02-18,2008,2,day,0.436,0.15715443990628153
+aml2008dso032,2008-02-18,2008,2,day,9.517,1.021891873919109
+aml2008dso033,2008-02-23,2008,2,day,2.562,0.5516939151272247
+aml2008dso034,2008-02-26,2008,2,night,11.744,1.1053057627930751
+aml2008dso035,2008-02-26,2008,2,night,0.178,0.07114529045108281
+aml2008dso036,2008-02-26,2008,2,day,2.191,0.5039268041935104
+aml2008dso037,2008-02-26,2008,2,day,128.941,2.113746204685859
+aml2008dso038,2008-02-26,2008,2,day,0.454,0.162564406523019
+aml2008dso039,2008-02-26,2008,2,night,118.425,2.0770952499553914
+aml2008dso042,2008-02-19,2008,2,day,0.133,0.05422990986339725
+aml2008dso043,2008-02-21,2008,2,night,2.271,0.5146805441249817
+aml2008dso044,2008-02-21,2008,2,day,161.63,2.2112006620002567
+aml2008dso045,2008-02-22,2008,2,night,0.06,0.02530586526477026
+aml2008dso046,2008-02-22,2008,2,day,11.135,1.0840397806679536
+aml2008dso047,2008-02-22,2008,2,day,17.865,1.2756568095370142
+aml2008dso048,2008-02-23,2008,2,night,1.041,0.3098430047160705
+aml2008dso049,2008-02-23,2008,2,day,672.312,2.82821635468381
+aml2008dso051,2008-02-25,2008,2,night,18.316,1.2859171967283087
+aml2008feb0201,2008-02-28,2008,2,night,212.648,2.3296988316581095
+aml2008feb0202,2008-02-27,2008,2,night,191.029,2.283366820312512
+aml2008feb0203,2008-02-27,2008,2,day,25.227,1.4187486162495742
+aml2008feb0204,2008-02-27,2008,2,day,107.948,2.0372192621459617
+aml2008feb0205,2008-02-27,2008,2,day,92.475,1.9706954738262572
+aml2008feb0206,2008-02-27,2008,2,day,0.234,0.09131515969722287
+aml2008feb0207,2008-02-27,2008,2,day,0.387,0.14207646107328487
+aml2008feb0208,2008-02-27,2008,2,night,10.886,1.0750357259221908
+aml2008feb0302,2008-02-28,2008,2,day,0.151,0.06107532362979181
+aml2008feb0304,2008-02-28,2008,2,day,8.894,0.9953719060281624
+aml2008feb0306,2008-02-28,2008,2,day,0.254,0.09829753649469763
+aml2008feb0308,2008-02-28,2008,2,day,0.279,0.10687054447865389
+aml2008feb0401,2008-02-29,2008,2,night,1.759,0.44075170047918544
+aml2008feb0402,2008-02-29,2008,2,day,4.812,0.764325605625984
+aml2008feb0403,2008-02-29,2008,2,day,33.246,1.5346098523429974
+aml2008feb0404,2008-02-29,2008,2,day,22.549,1.3719724696940931
+aml2008feb0405,2008-02-29,2008,2,day,73.427,1.8717305138232079
+aml2008feb0406,2008-02-29,2008,2,night,32.094,1.5197492625644744
+aml2008feb0407,2008-02-29,2008,2,night,0.404,0.14736710779378645
+aml2008feb0408,2008-02-28,2008,2,night,0.519,0.18155777386278638
+aml2008feb05501,2008-03-02,2008,3,night,3.528,0.655906418180215
+aml2008feb05502,2008-03-02,2008,3,night,45.632,1.6686840423113884
+aml2008feb05503,2008-03-02,2008,3,night,30.607,1.4997832764382577
+aml2008feb05504,2008-03-02,2008,3,night,4.047,0.7030333047336859
+aml2008feb05505,2008-03-02,2008,3,day,0,0
+aml2008feb05506,2008-03-02,2008,3,day,0,0
+aml2008feb05507,2008-03-02,2008,3,day,0,0
+aml2008feb05508,2008-03-02,2008,3,day,76.335,1.8883760898652473
+aml2008feb0612,2008-03-05,2008,3,day,1.955,0.47055748521727414
+aml2008feb0701,2008-03-03,2008,3,day,0.296,0.11260500153457453
+aml2008feb0702,2008-03-03,2008,3,day,1.962,0.47158505418518964
+aml2008feb0703,2008-03-03,2008,3,day,0.186,0.07408468902824378
+aml2008feb0704,2008-03-03,2008,3,day,0.683,0.22608411597582387
+aml2008feb0705,2008-03-03,2008,3,night,20.365,1.3297028971532154
+aml2008feb0706,2008-03-03,2008,3,night,64.927,1.8190633136765249
+aml2008feb0707,2008-03-02,2008,3,night,6.497,0.8748875108461125
+aml2008feb0708,2008-03-02,2008,3,day,10.124,1.0462609801921219
+aml2008feb0711,2008-03-05,2008,3,night,75.267,1.8823366635373942
+aml2008feb0810,2008-03-05,2008,3,night,2.413,0.5331362882786388
+aml2008feb0812,2008-03-05,2008,3,day,207.938,2.3200174332282075
+aml2008feb0901,2008-03-03,2008,3,night,0.173,0.06929801211552926
+aml2008feb0902,2008-03-04,2008,3,night,47.219,1.683218199426229
+aml2008feb0903,2008-03-04,2008,3,night,1.77,0.4424797690644485
+aml2008feb0904,2008-03-04,2008,3,day,0,0
+aml2008feb0905,2008-03-04,2008,3,day,1.669,0.4263485737875077
+aml2008feb0906,2008-03-04,2008,3,day,16.257,1.2369652991029796
+aml2008feb0907,2008-03-04,2008,3,day,3.653,0.6677330525332674
+aml2008feb0908,2008-03-04,2008,3,day,30.863,1.5032866635589146
+aml2008feb0909,2008-03-04,2008,3,night,0.127,0.05192391604610654
+aml2008feb0911,2008-03-05,2008,3,day,0.52,0.18184358794477254
+aml2008feb0913,2008-03-06,2008,3,night,166.197,2.2232284806679834
+aml2008feb1010,2008-03-05,2008,3,day,0,0
+aml2008feb1012,2008-03-06,2008,3,night,113.225,2.0577611667277425
+aml2008feb1111,2008-03-05,2008,3,night,5.807,0.8329557506045984
+aml2008feb1113,2008-03-06,2008,3,day,0.452,0.1619666163640749
+aml2008feb1212,2008-03-06,2008,3,day,0,0
+aml2008feb1214,2008-03-07,2008,3,night,346.945,2.541510600026287
+aml2008feb1311,2008-03-06,2008,3,day,0.913,0.28171497002729584
+aml2008feb1313,2008-03-06,2008,3,night,1453.917,3.1628382184283415
+aml2008feb1412,2008-03-06,2008,3,day,0.339,0.12678057701200898
+aml2008feb1414,2008-03-07,2008,3,night,33.182,1.5337974698403236
+aml2008feb1513,2008-03-07,2008,3,day,0.428,0.15472820744015556
+aml2008feb1515,2008-03-07,2008,3,day,0,0
+aml2008feb1614,2008-03-07,2008,3,day,2.026,0.4808689236871679
+aml2008feb1713,2008-03-07,2008,3,day,0.164,0.06595298031386966
+aml2008jan0205,2008-01-29,2008,1,day,13.451,1.1598979010340966
+aml2008jan0206,2008-01-29,2008,1,day,0.561,0.19340290306241747
+aml2008jan0207,2008-01-29,2008,1,day,10.892,1.0752549005329002
+aml2008jan0208,2008-01-30,2008,1,night,0.797,0.25454807710897387
+aml2008jan0209,2008-01-30,2008,1,day,186.631,2.2733045931939135
+aml2008jan0211,2008-01-30,2008,1,day,221.205,2.346753827098001
+aml2008jan0213,2008-01-30,2008,1,day,10.785,1.0713295868603425
+aml2008jan0302,2008-01-28,2008,1,day,5.605,0.819872821950546
+aml2008jan0304,2008-01-28,2008,1,day,26.515,1.4395695171471754
+aml2008jan0306,2008-01-28,2008,1,day,0.174,0.069668096911595645
+aml2008jan0308,2008-01-28,2008,1,day,12.182,1.119981307304154
+aml2008jan0314,2008-01-30,2008,1,day,273.717,2.438885535204146
+aml2008jan0401,2008-01-27,2008,1,night,2.545,0.5496162395190853
+aml2008jan0402,2008-01-27,2008,1,day,14.91,1.2016701796465816
+aml2008jan0403,2008-01-27,2008,1,day,23.998,1.3979052637236689
+aml2008jan0404,2008-01-27,2008,1,day,5.959,0.8425468364950149
+aml2008jan0405,2008-01-27,2008,1,day,20.885,1.3401465509491328
+aml2008jan0406,2008-01-27,2008,1,day,1.342,0.3695868907363443
+aml2008jan0407,2008-01-28,2008,1,night,0.87,0.271841606536499
+aml2008jan0408,2008-01-28,2008,1,night,0,0
+aml2008jan0409,2008-01-31,2008,1,day,0.102,0.04218159451576627
+aml2008jan0411,2008-01-30,2008,1,night,0.045,0.01911629044707278
+aml2008jan0413,2008-01-30,2008,1,day,0.812,0.25815819334079426
+aml2008jan05501,2008-01-26,2008,1,night,12.509,1.1306232016825952
+aml2008jan05502,2008-01-26,2008,1,day,2.305,0.5191714638216591
+aml2008jan05503,2008-01-26,2008,1,day,4.972,0.7761197990529878
+aml2008jan05504,2008-01-26,2008,1,day,291.762,2.466514705373688
+aml2008jan05505,2008-01-26,2008,1,day,0.202,0.0799044676667207
+aml2008jan05506,2008-01-26,2008,1,day,32.19,1.521007252408604
+aml2008jan05507,2008-01-26,2008,1,day,0.841,0.2650537885040147
+aml2008jan05508,2008-01-26,2008,1,day,143.154,2.15882669773932
+aml2008jan0609,2008-01-31,2008,1,day,0.444,0.15956719323362029
+aml2008jan0611,2008-01-31,2008,1,day,0,0
+aml2008jan0612,2008-01-31,2008,1,day,0,0
+aml2008jan0701,2008-01-25,2008,1,night,1.047,0.3111178426625056
+aml2008jan0702,2008-01-25,2008,1,day,0.203,0.08026562733984477
+aml2008jan0703,2008-01-25,2008,1,day,3.101,0.6128897692874847
+aml2008jan0704,2008-01-25,2008,1,day,3.913,0.6913467641348218
+aml2008jan0705,2008-01-25,2008,1,day,10.757,1.070296518197765
+aml2008jan0706,2008-01-25,2008,1,day,5.976,0.8436064719245108
+aml2008jan0708,2008-01-25,2008,1,night,410.619,2.6144954132349767
+aml2008jan0711,2008-01-31,2008,1,day,0.24,0.09342168516223506
+aml2008jan0802,2008-01-24,2008,1,night,3.824,0.6834072991320949
+aml2008jan0810,2008-01-31,2008,1,day,8.219,0.9646838149760406
+aml2008jan0812,2008-02-01,2008,2,day,0.083,0.03462845662532034
+aml2008jan0901,2008-01-23,2008,1,day,12.457,1.1289482524093308
+aml2008jan0902,2008-02-23,2008,2,day,9.565,1.023869501388332
+aml2008jan0909,2008-02-01,2008,2,night,0.444,0.15956719323362029
+aml2008jan0911,2008-02-01,2008,2,day,0.101,0.041787318971751766
+aml2008jan0913,2008-02-01,2008,2,day,9.569,1.024033897900905
+aml2008jan1010,2008-02-01,2008,2,day,0.327,0.1228709228644355
+aml2008jan1012,2008-02-01,2008,2,day,0.447,0.1604685311190375
+aml2008jan1101,2008-01-22,2008,1,day,0,0
+aml2008jan1107,2008-01-21,2008,1,day,105.203,2.026136784777877
+aml2008jan1111,2008-02-01,2008,2,day,0.357,0.13257984765973704
+aml2008jan1113,2008-02-02,2008,2,day,0.252,0.09760432887441087
+aml2008jan1208,2008-01-21,2008,1,day,0.077,0.03221570329798157
+aml2008jan1212,2008-02-02,2008,2,day,0.33,0.12385164096708583
+aml2008jan1214,2008-02-02,2008,2,day,18.948,1.2998993595463255
+aml2008jan1307,2008-01-21,2008,1,night,0.153,0.06182930729469903
+aml2008jan1309,2008-01-20,2008,1,night,0.655,0.21879799811173753
+aml2008jan1311,2008-02-02,2008,2,night,1.476,0.3937506403480804
+aml2008jan1313,2008-02-02,2008,2,day,165.451,2.2212864086436466
+aml2008jan1406,2008-01-20,2008,1,day,53.811,1.738867725635786
+aml2008jan1408,2008-01-20,2008,1,day,0.078,0.03261876085071993
+aml2008jan1410,2008-01-19,2008,1,day,0.527,0.1838390370564212
+aml2008jan1412,2008-02-02,2008,2,day,1.184,0.3392526340326997
+aml2008jan1414,2008-02-03,2008,2,night,0.245,0.09516935143175517
+aml2008jan1505,2008-01-20,2008,1,day,10.163,1.0477809247411964
+aml2008jan1507,2008-01-20,2008,1,day,16.028,1.2311636415050988
+aml2008jan1509,2008-01-19,2008,1,day,1.879,0.459241664878082
+aml2008jan1513,2008-02-02,2008,2,night,0.343,0.12807601266871535
+aml2008jan1515,2008-02-03,2008,2,day,0.2,0.07918124604762482
+aml2008jan1608,2008-01-19,2008,1,day,4.705,0.7562556487542335
+aml2008jan1610,2008-01-18,2008,1,day,2.103,0.4917817755841658
+aml2008jan1614,2008-02-03,2008,2,day,0,0
+aml2008jan1707,2008-01-19,2008,1,day,2.121,0.4942937686653327
+aml2008jan1709,2008-01-19,2008,1,night,0,0
+aml2008jan1711,2008-01-18,2008,1,day,18.176,1.2827580210255973
+aml2008jan1713,2008-02-03,2008,2,day,0,0
+aml2008jan1808,2008-01-19,2008,1,day,1.772,0.4427932259397692
+aml2008jan1810,2008-01-18,2008,1,day,7.182,0.912859475162355
+aml2008jan1812,2008-01-18,2008,1,day,12.234,1.1216911303075767
+aml2008jan1909,2008-01-18,2008,1,day,6.21,0.8579352647194289
+aml2008jan1911,2008-01-18,2008,1,day,0.567,0.1950689964685901
+aml2008jan2010,2008-01-18,2008,1,day,22.789,1.376376186315962
+aml2009jan0201,2009-01-15,2009,1,day,2.083,0.48897352472650824
+aml2009jan0202,2009-01-15,2009,1,NA,11.364,1.0921589963912675
+aml2009jan0203,2009-01-15,2009,1,night,0.353,0.13129779659762297
+aml2009jan0204,2009-01-15,2009,1,day,101.555,2.0109568389557193
+aml2009jan0205,2009-01-15,2009,1,day,2.399,0.5313511645830596
+aml2009jan0206,2009-01-15,2009,1,day,17.079,1.2571744047625357
+aml2009jan0207,2009-01-15,2009,1,day,3.736,0.6754116937148634
+aml2009jan0208,2009-01-15,2009,1,day,0.296,0.11260500153457453
+aml2009jan0209,2009-01-15,2009,1,night,0.033,0.014100321519620544
+aml2009jan0211,2009-01-15,2009,1,NA,0.165,0.0663259253620378
+aml2009jan0213,2009-01-15,2009,1,day,1.674,0.42716140292596555
+aml2009jan0304,2009-01-15,2009,1,day,1.275,0.3569814009931312
+aml2009jan0306,2009-01-15,2009,1,night,0.517,0.18098558078673047
+aml2009jan0308,2009-01-15,2009,1,NA,0,0
+aml2009jan0401,2009-01-15,2009,1,day,0.421,0.15259407792746976
+aml2009jan0402,2009-01-15,2009,1,NA,128.725,2.113023679366965
+aml2009jan0403,2009-01-15,2009,1,night,0.032,0.013679697291192559
+aml2009jan0404,2009-01-15,2009,1,day,4.883,0.7695988483874465
+aml2009jan0405,2009-01-15,2009,1,day,3.995,0.6985354925620011
+aml2009jan0406,2009-01-15,2009,1,day,0.532,0.18525876529658514
+aml2009jan0407,2009-01-15,2009,1,day,0.186,0.07408468902824378
+aml2009jan0408,2009-01-15,2009,1,day,4.749,0.7595923086459748
+aml2009jan0409,2009-01-15,2009,1,night,0.03,0.012837224705172217
+aml2009jan0410,2009-01-15,2009,1,NA,0.062,0.026124516745450282
+aml2009jan0411,2009-01-15,2009,1,day,0.079,0.033021444682910656
+aml2009jan0413,2009-01-15,2009,1,day,0,0
+aml2009jan05501,2009-01-15,2009,1,day,3.356,0.6390878710837373
+aml2009jan05502,2009-01-15,2009,1,day,5.094,0.7849024498866548
+aml2009jan05503,2009-01-15,2009,1,day,6.342,0.865814380167968
+aml2009jan05504,2009-01-15,2009,1,NA,0.842,0.26528962586083016
+aml2009jan05505,2009-01-15,2009,1,night,6.206,0.8576942577865496
+aml2009jan05506,2009-01-15,2009,1,night,0.888,0.2760019899620501
+aml2009jan05507,2009-01-15,2009,1,day,1.081,0.31827208021162695
+aml2009jan05508,2009-01-15,2009,1,day,0.374,0.13798673272353165
+aml2009jan0609,2009-01-15,2009,1,day,2.661,0.5635997288815311
+aml2009jan0611,2009-01-15,2009,1,day,0.078,0.03261876085071993
+aml2009jan0612,2009-01-15,2009,1,day,0.548,0.1897709563468738
+aml2009jan0701,2009-01-15,2009,1,day,0.942,0.288249225571986
+aml2009jan0702,2009-01-15,2009,1,day,6.548,0.8778318914928938
+aml2009jan0703,2009-01-15,2009,1,day,0.471,0.16761267272753014
+aml2009jan0704,2009-01-15,2009,1,night,0,0
+aml2009jan0705,2009-01-15,2009,1,day,0.878,0.27369558793009213
+aml2009jan0706,2009-01-15,2009,1,day,9.112,1.0048370609383097
+aml2009jan0707,2009-01-15,2009,1,day,17.433,1.2655960230875916
+aml2009jan0708,2009-01-15,2009,1,day,1.724,0.4352071032407476
+aml2009jan0711,2009-01-15,2009,1,day,9.071,1.003072595967675
+aml2009jan0802,2009-01-15,2009,1,day,3.13,0.615950051656401
+aml2009jan0804,2009-01-15,2009,1,day,1.516,0.4007106367732313
+aml2009jan0806,2009-01-15,2009,1,day,0.214,0.08421868673923877
+aml2009jan0808,2009-01-15,2009,1,NA,0,0
+aml2009jan0810,2009-01-15,2009,1,day,0,0
+aml2009jan0812,2009-01-15,2009,1,day,42.127,1.6347492488076665
+aml2009jan0901,2009-01-15,2009,1,day,2.17,0.5010592622177514
+aml2009jan0902,2009-01-15,2009,1,night,0.202,0.0799044676667207
+aml2009jan0903,2009-01-15,2009,1,NA,0,0
+aml2009jan0904,2009-01-15,2009,1,day,0.287,0.10957854690438665
+aml2009jan0905,2009-01-15,2009,1,day,0.283,0.10822665637492848
+aml2009jan0906,2009-01-15,2009,1,day,0.597,0.20330491613848292
+aml2009jan0907,2009-01-15,2009,1,day,2.903,0.5913985512812487
+aml2009jan0908,2009-01-15,2009,1,day,0.761,0.24575935596727694
+aml2009jan0909,2009-01-15,2009,1,night,0.206,0.08134730780413249
+aml2009jan0911,2009-01-15,2009,1,day,0,0
+aml2009jan0913,2009-01-15,2009,1,day,0.653,0.2182728535714475
+aml2009jan1010,2009-01-15,2009,1,night,3.008,0.6029277128591893
+aml2009jan1012,2009-01-15,2009,1,day,0.073,0.030599721965951062
+aml2009jan1101,2009-01-15,2009,1,day,0,0
+aml2009jan1103,2009-01-15,2009,1,day,0.621,0.20978301484851494
+aml2009jan1107,2009-01-15,2009,1,day,8.817,0.9919787909945836
+aml2009jan1111,2009-01-15,2009,1,day,0.364,0.13481437032046006
+aml2009jan1113,2009-01-15,2009,1,day,0.173,0.06929801211552926
+aml2009jan1208,2009-01-15,2009,1,day,0,0
+aml2009jan1212,2009-01-15,2009,1,NA,0.663,0.22089224921951925
+aml2009jan1214,2009-01-15,2009,1,day,0.345,0.12872228433842678
+aml2009jan1307,2009-01-15,2009,1,day,2.364,0.5268559871258746
+aml2009jan1309,2009-01-15,2009,1,day,0.358,0.1328997699444829
+aml2009jan1311,2009-01-15,2009,1,night,38.616,1.5978706230040052
+aml2009jan1313,2009-01-15,2009,1,day,0,0
+aml2009jan1406,2009-01-15,2009,1,NA,0,0
+aml2009jan1408,2009-01-15,2009,1,day,3.328,0.636287252098513
+aml2009jan1410,2009-01-15,2009,1,day,0.077,0.03221570329798157
+aml2009jan1412,2009-01-15,2009,1,day,0.251,0.0972573096934199
+aml2009jan1414,2009-01-15,2009,1,night,95.249,1.983396226006637
+aml2009jan1505,2009-01-15,2009,1,night,0,0
+aml2009jan1507,2009-01-15,2009,1,day,0.314,0.11859536522376199
+aml2009jan1509,2009-01-15,2009,1,night,3.777,0.6791552412833538
+aml2009jan1513,2009-01-15,2009,1,NA,0,0
+aml2009jan1515,2009-01-15,2009,1,NA,30.117,1.4929977198649071
+aml2009jan1606,2009-01-15,2009,1,day,0,0
+aml2009jan1608,2009-01-15,2009,1,NA,0.633,0.21298618473666814
+aml2009jan1610,2009-01-15,2009,1,NA,3.663,0.6686654154544921
+aml2009jan1614,2009-01-15,2009,1,day,0.084,0.03502928220236815
+aml2009jan1707,2009-01-15,2009,1,day,2.389,0.5300715688373783
+aml2009jan1711,2009-01-15,2009,1,night,0.504,0.17724783625562346
+aml2009jan1713,2009-01-15,2009,1,day,0,0
+aml2009jan1808,2009-01-15,2009,1,day,5.45,0.8095597146352678
+aml2009jan1810,2009-01-15,2009,1,day,1.774,0.44310645673726606
+aml2009jan1812,2009-01-15,2009,1,day,0.828,0.2619761913978126
+aml2009jan1909,2009-01-15,2009,1,day,5.171,0.7903555464143864
+aml2009jan1911,2009-01-15,2009,1,day,0.582,0.1992064791616577
+aml2009jan2010,2009-01-15,2009,1,day,1.524,0.4020893505720967
+aml2009sor0401,2009-02-14,2009,2,day,0.103,0.04257551244019059
+aml2009sor0567,2009-02-14,2009,2,night,316.396,2.5016014492339713
+aml2009sor0669,2009-02-14,2009,2,day,3.541,0.6571515019009667
+aml2009sor1004,2009-02-14,2009,2,night,0.362,0.13417710757676637
+aml2009sor2932,2009-02-14,2009,2,night,8.245,0.9659069154951918
+aml2009sor3128,2009-02-14,2009,2,night,7.575,0.933234128714808
+aml2009sor4241,2009-02-14,2009,2,day,0.222,0.08707120590653541
+aml2009sor5059,2009-02-14,2009,2,night,0.367,0.13576851456782227
+aml2009sor7808,2009-02-14,2009,2,night,0.055,0.023252459633711444
+aml2009sor8863,2009-02-14,2009,2,night,0.189,0.0751818546186916
+aml2009sor9343,2009-02-14,2009,2,night,NA,NA
+aml2009sor9616,2009-02-14,2009,2,night,3.031,0.6054127981530513
+aml2010feb0209,2010-02-14,2010,2,night,0.067,0.028164419424469872
+aml2010feb0210,2010-02-14,2010,2,day,3.702,0.6722826247889205
+aml2010feb0409,2010-02-14,2010,2,day,0.248,0.09621458534640517
+aml2010feb0410,2010-02-14,2010,2,day,0,0
+aml2010feb0609,2010-02-14,2010,2,night,4.557,0.7448403967853792
+aml2010feb0610,2010-02-14,2010,2,night,33.379,1.5362932399834328
+aml2010feb0611,2010-02-14,2010,2,night,154.504,2.1917415647814087
+aml2010feb0612,2010-02-14,2010,2,day,0,0
+aml2010feb0709,2010-02-14,2010,2,NA,1.196,0.34163233577805424
+aml2010feb0810,2010-02-14,2010,2,day,5.653,0.8230175234460492
+aml2010feb0812,2010-02-14,2010,2,night,61.376,1.7950175212126185
+aml2010feb0909,2010-02-14,2010,2,day,0.289,0.11025291735340301
+aml2010feb0911,2010-02-14,2010,2,night,21.072,1.343841687471129
+aml2010feb0913,2010-02-14,2010,2,NA,0.125,0.05115252244738129
+aml2010feb1010,2010-02-14,2010,2,night,0.331,0.1241780554746751
+aml2010feb1012,2010-02-14,2010,2,day,0,0
+aml2010feb1111,2010-02-14,2010,2,day,0.151,0.06107532362979181
+aml2010feb1113,2010-02-14,2010,2,night,1353.883,3.131901793624995
+aml2010feb1208,2010-02-14,2010,2,day,0.124,0.05076631123304232
+aml2010feb1212,2010-02-14,2010,2,night,2.13,0.49554433754644844
+aml2010feb1214,2010-02-14,2010,2,night,13.45,1.1598678470925665
+aml2010feb1307,2010-02-14,2010,2,day,5.969,0.8431704645198979
+aml2010feb1311,2010-02-14,2010,2,day,0.197,0.0780941504064107
+aml2010feb1313,2010-02-14,2010,2,NA,8.318,0.9693227061122022
+aml2010feb1406,2010-02-14,2010,2,night,0.319,0.12024479554636529
+aml2010feb1408,2010-02-14,2010,2,day,0,0
+aml2010feb1412,2010-02-14,2010,2,day,0,0
+aml2010feb1414,2010-02-14,2010,2,day,0.113,0.04649516433470831
+aml2010feb1505,2010-02-14,2010,2,night,0,0
+aml2010feb1507,2010-02-14,2010,2,NA,0,0
+aml2010feb1513,2010-02-14,2010,2,day,0,0
+aml2010feb1515,2010-02-14,2010,2,NA,0.712,0.2335037603411344
+aml2010feb1606,2010-02-14,2010,2,night,0.186,0.07408468902824378
+aml2010feb1610,2010-02-14,2010,2,night,72.317,1.865204686103311
+aml2010feb1614,2010-02-14,2010,2,night,22.672,1.3742349521525767
+aml2010feb1711,2010-02-14,2010,2,NA,0,0
+aml2010feb1713,2010-02-14,2010,2,night,0.225,0.0881360887005513
+aml2010feb1810,2010-02-14,2010,2,day,2.442,0.5368108659915415
+aml2010feb1812,2010-02-14,2010,2,night,0.84,0.26481782300953643
+aml2010feb1909,2010-02-14,2010,2,day,0.162,0.06520612805431189
+aml2010feb1911,2010-02-14,2010,2,NA,0.169,0.06781451116184012
+aml2010feb2010,2010-02-14,2010,2,day,0,0
+aml2010jan0201,2010-01-15,2010,1,day,16.26,1.2370407913791908
+aml2010jan0202,2010-01-15,2010,1,day,18.354,1.2867707367140557
+aml2010jan0204,2010-01-15,2010,1,night,1.466,0.39199307225971286
+aml2010jan0205,2010-01-15,2010,1,NA,0.367,0.13576851456782227
+aml2010jan0206,2010-01-15,2010,1,day,0.289,0.11025291735340301
+aml2010jan0207,2010-01-15,2010,1,day,0,0
+aml2010jan0208,2010-01-15,2010,1,day,0.15,0.06069784035361165
+aml2010jan0302,2010-01-15,2010,1,day,0.619,0.20924684875337374
+aml2010jan0304,2010-01-15,2010,1,day,1.583,0.41212440617331725
+aml2010jan0306,2010-01-15,2010,1,night,29.908,1.490070903572991
+aml2010jan0308,2010-01-15,2010,1,NA,6.713,0.8872233313625231
+aml2010jan05501,2010-01-15,2010,1,day,0.883,0.2748503200166648
+aml2010jan05502,2010-01-15,2010,1,NA,0.847,0.2664668954402414
+aml2010jan05503,2010-01-15,2010,1,night,0,0
+aml2010jan05504,2010-01-15,2010,1,NA,0,0
+aml2010jan05505,2010-01-15,2010,1,day,0,0
+aml2010jan05506,2010-01-15,2010,1,day,0,0
+aml2010jan05507,2010-01-15,2010,1,day,0.119,0.04883008652835004
+aml2010jan05508,2010-01-15,2010,1,day,0,0
+aml2010jan0701,2010-01-15,2010,1,NA,0.551,0.19061179781360496
+aml2010jan0702,2010-01-15,2010,1,day,0.749,0.24278980947867654
+aml2010jan0703,2010-01-15,2010,1,day,0,0
+aml2010jan0704,2010-01-15,2010,1,day,0.52,0.18184358794477254
+aml2010jan0705,2010-01-15,2010,1,day,19.188,1.3050932960616481
+aml2010jan0706,2010-01-15,2010,1,night,1.12,0.32633586092875144
+aml2010jan0707,2010-01-15,2010,1,night,0.682,0.22582599146189336
+aml2010jan0708,2010-01-15,2010,1,NA,16.981,1.254813841034874
+aml2010jan0711,2010-01-15,2010,1,night,1.402,0.38057300306688724
+aml2010jan0802,2010-01-15,2010,1,night,0.428,0.15472820744015556
+aml2010jan0804,2010-01-15,2010,1,NA,0.16,0.06445798922691845
+aml2010jan0806,2010-01-15,2010,1,day,0.748,0.2425414282983842
+aml2010jan0808,2010-01-15,2010,1,day,0.811,0.25791845031405836
+aml2010jan0810,2010-01-15,2010,1,day,0,0
+aml2010jan0909,2010-01-15,2010,1,day,0,0
+aml2010jan0911,2010-01-15,2010,1,night,0.052,0.02201573981772028
+aml2010jan1010,2010-01-15,2010,1,day,0,0
+aml2010jan1414,2010-01-15,2010,1,day,0.213,0.08386080086657301
+aml2010jan1614,2010-01-15,2010,1,night,0.784,0.25139485004010426
+aml2010jan1713,2010-01-15,2010,1,day,0,0
+aml2010jan2010,2010-01-15,2010,1,day,0.63,0.21218760440395779
+aml2011jan0201,2011-01-15,2011,1,day,1.701,0.4315245841874509
+aml2011jan0202,2011-01-15,2011,1,day,0.522,0.18241465243455401
+aml2011jan0203,2011-01-15,2011,1,NA,4.529,0.7426465899387362
+aml2011jan0204,2011-01-15,2011,1,night,8.126,0.9602804644366421
+aml2011jan0205,2011-01-15,2011,1,night,11.564,1.0991279277264672
+aml2011jan0206,2011-01-15,2011,1,NA,0.209,0.0824263008607719
+aml2011jan0207,2011-01-15,2011,1,day,0.323,0.12155984418750096
+aml2011jan0208,2011-01-15,2011,1,day,0.102,0.04218159451576627
+aml2011jan0210,2011-01-15,2011,1,day,1.062,0.3142886609474978
+aml2011jan0211,2011-01-15,2011,1,day,0.269,0.10346162209470482
+aml2011jan0302,2011-01-15,2011,1,day,0.475,0.16879202031418183
+aml2011jan0304,2011-01-15,2011,1,day,6.669,0.8847387377696317
+aml2011jan0306,2011-01-15,2011,1,day,1.848,0.45453998496481873
+aml2011jan0308,2011-01-15,2011,1,NA,0.137,0.05576046468773478
+aml2011jan0402,2011-01-15,2011,1,day,0.261,0.10071508657308166
+aml2011jan0403,2011-01-15,2011,1,day,0.865,0.2706788361447064
+aml2011jan0404,2011-01-15,2011,1,NA,0.587,0.20057692675484817
+aml2011jan0405,2011-01-15,2011,1,night,60.828,1.7911851982534908
+aml2011jan0406,2011-01-15,2011,1,NA,9.085,1.0036759025487842
+aml2011jan0407,2011-01-15,2011,1,NA,0.194,0.07700432679335026
+aml2011jan05502,2011-01-15,2011,1,NA,0.859,0.2692793897718986
+aml2011jan05503,2011-01-15,2011,1,night,10.151,1.0473138158153883
+aml2011jan05504,2011-01-15,2011,1,night,4.121,0.7093547758343961
+aml2011jan05505,2011-01-15,2011,1,NA,0.061,0.025715383901340642
+aml2011jan05506,2011-01-15,2011,1,day,0.1,0.04139268515822507
+aml2011jan0610,2011-01-15,2011,1,night,2.897,0.5907304057926903
+aml2011jan0612,2011-01-15,2011,1,NA,3.637,0.6662370958958045
+aml2011jan0702,2011-01-15,2011,1,NA,0.07,0.029383777685209667
+aml2011jan0703,2011-01-15,2011,1,night,0.098,0.04060234011407314
+aml2011jan0706,2011-01-15,2011,1,day,5.901,0.8389120274059986
+aml2011jan0707,2011-01-15,2011,1,day,7.165,0.9119561890726869
+aml2011jan0708,2011-01-15,2011,1,day,0.937,0.28712962071911086
+aml2011jan0711,2011-01-15,2011,1,day,0.465,0.16583762469012828
+aml2011jan0808,2011-01-15,2011,1,night,4.899,0.7707783961691477
+aml2011jan0810,2011-01-15,2011,1,day,3.437,0.6470894287165548
+aml2011jan0812,2011-01-15,2011,1,night,0.593,0.20221577580113148
+aml2011jan0901,2011-01-15,2011,1,night,0.039,0.016615547557177382
+aml2011jan0902,2011-01-15,2011,1,night,0.048,0.02036128264770786
+aml2011jan0904,2011-01-15,2011,1,day,0.079,0.033021444682910656
+aml2011jan0906,2011-01-15,2011,1,day,0.075,0.031408464251624114
+aml2011jan0907,2011-01-15,2011,1,day,2.785,0.5780658838360916
+aml2011jan0908,2011-01-15,2011,1,night,1.131,0.328583449714202
+aml2011jan0909,2011-01-15,2011,1,day,0.078,0.03261876085071993
+aml2011jan0913,2011-01-15,2011,1,night,6.187,0.856547644856748
+aml2011jan1010,2011-01-15,2011,1,day,0.545,0.18892848376085342
+aml2011jan1012,2011-01-15,2011,1,day,0.073,0.030599721965951062
+aml2011jan1107,2011-01-15,2011,1,day,6.019,0.8462752424122132
+aml2011jan1113,2011-01-15,2011,1,NA,0.09,0.03742649794062367
+aml2011jan1208,2011-01-15,2011,1,NA,6.028,0.8468317528232574
+aml2011jan1214,2011-01-15,2011,1,night,0.104,0.04296907339318013
+aml2011jan1309,2011-01-15,2011,1,night,10.085,1.044735697450507
+aml2011jan1311,2011-01-15,2011,1,day,2.04,0.48287358360875376
+aml2011jan1410,2011-01-15,2011,1,night,0.14,0.056904851336472634
+aml2011jan1412,2011-01-15,2011,1,day,1.512,0.40001963506515853
+aml2011jan1509,2011-01-15,2011,1,NA,8.191,0.9633627661037362
+aml2011jan1610,2011-01-15,2011,1,NA,10.715,1.0687422929329815
+aml2011jan1614,2011-01-15,2011,1,NA,0.166,0.06669855042299526
+aml2011jan1711,2011-01-15,2011,1,day,4.68,0.7543483357110189
+aml2011jan1713,2011-01-15,2011,1,night,0.624,0.21058602490515652
+aml2011jan1810,2011-01-15,2011,1,day,0.942,0.288249225571986
+aml2011jan1812,2011-01-15,2011,1,night,1.195,0.3414345245781402
+aml2011jan1911,2011-01-15,2011,1,day,2.222,0.5081255360831992
+aml2011jan2010,2011-01-15,2011,1,day,0.974,0.2953471483336179
+aus1981ana10,1981-01-23,1981,1,day,9.907,1.037705313135537
+aus1981ana11,1981-01-23,1981,1,night,0,0
+aus1981ana12a,1981-01-24,1981,1,day,139.216,2.146797573653734
+aus1981ana12b,1981-01-24,1981,1,day,0,0
+aus1981ana13,1981-01-24,1981,1,day,2.304,0.5190400386483446
+aus1981ana14a,1981-01-25,1981,1,day,6.02,0.8463371121298052
+aus1981ana14b,1981-01-25,1981,1,day,0,0
+aus1981ana15a,1981-01-25,1981,1,day,0,0
+aus1981ana15b,1981-01-25,1981,1,day,0,0
+aus1981ana17a,1981-01-29,1981,1,day,0,0
+aus1981ana17b,1981-01-29,1981,1,day,0,0
+aus1981ana18a,1981-01-29,1981,1,day,29.521,1.4845987588820284
+aus1981ana18b,1981-01-29,1981,1,day,70.086,1.8517840772215968
+aus1981ana19a,1981-01-30,1981,1,day,25.575,1.4244732731953345
+aus1981ana19b,1981-01-30,1981,1,day,58.766,1.776454190482185
+aus1981ana20,1981-01-30,1981,1,day,57.584,1.7677790211287947
+aus1981ana22,1981-02-13,1981,2,night,0.223,0.08742645703628549
+aus1981ana23,1981-02-14,1981,2,day,551.966,2.742698428833936
+aus1981ana24,1981-02-14,1981,2,night,13.575,1.1636085634310516
+aus1981ana26,1981-02-15,1981,2,night,13.375,1.157607853361668
+aus1981ana27,1981-02-16,1981,2,day,0,0
+aus1981ana28,1981-02-16,1981,2,night,8.752,0.9890936926103255
+aus1981ana29,1981-02-17,1981,2,day,63.076,1.80669539271181
+aus1981ana3,1981-01-19,1981,1,day,14.543,1.1915348470067837
+aus1981ana30,1981-02-17,1981,2,night,8.453,0.975569657893662
+aus1981ana31,1981-02-18,1981,2,day,3.289,0.6323560462390732
+aus1981ana34,1981-02-19,1981,2,day,25.103,1.416690423375564
+aus1981ana37,1981-02-20,1981,2,day,0,0
+aus1981ana38,1981-02-20,1981,2,night,0,0
+aus1981ana39,1981-02-21,1981,2,day,7.445,0.9265996539070275
+aus1981ana4,1981-01-20,1981,1,day,1.419,0.3836358683618797
+aus1981ana41,1981-02-21,1981,2,night,16.38,1.2400497721126476
+aus1981ana42,1981-02-22,1981,2,day,48.598,1.6954641642628556
+aus1981ana44,1981-02-22,1981,2,night,9.143,1.0061664254854314
+aus1981ana45,1981-02-23,1981,2,day,33.013,1.531644939077263
+aus1981ana46,1981-02-24,1981,2,night,3.655,0.6679196853173613
+aus1981ana48,1981-03-08,1981,3,day,0,0
+aus1981ana50,1981-03-09,1981,3,day,0,0
+aus1981ana51,1981-03-10,1981,3,day,0,0
+aus1981ana52,1981-03-10,1981,3,night,0,0
+aus1981ana5a,1981-01-20,1981,1,night,0,0
+aus1981ana5b,1981-01-20,1981,1,night,675.296,2.830136818767846
+aus1981ana6a,1981-01-21,1981,1,day,1.67,0.4265112613645752
+aus1981ana6b,1981-01-21,1981,1,day,0,0
+aus1981ana7a,1981-01-21,1981,1,night,17.82,1.274619619091238
+aus1981ana7b,1981-01-21,1981,1,night,3.155,0.6185710281201298
+aus1981ana8a,1981-01-22,1981,1,day,255.047,2.408319691745973
+aus1981ana8b,1981-01-22,1981,1,day,9.752,1.0314892557097501
+aus1981ana9a,1981-01-22,1981,1,night,0,0
+aus1981ana9b,1981-01-22,1981,1,night,1.615,0.4174716932032931
+aus1983ana10,1982-11-27,1982,11,day,0.046,0.01953168453125545
+aus1983ana11,1982-11-28,1982,11,day,0.004,0.0017337128090005314
+aus1983ana14,1982-11-29,1982,11,day,0,0
+aus1983ana15,1982-11-30,1982,11,day,0,0
+aus1983ana2,1982-11-20,1982,11,day,1.24,0.35024801833416286
+aus1983ana3,1982-11-21,1982,11,day,0,0
+aus1983ana38,1982-12-13,1982,12,day,13.669,1.1664005085750682
+aus1983ana42,1982-12-16,1982,12,day,4.866,0.7683420586445333
+aus1983ana43,1982-12-16,1982,12,day,21.621,1.3545117997442728
+aus1983ana44,1982-12-17,1982,12,day,0,0
+aus1983ana45,1982-12-17,1982,12,day,0,0
+aus1983ana46,1982-12-18,1982,12,day,23.032,1.3807899153240744
+aus1983ana47,1982-12-18,1982,12,day,0.808,0.2571984261393445
+aus1983ana48,1982-12-18,1982,12,day,1963.744,3.293305971184618
+aus1983ana49,1982-12-19,1982,12,day,1.211,0.34458874257871397
+aus1983ana50,1982-12-19,1982,12,day,1916.747,3.282791312036226
+aus1983ana6,1982-11-22,1982,11,night,0,0
+aus1983ana8,1982-11-25,1982,11,night,20.052,1.32329336134524
+aus1983ana9,1982-11-26,1982,11,night,0.139,0.05652372407910037
+aus1984adb10,1984-01-16,1984,1,night,0.614,0.20790353038605158
+aus1984adb11,1984-01-16,1984,1,day,0,0
+aus1984adb12,1984-01-17,1984,1,day,0,0
+aus1984adb13,1984-01-17,1984,1,day,0.269,0.10346162209470482
+aus1984adb16,1984-01-18,1984,1,night,0,0
+aus1984adb17,1984-01-19,1984,1,day,0,0
+aus1984adb19a,1984-01-19,1984,1,day,0,0
+aus1984adb19b,1984-01-19,1984,1,day,6.664,0.884455496070488
+aus1984adb20,1984-01-19,1984,1,day,0,0
+aus1984adb21,1984-01-19,1984,1,day,0,0
+aus1984adb22,1984-01-20,1984,1,day,0,0
+aus1984adb23,1984-01-20,1984,1,day,0,0
+aus1984adb24a,1984-01-20,1984,1,day,0,0
+aus1984adb24b,1984-01-21,1984,1,day,NA,NA
+aus1984adb25,1984-01-21,1984,1,day,0,0
+aus1984adb26,1984-01-21,1984,1,day,0,0
+aus1984adb27,1984-01-21,1984,1,night,0,0
+aus1984adb28,1984-01-22,1984,1,day,0,0
+aus1984adb29a,1984-01-23,1984,1,day,0,0
+aus1984adb29b,1984-01-23,1984,1,day,397.242,2.6001470603281547
+aus1984adb3,1984-01-13,1984,1,night,0,0
+aus1984adb30,1984-01-23,1984,1,day,0,0
+aus1984adb31,1984-01-23,1984,1,day,0,0
+aus1984adb33,1984-02-06,1984,2,day,0,0
+aus1984adb4,1984-01-14,1984,1,day,0,0
+aus1984adb7,1984-01-16,1984,1,day,0,0
+aus1984adb8,1984-01-16,1984,1,day,0,0
+aus1984adb9,1984-01-16,1984,1,day,0,0
+aus1985ana10,1985-01-07,1985,1,day,0.076,0.0318122713303704
+aus1985ana11,1985-01-07,1985,1,day,0,0
+aus1985ana12a,1985-01-07,1985,1,night,0.223,0.08742645703628549
+aus1985ana12b,1985-01-07,1985,1,night,0.078,0.03261876085071993
+aus1985ana13,1985-01-08,1985,1,day,0,0
+aus1985ana14,1985-01-08,1985,1,day,0,0
+aus1985ana16a,1985-01-09,1985,1,day,0,0
+aus1985ana16b,1985-01-09,1985,1,day,0,0
+aus1985ana17,1985-01-09,1985,1,day,0,0
+aus1985ana18,1985-01-09,1985,1,night,0.042,0.017867718963505686
+aus1985ana19a,1985-01-10,1985,1,day,0.135,0.05499586152914153
+aus1985ana19b,1985-01-10,1985,1,day,0,0
+aus1985ana1a,1985-01-04,1985,1,day,0,0
+aus1985ana1b,1985-01-04,1985,1,day,0,0
+aus1985ana2,1985-01-04,1985,1,day,0,0
+aus1985ana20,1985-01-10,1985,1,day,0,0
+aus1985ana21,1985-01-10,1985,1,day,0.128,0.05230909964732353
+aus1985ana22a,1985-01-10,1985,1,day,0.489,0.1728946977521761
+aus1985ana22b,1985-01-11,1985,1,day,0,0
+aus1985ana23,1985-01-11,1985,1,day,0,0
+aus1985ana26,1985-01-13,1985,1,day,1.344,0.3699576073460531
+aus1985ana29,1985-01-14,1985,1,day,5.947,0.8417972988743553
+aus1985ana3,1985-01-04,1985,1,day,0.501,0.17638069224327035
+aus1985ana31,1985-01-14,1985,1,day,15.589,1.2198202071472375
+aus1985ana35a,1985-01-15,1985,1,day,0,0
+aus1985ana35b,1985-01-15,1985,1,day,0.255,0.0986437258170569
+aus1985ana36,1985-01-16,1985,1,night,0.117,0.048053173115609055
+aus1985ana37,1985-01-16,1985,1,day,0.263,0.10140335055533072
+aus1985ana38a,1985-01-16,1985,1,day,0,0
+aus1985ana38b,1985-01-16,1985,1,day,0.057,0.024074987307426243
+aus1985ana44,1985-01-19,1985,1,day,0.219,0.08600370561838196
+aus1985ana45a,1985-01-19,1985,1,day,0.061,0.025715383901340642
+aus1985ana45b,1985-01-19,1985,1,day,0.142,0.05766610390982921
+aus1985ana46,1985-01-19,1985,1,day,2.772,0.5765716840652907
+aus1985ana47a,1985-01-20,1985,1,day,0.203,0.08026562733984477
+aus1985ana47b,1985-02-20,1985,2,night,1.111,0.3244882333076563
+aus1985ana48,1985-01-20,1985,1,day,0.081,0.03382569395331033
+aus1985ana49,1985-01-20,1985,1,day,0,0
+aus1985ana4a,1985-01-04,1985,1,day,27.081,1.4484125695100798
+aus1985ana4b,1985-01-04,1985,1,day,0.032,0.013679697291192559
+aus1985ana5,1985-01-05,1985,1,day,0,0
+aus1985ana50,1985-01-21,1985,1,day,13.845,1.1715802019320636
+aus1985ana51a,1985-01-21,1985,1,day,0.14,0.056904851336472634
+aus1985ana51b,1985-01-21,1985,1,day,0.058,0.024485667699166973
+aus1985ana52,1985-01-21,1985,1,night,0.183,0.07298474462793038
+aus1985ana53a,1985-01-22,1985,1,day,0,0
+aus1985ana53b,1985-01-22,1985,1,day,0.346,0.12904505988795806
+aus1985ana54,1985-01-22,1985,1,day,0,0
+aus1985ana55,1985-01-22,1985,1,night,0.132,0.05384642685225267
+aus1985ana56a,1985-01-23,1985,1,day,0,0
+aus1985ana56b,1985-01-23,1985,1,day,0,0
+aus1985ana57a,1985-01-23,1985,1,night,0.839,0.26458172923807743
+aus1985ana57b,1985-01-23,1985,1,night,0.477,0.16938049531194946
+aus1985ana58,1985-01-23,1985,1,day,0,0
+aus1985ana59,1985-01-24,1985,1,day,0.218,0.08564728829685654
+aus1985ana6,1985-01-05,1985,1,day,0.046,0.01953168453125545
+aus1985ana60a,1985-01-24,1985,1,day,0.526,0.18355453361886165
+aus1985ana60b,1985-01-24,1985,1,day,6.186,0.8564872128686307
+aus1985ana61,1985-01-24,1985,1,night,5.164,0.7898626300463817
+aus1985ana62a,1985-01-25,1985,1,day,0.655,0.21879799811173753
+aus1985ana62b,1985-01-25,1985,1,day,0.146,0.05918461763137114
+aus1985ana63,1985-01-26,1985,1,day,0.043,0.018284308426530838
+aus1985ana65a,1985-01-26,1985,1,day,99.702,2.0030380959790244
+aus1985ana65b,1985-01-26,1985,1,day,1.309,0.36342393291717634
+aus1985ana66,1985-01-26,1985,1,night,0.053,0.02242837118548649
+aus1985ana7a,1985-01-05,1985,1,day,1.096,0.32139127831168907
+aus1985ana7b,1985-01-05,1985,1,day,0.509,0.1786892397755898
+aus1985ana8,1985-01-06,1985,1,day,0,0
+aus1985ana9a,1985-01-06,1985,1,day,0,0
+aus1985ana9b,1985-01-06,1985,1,day,0.206,0.08134730780413249
+aus1986adb14a,1985-10-08,1985,10,day,0,0
+aus1986adb14b,1985-10-08,1985,10,day,0,0
+aus1986adb21a,1985-10-09,1985,10,day,0.066,0.027757204690553483
+aus1986adb21b,1985-10-09,1985,10,day,NA,NA
+aus1986adb21com,1985-10-09,1985,10,day,0.12,0.04921802267018165
+aus1986adb22a,1985-10-10,1985,10,night,0,0
+aus1986adb22b,1985-10-10,1985,10,night,NA,NA
+aus1986adb23,1985-10-11,1985,10,day,0,0
+aus1986adb24,1985-10-12,1985,10,night,0,0
+aus1986adb25a,1985-10-13,1985,10,day,NA,NA
+aus1986adb25b,1985-10-13,1985,10,day,0,0
+aus1986adb27a,1985-10-14,1985,10,day,0.211,0.08314414314305227
+aus1986adb27b,1985-10-14,1985,10,day,NA,NA
+aus1986adb27com,1985-10-14,1985,10,day,0.211,0.08314414314305227
+aus1986adb28a,1985-10-15,1985,10,night,0,0
+aus1986adb28b,1985-10-16,1985,10,day,0,0
+aus1986adb29,1985-10-17,1985,10,day,0,0
+aus1986adb31,1985-10-18,1985,10,day,0,0
+aus1986adb32,1985-10-18,1985,10,night,0,0
+aus1986adb33,1985-10-19,1985,10,night,0,0
+aus1986adb34a,1985-10-20,1985,10,night,0,0
+aus1986adb34b,1985-10-20,1985,10,night,NA,NA
+aus1986adb35a,1985-10-22,1985,10,day,0.08,0.03342375548694973
+aus1986adb35b,1985-10-22,1985,10,day,0,0
+aus1986adb36,1985-10-22,1985,10,night,0,0
+aus1986adb38,1985-10-23,1985,10,night,0,0
+aus1986adb39,1985-10-24,1985,10,day,0,0
+aus1986adb40,1985-10-25,1985,10,night,0,0
+aus1986adb41,1985-10-26,1985,10,night,1.884,0.45999525604739144
+aus1986adb43,1985-11-25,1985,11,day,0,0
+aus1987ana65,1987-03-07,1987,3,day,0.045,0.01911629044707278
+aus1987ana66,1987-03-07,1987,3,night,15.585,1.219715475855501
+aus1987ana69,1987-03-09,1987,3,day,0,0
+aus1987ana70a,1987-03-09,1987,3,day,0.185,0.07371835034612269
+aus1987ana70b,1987-03-09,1987,3,night,NA,NA
+aus1987ana71,1987-03-09,1987,3,night,19.912,1.3203955702364691
+aus1987ana72,1987-03-12,1987,3,day,0,0
+aus1987ana73,1987-03-12,1987,3,night,26.95,1.446381812222442
+aus1987ana74,1987-03-14,1987,3,day,0.777,0.24968742780530156
+aus1987ana75,1987-03-14,1987,3,night,17.631,1.2702361658370123
+aus1987ana78,1987-03-16,1987,3,day,7.762,0.9426032488421566
+aus1987ana80,1987-03-16,1987,3,day,0,0
+aus1987ana81,1987-03-16,1987,3,night,10.327,1.0541149005105859
+aus1987ana82a,1987-03-16,1987,3,night,2.383,0.5293019977879806
+aus1987ana82b,1987-03-16,1987,3,night,NA,NA
+aus1987ana83,1987-03-17,1987,3,night,0.127,0.05192391604610654
+aus1987ana84,1987-03-18,1987,3,night,0.554,0.19145101446489546
+aus1987ana85,1987-03-18,1987,3,night,2.729,0.5715923833613072
+aus1987ana86a,1987-03-19,1987,3,day,0.161,0.06483221973857385
+aus1987ana86b,1987-03-19,1987,3,day,0.17,0.06818586174616162
+aus1987ana87a,1987-03-19,1987,3,day,0,0
+aus1987ana87b,1987-03-19,1987,3,night,NA,NA
+aus1987ana87com,1987-03-19,1987,3,day,0.357,0.13257984765973704
+aus1987ana88a,1987-03-20,1987,3,day,0.163,0.06557971472844842
+aus1987ana88b,1987-03-20,1987,3,day,NA,NA
+aus1987ana88com,1987-03-20,1987,3,day,0.163,0.06557971472844842
+aus1987ana89,1987-03-20,1987,3,day,0,0
+aus1987ana90,1987-03-20,1987,3,night,33.777,1.5412921153423693
+aus1987ana91,1987-03-21,1987,3,day,1.013,0.3038437748886545
+aus1987ana92,1987-03-21,1987,3,night,0.607,0.20601587676334454
+aus1987ana93,1987-03-22,1987,3,night,0.183,0.07298474462793038
+aus1987ana94,1987-03-23,1987,3,day,0,0
+aus1987ana95a,1987-03-23,1987,3,day,0,0
+aus1987ana95b,1987-03-23,1987,3,day,NA,NA
+aus1987ana96,1987-03-23,1987,3,night,8.331,0.969928189428116
+aus1992fis11,1992-01-26,1992,1,day,0,0
+aus1992fis16,1992-01-27,1992,1,day,0,0
+aus1992fis18,1992-01-28,1992,1,day,0,0
+aus1992fis22,1992-01-29,1992,1,day,0,0
+aus1992fis27,1992-01-30,1992,1,day,0,0
+aus1992fis28,1992-01-30,1992,1,day,0,0
+aus1992fis32,1992-01-31,1992,1,day,0,0
+aus1992fis32e,1992-01-31,1992,1,day,NA,NA
+aus1992fis33,1992-01-31,1992,1,day,0,0
+aus1992fis33e,1992-01-31,1992,1,day,NA,NA
+aus1992fis36,1992-02-01,1992,2,day,0,0
+aus1992fis36e,1992-02-01,1992,2,day,NA,NA
+aus1992fis37,1992-02-01,1992,2,day,0,0
+aus1992fis37e,1992-02-01,1992,2,day,NA,NA
+aus1992fis38,1992-02-02,1992,2,day,0,0
+aus1992fis39,1992-02-02,1992,2,day,0,0
+aus1992fis39e,1992-02-02,1992,2,night,NA,NA
+aus1992fis43,1992-02-05,1992,2,day,0,0
+aus1992fis45,1992-02-05,1992,2,day,0,0
+aus1992fis45e,1992-02-05,1992,2,night,NA,NA
+aus1992fis47,1992-02-06,1992,2,day,0,0
+aus1992fis47e,1992-02-06,1992,2,day,NA,NA
+aus1992fis51,1992-02-08,1992,2,day,0,0
+aus1992fis51e,1992-02-08,1992,2,night,NA,NA
+aus1992fis52,1992-02-08,1992,2,day,0,0
+aus1992fis56,1992-02-09,1992,2,day,0,0
+aus1992fis56e,1992-02-09,1992,2,day,NA,NA
+aus1992fis58,1992-02-10,1992,2,day,0,0
+aus1992fis61,1992-02-10,1992,2,day,0,0
+aus1992fis61e,1992-02-10,1992,2,night,NA,NA
+aus1992fis67,1992-02-11,1992,2,day,0,0
+aus1992fis9,1992-01-25,1992,1,day,0,0
+aus1999mer1,1999-08-04,1999,8,night,NA,NA
+aus1999mer10,1999-08-11,1999,8,night,NA,NA
+aus1999mer11,1999-08-11,1999,8,day,NA,NA
+aus1999mer12,1999-08-18,1999,8,night,NA,NA
+aus1999mer13,1999-08-18,1999,8,night,NA,NA
+aus1999mer14,1999-08-18,1999,8,night,NA,NA
+aus1999mer15,1999-08-20,1999,8,day,NA,NA
+aus1999mer16,1999-08-20,1999,8,night,NA,NA
+aus1999mer17,1999-08-20,1999,8,day,NA,NA
+aus1999mer18,1999-08-30,1999,8,night,NA,NA
+aus1999mer2,1999-08-04,1999,8,night,NA,NA
+aus1999mer6,1999-08-07,1999,8,night,NA,NA
+aus1999mer7,1999-08-07,1999,8,night,NA,NA
+aus1999mer8,1999-08-08,1999,8,night,NA,NA
+aus1999mer9,1999-08-07,1999,8,night,NA,NA
+aus2001kac1,2001-01-14,2001,1,day,0.069,0.028977705208777998
+aus2001kac10,2001-01-16,2001,1,day,0.143,0.05804623039528174
+aus2001kac11,2001-01-16,2001,1,day,4.41,0.7331972651065695
+aus2001kac12,2001-01-16,2001,1,day,8.369,0.971693238949861
+aus2001kac13,2001-01-17,2001,1,day,14.615,1.1935419885662175
+aus2001kac14,2001-01-17,2001,1,day,1.841,0.45347123372293585
+aus2001kac15,2001-01-17,2001,1,day,2.674,0.5651391519697895
+aus2001kac16,2001-01-17,2001,1,day,10.1,1.0453229787866574
+aus2001kac17,2001-01-17,2001,1,day,0.059,0.024895960107484977
+aus2001kac18,2001-01-18,2001,1,day,0.046,0.01953168453125545
+aus2001kac19,2001-01-18,2001,1,day,1.182,0.33885474625232304
+aus2001kac2,2001-01-14,2001,1,night,0,0
+aus2001kac20,2001-01-18,2001,1,day,7.183,0.9129125511760969
+aus2001kac21,2001-01-18,2001,1,day,1.171,0.3366598234544201
+aus2001kac22,2001-01-18,2001,1,day,0.073,0.030599721965951062
+aus2001kac23,2001-01-18,2001,1,night,0.112,0.0461047872460387
+aus2001kac26,2001-01-19,2001,1,day,0,0
+aus2001kac27,2001-01-19,2001,1,day,0,0
+aus2001kac28,2001-01-19,2001,1,day,0.334,0.12515582958053018
+aus2001kac29,2001-01-19,2001,1,day,0,0
+aus2001kac3,2001-01-14,2001,1,day,7.656,0.9373172477624941
+aus2001kac30,2001-01-20,2001,1,day,0,0
+aus2001kac31,2001-01-20,2001,1,day,0,0
+aus2001kac32,2001-01-20,2001,1,day,6.689,0.8858698609039064
+aus2001kac34a,2001-01-21,2001,1,day,0.064,0.0269416279590294
+aus2001kac34b,2001-01-21,2001,1,day,0.096,0.039810554148350386
+aus2001kac34com,2001-01-21,2001,1,day,0.168,0.06744284277638066
+aus2001kac35,2001-01-21,2001,1,day,0.307,0.11627558758054428
+aus2001kac36,2001-01-21,2001,1,day,0.043,0.018284308426530838
+aus2001kac37,2001-01-21,2001,1,day,0.133,0.05422990986339725
+aus2001kac38,2001-01-21,2001,1,day,0.25,0.09691001300805642
+aus2001kac39,2001-01-21,2001,1,day,1.225,0.3473300153169504
+aus2001kac4,2001-01-15,2001,1,day,4.55,0.7442929831226762
+aus2001kac40,2001-01-21,2001,1,day,1.862,0.45666962942375755
+aus2001kac5,2001-01-15,2001,1,day,4.994,0.777716738609626
+aus2001kac6,2001-01-15,2001,1,day,1.275,0.3569814009931312
+aus2001kac7,2001-01-15,2001,1,day,2.288,0.5169318088680128
+aus2001kac8,2001-01-15,2001,1,day,1.41,0.3820170425748684
+aus2001kac9,2001-01-16,2001,1,day,0.253,0.09795107099415004
+aus2006bro10,2006-01-21,2006,1,day,0,0
+aus2006bro101,2006-02-19,2006,2,night,0.22,0.08635983067474821
+aus2006bro102,2006-02-20,2006,2,day,0.04,0.01703333929878037
+aus2006bro104,2006-02-20,2006,2,day,251.984,2.403093055043637
+aus2006bro105,2006-02-20,2006,2,day,0.043,0.018284308426530838
+aus2006bro11,2006-01-22,2006,1,day,2.45,0.5378190950732742
+aus2006bro110,2006-02-23,2006,2,day,0.031,0.01325866528351651
+aus2006bro112,2006-02-25,2006,2,day,0,0
+aus2006bro114,2006-02-26,2006,2,day,0.207,0.08170727009734924
+aus2006bro115,2006-02-26,2006,2,day,0,0
+aus2006bro116,2006-02-26,2006,2,night,1.473,0.3932241163612973
+aus2006bro117,2006-02-26,2006,2,night,3.052,0.6076694366882428
+aus2006bro119,2006-02-27,2006,2,day,182.634,2.2639530943234485
+aus2006bro120,2006-02-27,2006,2,day,0.811,0.25791845031405836
+aus2006bro122,2006-02-27,2006,2,day,2.338,0.523486332343228
+aus2006bro17,2006-01-23,2006,1,day,23.12,1.3823773034681137
+aus2006bro18,2006-01-23,2006,1,day,2.132,0.49582175338590584
+aus2006bro19,2006-01-23,2006,1,day,0.263,0.10140335055533072
+aus2006bro20,2006-01-23,2006,1,day,0.122,0.04999285692014256
+aus2006bro31,2006-01-27,2006,1,day,0,0
+aus2006bro34,2006-01-27,2006,1,night,0.733,0.238798562713917
+aus2006bro37,2006-01-28,2006,1,day,3.625,0.6651117370750514
+aus2006bro38,2006-01-29,2006,1,day,2.287,0.5167997040816243
+aus2006bro39,2006-01-29,2006,1,night,0.151,0.06107532362979181
+aus2006bro4,2006-01-19,2006,1,day,0,0
+aus2006bro42,2006-01-30,2006,1,day,4.089,0.7066324508732947
+aus2006bro43,2006-01-30,2006,1,day,0.187,0.07445071895459124
+aus2006bro44,2006-01-30,2006,1,night,0.014,0.006037954997317177
+aus2006bro5,2006-01-20,2006,1,day,0.092,0.038222638368718455
+aus2006bro50,2006-02-02,2006,2,night,1.387,0.37785241900675454
+aus2006bro51,2006-02-03,2006,2,day,0.044,0.01870049866624337
+aus2006bro52,2006-02-04,2006,2,day,0.18,0.07188200730612536
+aus2006bro53,2006-02-04,2006,2,night,0.11,0.04532297878665747
+aus2006bro54,2006-02-05,2006,2,day,0,0
+aus2006bro55,2006-02-05,2006,2,day,0.166,0.06669855042299526
+aus2006bro59,2006-02-05,2006,2,day,0,0
+aus2006bro61,2006-02-08,2006,2,day,0,0
+aus2006bro66,2006-02-08,2006,2,night,0.436,0.15715443990628153
+aus2006bro69,2006-02-09,2006,2,day,0.774,0.2489536154957076
+aus2006bro72,2006-02-10,2006,2,day,1.638,0.4212747912103465
+aus2006bro77,2006-02-10,2006,2,day,0.435,0.15685190107001115
+aus2006bro79,2006-02-10,2006,2,night,0.6,0.20411998265592482
+aus2006bro80,2006-02-11,2006,2,day,0.013,0.005609445360280386
+aus2006bro81,2006-02-11,2006,2,day,0.008,0.0034605321095064895
+aus2006bro87,2006-02-17,2006,2,day,0,0
+aus2006bro93,2006-02-17,2006,2,night,0.201,0.07954300740290607
+aus2006bro97,2006-02-18,2006,2,day,0.014,0.006037954997317177
+aus2006bro98,2006-02-19,2006,2,day,0.026,0.011147360775797477
+bas1982sgz108aa,1981-11-29,1981,11,day,0,0
+bas1982sgz108ab,1981-11-29,1981,11,day,0,0
+bas1982sgz108ba,1981-11-29,1981,11,day,NA,NA
+bas1982sgz108bb,1981-11-29,1981,11,day,NA,NA
+bas1982sgz118a,1981-11-30,1981,11,day,0.021,0.009025742086910206
+bas1982sgz118b,1981-11-30,1981,11,day,0,0
+bas1982sgz127a,1981-11-30,1981,11,day,0.258,0.09968064110925012
+bas1982sgz127b,1981-11-30,1981,11,day,1.358,0.37254380075907034
+bas1982sgz132a,1981-11-30,1981,11,night,0.037,0.01577875638904093
+bas1982sgz132b,1981-11-30,1981,11,night,0.268,0.10311925354571388
+bas1982sgz136,1981-12-01,1981,12,night,0.118,0.04844180355040444
+bas1982sgz141a,1981-12-01,1981,12,day,2.957,0.5973660502660276
+bas1982sgz141b,1981-12-01,1981,12,day,0,0
+bas1982sgz147aa,1981-12-01,1981,12,day,163.876,2.2171574426297203
+bas1982sgz147ab,1981-12-01,1981,12,day,56.603,1.7604451023384486
+bas1982sgz147ba,1981-12-01,1981,12,day,NA,NA
+bas1982sgz147bb,1981-12-01,1981,12,day,NA,NA
+bas1982sgz157aa,1981-12-02,1981,12,day,0.464,0.16554107672237303
+bas1982sgz157ab,1981-12-02,1981,12,day,0,0
+bas1982sgz157ba,1981-12-02,1981,12,day,NA,NA
+bas1982sgz157bb,1981-12-02,1981,12,day,NA,NA
+bas1982sgz159a,1981-12-02,1981,12,day,0.227,0.08884456272700426
+bas1982sgz159b,1981-12-02,1981,12,day,0.551,0.19061179781360496
+bas1982sgz164a,1981-12-03,1981,12,night,5.6,0.8195439355418687
+bas1982sgz164b,1981-12-03,1981,12,night,5.648,0.8226910107760546
+bas1982sgz169a,1981-12-03,1981,12,day,1.14,0.33041377334919075
+bas1982sgz169b,1981-12-03,1981,12,day,0.269,0.10346162209470482
+bas1982sgz174a,1981-12-03,1981,12,day,0,0
+bas1982sgz174b,1981-12-03,1981,12,day,0,0
+bas1982sgz179a,1981-12-03,1981,12,day,0.126,0.05153839051532738
+bas1982sgz179b,1981-12-03,1981,12,day,0,0
+bas1982sgz184aa,1981-12-04,1981,12,day,45.179,1.6644445240947097
+bas1982sgz184ab,1981-12-04,1981,12,day,3.687,0.6708949535202101
+bas1982sgz184ba,1981-12-04,1981,12,day,NA,NA
+bas1982sgz184bb,1981-12-04,1981,12,day,NA,NA
+bas1982sgz195aa,1981-12-05,1981,12,day,1.421,0.38399478944173293
+bas1982sgz195ab,1981-12-05,1981,12,day,1.762,0.4412236742426125
+bas1982sgz195ba,1981-12-05,1981,12,day,NA,NA
+bas1982sgz195bb,1981-12-05,1981,12,day,NA,NA
+bas1982sgz197a,1981-12-05,1981,12,day,0.021,0.009025742086910206
+bas1982sgz197b,1981-12-05,1981,12,day,0,0
+bas1982sgz203a,1981-12-06,1981,12,night,0.553,0.19117145572855848
+bas1982sgz203b,1981-12-06,1981,12,night,0,0
+bas1982sgz207a,1981-12-06,1981,12,day,4.868,0.7684901051712452
+bas1982sgz207b,1981-12-06,1981,12,day,7.587,0.9338414628987213
+bas1982sgz210a,1981-12-06,1981,12,day,166.184,2.2231947118338233
+bas1982sgz210b,1981-12-06,1981,12,day,42.067,1.634144620338793
+bas1982sgz215a,1981-12-06,1981,12,day,0,0
+bas1982sgz215b,1981-12-06,1981,12,day,0.943,0.28847280059978253
+bas1982sgz219a,1981-12-07,1981,12,night,2.053,0.4847268042986618
+bas1982sgz219b,1981-12-07,1981,12,night,3.728,0.6746774678731989
+bas1982sgz225aa,1981-12-07,1981,12,day,0.335,0.125481265700594
+bas1982sgz225ab,1981-12-07,1981,12,day,1.293,0.36040405472993886
+bas1982sgz225ba,1981-12-07,1981,12,day,NA,NA
+bas1982sgz225bb,1981-12-07,1981,12,day,NA,NA
+bas1982sgz225ca,1981-12-07,1981,12,day,NA,NA
+bas1982sgz225cb,1981-12-07,1981,12,day,NA,NA
+bas1982sgz225da,1981-12-07,1981,12,day,NA,NA
+bas1982sgz225db,1981-12-09,1981,12,day,NA,NA
+bas1982sgz235aa,1981-12-09,1981,12,night,1.405,0.3811150807098506
+bas1982sgz235ab,1981-12-09,1981,12,night,0.334,0.12515582958053018
+bas1982sgz235ba,1981-12-09,1981,12,day,NA,NA
+bas1982sgz235bb,1981-12-09,1981,12,day,NA,NA
+bas1982sgz235ca,1981-12-09,1981,12,day,NA,NA
+bas1982sgz235cb,1981-12-09,1981,12,day,NA,NA
+bas1982sgz235da,1981-12-09,1981,12,day,NA,NA
+bas1982sgz235db,1981-12-09,1981,12,day,NA,NA
+bas1982sgz237a,1981-12-09,1981,12,day,0.018,0.007747778000739948
+bas1982sgz237b,1981-12-09,1981,12,day,0,0
+bas1982sgz244a,1981-12-09,1981,12,day,0,0
+bas1982sgz244b,1981-12-09,1981,12,day,0,0
+bas1982sgz248a,1981-12-10,1981,12,night,0.222,0.08707120590653541
+bas1982sgz248b,1981-12-10,1981,12,night,0.948,0.2895889525425967
+bas1982sgz252a,1981-12-10,1981,12,day,0,0
+bas1982sgz252b,1981-12-10,1981,12,day,0,0
+bas1982sgz256a,1981-12-10,1981,12,day,0.426,0.15411952551584673
+bas1982sgz256b,1981-12-10,1981,12,day,0,0
+bas1982sgz262aa,1981-12-10,1981,12,day,3.193,0.6225248624035682
+bas1982sgz262ab,1981-12-10,1981,12,day,0,0
+bas1982sgz262ba,1981-12-10,1981,12,day,NA,NA
+bas1982sgz262bb,1981-12-10,1981,12,day,NA,NA
+bas1982sgz262ca,1981-12-10,1981,12,night,NA,NA
+bas1982sgz262cb,1981-12-10,1981,12,night,NA,NA
+bas1982sgz272aa,1981-12-11,1981,12,day,67.009,1.832566389054755
+bas1982sgz272ab,1981-12-11,1981,12,day,0,0
+bas1982sgz272ba,1981-12-11,1981,12,day,NA,NA
+bas1982sgz272bb,1981-12-11,1981,12,day,NA,NA
+bas1982sgz272ca,1981-12-11,1981,12,day,NA,NA
+bas1982sgz272cb,1981-12-11,1981,12,day,NA,NA
+bas1982sgz272da,1981-12-11,1981,12,day,NA,NA
+bas1982sgz272db,1981-12-11,1981,12,day,NA,NA
+bas1982sgz275a,1981-12-12,1981,12,day,0.25,0.09691001300805642
+bas1982sgz275b,1981-12-12,1981,12,day,0.987,0.29819786710981516
+bas1982sgz282a,1981-12-12,1981,12,night,2.719,0.5704261783589726
+bas1982sgz282b,1981-12-12,1981,12,night,0.969,0.2942457161381182
+bas1982sgz287a,1981-12-12,1981,12,day,100.572,2.006774003996861
+bas1982sgz287b,1981-12-12,1981,12,day,399.015,2.6020762770656782
+bas1982sgz293a,1981-12-12,1981,12,day,0.154,0.06220580881971259
+bas1982sgz293b,1981-12-12,1981,12,day,15.455,1.2162978866303924
+bas1982sgz300a,1981-12-13,1981,12,night,5.085,0.7842605825660838
+bas1982sgz300b,1981-12-13,1981,12,night,0.582,0.1992064791616577
+bas1982sgz305aa,1981-12-13,1981,12,day,0.221,0.0867156639448825
+bas1982sgz305ab,1981-12-13,1981,12,day,0,0
+bas1982sgz305ba,1981-12-13,1981,12,day,NA,NA
+bas1982sgz305bb,1981-12-13,1981,12,day,NA,NA
+bas1982sgz305ca,1981-12-13,1981,12,day,NA,NA
+bas1982sgz305cb,1981-12-13,1981,12,day,NA,NA
+bas1982sgz314aa,1981-12-14,1981,12,day,0,0
+bas1982sgz314ab,1981-12-14,1981,12,day,0,0
+bas1982sgz314ba,1981-12-13,1981,12,day,0,0
+bas1982sgz314bb,1981-12-13,1981,12,day,0,0
+bas1982sgz314ca,1981-12-13,1981,12,day,NA,NA
+bas1982sgz314cb,1981-12-13,1981,12,day,NA,NA
+bas1982sgz314da,1981-12-13,1981,12,night,NA,NA
+bas1982sgz314db,1981-12-13,1981,12,night,NA,NA
+bas1982sgz316a,1981-12-14,1981,12,day,0,0
+bas1982sgz316b,1981-12-14,1981,12,day,0,0
+bas1982sgz324,1981-12-14,1981,12,night,0.131,0.05346260492545529
+bas1982sgz329a,1981-12-15,1981,12,day,0.073,0.030599721965951062
+bas1982sgz329b,1981-12-15,1981,12,day,1.196,0.34163233577805424
+bas1982sgz335a,1981-12-15,1981,12,day,53.852,1.7391924673369554
+bas1982sgz335b,1981-12-15,1981,12,day,2250.873,3.352543893748365
+bas1982sgz340a,1981-12-16,1981,12,night,10.989,1.0787829601969634
+bas1982sgz340b,1981-12-16,1981,12,night,348.81,2.54383222047354
+bas1982sgz348aa,1981-12-16,1981,12,day,1401.963,3.147046217637256
+bas1982sgz348ab,1981-12-16,1981,12,day,1515.474,3.1808349687163644
+bas1982sgz348ba,1981-12-16,1981,12,day,NA,NA
+bas1982sgz348bb,1981-12-16,1981,12,day,NA,NA
+bas1982sgz348ca,1981-12-16,1981,12,day,NA,NA
+bas1982sgz348cb,1981-12-16,1981,12,day,NA,NA
+bas1982sgz348da,1981-12-16,1981,12,day,NA,NA
+bas1982sgz348db,1981-12-16,1981,12,day,NA,NA
+bas1982sgz34aa,1981-11-24,1981,11,night,201.966,2.3074232928438265
+bas1982sgz34ab,1981-11-24,1981,11,night,223.879,2.351948901367405
+bas1982sgz34ba,1981-11-24,1981,11,day,NA,NA
+bas1982sgz34bb,1981-11-24,1981,11,day,NA,NA
+bas1982sgz358aa,1981-12-17,1981,12,day,8445.385,3.9266708733906617
+bas1982sgz358ab,1981-12-17,1981,12,day,1286.406,3.1097155288420564
+bas1982sgz358ba,1981-12-17,1981,12,day,NA,NA
+bas1982sgz358bb,1981-12-17,1981,12,day,NA,NA
+bas1982sgz358ca,1981-12-17,1981,12,day,NA,NA
+bas1982sgz358cb,1981-12-17,1981,12,day,NA,NA
+bas1982sgz358da,1981-12-17,1981,12,night,NA,NA
+bas1982sgz358db,1981-12-17,1981,12,night,NA,NA
+bas1982sgz36,1981-11-25,1981,11,night,180.216,2.258196539946435
+bas1982sgz360a,1981-12-17,1981,12,day,0.131,0.05346260492545529
+bas1982sgz360b,1981-12-17,1981,12,day,0,0
+bas1982sgz366a,1981-12-17,1981,12,day,0.087,0.03622954408629452
+bas1982sgz366b,1981-12-17,1981,12,day,0,0
+bas1982sgz373a,1981-12-18,1981,12,day,0,0
+bas1982sgz373b,1981-12-18,1981,12,day,0,0
+bas1982sgz378a,1981-12-18,1981,12,day,0.125,0.05115252244738129
+bas1982sgz378b,1981-12-18,1981,12,day,0,0
+bas1982sgz384a,1981-12-18,1981,12,day,0.127,0.05192391604610654
+bas1982sgz384b,1981-12-18,1981,12,day,0,0
+bas1982sgz391aa,1981-12-19,1981,12,day,0.407,0.14829409743474573
+bas1982sgz391ab,1981-12-19,1981,12,day,0.846,0.26623169668989327
+bas1982sgz391ba,1981-12-19,1981,12,day,NA,NA
+bas1982sgz391bb,1981-12-19,1981,12,day,NA,NA
+bas1982sgz391ca,1981-12-19,1981,12,day,NA,NA
+bas1982sgz391cb,1981-12-19,1981,12,day,NA,NA
+bas1982sgz391da,1981-12-19,1981,12,day,NA,NA
+bas1982sgz391db,1981-12-19,1981,12,day,NA,NA
+bas1982sgz41a,1981-11-25,1981,11,day,12.151,1.1189587777664338
+bas1982sgz41b,1981-11-25,1981,11,day,0,0
+bas1982sgz46a,1981-11-25,1981,11,day,0.56,0.1931245983544616
+bas1982sgz46b,1981-11-25,1981,11,day,0,0
+bas1982sgz54a,1981-11-26,1981,11,night,17.356,1.2637780490226218
+bas1982sgz54b,1981-11-26,1981,11,night,15.659,1.221648928192229
+bas1982sgz58aa,1981-11-26,1981,11,day,2.909,0.5920656704322471
+bas1982sgz58ab,1981-11-26,1981,11,day,5.646,0.8225603369426923
+bas1982sgz58ba,1981-11-26,1981,11,day,NA,NA
+bas1982sgz58bb,1981-11-26,1981,11,day,NA,NA
+bas1982sgz68aa,1981-11-27,1981,11,day,0.128,0.05230909964732353
+bas1982sgz68ab,1981-11-27,1981,11,day,0.509,0.1786892397755898
+bas1982sgz68ba,1981-11-27,1981,11,night,NA,NA
+bas1982sgz68bb,1981-11-27,1981,11,night,NA,NA
+bas1982sgz70a,1981-11-27,1981,11,day,0,0
+bas1982sgz70b,1981-11-27,1981,11,day,0,0
+bas1982sgz76a,1981-11-27,1981,11,day,0.049,0.02077548819355783
+bas1982sgz76b,1981-11-27,1981,11,day,0,0
+bas1982sgz81a,1981-11-28,1981,11,night,219.846,2.34408953765074
+bas1982sgz81b,1981-11-28,1981,11,night,265.412,2.4255537828721727
+bas1982sgz86a,1981-11-28,1981,11,day,0,0
+bas1982sgz86b,1981-11-28,1981,11,day,0,0
+bas1982sgz92a,1981-11-28,1981,11,day,0.698,0.22993768590793384
+bas1982sgz92b,1981-11-28,1981,11,day,0.782,0.25090769970085597
+bas1982sgz97aa,1981-11-29,1981,11,night,57.819,1.7695176366590875
+bas1982sgz97ab,1981-11-29,1981,11,night,0.385,0.14144977340046735
+bas1982sgz97ba,1981-11-29,1981,11,day,NA,NA
+bas1982sgz97bb,1981-11-29,1981,11,day,NA,NA
+bas1985sib0a,1985-01-15,1985,1,night,0.04,0.01703333929878037
+bas1985sib0b,1985-01-15,1985,1,night,NA,NA
+bas1985sib0com,1985-01-15,1985,1,night,0.04,0.01703333929878037
+bas1985sib10,1985-01-19,1985,1,day,24.837,1.412242085209914
+bas1985sib11a,1985-01-19,1985,1,day,11.834,1.1083620349551717
+bas1985sib11b,1985-01-19,1985,1,day,NA,NA
+bas1985sib11com,1985-01-19,1985,1,day,11.898,1.1105223726013231
+bas1985sib12a,1985-01-20,1985,1,night,0.073,0.030599721965951062
+bas1985sib12b,1985-01-20,1985,1,night,NA,NA
+bas1985sib12com,1985-01-20,1985,1,night,0.086,0.03582982525282818
+bas1985sib13,1985-01-20,1985,1,day,0.346,0.12904505988795806
+bas1985sib14,1985-01-20,1985,1,day,0,0
+bas1985sib15a,1985-01-21,1985,1,day,0.073,0.030599721965951062
+bas1985sib15b,1985-01-21,1985,1,day,NA,NA
+bas1985sib15com,1985-01-21,1985,1,day,0.073,0.030599721965951062
+bas1985sib16a,1985-01-21,1985,1,night,0.82,0.26007138798507473
+bas1985sib16b,1985-01-21,1985,1,day,NA,NA
+bas1985sib16com,1985-01-21,1985,1,night,2.57,0.5526682161121932
+bas1985sib17a,1985-01-21,1985,1,day,0.147,0.059563417901267686
+bas1985sib17b,1985-01-21,1985,1,day,NA,NA
+bas1985sib17com,1985-01-21,1985,1,day,0.147,0.059563417901267686
+bas1985sib18a,1985-01-21,1985,1,day,0.308,0.11660774398824847
+bas1985sib18b,1985-01-21,1985,1,day,NA,NA
+bas1985sib19a,1985-01-22,1985,1,night,0,0
+bas1985sib19b,1985-01-22,1985,1,night,NA,NA
+bas1985sib19com,1985-01-22,1985,1,night,0.031,0.01325866528351651
+bas1985sib1a,1985-01-16,1985,1,day,139.912,2.148947978999141
+bas1985sib1b,1985-01-16,1985,1,day,NA,NA
+bas1985sib1com,1985-01-16,1985,1,day,141.569,2.15402510340831
+bas1985sib20a,1985-01-22,1985,1,day,0.047,0.019946681678842303
+bas1985sib20b,1985-01-22,1985,1,day,NA,NA
+bas1985sib20com,1985-01-22,1985,1,day,0.047,0.019946681678842303
+bas1985sib21,1985-01-22,1985,1,day,0,0
+bas1985sib22a,1985-01-22,1985,1,day,0.731,0.23829706787539381
+bas1985sib22b,1985-01-22,1985,1,day,NA,NA
+bas1985sib22com,1985-01-22,1985,1,day,1.154,0.33324569896196277
+bas1985sib23a,1985-01-23,1985,1,night,1.348,0.37069809257557684
+bas1985sib23b,1985-01-23,1985,1,night,NA,NA
+bas1985sib23com,1985-01-23,1985,1,night,1.441,0.38756777941718856
+bas1985sib24a,1985-01-23,1985,1,day,0.068,0.028571252692537637
+bas1985sib24b,1985-01-23,1985,1,day,NA,NA
+bas1985sib24com,1985-01-23,1985,1,day,0.125,0.05115252244738129
+bas1985sib25,1985-01-23,1985,1,day,156.683,2.1977848740253925
+bas1985sib26a,1985-01-24,1985,1,night,0.095,0.03941411917613713
+bas1985sib26b,1985-01-24,1985,1,night,NA,NA
+bas1985sib27,1985-01-24,1985,1,day,0.484,0.17143390094300825
+bas1985sib28,1985-01-24,1985,1,day,0.023,0.009875633712160119
+bas1985sib29,1985-01-24,1985,1,day,1.109,0.3240765797394864
+bas1985sib2a,1985-01-16,1985,1,day,16.385,1.2401746950192774
+bas1985sib2b,1985-01-16,1985,1,day,NA,NA
+bas1985sib2com,1985-01-16,1985,1,day,16.916,1.2532410537726502
+bas1985sib30a,1985-01-25,1985,1,night,0.032,0.013679697291192559
+bas1985sib30b,1985-01-25,1985,1,night,NA,NA
+bas1985sib30com,1985-01-25,1985,1,night,0.052,0.02201573981772028
+bas1985sib31a,1985-01-25,1985,1,day,0,0
+bas1985sib31b,1985-01-25,1985,1,day,NA,NA
+bas1985sib32,1985-01-26,1985,1,day,8.579,0.9813201732591074
+bas1985sib33a,1985-01-26,1985,1,night,1.799,0.44700289846616237
+bas1985sib33b,1985-01-26,1985,1,day,NA,NA
+bas1985sib33com,1985-01-26,1985,1,night,1.799,0.44700289846616237
+bas1985sib34a,1985-01-27,1985,1,day,19.781,1.3176664423565012
+bas1985sib34b,1985-01-27,1985,1,day,NA,NA
+bas1985sib34com,1985-01-27,1985,1,day,19.807,1.3182094671713804
+bas1985sib35a,1985-01-27,1985,1,day,0.965,0.2933625547114455
+bas1985sib35b,1985-01-27,1985,1,day,NA,NA
+bas1985sib35com,1985-01-27,1985,1,day,1.001,0.30124708863621136
+bas1985sib36a,1985-01-28,1985,1,day,0.14,0.056904851336472634
+bas1985sib36b,1985-01-28,1985,1,day,NA,NA
+bas1985sib36com,1985-01-28,1985,1,day,0.14,0.056904851336472634
+bas1985sib37a,1985-01-28,1985,1,day,7.285,0.9182925127553555
+bas1985sib37b,1985-01-28,1985,1,day,NA,NA
+bas1985sib37com,1985-01-28,1985,1,day,7.45,0.9268567089496923
+bas1985sib38a,1985-01-29,1985,1,day,60.582,1.7894537893878852
+bas1985sib38b,1985-01-29,1985,1,day,NA,NA
+bas1985sib38com,1985-01-29,1985,1,day,60.612,1.789665306746111
+bas1985sib39a,1985-01-30,1985,1,day,139.726,2.148374343443015
+bas1985sib39b,1985-01-30,1985,1,day,NA,NA
+bas1985sib39com,1985-01-30,1985,1,day,142.23,2.156033991923621
+bas1985sib3a,1985-01-17,1985,1,night,7.979,0.9532279715598538
+bas1985sib3b,1985-01-17,1985,1,night,NA,NA
+bas1985sib3com,1985-01-17,1985,1,night,8.155,0.9616583486377153
+bas1985sib40a,1985-01-31,1985,1,night,0.456,0.16316137497701835
+bas1985sib40b,1985-01-31,1985,1,night,NA,NA
+bas1985sib40com,1985-01-31,1985,1,night,0.85,0.2671717284030138
+bas1985sib41a,1985-01-31,1985,1,day,4.073,0.7052648623174043
+bas1985sib41b,1985-01-31,1985,1,day,NA,NA
+bas1985sib41c,1985-01-31,1985,1,day,NA,NA
+bas1985sib41com,1985-01-31,1985,1,day,4.992,0.7775718046914101
+bas1985sib41d,1985-01-31,1985,1,day,NA,NA
+bas1985sib42a,1985-02-02,1985,2,day,0.035,0.014940349792936524
+bas1985sib42b,1985-02-02,1985,2,night,NA,NA
+bas1985sib42c,1985-02-02,1985,2,day,NA,NA
+bas1985sib42com,1985-02-02,1985,2,day,0.12,0.04921802267018165
+bas1985sib42d,1985-02-02,1985,2,day,NA,NA
+bas1985sib43,1985-02-02,1985,2,night,0.029,0.012415374762432893
+bas1985sib44a,1985-02-02,1985,2,day,0.034,0.014520538757923713
+bas1985sib44b,1985-02-02,1985,2,day,NA,NA
+bas1985sib44com,1985-02-02,1985,2,day,0.034,0.014520538757923713
+bas1985sib45,1985-02-03,1985,2,night,1.97,0.47275644931721233
+bas1985sib46a,1985-02-03,1985,2,day,25.402,1.4216368267207542
+bas1985sib46b,1985-02-03,1985,2,day,NA,NA
+bas1985sib46com,1985-02-03,1985,2,day,25.449,1.4224092566096984
+bas1985sib47,1985-02-03,1985,2,day,0.624,0.21058602490515652
+bas1985sib48a,1985-02-04,1985,2,night,0,0
+bas1985sib48b,1985-02-04,1985,2,night,NA,NA
+bas1985sib48com,1985-02-04,1985,2,night,0,0
+bas1985sib49a,1985-02-04,1985,2,day,0,0
+bas1985sib49b,1985-02-04,1985,2,day,NA,NA
+bas1985sib49com,1985-02-04,1985,2,day,0.037,0.01577875638904093
+bas1985sib4a,1985-01-17,1985,1,day,0.762,0.24600590407602912
+bas1985sib4b,1985-01-17,1985,1,day,NA,NA
+bas1985sib4com,1985-01-17,1985,1,day,5.231,0.7945577512547615
+bas1985sib5,1985-01-17,1985,1,day,0.499,0.1758016328482795
+bas1985sib50a,1985-02-05,1985,2,night,0.015,0.006466042249231681
+bas1985sib50b,1985-02-05,1985,2,night,NA,NA
+bas1985sib50com,1985-02-05,1985,2,night,0.015,0.006466042249231681
+bas1985sib51a,1985-02-05,1985,2,day,0,0
+bas1985sib51b,1985-02-05,1985,2,day,NA,NA
+bas1985sib51com,1985-02-05,1985,2,day,0,0
+bas1985sib52,1985-02-05,1985,2,day,0,0
+bas1985sib53a,1985-02-06,1985,2,day,0.55,0.1903316981702915
+bas1985sib53b,1985-02-06,1985,2,day,NA,NA
+bas1985sib6a,1985-01-16,1985,1,night,5.184,0.7912694809102685
+bas1985sib6b,1985-01-16,1985,1,night,NA,NA
+bas1985sib7,1985-01-18,1985,1,day,0.026,0.011147360775797477
+bas1985sib8a,1985-01-18,1985,1,day,21.186,1.3460790086529422
+bas1985sib8b,1985-01-18,1985,1,day,NA,NA
+bas1985sib8com,1985-01-18,1985,1,day,21.216,1.3466658667422011
+bas1985sib9a,1985-01-19,1985,1,night,95.746,1.985633017992352
+bas1985sib9b,1985-01-19,1985,1,night,NA,NA
+bas1996bon1,1996-01-04,1996,1,night,0,0
+bas1996bon10,1996-01-07,1996,1,night,15.298,1.2121343134680171
+bas1996bon11,1996-01-07,1996,1,day,0,0
+bas1996bon12,1996-01-07,1996,1,day,0,0
+bas1996bon13,1996-01-07,1996,1,day,0,0
+bas1996bon14,1996-01-08,1996,1,day,0,0
+bas1996bon15,1996-01-08,1996,1,day,0,0
+bas1996bon16,1996-01-09,1996,1,night,0,0
+bas1996bon17,1996-01-09,1996,1,day,0,0
+bas1996bon18,1996-01-09,1996,1,day,0,0
+bas1996bon19,1996-01-09,1996,1,day,0,0
+bas1996bon2,1996-01-05,1996,1,day,0,0
+bas1996bon20,1996-01-09,1996,1,day,0,0
+bas1996bon21,1996-01-10,1996,1,night,0,0
+bas1996bon22,1996-01-10,1996,1,day,0,0
+bas1996bon23,1996-01-12,1996,1,night,0,0
+bas1996bon24,1996-01-13,1996,1,night,8.986,0.9993915617190909
+bas1996bon25,1996-01-14,1996,1,night,0,0
+bas1996bon26,1996-01-13,1996,1,day,0,0
+bas1996bon27,1996-01-15,1996,1,night,0,0
+bas1996bon28,1996-01-14,1996,1,day,50.958,1.7156524255865035
+bas1996bon29,1996-01-18,1996,1,day,0,0
+bas1996bon3,1996-01-05,1996,1,day,0,0
+bas1996bon30,1996-01-20,1996,1,night,0,0
+bas1996bon31,1996-01-19,1996,1,night,0,0
+bas1996bon32,1996-01-21,1996,1,night,84.616,1.9325549336301795
+bas1996bon33,1996-01-21,1996,1,day,0,0
+bas1996bon4,1996-01-05,1996,1,day,45.632,1.6686840423113884
+bas1996bon5,1996-01-06,1996,1,night,0,0
+bas1996bon6,1996-01-06,1996,1,day,0,0
+bas1996bon7,1996-01-06,1996,1,day,0,0
+bas1996bon8,1996-01-06,1996,1,day,0,0
+bas1996bon9,1996-01-07,1996,1,night,0,0
+bas1996rmt136a,1996-01-13,1996,1,night,8.33,0.9698816437464999
+bas1996rmt136b,1996-01-13,1996,1,night,0.25,0.09691001300805642
+bas1996rmt146a,1996-01-14,1996,1,day,525.961,2.7217784745834304
+bas1996rmt146b,1996-01-14,1996,1,day,145.232,2.1650424198360754
+bas1996rmt152a,1996-01-14,1996,1,night,188.065,2.2766111390315453
+bas1996rmt152b,1996-01-14,1996,1,night,594.663,2.775000624448542
+bas1996rmt163,1996-01-15,1996,1,day,390.442,2.592667421704326
+bas1996rmt193a,1996-01-20,1996,1,day,33.959,1.5435590011736546
+bas1996rmt193b,1996-01-20,1996,1,day,1.664,0.4255342204982634
+bas1996rmt206a,1996-01-21,1996,1,night,148.416,2.174397105776908
+bas1996rmt206b,1996-01-21,1996,1,night,3.033,0.6056282220076186
+bas1996rmt207a,1996-01-21,1996,1,day,0.423,0.15320490008428433
+bas1996rmt207b,1996-01-21,1996,1,day,0,0
+bas1996rmt215a,1996-01-22,1996,1,night,0.829,0.26221370547641687
+bas1996rmt215b,1996-01-22,1996,1,night,3.291,0.6325585145326719
+bas1996rmt345a,1996-01-30,1996,1,night,45.681,1.6691401509258206
+bas1996rmt350a,1996-01-30,1996,1,night,5.364,0.8037301709745439
+bas1996rmt350b,1996-01-30,1996,1,night,0,0
+bas1996rmt355a,1996-01-30,1996,1,day,0.396,0.14488541828714224
+bas1996rmt355b,1996-01-30,1996,1,day,1.315,0.36455099535397195
+bas1997bon1,1996-12-16,1996,12,night,0,0
+bas1997bon10,1996-12-17,1996,12,day,0,0
+bas1997bon11,1996-12-18,1996,12,night,0,0
+bas1997bon12,1996-12-18,1996,12,day,0,0
+bas1997bon13,1996-12-18,1996,12,day,0,0
+bas1997bon14,1996-12-19,1996,12,night,0,0
+bas1997bon16,1996-12-20,1996,12,night,0,0
+bas1997bon17,1996-12-20,1996,12,night,0,0
+bas1997bon18,1996-12-20,1996,12,day,0,0
+bas1997bon19,1996-12-20,1996,12,day,0,0
+bas1997bon2,1996-12-16,1996,12,day,0,0
+bas1997bon20,1996-12-20,1996,12,day,0,0
+bas1997bon21,1996-12-20,1996,12,night,0,0
+bas1997bon22,1996-12-21,1996,12,night,0,0
+bas1997bon23,1996-12-24,1996,12,night,0,0
+bas1997bon24,1996-12-23,1996,12,day,0,0
+bas1997bon25,1996-12-24,1996,12,day,0,0
+bas1997bon26,1996-12-25,1996,12,night,204.607,2.3130378963622578
+bas1997bon27,1996-12-26,1996,12,night,0,0
+bas1997bon28,1996-12-26,1996,12,night,0,0
+bas1997bon29,1996-12-26,1996,12,day,0,0
+bas1997bon3,1996-12-16,1996,12,day,0,0
+bas1997bon30,1996-12-27,1996,12,night,0,0
+bas1997bon31,1996-12-29,1996,12,day,73.813,1.8739770702937149
+bas1997bon32,1996-12-30,1996,12,night,0,0
+bas1997bon33,1996-12-30,1996,12,day,0,0
+bas1997bon34,1996-12-31,1996,12,night,334.106,2.5251822038530674
+bas1997bon35,1996-12-31,1996,12,day,0,0
+bas1997bon36,1997-01-02,1997,1,day,0,0
+bas1997bon4,1996-12-16,1996,12,day,0,0
+bas1997bon5,1996-12-16,1996,12,night,0,0
+bas1997bon6,1996-12-17,1996,12,night,0,0
+bas1997bon7,1996-12-17,1996,12,day,0,0
+bas1997bon8,1996-12-17,1996,12,day,0,0
+bas1997bon9,1996-12-17,1996,12,day,0,0
+bas1997rmt193a,1996-12-24,1996,12,night,1.641,0.42176840120692405
+bas1997rmt193b,1996-12-24,1996,12,night,0.301,0.11427729656158624
+bas1997rmt202a,1996-12-25,1996,12,night,0.433,0.15624619039734447
+bas1997rmt202b,1996-12-25,1996,12,night,0.018,0.007747778000739948
+bas1997rmt227a,1996-12-26,1996,12,night,0.137,0.05576046468773478
+bas1997rmt227b,1996-12-27,1996,12,night,1.823,0.45071087814691935
+bas1997rmt233a,1996-12-27,1996,12,night,0.019,0.008174184006426354
+bas1997rmt233b,1996-12-27,1996,12,night,0.096,0.039810554148350386
+bas1997rmt257a,1996-12-29,1996,12,night,40.048,1.6132920016473284
+bas1997rmt257b,1996-12-30,1996,12,night,0.408,0.14860265480609336
+bas1997rmt273a,1996-12-30,1996,12,night,4.615,0.7493497605974766
+bas1997rmt273b,1996-12-31,1996,12,night,0,0
+bas1997rmt279a,1996-12-31,1996,12,night,47.488,1.6856342710019536
+bas1997rmt279b,1996-12-31,1996,12,night,171.979,2.2379933821016165
+bas1997rmt291a,1997-01-02,1997,1,night,0,0
+bas1997rmt291b,1997-01-02,1997,1,night,0.073,0.030599721965951062
+bas1998bon1,1998-01-16,1998,1,day,0,0
+bas1998bon10,1998-01-19,1998,1,day,0,0
+bas1998bon11,1998-01-19,1998,1,day,0,0
+bas1998bon12,1998-01-19,1998,1,day,0,0
+bas1998bon13,1998-01-19,1998,1,night,0,0
+bas1998bon14,1998-01-20,1998,1,night,0,0
+bas1998bon15,1998-01-20,1998,1,day,0,0
+bas1998bon16,1998-01-20,1998,1,day,0,0
+bas1998bon17,1998-01-20,1998,1,day,0,0
+bas1998bon18,1998-01-21,1998,1,night,0,0
+bas1998bon19,1998-01-21,1998,1,night,0,0
+bas1998bon2,1998-01-17,1998,1,day,0,0
+bas1998bon20,1998-01-21,1998,1,day,0,0
+bas1998bon21,1998-01-21,1998,1,day,0,0
+bas1998bon22,1998-01-21,1998,1,day,0,0
+bas1998bon23,1998-01-25,1998,1,night,162.071,2.2123767344320955
+bas1998bon24,1998-01-24,1998,1,day,43.923,1.6524687511499914
+bas1998bon25,1998-01-25,1998,1,day,0,0
+bas1998bon26,1998-01-26,1998,1,night,4.921,0.7723950610820003
+bas1998bon27,1998-01-27,1998,1,night,245.398,2.3916371783605332
+bas1998bon28,1998-01-26,1998,1,day,0,0
+bas1998bon29,1998-01-27,1998,1,day,22.22,1.365862215402555
+bas1998bon3,1998-01-17,1998,1,day,0,0
+bas1998bon30,1998-01-28,1998,1,night,0,0
+bas1998bon31,1998-01-30,1998,1,day,0,0
+bas1998bon32,1998-01-31,1998,1,night,0,0
+bas1998bon33,1998-02-01,1998,2,night,0,0
+bas1998bon34,1998-01-31,1998,1,day,0,0
+bas1998bon35,1998-02-01,1998,2,day,0,0
+bas1998bon36,1998-02-02,1998,2,night,0,0
+bas1998bon37,1998-02-03,1998,2,night,0,0
+bas1998bon38,1998-02-02,1998,2,day,0,0
+bas1998bon4,1998-01-18,1998,1,night,0,0
+bas1998bon5,1998-01-18,1998,1,day,0,0
+bas1998bon6,1998-01-18,1998,1,day,0,0
+bas1998bon7,1998-01-18,1998,1,day,0,0
+bas1998bon8,1998-01-18,1998,1,day,0,0
+bas1998bon9,1998-01-19,1998,1,night,0,0
+bas1998rmt128a,1998-01-25,1998,1,night,31.966,1.5180662542916077
+bas1998rmt128b,1998-01-25,1998,1,night,1337.885,3.126743276033476
+bas1998rmt136a,1998-01-25,1998,1,night,104.608,2.0236968180487165
+bas1998rmt136b,1998-01-25,1998,1,day,653.583,2.815964722172623
+bas1998rmt145a,1998-01-25,1998,1,night,0.402,0.14674801363063988
+bas1998rmt145b,1998-01-25,1998,1,night,5.8,0.8325089127062363
+bas1998rmt154a,1998-01-26,1998,1,night,831.403,2.9203336367499424
+bas1998rmt154b,1998-01-26,1998,1,night,1.193,0.3410386316775229
+bas1998rmt164a,1998-01-26,1998,1,night,2.97,0.5987905067631151
+bas1998rmt164b,1998-01-27,1998,1,night,1.937,0.4679039465228003
+bas1998rmt173a,1998-01-27,1998,1,night,1054.45,3.0234376642297156
+bas1998rmt173b,1998-01-27,1998,1,night,27.182,1.4499718105668926
+bas1998rmt193a,1998-01-28,1998,1,night,7.356,0.921998431308271
+bas1998rmt193b,1998-01-28,1998,1,night,0.153,0.06182930729469903
+bas1998rmt208a,1998-01-30,1998,1,night,0.173,0.06929801211552926
+bas1998rmt208b,1998-01-30,1998,1,night,0.127,0.05192391604610654
+bas1998rmt217a,1998-01-31,1998,1,night,0.052,0.02201573981772028
+bas1998rmt217b,1998-01-31,1998,1,night,0,0
+bas1998rmt226a,1998-01-31,1998,1,night,0,0
+bas1998rmt226b,1998-02-01,1998,2,night,0,0
+bas1998rmt234a,1998-02-01,1998,2,night,0.051,0.02160271602824219
+bas1998rmt234b,1998-02-01,1998,2,night,0.037,0.01577875638904093
+bas1998rmt245a,1998-02-01,1998,2,night,8.118,0.9598995878659435
+bas1998rmt245b,1998-02-01,1998,2,night,0.888,0.2760019899620501
+bas1998rmt254a,1998-02-02,1998,2,night,0,0
+bas1998rmt254b,1998-02-02,1998,2,night,0.157,0.06333335895174956
+bas1998rmt259a,1998-02-02,1998,2,day,NA,NA
+bas1998rmt259b,1998-02-02,1998,2,day,0,0
+bas1998rmt264a,1998-02-02,1998,2,night,0.018,0.007747778000739948
+bas1998rmt264b,1998-02-02,1998,2,night,0.027,0.011570443597278159
+bas1998rmt272a,1998-02-02,1998,2,night,1.159,0.33425264233423074
+bas1998rmt272b,1998-02-02,1998,2,night,7.114,0.9092350033683074
+bas1999bon1,1998-12-20,1998,12,day,0,0
+bas1999bon10,1998-12-22,1998,12,day,0,0
+bas1999bon11,1998-12-22,1998,12,day,0,0
+bas1999bon12,1998-12-23,1998,12,night,0,0
+bas1999bon13,1998-12-23,1998,12,day,0,0
+bas1999bon14,1998-12-23,1998,12,day,0,0
+bas1999bon15,1998-12-23,1998,12,day,0,0
+bas1999bon16,1998-12-23,1998,12,day,0,0
+bas1999bon17,1998-12-24,1998,12,night,0,0
+bas1999bon18,1998-12-24,1998,12,day,0,0
+bas1999bon19,1998-12-24,1998,12,day,0,0
+bas1999bon2,1998-12-21,1998,12,night,0,0
+bas1999bon20,1998-12-25,1998,12,night,0,0
+bas1999bon21,1998-12-25,1998,12,day,0,0
+bas1999bon22,1998-12-25,1998,12,day,0,0
+bas1999bon23,1998-12-28,1998,12,day,0,0
+bas1999bon24a,1998-12-29,1998,12,night,0,0
+bas1999bon24b,1998-12-29,1998,12,night,0,0
+bas1999bon25,1998-12-29,1998,12,night,0,0
+bas1999bon26,1998-12-29,1998,12,day,0,0
+bas1999bon27,1998-12-30,1998,12,day,0,0
+bas1999bon28a,1998-12-31,1998,12,night,0,0
+bas1999bon28b,1998-12-31,1998,12,night,0,0
+bas1999bon29,1998-12-31,1998,12,day,0,0
+bas1999bon3,1998-12-21,1998,12,night,0,0
+bas1999bon30,1999-01-02,1999,1,day,0,0
+bas1999bon31,1999-01-03,1999,1,night,0,0
+bas1999bon32,1999-01-04,1999,1,night,0,0
+bas1999bon33,1999-01-03,1999,1,day,0,0
+bas1999bon34,1999-01-04,1999,1,day,0,0
+bas1999bon35,1999-01-05,1999,1,night,0,0
+bas1999bon36,1999-01-06,1999,1,night,0,0
+bas1999bon37,1999-01-05,1999,1,day,0,0
+bas1999bon4,1998-12-21,1998,12,day,0,0
+bas1999bon5,1998-12-21,1998,12,day,0,0
+bas1999bon6,1998-12-21,1998,12,day,0,0
+bas1999bon7,1998-12-22,1998,12,night,0,0
+bas1999bon8,1998-12-22,1998,12,night,0,0
+bas1999bon9,1998-12-22,1998,12,day,0,0
+bas1999rmt165a,1998-12-28,1998,12,day,1.71,0.4329692908744057
+bas1999rmt165b,1998-12-28,1998,12,night,0.414,0.15044940946088056
+bas1999rmt175a,1998-12-29,1998,12,night,0,0
+bas1999rmt175b,1998-12-29,1998,12,day,0,0
+bas1999rmt185a,1998-12-29,1998,12,night,0.004,0.0017337128090005314
+bas1999rmt185b,1998-12-30,1998,12,night,0.044,0.01870049866624337
+bas1999rmt189a,1998-12-30,1998,12,night,0.063,0.026533264523296733
+bas1999rmt189b,1998-12-30,1998,12,night,0.656,0.2190603324488614
+bas1999rmt212a,1998-12-31,1998,12,night,0,0
+bas1999rmt212b,1998-12-31,1998,12,night,0,0
+bas1999rmt238a,1999-01-02,1999,1,day,9.295,1.01262635095405
+bas1999rmt238b,1999-01-02,1999,1,night,0.409,0.14891099310935643
+bas1999rmt245a,1999-01-03,1999,1,night,0,0
+bas1999rmt245b,1999-01-03,1999,1,night,0.042,0.017867718963505686
+bas1999rmt261,1999-01-03,1999,1,night,0.006,0.0025979807199085943
+bas1999rmt266a,1999-01-04,1999,1,night,0.083,0.03462845662532034
+bas1999rmt266b,1999-01-04,1999,1,night,0,0
+bas1999rmt283a,1999-01-04,1999,1,night,0.012,0.005180512503780314
+bas1999rmt283b,1999-01-04,1999,1,night,0.144,0.05842602445700535
+bas1999rmt288a,1999-01-05,1999,1,night,0.002,8.677215312269133e-4
+bas1999rmt288b,1999-01-05,1999,1,night,0.084,0.03502928220236815
+bas1999rmt306a,1999-01-05,1999,1,night,0,0
+bas1999rmt306b,1999-01-06,1999,1,night,0,0
+bas2001bon1,2000-12-30,2000,12,day,0,0
+bas2001bon10,2001-01-05,2001,1,night,0,0
+bas2001bon11,2001-01-07,2001,1,day,0,0
+bas2001bon12,2001-01-08,2001,1,night,0,0
+bas2001bon13,2001-01-08,2001,1,day,0,0
+bas2001bon14,2001-01-09,2001,1,night,0,0
+bas2001bon15,2000-12-22,2000,12,day,0,0
+bas2001bon16,2000-12-23,2000,12,night,0,0
+bas2001bon2,2000-12-31,2000,12,night,136.357,2.1378507968756786
+bas2001bon3,2000-12-31,2000,12,day,0,0
+bas2001bon4,2001-01-01,2001,1,night,0,0
+bas2001bon5,2001-01-02,2001,1,day,38.624,1.5979583149743997
+bas2001bon6,2001-01-03,2001,1,night,0,0
+bas2001bon7,2001-01-03,2001,1,day,19.372,1.3090336675041405
+bas2001bon8,2001-01-04,2001,1,night,0,0
+bas2001bon9,2001-01-04,2001,1,day,0,0
+bas2001rmt125,2000-12-30,2000,12,night,12.274,1.123001813306024
+bas2001rmt136a,2000-12-30,2000,12,night,28.846,1.4748861346502506
+bas2001rmt136b,2000-12-30,2000,12,night,7.361,0.9222582234329667
+bas2001rmt138a,2000-12-31,2000,12,night,1.097,0.32159843046534387
+bas2001rmt138b,2000-12-31,2000,12,night,0,0
+bas2001rmt153a,2000-12-31,2000,12,night,0.11,0.04532297878665747
+bas2001rmt153b,2001-01-01,2001,1,night,0,0
+bas2001rmt156a,2001-01-01,2001,1,night,748,2.8744818176994666
+bas2001rmt156b,2001-01-01,2001,1,night,69.448,1.8478686681154506
+bas2001rmt172a,2001-01-02,2001,1,night,0.193,0.0766404436703419
+bas2001rmt172b,2001-01-03,2001,1,night,1.137,0.3298045221640694
+bas2001rmt174a,2001-01-03,2001,1,day,0.061,0.025715383901340642
+bas2001rmt174b,2001-01-03,2001,1,night,0.073,0.030599721965951062
+bas2001rmt189a,2001-01-04,2001,1,night,36.936,1.579051536634113
+bas2001rmt189b,2001-01-04,2001,1,night,13.22,1.1528995963937474
+bas2001rmt200a,2001-01-04,2001,1,night,0.013,0.005609445360280386
+bas2001rmt200b,2001-01-04,2001,1,night,0,0
+bas2001rmt203a,2001-01-05,2001,1,night,0.156,0.06295783408451021
+bas2001rmt203b,2001-01-05,2001,1,night,0.064,0.0269416279590294
+bas2001rmt216,2001-01-06,2001,1,night,2.08,0.48855071650044424
+bas2001rmt219a,2001-01-06,2001,1,night,0.671,0.22297644989339133
+bas2001rmt219b,2001-01-06,2001,1,night,1.118,0.32592595577146627
+bas2001rmt229a,2001-01-06,2001,1,day,6.455,0.8724476477890133
+bas2001rmt229b,2001-01-06,2001,1,night,0.492,0.17376882313665
+bas2001rmt232a,2001-01-07,2001,1,night,2.101,0.4915017662373264
+bas2001rmt232b,2001-01-07,2001,1,night,1.265,0.3550682063488506
+bas2001rmt244a,2001-01-07,2001,1,night,0.075,0.031408464251624114
+bas2001rmt244b,2001-01-07,2001,1,night,3.987,0.697839368218363
+bas2001rmt247a,2001-01-08,2001,1,night,0.131,0.05346260492545529
+bas2001rmt247b,2001-01-08,2001,1,night,20.679,1.3360392453710435
+bas2001rmt258a,2001-01-08,2001,1,day,1.603,0.41547416810923565
+bas2001rmt258b,2001-01-08,2001,1,night,22.247,1.3663669156126421
+bas2001rmt261a,2001-01-09,2001,1,night,18.911,1.2990930723617053
+bas2001rmt261b,2001-01-09,2001,1,night,11.1,1.08278537031645
+bas2001rmt44a,2000-12-22,2000,12,night,0.049,0.02077548819355783
+bas2001rmt44b,2000-12-22,2000,12,night,0.116,0.04766419460155998
+bas2001rmt47,2000-12-23,2000,12,night,0.235,0.09166695759568448
+bas2002bon1,2002-01-10,2002,1,day,0,0
+bas2002bon10,2002-01-08,2002,1,day,0,0
+bas2002bon11,2002-01-11,2002,1,day,0,0
+bas2002bon12,2002-01-11,2002,1,day,0,0
+bas2002bon13,2002-01-11,2002,1,night,0,0
+bas2002bon14,2002-01-10,2002,1,day,0,0
+bas2002bon15,2002-01-10,2002,1,day,0,0
+bas2002bon16,2002-01-10,2002,1,day,0,0
+bas2002bon17,2002-01-12,2002,1,day,0,0
+bas2002bon18,2002-01-12,2002,1,day,0,0
+bas2002bon19,2002-01-12,2002,1,day,0,0
+bas2002bon2,2002-01-10,2002,1,day,0,0
+bas2002bon20,2002-01-12,2002,1,day,0,0
+bas2002bon21,2002-01-12,2002,1,night,0,0
+bas2002bon22,2002-01-12,2002,1,night,0,0
+bas2002bon23,2002-01-11,2002,1,day,0,0
+bas2002bon24,2002-01-11,2002,1,day,0,0
+bas2002bon25,2002-01-11,2002,1,day,0,0
+bas2002bon26,2002-01-22,2002,1,day,0,0
+bas2002bon27,2002-01-22,2002,1,day,0,0
+bas2002bon28,2002-01-22,2002,1,day,0,0
+bas2002bon29,2002-01-22,2002,1,day,0,0
+bas2002bon3,2002-01-10,2002,1,night,0,0
+bas2002bon30,2002-01-22,2002,1,night,0,0
+bas2002bon31,2002-01-21,2002,1,night,0,0
+bas2002bon32,2002-01-21,2002,1,day,0,0
+bas2002bon33,2002-01-21,2002,1,day,0,0
+bas2002bon34,2002-01-21,2002,1,day,0,0
+bas2002bon35,2002-01-21,2002,1,day,0,0
+bas2002bon36,2002-01-21,2002,1,night,0,0
+bas2002bon37,2002-01-20,2002,1,day,0,0
+bas2002bon38,2002-01-20,2002,1,day,0,0
+bas2002bon39,2002-01-20,2002,1,day,0,0
+bas2002bon4,2002-01-09,2002,1,day,0,0
+bas2002bon40,2002-01-20,2002,1,day,0,0
+bas2002bon41,2002-01-20,2002,1,night,0,0
+bas2002bon42,2002-01-19,2002,1,day,0,0
+bas2002bon43,2002-01-31,2002,1,day,0,0
+bas2002bon44,2002-01-31,2002,1,day,0,0
+bas2002bon45,2002-01-31,2002,1,day,0,0
+bas2002bon46,2002-01-31,2002,1,day,0,0
+bas2002bon47,2002-01-31,2002,1,day,0,0
+bas2002bon48,2002-02-01,2002,2,night,0,0
+bas2002bon49,2002-02-01,2002,2,night,0,0
+bas2002bon5,2002-01-09,2002,1,day,0,0
+bas2002bon50,2002-02-01,2002,2,day,0,0
+bas2002bon51,2002-02-01,2002,2,day,0,0
+bas2002bon52,2002-02-01,2002,2,day,0,0
+bas2002bon53,2002-02-01,2002,2,night,0,0
+bas2002bon54,2002-02-02,2002,2,night,0,0
+bas2002bon55,2002-02-02,2002,2,night,0,0
+bas2002bon56,2002-02-02,2002,2,day,0,0
+bas2002bon57,2002-02-02,2002,2,day,0,0
+bas2002bon58,2002-02-02,2002,2,day,0,0
+bas2002bon59,2002-02-03,2002,2,night,0,0
+bas2002bon6,2002-01-09,2002,1,day,0,0
+bas2002bon7,2002-01-09,2002,1,day,0,0
+bas2002bon8,2002-01-09,2002,1,night,0,0
+bas2002bon9,2002-01-08,2002,1,day,0,0
+bas2002rmt134a,2002-01-13,2002,1,day,1.085,0.3191060593097763
+bas2002rmt134b,2002-01-13,2002,1,night,0.878,0.27369558793009213
+bas2002rmt137a,2002-01-14,2002,1,night,1.114,0.32510498297140744
+bas2002rmt137b,2002-01-14,2002,1,night,0.231,0.09025805293131633
+bas2002rmt149a,2002-01-14,2002,1,night,0,0
+bas2002rmt149b,2002-01-14,2002,1,night,4.392,0.7317498835272634
+bas2002rmt152a,2002-01-15,2002,1,night,1.003,0.30168094929357625
+bas2002rmt152b,2002-01-15,2002,1,night,0.38,0.13987908640123647
+bas2002rmt260,2002-01-25,2002,1,night,1.464,0.39164070349238783
+bas2002rmt263a,2002-01-25,2002,1,night,0.003,0.001300933020418072
+bas2002rmt263b,2002-01-25,2002,1,night,1.956,0.47070442972278814
+bas2002rmt276a,2002-01-26,2002,1,night,32.919,1.5304430399029612
+bas2002rmt276b,2002-01-26,2002,1,night,93.459,1.975243343561444
+bas2002rmt286a,2002-01-26,2002,1,day,39.926,1.6119992999147918
+bas2002rmt286b,2002-01-26,2002,1,day,0.147,0.059563417901267686
+bas2003bon11,2003-01-09,2003,1,day,0,0
+bas2003bon12,2003-01-10,2003,1,night,0,0
+bas2003bon13,2003-01-10,2003,1,day,19.303,1.307560214621736
+bas2003bon14,2003-01-11,2003,1,day,0,0
+bas2003bon15,2003-01-11,2003,1,day,19.403,1.3096940295658421
+bas2003bon22,2003-01-12,2003,1,night,0,0
+bas2003bon23,2003-01-12,2003,1,day,0,0
+bas2003bon24,2003-01-13,2003,1,day,0,0
+bas2003bon25,2003-01-13,2003,1,day,0,0
+bas2003bon26,2003-01-14,2003,1,night,4.376,0.7304592600457689
+bas2003bon27,2003-01-14,2003,1,day,9.867,1.0361096670605796
+bas2003bon31,2003-01-15,2003,1,night,0,0
+bas2003bon32,2003-01-15,2003,1,day,0,0
+bas2003bon33,2003-01-16,2003,1,night,0,0
+bas2003bon34,2003-01-16,2003,1,day,0,0
+bas2003bon35,2003-01-17,2003,1,night,4.461,0.7372721765355434
+bas2003bon36,2003-01-17,2003,1,day,0,0
+bas2003bon37,2003-01-18,2003,1,day,0,0
+bas2003bon41,2003-01-18,2003,1,day,0,0
+bas2003bon42,2003-01-18,2003,1,day,0,0
+bas2003bon43,2003-01-19,2003,1,day,0,0
+bas2003bon44,2003-01-20,2003,1,night,0,0
+bas2003bon45,2003-01-20,2003,1,day,0,0
+bas2003bon46,2003-01-21,2003,1,night,0,0
+bas2003bon51,2003-01-21,2003,1,day,0,0
+bas2003bon52,2003-01-22,2003,1,day,0,0
+bas2003bon53,2003-01-22,2003,1,day,0,0
+bas2003bon54,2003-01-23,2003,1,day,0,0
+bas2003bon55,2003-01-23,2003,1,day,0,0
+bas2003bon56,2003-01-26,2003,1,day,0,0
+bas2003bon61,2003-01-26,2003,1,day,0,0
+bas2003bon62,2003-01-27,2003,1,night,0,0
+bas2003bon63,2003-01-27,2003,1,day,0,0
+bas2003bon64,2003-01-28,2003,1,day,0,0
+bas2003bon65,2003-01-28,2003,1,night,0,0
+bas2003bon66,2003-01-29,2003,1,day,0,0
+bas2003bon67,2003-01-30,2003,1,night,0,0
+bas2003bon71,2003-01-30,2003,1,day,0,0
+bas2003bon710,2003-02-04,2003,2,day,0,0
+bas2003bon711,2003-02-05,2003,2,day,0,0
+bas2003bon72,2003-01-31,2003,1,day,0,0
+bas2003bon73,2003-01-31,2003,1,day,0,0
+bas2003bon74,2003-02-01,2003,2,day,0,0
+bas2003bon75,2003-02-02,2003,2,night,0,0
+bas2003bon76,2003-02-02,2003,2,day,0,0
+bas2003bon77,2003-02-03,2003,2,night,5.199,0.7923216363515735
+bas2003bon78,2003-02-03,2003,2,day,0,0
+bas2003bon79,2003-02-04,2003,2,night,0,0
+bas2003bon81,2003-02-06,2003,2,day,0,0
+bas2003bon82,2003-02-06,2003,2,night,0,0
+bas2003bon83,2003-02-07,2003,2,day,0,0
+bas2003bon84,2003-02-08,2003,2,night,0,0
+bas2003bon85,2003-02-08,2003,2,day,0,0
+bas2003bon86,2003-02-09,2003,2,night,0,0
+bas2003bon87,2003-02-09,2003,2,day,100.852,2.0079695613718807
+bas2003rmt616a,2003-02-14,2003,2,night,0.276,0.10585067438514352
+bas2003rmt616b,2003-02-14,2003,2,night,3.294,0.63286204010023
+bas2003rmt619a,2003-02-14,2003,2,night,0,0
+bas2003rmt619b,2003-02-14,2003,2,night,0,0
+bas2003rmt634a,2003-02-14,2003,2,night,0.101,0.041787318971751766
+bas2003rmt634b,2003-02-14,2003,2,night,0.123,0.050379756261457784
+bas2003rmt637a,2003-02-15,2003,2,night,2.334,0.5229655954919865
+bas2003rmt637b,2003-02-15,2003,2,night,63.633,1.8104543145673886
+bas2003rmt655a,2003-02-15,2003,2,night,37.744,1.588204456038483
+bas2003rmt655b,2003-02-15,2003,2,night,10.992,1.0788916198402232
+bas2003rmt658a,2003-02-16,2003,2,night,1.191,0.34064237756070526
+bas2003rmt658b,2003-02-16,2003,2,night,0.375,0.13830269816628143
+bas2004bon006,2004-03-12,2004,3,night,0,0
+bas2004bon015,2004-03-12,2004,3,night,0,0
+bas2004bon033,2004-03-14,2004,3,day,0,0
+bas2004bon038,2004-03-14,2004,3,day,0,0
+bas2004bon047,2004-03-15,2004,3,day,0,0
+bas2004bon055,2004-03-15,2004,3,night,0,0
+bas2004bon065,2004-03-17,2004,3,night,0,0
+bas2004bon074,2004-03-17,2004,3,day,0,0
+bas2004bon078,2004-03-17,2004,3,night,0,0
+bas2004bon086,2004-03-18,2004,3,day,0,0
+bas2004bon097,2004-03-19,2004,3,day,0,0
+bas2004bon105,2004-03-20,2004,3,night,0,0
+bas2004bon116,2004-03-20,2004,3,day,0,0
+bas2004bon125,2004-03-21,2004,3,night,0,0
+bas2004bon164,2004-03-27,2004,3,night,0,0
+bas2004bon175,2004-03-28,2004,3,night,0,0
+bas2004bon180,2004-03-28,2004,3,night,0,0
+bas2004bon192,2004-03-29,2004,3,night,0,0
+bas2004bon202,2004-03-31,2004,3,day,0,0
+bas2004lhp129,2004-03-21,2004,3,day,8.443,0.975109989686161
+bas2004lhp131,2004-03-22,2004,3,night,8.071,0.9576551669434912
+bas2004lhp142,2004-03-24,2004,3,night,9.813,1.0339462029903608
+bas2004lhp213,2004-04-02,2004,4,night,0,0
+bas2005bon11,2005-01-12,2005,1,day,7.443,0.9264967892732203
+bas2005bon12,2005-01-12,2005,1,day,0,0
+bas2005bon13,2005-01-09,2005,1,day,0,0
+bas2005bon14,2005-01-09,2005,1,day,0,0
+bas2005bon15,2005-01-09,2005,1,night,0,0
+bas2005bon21,2005-01-08,2005,1,day,0,0
+bas2005bon22,2005-01-08,2005,1,day,0,0
+bas2005bon23,2005-01-08,2005,1,day,0,0
+bas2005bon24,2005-01-08,2005,1,night,0,0
+bas2005bon25,2005-01-07,2005,1,day,0,0
+bas2005bon286,2005-01-06,2005,1,night,0,0
+bas2005bon291,2005-01-07,2005,1,night,0,0
+bas2005bon31,2005-01-06,2005,1,day,0,0
+bas2005bon32,2005-01-06,2005,1,night,0,0
+bas2005bon33,2005-01-06,2005,1,night,0,0
+bas2005bon34,2005-01-06,2005,1,night,0,0
+bas2005bon35,2005-01-05,2005,1,day,0,0
+bas2005bon36,2005-01-05,2005,1,day,0,0
+bas2005bon37,2005-01-05,2005,1,day,0,0
+bas2005bon38,2005-01-05,2005,1,night,0,0
+bas2005bon41,2005-01-04,2005,1,day,0,0
+bas2005bon42,2005-01-04,2005,1,day,0,0
+bas2005bon43,2005-01-04,2005,1,day,0,0
+bas2005bon44,2005-01-04,2005,1,day,0,0
+bas2005bon45,2005-01-04,2005,1,night,0,0
+bas2005bon46,2005-01-04,2005,1,night,0,0
+bas2005bon47,2005-01-03,2005,1,day,0,0
+bas2005bon51,2005-01-02,2005,1,day,0,0
+bas2005bon52,2005-01-02,2005,1,day,0,0
+bas2005bon53,2005-01-02,2005,1,day,0,0
+bas2005bon54,2005-01-02,2005,1,night,0,0
+bas2005bon55,2005-01-01,2005,1,day,0,0
+bas2005bon56,2005-01-01,2005,1,day,0,0
+bas2005bon61,2005-01-03,2005,1,day,0,0
+bas2005bon62,2005-01-03,2005,1,night,0,0
+bas2005bon63,2005-01-02,2005,1,night,0,0
+bas2005bon64,2005-01-02,2005,1,day,0,0
+bas2005bon65,2004-12-31,2004,12,day,0,0
+bas2005bon66,2004-12-31,2004,12,day,0,0
+bas2005bon71,2004-12-30,2004,12,night,0,0
+bas2005bon72,2004-12-30,2004,12,day,0,0
+bas2005bon73,2004-12-30,2004,12,day,0,0
+bas2005bon74,2004-12-30,2004,12,day,0,0
+bas2005bon75,2004-12-30,2004,12,night,0,0
+bas2005bon81,2004-12-29,2004,12,day,0,0
+bas2005bon82,2004-12-29,2004,12,day,0,0
+bas2005bon83,2004-12-29,2004,12,day,0,0
+bas2005bon84,2004-12-29,2004,12,night,0,0
+bas2005bon85,2004-12-28,2004,12,day,0,0
+bas2005bon86,2004-12-28,2004,12,day,0,0
+bas2005bon91,2004-12-28,2004,12,night,0,0
+bas2005bon92,2004-12-28,2004,12,night,0,0
+bas2005bon93,2004-12-27,2004,12,day,0,0
+bas2005bon94,2004-12-27,2004,12,day,0,0
+bas2005bon95,2004-12-27,2004,12,day,0,0
+bas2005bon96,2004-12-27,2004,12,day,0,0
+bas2005bon97,2004-12-27,2004,12,night,0,0
+bas2007bonl025,2006-10-27,2006,10,day,0,0
+bas2007bonl051,2006-10-29,2006,10,day,0,0
+bas2007bonl082,2006-11-01,2006,11,day,0,0
+bas2007bonl090,2006-11-02,2006,11,day,0,0
+bas2007bonl100,2006-11-05,2006,11,day,0,0
+bas2007bonl133,2006-11-06,2006,11,day,24.181,1.4010729730259477
+bas2007bonl140,2006-11-09,2006,11,day,244.888,2.390737334509562
+bas2007bonl151,2006-11-16,2006,11,day,0,0
+bas2007bonl180,2006-11-18,2006,11,day,0,0
+bas2007bonl196,2006-11-19,2006,11,day,0,0
+bas2007bonl233,2006-11-23,2006,11,day,0,0
+bas2007bonl258,2006-11-25,2006,11,day,0,0
+bas2007bonl277,2006-11-28,2006,11,day,0,0
+bas2007bonl290,2006-11-29,2006,11,day,0,0
+bas2007bons025,2006-10-27,2006,10,day,0,0
+bas2007bons051,2006-10-29,2006,10,day,0,0
+bas2007bons082,2006-11-01,2006,11,day,0,0
+bas2007bons090,2006-11-02,2006,11,day,89.197,1.9551920929176316
+bas2007bons100,2006-11-05,2006,11,day,0,0
+bas2007bons113,2006-11-06,2006,11,day,24.181,1.4010729730259477
+bas2007bons140,2006-11-09,2006,11,day,0,0
+bas2007bons151,2006-11-16,2006,11,day,54.155,1.7415848889245436
+bas2007bons180,2006-11-18,2006,11,day,0,0
+bas2007bons196,2006-11-19,2006,11,day,0,0
+bas2007bons233,2006-11-23,2006,11,day,0,0
+bas2007bons258,2006-11-25,2006,11,day,0,0
+bas2007bons277,2006-11-28,2006,11,day,0,0
+bas2007bons290,2006-11-29,2006,11,day,0,0
+bas2007lhp053,2006-10-29,2006,10,day,0,0
+bas2007lhp072,2006-11-01,2006,11,night,6.476,0.8736692927067944
+bas2007lhp210,2006-11-20,2006,11,day,0,0
+bas2007lhp250,2006-11-24,2006,11,day,0,0
+bas2008bonl051,2008-01-04,2008,1,day,0,0
+bas2008bonl066,2008-01-05,2008,1,day,0,0
+bas2008bonl090,2008-01-08,2008,1,day,0,0
+bas2008bonl105,2008-01-09,2008,1,day,9.924,1.0383816914676958
+bas2008bonl147,2008-01-15,2008,1,day,0,0
+bas2008bonl163,2008-01-16,2008,1,day,0,0
+bas2008bonl186,2008-01-19,2008,1,day,0,0
+bas2008bonl202,2008-01-20,2008,1,day,0,0
+bas2008bonl217,2008-01-22,2008,1,day,0,0
+bas2008bonl228,2008-01-25,2008,1,day,0,0
+bas2008bonl244,2008-01-27,2008,1,day,0,0
+bas2008bonl280,2008-02-01,2008,2,day,0,0
+bas2008bonl296,2008-02-02,2008,2,day,11.979,1.1132412324325989
+bas2008bonl318,2008-02-04,2008,2,day,0,0
+bas2008bonl330,2008-02-05,2008,2,day,0,0
+bas2008bonl370,2008-02-09,2008,2,day,0,0
+bas2008bons051,2008-01-04,2008,1,day,0,0
+bas2008bons066,2008-01-05,2008,1,day,0,0
+bas2008bons090,2008-01-08,2008,1,day,0,0
+bas2008bons105,2008-01-09,2008,1,day,0,0
+bas2008bons147,2008-01-15,2008,1,day,0,0
+bas2008bons163,2008-01-16,2008,1,day,0,0
+bas2008bons186,2008-01-19,2008,1,day,0,0
+bas2008bons202,2008-01-20,2008,1,day,0,0
+bas2008bons217,2008-01-22,2008,1,day,0,0
+bas2008bons228,2008-01-25,2008,1,day,0,0
+bas2008bons244,2008-01-27,2008,1,day,0,0
+bas2008bons280,2008-02-01,2008,2,day,0,0
+bas2008bons296,2008-02-02,2008,2,day,0,0
+bas2008bons318,2008-02-04,2008,2,day,0,0
+bas2008bons330,2008-02-05,2008,2,day,0,0
+bas2008bons370,2008-02-09,2008,2,day,0,0
+bas2008lhp048,2008-01-04,2008,1,day,0,0
+bas2008lhp055,2008-01-04,2008,1,day,0,0
+bas2008lhp096,2008-01-08,2008,1,day,0,0
+bas2008lhp152,2008-01-15,2008,1,day,0,0
+bas2008lhp194,2008-01-19,2008,1,day,0,0
+bas2008lhp210,2008-01-21,2008,1,day,0,0
+bas2008lhp256,2008-01-28,2008,1,day,0,0
+bas2008lhp261,2008-01-29,2008,1,night,1.918,0.46508528755743284
+bas2008lhp278,2008-02-01,2008,2,night,0,0
+bas2008lhp287,2008-02-01,2008,2,day,0,0
+bas2008lhp325,2008-02-04,2008,2,day,0,0
+bas2009bonl002,2009-03-13,2009,3,day,0,0
+bas2009bonl014,2009-03-16,2009,3,night,0,0
+bas2009bonl015,2009-03-16,2009,3,night,10.884,1.0749626431316264
+bas2009bonl024,2009-03-16,2009,3,day,0,0
+bas2009bonl045,2009-03-19,2009,3,day,0,0
+bas2009bonl049,2009-03-19,2009,3,day,0,0
+bas2009bonl058,2009-03-20,2009,3,day,0,0
+bas2009bonl069,2009-03-21,2009,3,day,0,0
+bas2009bonl083,2009-03-23,2009,3,night,0,0
+bas2009bonl096,2009-03-24,2009,3,day,0,0
+bas2009bonl117,2009-03-27,2009,3,night,0,0
+bas2009bonl122,2009-03-27,2009,3,day,0,0
+bas2009bonl136,2009-03-29,2009,3,day,0,0
+bas2009bonl149,2009-03-31,2009,3,day,0,0
+bas2009bonl166,2009-04-02,2009,4,day,0,0
+bas2009bonl174,2009-04-03,2009,4,day,0,0
+bas2009bonl178,2009-04-03,2009,4,day,0,0
+bas2009bonl188,2009-04-06,2009,4,day,0,0
+bas2009bonl189,2009-04-06,2009,4,day,0,0
+bas2009bonl228,2009-04-10,2009,4,day,0,0
+bas2009bonl239,2009-04-11,2009,4,day,0,0
+bas2009bons002,2009-03-13,2009,3,day,0,0
+bas2009bons014,2009-03-16,2009,3,night,0,0
+bas2009bons015,2009-03-16,2009,3,night,0,0
+bas2009bons024,2009-03-16,2009,3,day,0,0
+bas2009bons045,2009-03-19,2009,3,day,0,0
+bas2009bons049,2009-03-19,2009,3,day,0,0
+bas2009bons058,2009-03-20,2009,3,day,0,0
+bas2009bons069,2009-03-21,2009,3,day,0,0
+bas2009bons083,2009-03-23,2009,3,night,0,0
+bas2009bons096,2009-03-24,2009,3,day,0,0
+bas2009bons117,2009-03-27,2009,3,night,0,0
+bas2009bons136,2009-03-29,2009,3,day,0,0
+bas2009bons149,2009-03-31,2009,3,day,0,0
+bas2009bons166,2009-04-02,2009,4,day,0,0
+bas2009bons174,2009-04-03,2009,4,day,0,0
+bas2009bons178,2009-04-03,2009,4,day,0,0
+bas2009bons188,2009-04-06,2009,4,day,0,0
+bas2009bons189,2009-04-06,2009,4,day,0,0
+bas2009bons228,2009-04-10,2009,4,day,0,0
+bas2009bons239,2009-04-11,2009,4,day,0,0
+bas2009lhp027,2009-03-16,2009,3,night,0,0
+bas2009lhp036,2009-03-18,2009,3,night,0,0
+bas2009lhp065,2009-03-21,2009,3,night,0,0
+bas2009lhp093,2009-03-24,2009,3,day,0,0
+bas2009lhp120,2009-03-27,2009,3,day,0,0
+bas2009lhp147,2009-03-31,2009,3,night,0,0
+bas2009lhp172,2009-04-03,2009,4,night,6.359,0.8668188029260482
+bas2009lhp180,2009-04-03,2009,4,day,0,0
+bas2009lhp234,2009-04-10,2009,4,night,0,0
+boy1983rmt,1983-03-15,1983,3,day,23.533,1.3897506588375064
+bri1981bon1,1981-03-15,1981,3,night,199.268,2.3016115607081153
+bri1981bon2,1981-03-15,1981,3,day,22.16,1.3647385550553985
+bri1981bon3,1981-03-15,1981,3,night,518.643,2.7157050813733625
+bri1981bon4,1981-03-15,1981,3,day,16.004,1.2305510962950852
+bri1981bon5,1981-03-15,1981,3,day,0,0
+bri1984bon1,1984-03-14,1984,3,night,131.026,2.1206594656347773
+bri1984bon2,1984-03-14,1984,3,day,73.866,1.8742846299180156
+bri1984bon3,1984-03-14,1984,3,night,11.738,1.1051012445496424
+bri1984bon4,1984-03-14,1984,3,day,3.078,0.6104472214421212
+bri1984bon5,1984-03-14,1984,3,day,61.555,1.7962620284279147
+bri1987bra1,1987-01-15,1987,1,day,0.656,0.2190603324488614
+bri1987bra2,1987-02-14,1987,2,day,379.21,2.5800235351693668
+bri1987bra3,1987-03-15,1987,3,day,3.693,0.6714505542124947
+bri1987bra4,1987-01-15,1987,1,night,0,0
+bri1987bra5,1987-02-14,1987,2,night,1.822,0.450557009418329
+bri1987bra6,1987-03-15,1987,3,night,177.43,2.2514678754835242
+bri1987dra1,1987-01-15,1987,1,day,4.918,0.7721749608246142
+bri1987dra2,1987-02-14,1987,2,day,122.491,2.0916353074518423
+bri1987dra3,1987-03-15,1987,3,day,4.924,0.7726150498491708
+bri1987dra4,1987-01-15,1987,1,night,0.485,0.17172645365323114
+bri1987dra5,1987-02-14,1987,2,night,65.379,1.8220307054731093
+bri1987dra6,1987-03-15,1987,3,night,125.02,2.100439475279108
+bri1987ger1,1987-01-15,1987,1,day,4.098,0.7073998311332487
+bri1987ger2,1987-02-14,1987,2,day,0,0
+bri1987ger3,1987-03-15,1987,3,day,12.311,1.1242106834362173
+bri1987ger4,1987-01-15,1987,1,night,1.042,0.3100557377508914
+bri1987ger5,1987-02-14,1987,2,night,20.492,1.3322768318966565
+bri1987ger6,1987-03-15,1987,3,night,50.226,1.7094904451648454
+chi1988jar27,1988-03-05,1988,3,NA,0,0
+chi1988jar29,1988-03-06,1988,3,NA,0,0
+chi1988jar31,1988-03-07,1988,3,NA,0,0
+chi1988jar33,1988-03-08,1988,3,NA,0,0
+chi1988jar35,1988-03-09,1988,3,NA,0,0
+chi1988jar37,1988-03-10,1988,3,NA,0,0
+chi1988jar39,1988-03-11,1988,3,NA,0,0
+chi1988jar41,1988-03-12,1988,3,NA,0,0
+chi1989jar11,1989-03-14,1989,3,NA,382.984,2.584313128387116
+chi1989jar13,1989-03-15,1989,3,NA,0,0
+chi1990jar25,1990-03-14,1990,3,NA,0,0
+chi1990jar27,1990-03-15,1990,3,NA,0,0
+chi1992jar17,1992-03-08,1992,3,NA,0,0
+chi1992jar21,1992-03-10,1992,3,NA,47.577,1.6864306903028627
+chi1992jar23,1992-03-12,1992,3,NA,0,0
+chi1992jar25,1992-03-14,1992,3,NA,0,0
+chi1992jar27,1992-03-15,1992,3,NA,0,0
+chi1993jar21,1993-03-07,1993,3,NA,55.992,1.7558138977831494
+chi1993jar23,1993-03-08,1993,3,NA,0,0
+chi1993jar25,1993-03-09,1993,3,NA,0,0
+chi1993jar27,1993-03-10,1993,3,NA,0,0
+chi1993jar29,1993-03-11,1993,3,NA,0,0
+chi1993jar31,1993-03-15,1993,3,NA,0,0
+chi1994jar15,1994-03-06,1994,3,NA,0,0
+chi1994jar17,1994-03-07,1994,3,NA,85.635,1.9376933797314164
+chi1994jar19,1994-03-08,1994,3,NA,0,0
+chi1994jar21,1994-03-09,1994,3,NA,0,0
+chi1994jar23,1994-03-12,1994,3,NA,0,0
+chi1994jar25,1994-03-15,1994,3,NA,100.477,2.0063676195399935
+chi1995jar19,1995-03-05,1995,3,NA,0,0
+chi1995jar21,1995-03-06,1995,3,NA,0,0
+chi1995jar23,1995-03-08,1995,3,NA,0,0
+chi1995jar24,1995-03-09,1995,3,NA,0,0
+chi1995jar26,1995-03-10,1995,3,NA,0,0
+chi1995jar28,1995-03-11,1995,3,NA,0,0
+chi1995jar30,1995-03-14,1995,3,NA,0,0
+chi1995jar32,1995-03-16,1995,3,NA,0,0
+chi1996jar15,1996-03-03,1996,3,NA,0,0
+chi1996jar17,1996-03-04,1996,3,NA,0,0
+chi1996jar19,1996-03-05,1996,3,NA,0,0
+chi1996jar21,1996-03-06,1996,3,NA,563.213,2.751443088489361
+chi1996jar23,1996-03-07,1996,3,NA,0,0
+chi1996jar25,1996-03-12,1996,3,NA,0,0
+chi1996jar27,1996-03-13,1996,3,NA,0,0
+chi1996uma10,1996-01-28,1996,1,day,5.012,0.7790189719148706
+chi1996uma11,1996-01-29,1996,1,day,0,0
+chi1996uma12,1996-01-29,1996,1,day,0,0
+chi1996uma13,1996-01-29,1996,1,night,0,0
+chi1996uma14,1996-01-30,1996,1,day,0,0
+chi1996uma15,1996-01-30,1996,1,day,7.526,0.9307453283111133
+chi1996uma16,1996-01-30,1996,1,day,0,0
+chi1996uma17,1996-01-30,1996,1,day,0,0
+chi1996uma18,1996-01-30,1996,1,day,0,0
+chi1996uma19,1996-01-30,1996,1,day,0,0
+chi1996uma20,1996-01-30,1996,1,night,0,0
+chi1996uma21,1996-01-31,1996,1,night,0,0
+chi1996uma22,1996-01-31,1996,1,day,0,0
+chi1996uma23,1996-01-31,1996,1,day,24.848,1.4124269450095877
+chi1996uma24,1996-01-31,1996,1,day,7.97,0.952792443044092
+chi1996uma25,1996-01-31,1996,1,day,6.929,0.8992184178513728
+chi1996uma27,1996-02-02,1996,2,day,3.808,0.681964458994683
+chi1996uma29,1996-02-02,1996,2,day,7.876,0.9482172935599708
+chi1996uma30,1996-02-03,1996,2,night,0,0
+chi1996uma31,1996-02-03,1996,2,day,0,0
+chi1996uma6,1996-01-26,1996,1,day,0,0
+chi1996uma7,1996-01-27,1996,1,night,0.077,0.03221570329798157
+chi1996uma8,1996-01-27,1996,1,day,0,0
+chi1996uma9,1996-01-28,1996,1,night,0,0
+dal1984amer1,1983-11-15,1983,11,day,18.494,1.2899009617286263
+dal1984amer10,1983-11-15,1983,11,day,2.132,0.49582175338590584
+dal1984amer12,1983-11-15,1983,11,night,2.579,0.5537616983900043
+dal1984amer13,1983-11-15,1983,11,night,10.389,1.0564855929511157
+dal1984amer13a,1983-11-15,1983,11,day,8.747,0.9888709660646452
+dal1984amer15,1983-11-15,1983,11,night,44.905,1.6618599917278098
+dal1984amer16,1983-11-15,1983,11,day,0.973,0.29512708525219117
+dal1984amer16a,1983-11-15,1983,11,day,4.785,0.7623033632877684
+dal1984amer18,1983-11-15,1983,11,day,1.31,0.36361197989214433
+dal1984amer19a,1983-11-15,1983,11,night,1.487,0.395675785269936
+dal1984amer19b,1983-11-15,1983,11,day,37.518,1.585663728828571
+dal1984amer19c,1983-11-15,1983,11,night,2.577,0.5535189401489695
+dal1984amer20,1983-11-15,1983,11,day,5.19,0.791690649020118
+dal1984amer21,1983-11-15,1983,11,night,5.479,0.8115079799453266
+dal1984amer21a,1983-11-15,1983,11,day,2.194,0.5043349118024641
+dal1984amer22,1983-11-15,1983,11,day,1.692,0.43007505555193926
+dal1984amer23,1983-11-15,1983,11,night,9.186,1.0080036718401593
+dal1984amer23a,1983-11-15,1983,11,day,1.768,0.44216608578472016
+dal1984amer24,1983-11-15,1983,11,day,8.278,0.9674543681827408
+dal1984amer25,1983-11-15,1983,11,night,19.506,1.311880953037904
+dal1984amer26,1983-11-15,1983,11,day,61.333,1.7947180293802423
+dal1984amer27a,1983-11-15,1983,11,day,26.276,1.4357806817725955
+dal1984amer27b,1983-11-15,1983,11,day,11.924,1.1113969493456064
+dal1984amer28,1983-11-15,1983,11,night,7.053,0.9059576990924271
+dal1984amer29,1983-11-15,1983,11,day,2.804,0.5802405082653763
+dal1984amer2a,1983-11-15,1983,11,day,15.756,1.224170351689182
+dal1984amer2b,1983-11-15,1983,11,day,35.665,1.5642516893316294
+dal1984amer30,1983-11-15,1983,11,night,0.87,0.271841606536499
+dal1984amer33,1983-11-15,1983,11,day,1.005,0.3021143769562011
+dal1984amer4,1983-11-15,1983,11,day,10.618,1.06513137214021
+dal1984amer5,1983-11-15,1983,11,day,8.269,0.9670328821587023
+dal1984amer5a,1983-11-15,1983,11,day,9257.056,3.9665198034091413
+dal1984amer6,1983-11-15,1983,11,day,5.384,0.8050928783426731
+dal1984amer70,1983-11-15,1983,11,night,0.413,0.1501421618485586
+dal1984amer7a,1983-11-15,1983,11,night,54.229,1.7421671797724072
+dal1984amer7b,1983-11-15,1983,11,night,26.726,1.4428872189023485
+dal1984amer7c,1983-11-15,1983,11,night,14.838,1.199700338629511
+dal1984amer8,1983-11-15,1983,11,day,6.862,0.8955330394840704
+dal1984amer8a,1983-11-15,1983,11,night,1.019,0.3051363189436393
+dal1984amer9,1983-11-15,1983,11,day,4.486,0.7392558032685106
+dal1984amerzero,1983-11-15,1983,11,NA,0,0
+dal1986amer1,1986-03-15,1986,3,night,7.386,0.9235548580675493
+dal1986amer10,1986-03-15,1986,3,day,58.882,1.777296296922495
+dal1986amer11,1986-03-15,1986,3,day,4.611,0.7490402687034572
+dal1986amer12,1986-03-15,1986,3,day,19.96,1.321391278311689
+dal1986amer13,1986-03-15,1986,3,day,325.527,2.5139190982181305
+dal1986amer14,1986-03-15,1986,3,day,8.997,0.9998696921082678
+dal1986amer16,1986-03-15,1986,3,day,1.07,0.3159703454569178
+dal1986amer18,1986-03-15,1986,3,day,4.4,0.7323937598229685
+dal1986amer19,1986-03-15,1986,3,day,70.276,1.85294331916193
+dal1986amer2,1986-03-15,1986,3,day,27.527,1.455256102117952
+dal1986amer20,1986-03-15,1986,3,day,21.086,1.3441170678303103
+dal1986amer21,1986-03-15,1986,3,day,27.699,1.4578667642584457
+dal1986amer23,1986-03-15,1986,3,day,4.346,0.7280289544205184
+dal1986amer28,1986-03-15,1986,3,night,599.731,2.778680043769494
+dal1986amer34,1986-03-15,1986,3,day,42.548,1.6389682142481814
+dal1986amer35,1986-03-15,1986,3,day,NA,NA
+dal1986amer37,1986-03-15,1986,3,night,5.733,0.8282086144679454
+dal1986amer4,1986-03-15,1986,3,day,52.29,1.7266457202409118
+dal1986amer43,1986-03-15,1986,3,day,NA,NA
+dal1986amer46,1986-03-15,1986,3,day,265.123,2.4250824105958286
+dal1986amer9,1986-03-15,1986,3,day,4.4,0.7323937598229685
+dal1986amersalp,1986-03-15,1986,3,NA,NA,NA
+don1994edge,1993-12-15,1993,12,NA,3.449,0.6482624057480444
+don1994open,1993-12-15,1993,12,NA,6.893,0.8972421028053654
+don1994pack,1993-12-15,1993,12,NA,3.07,0.6095944092252201
+epa1980nbn20,1979-12-22,1979,12,night,0,0
+epa1980nbn21,1979-12-23,1979,12,night,0,0
+epa1980nbn22,1979-12-25,1979,12,night,0,0
+epa1980nbn23,1979-12-26,1979,12,night,0.001,4.340774793185929e-4
+epa1980nbn24,1979-12-27,1979,12,night,0,0
+epa1980nbn25,1979-12-28,1979,12,night,0,0
+epa1980nbn26,1979-12-29,1979,12,night,0.005,0.0021660617565076304
+epa1980nbn27,1979-12-30,1979,12,night,0.072,0.03019478535675124
+epa1980nbn28,1979-12-31,1979,12,night,0.019,0.008174184006426354
+epa1980nbn29,1980-01-01,1980,1,night,0.05,0.021189299069938092
+epa1980nbn30,1980-01-18,1980,1,night,0.013,0.005609445360280386
+epa1980nbn31,1980-01-19,1980,1,night,0.03,0.012837224705172217
+epa1980nbn32,1980-01-20,1980,1,night,0.001,4.340774793185929e-4
+epa1980nbn33,1980-01-21,1980,1,night,0.055,0.023252459633711444
+epa1981nbn1,1981-01-09,1981,1,day,0,0
+epa1981nbn10,1981-02-16,1981,2,night,0,0
+epa1981nbn11,1981-02-17,1981,2,night,0,0
+epa1981nbn12,1981-02-18,1981,2,night,0,0
+epa1981nbn13,1981-02-19,1981,2,night,0,0
+epa1981nbn14,1981-02-20,1981,2,night,0,0
+epa1981nbn15,1981-02-21,1981,2,night,0,0
+epa1981nbn16,1981-02-22,1981,2,night,0,0
+epa1981nbn17,1981-02-23,1981,2,night,0,0
+epa1981nbn18,1981-02-24,1981,2,night,0,0
+epa1981nbn19,1981-02-25,1981,2,night,0,0
+epa1981nbn2,1981-01-09,1981,1,night,0,0
+epa1981nbn3,1981-01-10,1981,1,night,0,0
+epa1981nbn4,1981-01-11,1981,1,night,0,0
+epa1981nbn5,1981-01-12,1981,1,night,0,0
+epa1981nbn6,1981-01-13,1981,1,night,0,0
+epa1981nbn7,1981-01-16,1981,1,night,0,0
+epa1981nbn8,1981-01-17,1981,1,night,0,0
+epa1981nbn9,1981-01-18,1981,1,night,0,0
+epa1981saf257,1980-12-26,1980,12,day,0,0
+epa1981saf258,1980-12-27,1980,12,night,0,0
+epa1981saf259,1980-12-28,1980,12,day,0,0
+epa1981saf260,1980-12-28,1980,12,night,0,0
+epa1981saf261,1980-12-30,1980,12,day,0,0
+epa1981saf262,1981-01-02,1981,1,day,0,0
+epa1981saf263,1981-01-05,1981,1,day,2.907,0.5918434112247845
+epa1981saf264,1981-01-06,1981,1,night,1.294,0.3605934135652489
+epa1981saf265,1981-01-06,1981,1,day,0,0
+epa1981saf266,1981-02-02,1981,2,night,0,0
+epa1981saf267,1981-02-04,1981,2,day,0,0
+epa1981saf268,1981-02-05,1981,2,night,0,0
+epa1981saf269,1981-02-05,1981,2,night,0,0
+epa1981saf270,1981-02-06,1981,2,day,0,0
+epa1981saf271,1981-02-07,1981,2,night,0,0
+epa1983yun140,1982-12-19,1982,12,day,0,0
+epa1983yun141,1982-12-19,1982,12,day,0,0
+epa1983yun142,1982-12-19,1982,12,day,0,0
+epa1983yun143,1982-12-20,1982,12,day,0,0
+epa1983yun144,1982-12-20,1982,12,day,0,0
+epa1983yun145,1982-12-21,1982,12,night,0,0
+epa1983yun146,1982-12-21,1982,12,night,0,0
+epa1983yun147,1982-12-22,1982,12,night,0,0
+epa1983yun148,1982-12-22,1982,12,night,0,0
+epa1983yun149,1982-12-23,1982,12,night,0,0
+epa1983yun150,1982-12-23,1982,12,day,0,0
+epa1983yun151,1982-12-24,1982,12,day,0,0
+epa1983yun152,1982-12-24,1982,12,day,0,0
+epa1983yun153,1982-12-25,1982,12,day,0,0
+epa1983yun154,1982-12-25,1982,12,day,0,0
+epa1983yun155,1982-12-26,1982,12,night,0,0
+epa1983yun156,1982-12-26,1982,12,night,0,0
+epa1983yun157,1982-12-27,1982,12,night,0,0
+epa1983yun158,1982-12-28,1982,12,night,0,0
+epa1983yun159,1982-12-28,1982,12,night,0,0
+epa1983yun160,1982-12-29,1982,12,day,0,0
+epa1983yun161,1982-12-29,1982,12,day,0,0
+epa1983yun162,1982-12-30,1982,12,day,0,0
+epa1983yun163,1982-12-30,1982,12,day,0,0
+epa1983yun164,1982-12-31,1982,12,day,0,0
+epa1983yun165,1983-01-01,1983,1,night,0,0
+epa1983yun166,1983-01-01,1983,1,night,0,0
+epa1983yun167,1983-01-02,1983,1,night,0,0
+epa1983yun168,1983-01-03,1983,1,night,0,0
+epa1983yun169,1983-01-03,1983,1,night,0,0
+epa1983yun170,1983-01-04,1983,1,day,0,0
+epa1983yun171,1983-01-05,1983,1,day,0,0
+epa1983yun172,1983-01-06,1983,1,day,0,0
+epa1983yun173,1983-01-06,1983,1,day,0,0
+epa1983yun174,1983-01-07,1983,1,day,0,0
+epa1983yun175,1983-01-08,1983,1,night,0,0
+epa1983yun176,1983-01-09,1983,1,night,0,0
+epa1983yun177,1983-01-10,1983,1,night,0,0
+epa1983yun178,1983-01-10,1983,1,night,0,0
+epa1983yun179,1983-01-11,1983,1,night,0,0
+epa1983yun180,1983-01-11,1983,1,day,0,0
+epa1983yun181,1983-01-12,1983,1,day,0,0
+epa1983yun182,1983-01-12,1983,1,day,0,0
+epa1983yun183,1983-01-13,1983,1,day,0,0
+epa1983yun184,1983-01-13,1983,1,night,0,0
+epa1983yun185,1983-01-14,1983,1,night,0,0
+epa1983yun186,1983-01-14,1983,1,night,0,0
+epa1984yun187,1983-12-01,1983,12,night,1.34,0.36921585741014284
+epa1984yun188,1983-12-02,1983,12,night,0,0
+epa1984yun189,1983-12-05,1983,12,night,0,0
+epa1984yun190,1984-01-22,1984,1,night,0,0
+epa1984yun191,1984-01-22,1984,1,night,2.878,0.5886078047426866
+epa1984yun192,1984-01-23,1984,1,day,0,0
+epa1984yun193,1984-01-23,1984,1,day,0,0
+epa1984yun194,1984-01-23,1984,1,night,0,0
+epa1984yun195,1984-01-23,1984,1,night,0,0
+epa1984yun196,1984-01-24,1984,1,day,0,0
+epa1984yun197,1984-01-24,1984,1,day,0,0
+epa1984yun198,1984-01-24,1984,1,night,0,0
+epa1984yun199,1984-01-24,1984,1,night,0,0
+epa1984yun200,1984-01-25,1984,1,day,0,0
+epa1984yun201,1984-01-25,1984,1,day,0,0
+epa1984yun202,1984-01-25,1984,1,night,2.954,0.5970366649776535
+epa1984yun203,1984-01-25,1984,1,night,2.954,0.5970366649776535
+epa1984yun204,1984-01-26,1984,1,day,0,0
+epa1984yun205,1984-01-26,1984,1,day,0,0
+epa1984yun206,1984-01-26,1984,1,night,0,0
+epa1984yun207,1984-01-26,1984,1,night,0,0
+epa1984yun208,1984-01-27,1984,1,day,0,0
+epa1984yun209,1984-01-27,1984,1,night,0,0
+epa1984yun210,1984-01-28,1984,1,day,0,0
+epa1984yun211,1984-01-28,1984,1,night,0,0
+epa1984yun212,1984-01-28,1984,1,night,0,0
+epa1984yun213,1984-01-28,1984,1,day,0,0
+epa1984yun214,1984-01-28,1984,1,day,0,0
+epa1984yun215,1984-01-29,1984,1,night,0,0
+epa1984yun216,1984-01-29,1984,1,night,0,0
+epa1984yun217,1984-01-29,1984,1,day,0,0
+epa1984yun218,1984-01-29,1984,1,day,0,0
+epa1984yun219,1984-01-30,1984,1,night,0,0
+epa1984yun220,1984-01-30,1984,1,night,0,0
+epa1984yun221,1984-01-30,1984,1,day,0,0
+epa1984yun222,1984-01-31,1984,1,day,14.141,1.1801545594533482
+epa1984yun223,1984-01-31,1984,1,night,3.136,0.6165805300858861
+epa1984yun224,1984-02-01,1984,2,day,0,0
+epa1984yun225,1984-02-01,1984,2,night,6.34,0.8656960599160706
+epa1984yun226,1984-02-01,1984,2,day,0,0
+epa1984yun227,1984-02-02,1984,2,day,0,0
+epa1984yun228,1984-02-02,1984,2,night,0,0
+epa1984yun229,1984-02-02,1984,2,night,0,0
+epa1984yun230,1984-02-03,1984,2,night,0,0
+epa1984yun231,1984-02-03,1984,2,day,0,0
+epa1984yun232,1984-02-10,1984,2,day,0,0
+epa1984yun233,1984-02-10,1984,2,night,7.081,0.9074651067658561
+epa1984yun234,1984-02-10,1984,2,night,106.217,2.03026365122287
+epa1984yun235,1984-02-10,1984,2,day,0,0
+epa1984yun236,1984-02-10,1984,2,day,0,0
+epa1984yun237,1984-02-10,1984,2,night,0,0
+epa1984yun238,1984-02-11,1984,2,night,0,0
+epa1984yun239,1984-02-11,1984,2,night,7.179,0.9127002081908604
+epa1984yun240,1984-02-11,1984,2,night,0,0
+epa1984yun241,1984-02-11,1984,2,day,0,0
+epa1984yun242,1984-02-11,1984,2,day,0,0
+epa1984yun243,1984-02-11,1984,2,day,0,0
+epa1984yun244,1984-02-11,1984,2,day,0,0
+epa1984yun245,1984-02-12,1984,2,day,0,0
+epa1984yun246,1984-02-12,1984,2,night,0,0
+epa1984yun247,1984-02-12,1984,2,night,0,0
+epa1984yun248,1984-02-12,1984,2,night,0,0
+epa1984yun249,1984-02-29,1984,2,night,0,0
+epa1984yun250,1984-02-29,1984,2,night,0,0
+epa1984yun251,1984-02-29,1984,2,day,0,0
+epa1984yun252,1984-03-01,1984,3,day,0,0
+epa1984yun253,1984-03-01,1984,3,day,0,0
+epa1984yun254,1984-03-01,1984,3,night,35.736,1.5650918663818607
+epa1985ikt141,1985-02-05,1985,2,day,0,0
+epa1985ikt142,1985-02-05,1985,2,night,0,0
+epa1985ikt143,1985-02-06,1985,2,NA,0,0
+epa1985ikt144,1985-02-06,1985,2,day,0.116,0.04766419460155998
+epa1985ikt145,1985-02-07,1985,2,night,0.027,0.011570443597278159
+epa1985ikt146,1985-02-07,1985,2,day,0.454,0.162564406523019
+epa1985ikt147,1985-02-07,1985,2,night,0.07,0.029383777685209667
+epa1985ikt148,1985-02-08,1985,2,night,0.021,0.009025742086910206
+epa1985ikt149,1985-02-08,1985,2,day,1.439,0.3872118003137306
+epa1985ikt150,1985-02-08,1985,2,night,0.315,0.11892575282577666
+epa1985ikt151,1985-02-09,1985,2,day,0.41,0.14921911265537988
+epa1985ikt152,1985-02-09,1985,2,day,0,0
+epa1985ikt153,1985-02-09,1985,2,day,0,0
+epa1985ikt154,1985-02-09,1985,2,night,0.229,0.08955188288645412
+epa1985ikt155,1985-02-10,1985,2,day,33.253,1.5346986145551145
+epa1985ikt156,1985-02-10,1985,2,day,0.503,0.17695898058690818
+epa1985ikt157,1985-02-11,1985,2,day,1.51,0.3996737214810381
+epa1985ikt158,1985-02-11,1985,2,day,0.015,0.006466042249231681
+epa1985ikt159,1985-02-11,1985,2,night,0.02,0.008600171761917567
+epa1985ikt160,1985-02-12,1985,2,day,0,0
+epa1985ikt161,1985-02-12,1985,2,day,0,0
+epa1985ikt162,1985-02-12,1985,2,night,0,0
+epa1985ikt163,1985-02-13,1985,2,day,0,0
+epa1985ikt164,1985-02-13,1985,2,day,0,0
+epa1985ikt165,1985-02-14,1985,2,night,0,0
+epa1985ikt166,1985-02-14,1985,2,day,0,0
+epa1985ikt167,1985-02-14,1985,2,day,0,0
+epa1985ikt168,1985-02-14,1985,2,night,0.011,0.00475115559100102
+epa1985ikt169,1985-02-15,1985,2,day,0.047,0.019946681678842303
+epa1985ikt170,1985-02-15,1985,2,day,6.903,0.8977919819392246
+epa1985ikt171,1985-02-16,1985,2,night,0.184,0.07335170238690095
+epa1985ikt172,1985-02-16,1985,2,day,21.53,1.352761191723831
+epa1985ikt173,1985-02-17,1985,2,night,1.01,0.30319605742048883
+epa1985ikt174,1985-02-17,1985,2,day,0,0
+epa1985ikt175,1985-02-18,1985,2,night,0.366,0.13545069934551376
+epa1985ikt176,1985-02-18,1985,2,day,0.061,0.025715383901340642
+epa1985ikt177,1985-02-19,1985,2,night,0.015,0.006466042249231681
+epa1985ikt178,1985-02-19,1985,2,day,0,0
+epa1985ikt179,1985-02-20,1985,2,night,0.042,0.017867718963505686
+epa1985ikt180,1985-02-20,1985,2,day,0,0
+epa1985ikt181,1985-02-20,1985,2,night,0.022,0.009450895798693935
+epa1985ikt182,1985-02-21,1985,2,day,0,0
+epa1985ikt183,1985-02-21,1985,2,day,0,0
+epa1985ikt184,1985-02-22,1985,2,night,0.005,0.0021660617565076304
+epa1985ikt185,1985-02-22,1985,2,day,3.431,0.6465017500316119
+epa1985ikt186,1985-02-22,1985,2,day,8.479,0.9767625232674606
+epa1985ikt187,1985-02-23,1985,2,day,10.932,1.0767132446900252
+epa1985ikt188,1985-03-03,1985,3,night,3.602,0.6629466143326246
+epa1985ikt189,1985-03-03,1985,3,night,0.042,0.017867718963505686
+epa1985ikt190,1985-03-04,1985,3,night,39.86,1.6112983622964288
+epa1985ikt191,1985-03-04,1985,3,night,36.22,1.5707763687947482
+epa1985ikt192,1985-03-05,1985,3,day,20.455,1.3315285188686907
+epa1985ikt193,1985-03-05,1985,3,night,4.504,0.7406784252274549
+epa1985ikt194,1985-03-06,1985,3,day,0.006,0.0025979807199085943
+epa1985ikt195,1985-03-06,1985,3,night,52.697,1.7299500227602547
+epa1985ikt196,1985-03-08,1985,3,day,4.75,0.7596678446896306
+epa1985ikt197,1985-03-29,1985,3,day,59.537,1.7820208950525276
+epa1985ikt198,1985-03-29,1985,3,day,96.931,1.9909201892274624
+epa1985yun100,1985-02-05,1985,2,day,0,0
+epa1985yun101,1985-02-05,1985,2,night,0,0
+epa1985yun102,1985-02-06,1985,2,night,0,0
+epa1985yun103,1985-02-06,1985,2,day,0,0
+epa1985yun104,1985-02-06,1985,2,night,0,0
+epa1985yun105,1985-02-07,1985,2,day,0,0
+epa1985yun106,1985-02-07,1985,2,night,0,0
+epa1985yun107,1985-02-08,1985,2,day,0,0
+epa1985yun108,1985-02-09,1985,2,day,0,0
+epa1985yun109,1985-02-09,1985,2,day,0,0
+epa1985yun110,1985-02-10,1985,2,night,3.541,0.6571515019009667
+epa1985yun111,1985-02-10,1985,2,night,0,0
+epa1985yun112,1985-02-11,1985,2,night,0,0
+epa1985yun113,1985-02-11,1985,2,day,0,0
+epa1985yun114,1985-02-11,1985,2,night,0,0
+epa1985yun115,1985-02-12,1985,2,night,0,0
+epa1985yun116,1985-02-12,1985,2,day,0,0
+epa1985yun117,1985-02-12,1985,2,night,0,0
+epa1985yun118,1985-02-13,1985,2,day,0,0
+epa1985yun119,1985-02-14,1985,2,day,0,0
+epa1985yun120,1985-02-14,1985,2,night,0,0
+epa1985yun121,1985-02-15,1985,2,night,0,0
+epa1985yun122,1985-02-15,1985,2,day,0,0
+epa1985yun123,1985-02-15,1985,2,night,0,0
+epa1985yun124,1985-02-16,1985,2,night,0,0
+epa1985yun125,1985-02-16,1985,2,night,3.861,0.6867256210745423
+epa1985yun126,1985-02-17,1985,2,night,0,0
+epa1985yun127,1985-02-17,1985,2,day,0,0
+epa1985yun128,1985-02-17,1985,2,night,0,0
+epa1985yun129,1985-02-18,1985,2,night,0,0
+epa1985yun130,1985-02-18,1985,2,night,0,0
+epa1985yun131,1985-02-19,1985,2,night,0,0
+epa1985yun132,1985-02-19,1985,2,day,0,0
+epa1985yun133,1985-02-20,1985,2,night,0,0
+epa1985yun134,1985-02-20,1985,2,night,0,0
+epa1985yun135,1985-02-21,1985,2,day,0,0
+epa1985yun136,1985-02-21,1985,2,day,0,0
+epa1985yun137,1985-02-21,1985,2,night,3.975,0.6967930850817442
+epa1985yun138,1985-02-22,1985,2,day,0,0
+epa1985yun139,1985-02-22,1985,2,day,0,0
+epa1985yuv10a,1985-02-15,1985,2,night,0,0
+epa1985yuv10b,1985-02-15,1985,2,night,NA,NA
+epa1985yuv10c,1985-02-15,1985,2,night,NA,NA
+epa1985yuv10com,1985-02-15,1985,2,night,0,0
+epa1985yuv11a,1985-02-16,1985,2,night,0,0
+epa1985yuv11b,1985-02-16,1985,2,night,NA,NA
+epa1985yuv11c,1985-02-16,1985,2,night,NA,NA
+epa1985yuv11com,1985-02-16,1985,2,night,0,0
+epa1985yuv12a,1985-02-16,1985,2,night,0,0
+epa1985yuv12b,1985-02-16,1985,2,night,NA,NA
+epa1985yuv12c,1985-02-16,1985,2,night,NA,NA
+epa1985yuv12com,1985-02-16,1985,2,night,0,0
+epa1985yuv13a,1985-02-17,1985,2,night,0,0
+epa1985yuv13b,1985-02-17,1985,2,night,NA,NA
+epa1985yuv13c,1985-02-17,1985,2,night,NA,NA
+epa1985yuv13com,1985-02-17,1985,2,night,0,0
+epa1985yuv14a,1985-02-17,1985,2,day,0,0
+epa1985yuv14b,1985-02-17,1985,2,day,NA,NA
+epa1985yuv14c,1985-02-17,1985,2,day,NA,NA
+epa1985yuv14com,1985-02-17,1985,2,day,0,0
+epa1985yuv15a,1985-02-17,1985,2,night,0,0
+epa1985yuv15b,1985-02-17,1985,2,night,NA,NA
+epa1985yuv15c,1985-02-17,1985,2,night,NA,NA
+epa1985yuv15com,1985-02-17,1985,2,night,1.571,0.4101020766428606
+epa1985yuv16a,1985-02-22,1985,2,day,0,0
+epa1985yuv16b,1985-02-22,1985,2,day,NA,NA
+epa1985yuv16c,1985-02-22,1985,2,day,NA,NA
+epa1985yuv16com,1985-02-22,1985,2,day,0,0
+epa1985yuv1a,1985-02-07,1985,2,day,0,0
+epa1985yuv1b,1985-02-07,1985,2,day,NA,NA
+epa1985yuv1c,1985-02-07,1985,2,day,NA,NA
+epa1985yuv1com,1985-02-07,1985,2,day,0,0
+epa1985yuv2a,1985-02-07,1985,2,night,1.325,0.36642295722597273
+epa1985yuv2b,1985-02-07,1985,2,night,NA,NA
+epa1985yuv2c,1985-02-07,1985,2,night,NA,NA
+epa1985yuv2com,1985-02-07,1985,2,night,1.385,0.37748838337613266
+epa1985yuv34a,1985-02-05,1985,2,day,0,0
+epa1985yuv34b,1985-02-05,1985,2,day,0,0
+epa1985yuv34c,1985-02-05,1985,2,day,0,0
+epa1985yuv34com,1985-02-05,1985,2,day,0,0
+epa1985yuv34d,1985-02-05,1985,2,day,NA,NA
+epa1985yuv34e,1985-02-05,1985,2,day,NA,NA
+epa1985yuv35a,1985-02-05,1985,2,night,0,0
+epa1985yuv35b,1985-02-05,1985,2,night,0,0
+epa1985yuv35c,1985-02-05,1985,2,night,0,0
+epa1985yuv35com,1985-02-05,1985,2,night,0,0
+epa1985yuv35d,1985-02-05,1985,2,night,NA,NA
+epa1985yuv35e,1985-02-05,1985,2,night,NA,NA
+epa1985yuv36a,1985-02-06,1985,2,night,0,0
+epa1985yuv36b,1985-02-06,1985,2,night,0,0
+epa1985yuv36c,1985-02-06,1985,2,night,0,0
+epa1985yuv36com,1985-02-06,1985,2,night,0,0
+epa1985yuv36d,1985-02-06,1985,2,night,NA,NA
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+epa1985yuv37a,1985-02-06,1985,2,day,0,0
+epa1985yuv37b,1985-02-06,1985,2,day,0,0
+epa1985yuv37c,1985-02-06,1985,2,day,0,0
+epa1985yuv37com,1985-02-06,1985,2,day,0,0
+epa1985yuv37d,1985-02-06,1985,2,day,NA,NA
+epa1985yuv37e,1985-02-06,1985,2,day,NA,NA
+epa1985yuv38a,1985-02-06,1985,2,night,0,0
+epa1985yuv38b,1985-02-06,1985,2,night,0,0
+epa1985yuv38c,1985-02-06,1985,2,night,0,0
+epa1985yuv38com,1985-02-06,1985,2,night,0,0
+epa1985yuv38d,1985-02-06,1985,2,night,NA,NA
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+epa1985yuv39a,1985-02-07,1985,2,day,0,0
+epa1985yuv39b,1985-02-07,1985,2,day,0,0
+epa1985yuv39c,1985-02-07,1985,2,day,0,0
+epa1985yuv39com,1985-02-07,1985,2,day,0,0
+epa1985yuv39d,1985-02-07,1985,2,day,NA,NA
+epa1985yuv39e,1985-02-07,1985,2,day,NA,NA
+epa1985yuv3a,1985-02-08,1985,2,day,0,0
+epa1985yuv3b,1985-02-08,1985,2,day,NA,NA
+epa1985yuv3c,1985-02-08,1985,2,day,NA,NA
+epa1985yuv3com,1985-02-08,1985,2,day,3.159,0.6189889203649336
+epa1985yuv40a,1985-02-07,1985,2,night,0,0
+epa1985yuv40b,1985-02-07,1985,2,night,0,0
+epa1985yuv40c,1985-02-07,1985,2,night,0,0
+epa1985yuv40com,1985-02-07,1985,2,night,0,0
+epa1985yuv40d,1985-02-07,1985,2,night,NA,NA
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+epa1985yuv41a,1985-02-08,1985,2,day,0,0
+epa1985yuv41b,1985-02-08,1985,2,day,0,0
+epa1985yuv41c,1985-02-08,1985,2,day,0,0
+epa1985yuv41com,1985-02-08,1985,2,day,0,0
+epa1985yuv41d,1985-02-08,1985,2,day,NA,NA
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+epa1985yuv42a,1985-02-09,1985,2,day,0,0
+epa1985yuv42b,1985-02-09,1985,2,day,0,0
+epa1985yuv42c,1985-02-09,1985,2,day,0,0
+epa1985yuv42com,1985-02-09,1985,2,day,0,0
+epa1985yuv42d,1985-02-09,1985,2,day,NA,NA
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+epa1985yuv43a,1985-02-09,1985,2,day,0,0
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+epa1985yuv43c,1985-02-09,1985,2,day,0,0
+epa1985yuv43com,1985-02-09,1985,2,day,0,0
+epa1985yuv43d,1985-02-09,1985,2,day,NA,NA
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+epa1985yuv44a,1985-02-10,1985,2,night,2.808,0.5806969397124366
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+epa1985yuv44c,1985-02-10,1985,2,night,0,0
+epa1985yuv44com,1985-02-10,1985,2,night,3.701,0.6721902511882526
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+epa1985yuv44e,1985-02-10,1985,2,night,NA,NA
+epa1985yuv45a,1985-02-10,1985,2,night,0,0
+epa1985yuv45b,1985-02-10,1985,2,night,0,0
+epa1985yuv45c,1985-02-10,1985,2,night,0,0
+epa1985yuv45com,1985-02-10,1985,2,night,0,0
+epa1985yuv45d,1985-02-10,1985,2,night,NA,NA
+epa1985yuv45e,1985-02-10,1985,2,night,NA,NA
+epa1985yuv46a,1985-02-11,1985,2,night,0,0
+epa1985yuv46b,1985-02-11,1985,2,night,0,0
+epa1985yuv46c,1985-02-11,1985,2,night,0,0
+epa1985yuv46com,1985-02-11,1985,2,night,0,0
+epa1985yuv46d,1985-02-11,1985,2,night,NA,NA
+epa1985yuv46e,1985-02-11,1985,2,night,NA,NA
+epa1985yuv47a,1985-02-11,1985,2,day,0,0
+epa1985yuv47b,1985-02-11,1985,2,day,0,0
+epa1985yuv47c,1985-02-11,1985,2,day,0,0
+epa1985yuv47com,1985-02-11,1985,2,day,0,0
+epa1985yuv47d,1985-02-11,1985,2,day,NA,NA
+epa1985yuv47e,1985-02-11,1985,2,day,NA,NA
+epa1985yuv48a,1985-02-11,1985,2,night,0,0
+epa1985yuv48b,1985-02-11,1985,2,night,0,0
+epa1985yuv48c,1985-02-11,1985,2,night,0,0
+epa1985yuv48com,1985-02-11,1985,2,night,0,0
+epa1985yuv48d,1985-02-11,1985,2,night,NA,NA
+epa1985yuv48e,1985-02-11,1985,2,night,NA,NA
+epa1985yuv49a,1985-02-12,1985,2,night,0,0
+epa1985yuv49b,1985-02-12,1985,2,night,0,0
+epa1985yuv49c,1985-02-12,1985,2,night,0,0
+epa1985yuv49com,1985-02-12,1985,2,night,0,0
+epa1985yuv49d,1985-02-12,1985,2,night,NA,NA
+epa1985yuv49e,1985-02-12,1985,2,night,NA,NA
+epa1985yuv4a,1985-02-09,1985,2,night,0,0
+epa1985yuv4b,1985-02-09,1985,2,night,NA,NA
+epa1985yuv4c,1985-02-09,1985,2,night,NA,NA
+epa1985yuv4com,1985-02-09,1985,2,night,1.417,0.38327665040765035
+epa1985yuv50a,1985-02-12,1985,2,day,0,0
+epa1985yuv50b,1985-02-12,1985,2,day,0,0
+epa1985yuv50c,1985-02-12,1985,2,day,0,0
+epa1985yuv50com,1985-02-12,1985,2,day,0,0
+epa1985yuv50d,1985-02-12,1985,2,day,NA,NA
+epa1985yuv50e,1985-02-12,1985,2,day,NA,NA
+epa1985yuv51a,1985-02-12,1985,2,night,0,0
+epa1985yuv51b,1985-02-12,1985,2,night,0,0
+epa1985yuv51c,1985-02-12,1985,2,night,0,0
+epa1985yuv51com,1985-02-12,1985,2,night,0,0
+epa1985yuv51d,1985-02-12,1985,2,night,NA,NA
+epa1985yuv51e,1985-02-12,1985,2,night,NA,NA
+epa1985yuv52a,1985-02-13,1985,2,day,0,0
+epa1985yuv52b,1985-02-13,1985,2,day,0,0
+epa1985yuv52c,1985-02-13,1985,2,day,0,0
+epa1985yuv52com,1985-02-13,1985,2,day,0,0
+epa1985yuv52d,1985-02-13,1985,2,day,NA,NA
+epa1985yuv52e,1985-02-13,1985,2,day,NA,NA
+epa1985yuv53a,1985-02-14,1985,2,day,0,0
+epa1985yuv53b,1985-02-14,1985,2,day,0,0
+epa1985yuv53c,1985-02-14,1985,2,day,0,0
+epa1985yuv53com,1985-02-14,1985,2,day,0,0
+epa1985yuv53d,1985-02-14,1985,2,day,NA,NA
+epa1985yuv53e,1985-02-14,1985,2,day,NA,NA
+epa1985yuv54a,1985-02-14,1985,2,night,0,0
+epa1985yuv54b,1985-02-14,1985,2,night,0,0
+epa1985yuv54c,1985-02-14,1985,2,night,0,0
+epa1985yuv54com,1985-02-14,1985,2,night,0,0
+epa1985yuv54d,1985-02-14,1985,2,night,NA,NA
+epa1985yuv54e,1985-02-14,1985,2,night,NA,NA
+epa1985yuv55a,1985-02-15,1985,2,night,0,0
+epa1985yuv55b,1985-02-15,1985,2,night,0,0
+epa1985yuv55c,1985-02-15,1985,2,night,0,0
+epa1985yuv55com,1985-02-15,1985,2,night,0,0
+epa1985yuv55d,1985-02-15,1985,2,night,NA,NA
+epa1985yuv55e,1985-02-15,1985,2,night,NA,NA
+epa1985yuv56a,1985-02-15,1985,2,day,0,0
+epa1985yuv56b,1985-02-15,1985,2,day,0,0
+epa1985yuv56c,1985-02-15,1985,2,day,0,0
+epa1985yuv56com,1985-02-15,1985,2,day,0,0
+epa1985yuv56d,1985-02-15,1985,2,day,NA,NA
+epa1985yuv56e,1985-02-15,1985,2,day,NA,NA
+epa1985yuv57a,1985-02-15,1985,2,night,0,0
+epa1985yuv57b,1985-02-15,1985,2,night,0,0
+epa1985yuv57c,1985-02-15,1985,2,night,0,0
+epa1985yuv57com,1985-02-15,1985,2,night,0,0
+epa1985yuv57d,1985-02-15,1985,2,night,NA,NA
+epa1985yuv57e,1985-02-15,1985,2,night,NA,NA
+epa1985yuv58a,1985-02-16,1985,2,night,0,0
+epa1985yuv58b,1985-02-16,1985,2,night,0,0
+epa1985yuv58c,1985-02-16,1985,2,night,0,0
+epa1985yuv58com,1985-02-16,1985,2,night,0,0
+epa1985yuv58d,1985-02-16,1985,2,night,NA,NA
+epa1985yuv58e,1985-02-16,1985,2,night,NA,NA
+epa1985yuv59a,1985-02-16,1985,2,night,3.062,0.6087399190687879
+epa1985yuv59b,1985-02-16,1985,2,night,0,0
+epa1985yuv59c,1985-02-16,1985,2,night,0,0
+epa1985yuv59com,1985-02-16,1985,2,night,4.037,0.7021719508577112
+epa1985yuv59d,1985-02-16,1985,2,night,NA,NA
+epa1985yuv59e,1985-02-16,1985,2,night,NA,NA
+epa1985yuv5a,1985-02-09,1985,2,day,0,0
+epa1985yuv5b,1985-02-09,1985,2,day,NA,NA
+epa1985yuv5c,1985-02-09,1985,2,day,NA,NA
+epa1985yuv5com,1985-02-09,1985,2,day,0,0
+epa1985yuv60a,1985-02-17,1985,2,night,0,0
+epa1985yuv60b,1985-02-17,1985,2,night,0,0
+epa1985yuv60c,1985-02-17,1985,2,night,0,0
+epa1985yuv60com,1985-02-17,1985,2,night,0,0
+epa1985yuv60d,1985-02-17,1985,2,night,NA,NA
+epa1985yuv60e,1985-02-17,1985,2,night,NA,NA
+epa1985yuv61a,1985-02-17,1985,2,day,0,0
+epa1985yuv61b,1985-02-17,1985,2,day,0,0
+epa1985yuv61c,1985-02-17,1985,2,day,0,0
+epa1985yuv61com,1985-02-17,1985,2,day,0,0
+epa1985yuv61d,1985-02-17,1985,2,day,NA,NA
+epa1985yuv61e,1985-02-17,1985,2,day,NA,NA
+epa1985yuv62a,1985-02-17,1985,2,night,0,0
+epa1985yuv62b,1985-02-17,1985,2,night,0,0
+epa1985yuv62c,1985-02-17,1985,2,night,0,0
+epa1985yuv62com,1985-02-17,1985,2,night,0,0
+epa1985yuv62d,1985-02-17,1985,2,night,NA,NA
+epa1985yuv62e,1985-02-17,1985,2,night,NA,NA
+epa1985yuv63a,1985-02-18,1985,2,night,0,0
+epa1985yuv63b,1985-02-18,1985,2,night,0,0
+epa1985yuv63c,1985-02-18,1985,2,night,0,0
+epa1985yuv63com,1985-02-18,1985,2,night,0,0
+epa1985yuv63d,1985-02-18,1985,2,night,NA,NA
+epa1985yuv63e,1985-02-18,1985,2,night,NA,NA
+epa1985yuv64a,1985-02-18,1985,2,night,0,0
+epa1985yuv64b,1985-02-18,1985,2,night,0,0
+epa1985yuv64c,1985-02-18,1985,2,night,0,0
+epa1985yuv64com,1985-02-18,1985,2,night,0,0
+epa1985yuv64d,1985-02-18,1985,2,night,NA,NA
+epa1985yuv64e,1985-02-18,1985,2,night,NA,NA
+epa1985yuv65a,1985-02-19,1985,2,night,0,0
+epa1985yuv65b,1985-02-19,1985,2,night,0,0
+epa1985yuv65c,1985-02-19,1985,2,night,0,0
+epa1985yuv65com,1985-02-19,1985,2,night,0,0
+epa1985yuv65d,1985-02-19,1985,2,night,NA,NA
+epa1985yuv65e,1985-02-19,1985,2,night,NA,NA
+epa1985yuv66a,1985-02-19,1985,2,day,0,0
+epa1985yuv66b,1985-02-19,1985,2,day,0,0
+epa1985yuv66c,1985-02-19,1985,2,day,0,0
+epa1985yuv66com,1985-02-19,1985,2,day,0,0
+epa1985yuv66d,1985-02-19,1985,2,day,NA,NA
+epa1985yuv66e,1985-02-19,1985,2,day,NA,NA
+epa1985yuv67a,1985-02-20,1985,2,night,0,0
+epa1985yuv67b,1985-02-20,1985,2,night,0,0
+epa1985yuv67c,1985-02-20,1985,2,night,0,0
+epa1985yuv67com,1985-02-20,1985,2,night,0,0
+epa1985yuv67d,1985-02-20,1985,2,night,NA,NA
+epa1985yuv67e,1985-02-20,1985,2,night,NA,NA
+epa1985yuv68a,1985-02-21,1985,2,day,0,0
+epa1985yuv68b,1985-02-21,1985,2,day,0,0
+epa1985yuv68c,1985-02-21,1985,2,day,0,0
+epa1985yuv68com,1985-02-21,1985,2,day,0,0
+epa1985yuv68d,1985-02-21,1985,2,day,NA,NA
+epa1985yuv68e,1985-02-21,1985,2,day,NA,NA
+epa1985yuv69a,1985-02-21,1985,2,day,0,0
+epa1985yuv69b,1985-02-21,1985,2,day,0,0
+epa1985yuv69c,1985-02-21,1985,2,day,0,0
+epa1985yuv69com,1985-02-21,1985,2,day,0,0
+epa1985yuv69d,1985-02-21,1985,2,day,NA,NA
+epa1985yuv69e,1985-02-21,1985,2,day,NA,NA
+epa1985yuv6a,1985-02-10,1985,2,day,0,0
+epa1985yuv6b,1985-02-10,1985,2,day,NA,NA
+epa1985yuv6c,1985-02-10,1985,2,day,NA,NA
+epa1985yuv6com,1985-02-10,1985,2,day,0,0
+epa1985yuv70a,1985-02-21,1985,2,night,0,0
+epa1985yuv70b,1985-02-21,1985,2,night,0,0
+epa1985yuv70c,1985-02-21,1985,2,night,3.975,0.6967930850817442
+epa1985yuv70com,1985-02-21,1985,2,night,4.156,0.7123129086813654
+epa1985yuv70d,1985-02-21,1985,2,night,NA,NA
+epa1985yuv70e,1985-02-21,1985,2,night,NA,NA
+epa1985yuv71a,1985-02-22,1985,2,day,0,0
+epa1985yuv71b,1985-02-22,1985,2,day,0,0
+epa1985yuv71c,1985-02-22,1985,2,day,0,0
+epa1985yuv71com,1985-02-22,1985,2,day,0,0
+epa1985yuv71d,1985-02-22,1985,2,day,NA,NA
+epa1985yuv71e,1985-02-22,1985,2,day,NA,NA
+epa1985yuv72a,1985-02-22,1985,2,day,0,0
+epa1985yuv72b,1985-02-22,1985,2,day,0,0
+epa1985yuv72c,1985-02-22,1985,2,day,0,0
+epa1985yuv72com,1985-02-22,1985,2,day,0,0
+epa1985yuv72d,1985-02-22,1985,2,day,NA,NA
+epa1985yuv72e,1985-02-22,1985,2,day,NA,NA
+epa1985yuv7a,1985-02-11,1985,2,night,0,0
+epa1985yuv7b,1985-02-11,1985,2,night,NA,NA
+epa1985yuv7c,1985-02-11,1985,2,night,NA,NA
+epa1985yuv7com,1985-02-11,1985,2,night,0,0
+epa1985yuv8a,1985-02-11,1985,2,day,0,0
+epa1985yuv8b,1985-02-11,1985,2,day,NA,NA
+epa1985yuv8c,1985-02-11,1985,2,day,NA,NA
+epa1985yuv8com,1985-02-11,1985,2,day,0,0
+epa1985yuv9a,1985-02-15,1985,2,day,6.595,0.8805277781988051
+epa1985yuv9b,1985-02-15,1985,2,day,NA,NA
+epa1985yuv9c,1985-02-15,1985,2,day,NA,NA
+epa1985yuv9com,1985-02-15,1985,2,day,20.685,1.3361594264847807
+epa1986ikt199,1985-12-26,1985,12,day,0,0
+epa1986ikt200,1985-12-26,1985,12,day,0.008,0.0034605321095064895
+epa1986ikt201,1985-12-26,1985,12,day,0,0
+epa1986ikt202,1985-12-26,1985,12,night,0.218,0.08564728829685654
+epa1986ikt203,1985-12-27,1985,12,day,0.334,0.12515582958053018
+epa1986ikt204,1985-12-27,1985,12,day,0,0
+epa1986ikt205,1985-12-27,1985,12,NA,0.016,0.006893707947900456
+epa1986ikt206,1985-12-28,1985,12,day,0.397,0.14519640611418191
+epa1986ikt207,1985-12-29,1985,12,day,0.046,0.01953168453125545
+epa1986ikt208,1985-12-29,1985,12,day,0.08,0.03342375548694973
+epa1986ikt209,1985-12-29,1985,12,night,0.031,0.01325866528351651
+epa1986ikt210,1985-12-29,1985,12,night,0,0
+epa1986ikt211,1985-12-30,1985,12,night,2.528,0.5475285764597821
+epa1986ikt212,1985-12-30,1985,12,day,0,0
+epa1986ikt213,1985-12-30,1985,12,night,0,0
+epa1986ikt214,1985-12-31,1985,12,night,0.031,0.01325866528351651
+epa1986ikt215,1985-12-31,1985,12,night,0,0
+epa1986ikt216,1985-12-31,1985,12,day,1.209,0.34419571587143494
+epa1986ikt217,1985-12-31,1985,12,NA,0.222,0.08707120590653541
+epa1986ikt218,1986-01-01,1986,1,day,0.011,0.00475115559100102
+epa1986ikt219,1986-01-01,1986,1,NA,1.573,0.41043978621034655
+epa1986ikt220,1986-01-02,1986,1,night,0.047,0.019946681678842303
+epa1986ikt221,1986-01-03,1986,1,day,4.025,0.7011360660925265
+epa1986ikt222,1986-01-04,1986,1,night,0.119,0.04883008652835004
+epa1986ikt223,1986-01-10,1986,1,NA,0.065,0.027349607774756507
+epa1986ikt224,1986-01-10,1986,1,day,0.32,0.1205739312058499
+epa1986ikt225,1986-01-10,1986,1,NA,0.06,0.02530586526477026
+epa1986ikt226,1986-01-10,1986,1,night,0.012,0.005180512503780314
+epa1986ikt227,1986-01-11,1986,1,day,0.077,0.03221570329798157
+epa1986ikt228,1986-01-11,1986,1,day,0.093,0.03862016194970278
+epa1986ikt229,1986-01-12,1986,1,night,0.026,0.011147360775797477
+epa1986ikt230,1986-01-12,1986,1,night,0.798,0.25478968739721
+epa1986ikt231,1986-02-01,1986,2,day,46.115,1.673159195454186
+epa1986ikt232,1986-02-08,1986,2,night,0,0
+epa1986ikt233,1986-02-08,1986,2,night,0.01,0.004321373782642578
+epa1986ikt234,1986-02-09,1986,2,night,0.022,0.009450895798693935
+epa1986ikt235,1986-02-09,1986,2,night,0,0
+epa1986ikt236,1986-02-09,1986,2,day,0.017,0.0073209529227445565
+epa1986ikt237,1986-02-09,1986,2,day,1.567,0.40942586867144337
+epa1986ikt238,1986-02-09,1986,2,night,13.129,1.1501114251287818
+epa1986ikt239,1986-02-10,1986,2,night,41.687,1.6302956340911963
+epa1986ikt240,1986-02-11,1986,2,NA,0.005,0.0021660617565076304
+epa1986ikt241,1986-02-11,1986,2,night,0.058,0.024485667699166973
+epa1986ikt242,1986-02-12,1986,2,day,0,0
+epa1986ikt243,1986-02-13,1986,2,day,0.024,0.010299956639811961
+epa1986ikt244,1986-02-13,1986,2,day,1.515,0.4005379893919462
+epa1986ikt245,1986-02-13,1986,2,night,0.135,0.05499586152914153
+epa1986ikt246,1986-02-14,1986,2,night,0.016,0.006893707947900456
+epa1986ikt247,1986-02-14,1986,2,day,0.699,0.23019337886904556
+epa1986ikt248,1986-02-15,1986,2,day,0.483,0.17114115102838204
+epa1986ikt249,1986-02-15,1986,2,day,0.236,0.09201847075279702
+epa1986ikt250,1986-02-15,1986,2,night,0,0
+epa1986ikt251,1986-02-16,1986,2,day,0,0
+epa1986ikt252,1986-02-17,1986,2,night,0.048,0.02036128264770786
+epa1986ikt253,1986-02-17,1986,2,night,0.01,0.004321373782642578
+epa1986ikt254,1986-02-18,1986,2,NA,0,0
+epa1986ikt255,1986-02-18,1986,2,night,0.052,0.02201573981772028
+epa1986ikt256,1986-02-19,1986,2,NA,1.914,0.4644895474339713
+epa1986ikt257,1986-02-19,1986,2,day,0.036,0.01535975540921423
+epa1986ikt258,1986-02-19,1986,2,night,0.293,0.11159852488039401
+epa1986ikt259,1986-02-20,1986,2,NA,0.015,0.006466042249231681
+epa1986ikt260,1986-02-24,1986,2,day,0,0
+epa1986ikt261,1986-02-21,1986,2,day,0,0
+epa1986ikt262,1986-02-21,1986,2,night,0.547,0.18949031369936756
+epa1986ikt263,1986-02-22,1986,2,day,0.177,0.0707764628434347
+epa1986ikt264,1986-02-23,1986,2,night,0.24,0.09342168516223506
+epa1986ikt265,1986-02-23,1986,2,day,0,0
+epa1986ikt266,1986-02-23,1986,2,NA,0,0
+epa1986ikt267,1986-02-24,1986,2,night,0,0
+epa1986ikt268,1986-02-28,1986,2,night,0,0
+epa1986ikt269,1986-02-28,1986,2,day,3.109,0.6137361412618713
+epa1986ikt270,1986-02-28,1986,2,day,4.194,0.7155019452932839
+epa1986ikt271,1986-02-28,1986,2,day,0.518,0.18127177155946156
+epa1986ikt272,1986-03-01,1986,3,night,1.006,0.30233092868439937
+epa1986ikt273,1986-03-01,1986,3,day,0,0
+epa1986ikt274,1986-03-01,1986,3,night,0.086,0.03582982525282818
+epa1986ikt275,1986-03-02,1986,3,day,0,0
+epa1986ikt276,1986-03-02,1986,3,day,0,0
+epa1986yun67,1986-02-13,1986,2,day,0,0
+epa1986yun68,1986-02-13,1986,2,day,0,0
+epa1986yun69,1986-02-13,1986,2,night,0,0
+epa1986yun70,1986-02-14,1986,2,night,0,0
+epa1986yun71,1986-02-15,1986,2,night,7.606,0.9348013417465367
+epa1986yun72,1986-02-15,1986,2,night,3.976,0.6968803716827623
+epa1986yun73,1986-02-15,1986,2,day,0,0
+epa1986yun74,1986-02-16,1986,2,night,0,0
+epa1986yun75,1986-02-16,1986,2,day,0,0
+epa1986yun76,1986-02-17,1986,2,night,0,0
+epa1986yun77,1986-02-17,1986,2,night,0,0
+epa1986yun78,1986-02-18,1986,2,night,0,0
+epa1986yun79,1986-02-18,1986,2,day,0,0
+epa1986yun80,1986-02-18,1986,2,night,0,0
+epa1986yun81,1986-02-19,1986,2,night,0,0
+epa1986yun82,1986-02-19,1986,2,day,0,0
+epa1986yun83,1986-02-19,1986,2,night,0,0
+epa1986yun84,1986-02-20,1986,2,night,0,0
+epa1986yun85,1986-02-21,1986,2,day,0,0
+epa1986yun86,1986-02-21,1986,2,night,0,0
+epa1986yun87,1986-02-22,1986,2,night,0,0
+epa1986yun88,1986-02-22,1986,2,night,0,0
+epa1986yun89,1986-02-23,1986,2,day,0,0
+epa1986yun90,1986-02-23,1986,2,day,0,0
+epa1986yun91,1986-02-24,1986,2,night,0,0
+epa1986yun92,1986-02-24,1986,2,day,0,0
+epa1986yun93,1986-02-28,1986,2,night,0,0
+epa1986yun94,1986-02-28,1986,2,night,0,0
+epa1986yun95,1986-02-28,1986,2,day,0,0
+epa1986yun96,1986-02-28,1986,2,day,0,0
+epa1986yun97,1986-03-01,1986,3,night,0,0
+epa1986yun98,1986-03-02,1986,3,night,0,0
+epa1986yun99,1986-03-02,1986,3,day,0,0
+epa1986yuv10a,1986-02-17,1986,2,night,0,0
+epa1986yuv10b,1986-02-17,1986,2,night,0,0
+epa1986yuv10c,1986-02-17,1986,2,night,0,0
+epa1986yuv10com,1986-02-17,1986,2,night,0,0
+epa1986yuv10d,1986-02-17,1986,2,night,NA,NA
+epa1986yuv10e,1986-02-17,1986,2,night,NA,NA
+epa1986yuv11a,1986-02-17,1986,2,night,0,0
+epa1986yuv11b,1986-02-17,1986,2,night,0,0
+epa1986yuv11c,1986-02-17,1986,2,night,0,0
+epa1986yuv11com,1986-02-17,1986,2,night,0,0
+epa1986yuv11d,1986-02-17,1986,2,night,NA,NA
+epa1986yuv11e,1986-02-17,1986,2,night,NA,NA
+epa1986yuv12a,1986-02-18,1986,2,night,0,0
+epa1986yuv12b,1986-02-18,1986,2,night,0,0
+epa1986yuv12c,1986-02-18,1986,2,night,0,0
+epa1986yuv12com,1986-02-18,1986,2,night,0,0
+epa1986yuv12d,1986-02-18,1986,2,night,NA,NA
+epa1986yuv12e,1986-02-18,1986,2,night,NA,NA
+epa1986yuv13a,1986-02-18,1986,2,day,0,0
+epa1986yuv13b,1986-02-18,1986,2,day,0,0
+epa1986yuv13c,1986-02-18,1986,2,day,0,0
+epa1986yuv13com,1986-02-18,1986,2,day,0,0
+epa1986yuv13d,1986-02-18,1986,2,day,NA,NA
+epa1986yuv13e,1986-02-18,1986,2,day,NA,NA
+epa1986yuv14a,1986-02-18,1986,2,night,0,0
+epa1986yuv14b,1986-02-18,1986,2,night,0,0
+epa1986yuv14c,1986-02-18,1986,2,night,0,0
+epa1986yuv14com,1986-02-18,1986,2,night,0,0
+epa1986yuv14d,1986-02-18,1986,2,night,NA,NA
+epa1986yuv14e,1986-02-18,1986,2,night,NA,NA
+epa1986yuv15a,1986-02-19,1986,2,night,0,0
+epa1986yuv15b,1986-02-19,1986,2,night,0,0
+epa1986yuv15c,1986-02-19,1986,2,night,0,0
+epa1986yuv15com,1986-02-19,1986,2,night,4.232,0.7186677353162109
+epa1986yuv15d,1986-02-19,1986,2,night,NA,NA
+epa1986yuv15e,1986-02-19,1986,2,night,NA,NA
+epa1986yuv16a,1986-02-19,1986,2,day,0,0
+epa1986yuv16b,1986-02-19,1986,2,day,0,0
+epa1986yuv16c,1986-02-19,1986,2,day,0,0
+epa1986yuv16com,1986-02-19,1986,2,day,0,0
+epa1986yuv16d,1986-02-19,1986,2,day,NA,NA
+epa1986yuv16e,1986-02-19,1986,2,day,NA,NA
+epa1986yuv17a,1986-02-19,1986,2,night,0,0
+epa1986yuv17b,1986-02-19,1986,2,night,0,0
+epa1986yuv17c,1986-02-19,1986,2,night,0,0
+epa1986yuv17com,1986-02-19,1986,2,night,0,0
+epa1986yuv17d,1986-02-19,1986,2,night,NA,NA
+epa1986yuv17e,1986-02-19,1986,2,night,NA,NA
+epa1986yuv18a,1986-02-20,1986,2,night,0,0
+epa1986yuv18b,1986-02-20,1986,2,night,0,0
+epa1986yuv18c,1986-02-20,1986,2,night,0,0
+epa1986yuv18com,1986-02-20,1986,2,night,0,0
+epa1986yuv18d,1986-02-20,1986,2,night,NA,NA
+epa1986yuv18e,1986-02-20,1986,2,night,NA,NA
+epa1986yuv19a,1986-02-21,1986,2,day,0,0
+epa1986yuv19b,1986-02-21,1986,2,day,0,0
+epa1986yuv19c,1986-02-21,1986,2,day,0,0
+epa1986yuv19com,1986-02-21,1986,2,day,0,0
+epa1986yuv19d,1986-02-21,1986,2,day,NA,NA
+epa1986yuv19e,1986-02-21,1986,2,day,NA,NA
+epa1986yuv1a,1986-02-13,1986,2,day,0,0
+epa1986yuv1b,1986-02-13,1986,2,day,0,0
+epa1986yuv1c,1986-02-13,1986,2,day,0,0
+epa1986yuv1com,1986-02-13,1986,2,day,0,0
+epa1986yuv1d,1986-02-13,1986,2,day,NA,NA
+epa1986yuv1e,1986-02-13,1986,2,day,NA,NA
+epa1986yuv20a,1986-02-21,1986,2,night,0,0
+epa1986yuv20b,1986-02-21,1986,2,night,0,0
+epa1986yuv20c,1986-02-21,1986,2,night,0,0
+epa1986yuv20com,1986-02-21,1986,2,night,0,0
+epa1986yuv20d,1986-02-21,1986,2,night,NA,NA
+epa1986yuv20e,1986-02-21,1986,2,night,NA,NA
+epa1986yuv21a,1986-02-22,1986,2,night,0,0
+epa1986yuv21b,1986-02-22,1986,2,night,0,0
+epa1986yuv21c,1986-02-22,1986,2,night,0,0
+epa1986yuv21com,1986-02-22,1986,2,night,0,0
+epa1986yuv21d,1986-02-22,1986,2,night,NA,NA
+epa1986yuv21e,1986-02-22,1986,2,night,NA,NA
+epa1986yuv22a,1986-02-22,1986,2,night,0,0
+epa1986yuv22b,1986-02-22,1986,2,night,0,0
+epa1986yuv22c,1986-02-22,1986,2,night,0,0
+epa1986yuv22com,1986-02-22,1986,2,night,0,0
+epa1986yuv22d,1986-02-22,1986,2,night,NA,NA
+epa1986yuv22e,1986-02-22,1986,2,night,NA,NA
+epa1986yuv23a,1986-02-23,1986,2,day,0,0
+epa1986yuv23b,1986-02-23,1986,2,day,0,0
+epa1986yuv23c,1986-02-23,1986,2,day,0,0
+epa1986yuv23com,1986-02-23,1986,2,day,0,0
+epa1986yuv23d,1986-02-23,1986,2,day,NA,NA
+epa1986yuv23e,1986-02-23,1986,2,day,NA,NA
+epa1986yuv24a,1986-02-23,1986,2,day,0,0
+epa1986yuv24b,1986-02-23,1986,2,day,0,0
+epa1986yuv24c,1986-02-23,1986,2,day,0,0
+epa1986yuv24com,1986-02-23,1986,2,day,0,0
+epa1986yuv24d,1986-02-23,1986,2,day,NA,NA
+epa1986yuv24e,1986-02-23,1986,2,day,NA,NA
+epa1986yuv25a,1986-02-24,1986,2,night,0,0
+epa1986yuv25b,1986-02-24,1986,2,night,0,0
+epa1986yuv25c,1986-02-24,1986,2,night,0,0
+epa1986yuv25com,1986-02-24,1986,2,night,0,0
+epa1986yuv25d,1986-02-24,1986,2,night,NA,NA
+epa1986yuv25e,1986-02-24,1986,2,night,NA,NA
+epa1986yuv26a,1986-02-24,1986,2,day,0,0
+epa1986yuv26b,1986-02-24,1986,2,day,0,0
+epa1986yuv26c,1986-02-24,1986,2,day,0,0
+epa1986yuv26com,1986-02-24,1986,2,day,0,0
+epa1986yuv26d,1986-02-24,1986,2,day,NA,NA
+epa1986yuv26e,1986-02-24,1986,2,day,NA,NA
+epa1986yuv27a,1986-02-28,1986,2,night,0,0
+epa1986yuv27b,1986-02-28,1986,2,night,0,0
+epa1986yuv27c,1986-02-28,1986,2,night,0,0
+epa1986yuv27com,1986-02-28,1986,2,night,0,0
+epa1986yuv27d,1986-02-28,1986,2,night,NA,NA
+epa1986yuv27e,1986-02-28,1986,2,night,NA,NA
+epa1986yuv28a,1986-02-28,1986,2,night,0,0
+epa1986yuv28b,1986-02-28,1986,2,night,0,0
+epa1986yuv28c,1986-02-28,1986,2,night,0,0
+epa1986yuv28com,1986-02-28,1986,2,night,0,0
+epa1986yuv28d,1986-02-28,1986,2,night,NA,NA
+epa1986yuv28e,1986-02-28,1986,2,night,NA,NA
+epa1986yuv29a,1986-02-28,1986,2,day,0,0
+epa1986yuv29b,1986-02-28,1986,2,day,0,0
+epa1986yuv29c,1986-02-28,1986,2,day,0,0
+epa1986yuv29com,1986-02-28,1986,2,day,0,0
+epa1986yuv29d,1986-02-28,1986,2,day,NA,NA
+epa1986yuv29e,1986-02-28,1986,2,day,NA,NA
+epa1986yuv2a,1986-02-13,1986,2,day,0,0
+epa1986yuv2b,1986-02-13,1986,2,day,0,0
+epa1986yuv2c,1986-02-13,1986,2,day,0,0
+epa1986yuv2com,1986-02-13,1986,2,day,0,0
+epa1986yuv2d,1986-02-13,1986,2,day,NA,NA
+epa1986yuv2e,1986-02-13,1986,2,day,NA,NA
+epa1986yuv30a,1986-02-28,1986,2,day,0,0
+epa1986yuv30b,1986-02-28,1986,2,day,0,0
+epa1986yuv30c,1986-02-28,1986,2,day,0,0
+epa1986yuv30com,1986-02-28,1986,2,day,0,0
+epa1986yuv30d,1986-02-28,1986,2,day,NA,NA
+epa1986yuv30e,1986-02-28,1986,2,day,NA,NA
+epa1986yuv31a,1986-03-01,1986,3,night,0,0
+epa1986yuv31b,1986-03-01,1986,3,night,0,0
+epa1986yuv31c,1986-03-01,1986,3,night,0,0
+epa1986yuv31com,1986-03-01,1986,3,night,5.054,0.7820424166205542
+epa1986yuv31d,1986-03-01,1986,3,night,NA,NA
+epa1986yuv31e,1986-03-01,1986,3,night,NA,NA
+epa1986yuv32a,1986-03-02,1986,3,night,0,0
+epa1986yuv32b,1986-03-02,1986,3,night,0,0
+epa1986yuv32c,1986-03-02,1986,3,night,0,0
+epa1986yuv32com,1986-03-02,1986,3,night,0,0
+epa1986yuv32d,1986-03-02,1986,3,night,NA,NA
+epa1986yuv32e,1986-03-02,1986,3,night,NA,NA
+epa1986yuv33a,1986-03-02,1986,3,day,0,0
+epa1986yuv33b,1986-03-02,1986,3,day,0,0
+epa1986yuv33c,1986-03-02,1986,3,day,0,0
+epa1986yuv33com,1986-03-02,1986,3,day,0,0
+epa1986yuv33d,1986-03-02,1986,3,day,NA,NA
+epa1986yuv33e,1986-03-02,1986,3,day,NA,NA
+epa1986yuv3a,1986-02-13,1986,2,night,0,0
+epa1986yuv3b,1986-02-13,1986,2,night,0,0
+epa1986yuv3c,1986-02-13,1986,2,night,0,0
+epa1986yuv3com,1986-02-13,1986,2,night,0,0
+epa1986yuv3d,1986-02-13,1986,2,night,NA,NA
+epa1986yuv3e,1986-02-13,1986,2,night,NA,NA
+epa1986yuv4a,1986-02-14,1986,2,night,0,0
+epa1986yuv4b,1986-02-14,1986,2,night,0,0
+epa1986yuv4c,1986-02-14,1986,2,night,0,0
+epa1986yuv4com,1986-02-14,1986,2,night,0,0
+epa1986yuv4d,1986-02-14,1986,2,night,NA,NA
+epa1986yuv4e,1986-02-14,1986,2,night,NA,NA
+epa1986yuv5a,1986-02-14,1986,2,day,0,0
+epa1986yuv5b,1986-02-14,1986,2,day,0,0
+epa1986yuv5c,1986-02-14,1986,2,day,0,0
+epa1986yuv5com,1986-02-14,1986,2,day,0,0
+epa1986yuv5d,1986-02-14,1986,2,day,NA,NA
+epa1986yuv5e,1986-02-14,1986,2,day,NA,NA
+epa1986yuv6a,1986-02-15,1986,2,night,3.016,0.6037937041369629
+epa1986yuv6b,1986-02-15,1986,2,night,3.499,0.6531159931655669
+epa1986yuv6c,1986-02-15,1986,2,night,0,0
+epa1986yuv6com,1986-02-15,1986,2,night,7.952,0.9519200735202936
+epa1986yuv6d,1986-02-15,1986,2,night,NA,NA
+epa1986yuv6e,1986-02-15,1986,2,night,NA,NA
+epa1986yuv7a,1986-02-15,1986,2,day,0,0
+epa1986yuv7b,1986-02-15,1986,2,day,0,0
+epa1986yuv7c,1986-02-15,1986,2,day,0,0
+epa1986yuv7com,1986-02-15,1986,2,day,0,0
+epa1986yuv7d,1986-02-15,1986,2,day,NA,NA
+epa1986yuv7e,1986-02-15,1986,2,day,NA,NA
+epa1986yuv8a,1986-02-15,1986,2,night,0,0
+epa1986yuv8b,1986-02-15,1986,2,night,0,0
+epa1986yuv8c,1986-02-15,1986,2,night,0,0
+epa1986yuv8com,1986-02-15,1986,2,night,0,0
+epa1986yuv8d,1986-02-15,1986,2,night,NA,NA
+epa1986yuv8e,1986-02-15,1986,2,night,NA,NA
+epa1986yuv9a,1986-02-16,1986,2,day,0,0
+epa1986yuv9b,1986-02-16,1986,2,day,0,0
+epa1986yuv9c,1986-02-16,1986,2,day,0,0
+epa1986yuv9com,1986-02-16,1986,2,day,0,0
+epa1986yuv9d,1986-02-16,1986,2,day,NA,NA
+epa1986yuv9e,1986-02-16,1986,2,day,NA,NA
+epa1987ikt10,1987-01-26,1987,1,night,1.065,0.3149200559924198
+epa1987ikt11,1987-01-26,1987,1,day,1.513,0.40019248859257595
+epa1987ikt12,1987-01-27,1987,1,day,155.464,2.1944144287843637
+epa1987ikt13,1987-01-29,1987,1,day,0.363,0.13449585583467355
+epa1987ikt14,1987-01-29,1987,1,day,31.885,1.5169978461952751
+epa1987ikt15,1987-01-30,1987,1,night,0.216,0.08493357493671612
+epa1987ikt16,1987-01-31,1987,1,day,13.566,1.1633403056182516
+epa1987ikt17,1987-02-01,1987,2,day,2.269,0.514414920580369
+epa1987ikt18,1987-02-02,1987,2,day,89.905,1.958587771150353
+epa1987ikt19,1987-02-02,1987,2,night,21.319,1.348674732191571
+epa1987ikt20,1987-02-04,1987,2,night,0.013,0.005609445360280386
+epa1987ikt21,1987-03-18,1987,3,day,415.256,2.619360506569899
+epa1987ikt22,1987-03-19,1987,3,night,45.554,1.667957002251729
+epa1987ikt23,1987-03-19,1987,3,day,29.315,1.4816575725709853
+epa1987ikt24,1987-03-20,1987,3,day,337.082,2.529022048882382
+epa1987ikt25,1987-02-20,1987,2,day,0.089,0.03702787975577494
+epa1987ikt26,1987-02-20,1987,2,day,3.697,0.6718205601832489
+epa1987ikt27,1987-02-20,1987,2,night,0.656,0.2190603324488614
+epa1987ikt28,1987-02-21,1987,2,day,3.867,0.6872613462435064
+epa1987ikt29,1987-02-21,1987,2,day,7.139,0.9105710484812585
+epa1987ikt30,1987-02-21,1987,2,NA,0.062,0.026124516745450282
+epa1987ikt31,1987-02-22,1987,2,day,0,0
+epa1987ikt32,1987-02-22,1987,2,day,0,0
+epa1987ikt33,1987-02-23,1987,2,day,0,0
+epa1987ikt34,1987-02-26,1987,2,day,0,0
+epa1987ikt35,1987-02-26,1987,2,day,0,0
+epa1987ikt36,1987-02-26,1987,2,night,1.212,0.3447851226326606
+epa1987ikt37,1987-02-27,1987,2,night,0.107,0.0441476208787228
+epa1987ikt38,1987-02-27,1987,2,day,4.583,0.7468676278504295
+epa1987ikt39,1987-02-27,1987,2,day,0.271,0.10414555055400815
+epa1987ikt40,1987-02-27,1987,2,night,8.719,0.9876215821254837
+epa1987ikt41,1987-02-28,1987,2,night,0.472,0.16790781000148003
+epa1987ikt42,1987-02-28,1987,2,day,0.118,0.04844180355040444
+epa1987ikt43,1987-02-28,1987,2,day,15.014,1.2044998241711513
+epa1987ikt44,1987-02-28,1987,2,NA,0.077,0.03221570329798157
+epa1987ikt45,1987-03-01,1987,3,day,0,0
+epa1987ikt46,1987-03-04,1987,3,day,0.043,0.018284308426530838
+epa1987ikt47,1987-03-05,1987,3,day,0.228,0.08919836680514887
+epa1987ikt48,1987-03-05,1987,3,day,43.457,1.6479401527533313
+epa1987ikt49,1987-03-06,1987,3,night,0.915,0.28216877830464154
+epa1987ikt5,1987-01-21,1987,1,night,10.295,1.052886235256382
+epa1987ikt50,1987-03-06,1987,3,night,1.596,0.41430468812833166
+epa1987ikt51,1987-03-06,1987,3,day,1.919,0.4652340949880144
+epa1987ikt52,1987-03-06,1987,3,day,239.517,2.3811457781953673
+epa1987ikt53,1987-03-06,1987,3,day,12.228,1.1214941862416652
+epa1987ikt54,1987-03-06,1987,3,night,2.037,0.48244479191826517
+epa1987ikt55,1987-03-07,1987,3,night,2.008,0.47827783191960466
+epa1987ikt56,1987-03-07,1987,3,day,1.743,0.4382258076045295
+epa1987ikt57,1987-03-07,1987,3,day,51.907,1.723513136312176
+epa1987ikt58,1987-03-07,1987,3,day,8.908,0.9959859979137994
+epa1987ikt59,1987-03-09,1987,3,day,6.57,0.8790958795000727
+epa1987ikt6,1987-01-22,1987,1,day,99.014,2.000060796971778
+epa1987ikt60,1987-03-08,1987,3,day,13.354,1.1569729418962973
+epa1987ikt61,1987-03-09,1987,3,day,0,0
+epa1987ikt62,1987-03-09,1987,3,day,11.612,1.100783962075867
+epa1987ikt63,1987-03-11,1987,3,NA,6.366,0.8672317145188941
+epa1987ikt64,1987-03-12,1987,3,night,1.667,0.4260230156898761
+epa1987ikt65,1987-03-12,1987,3,night,0.455,0.1628629933219261
+epa1987ikt66,1987-03-12,1987,3,day,58.578,1.7750859201018736
+epa1987ikt67,1987-03-14,1987,3,day,5.314,0.8003045775561985
+epa1987ikt68,1987-03-14,1987,3,day,126.808,2.10655803885459
+epa1987ikt69,1987-03-14,1987,3,night,4.193,0.7154183225950563
+epa1987ikt7,1987-01-23,1987,1,day,1.371,0.37493155397818817
+epa1987ikt8,1987-01-25,1987,1,day,4.529,0.7426465899387362
+epa1987ikt9,1987-01-25,1987,1,day,2.752,0.5742628297070268
+epa1987mkx100,1987-02-17,1987,2,NA,42.899,1.6424546273166456
+epa1987mkx120,1987-02-20,1987,2,day,0,0
+epa1987mkx121,1987-02-20,1987,2,day,7.235,0.9156636035057731
+epa1987mkx122,1987-02-20,1987,2,night,2.617,0.5583485087616198
+epa1987mkx123,1987-02-21,1987,2,day,0,0
+epa1987mkx124,1987-02-21,1987,2,day,0,0
+epa1987mkx125,1987-02-21,1987,2,day,5.991,0.8445393021290079
+epa1987mkx126,1987-02-22,1987,2,day,0,0
+epa1987mkx127,1987-02-22,1987,2,day,0,0
+epa1987mkx128,1987-02-22,1987,2,night,0,0
+epa1987mkx129,1987-02-26,1987,2,day,0,0
+epa1987mkx130,1987-02-26,1987,2,day,0,0
+epa1987mkx131,1987-02-26,1987,2,NA,0,0
+epa1987mkx132,1987-02-27,1987,2,day,4.534,0.7430391548049331
+epa1987mkx133,1987-02-28,1987,2,night,2.803,0.5801263254115825
+epa1987mkx134,1987-02-28,1987,2,day,0,0
+epa1987mkx135,1987-02-28,1987,2,day,0,0
+epa1987mkx136,1987-03-01,1987,3,night,0,0
+epa1987mkx137,1987-03-01,1987,3,day,0,0
+epa1987mkx138,1987-03-01,1987,3,NA,0,0
+epa1987mkx139,1987-03-02,1987,3,night,0,0
+epa1987mkx140,1987-03-02,1987,3,night,0,0
+epa1987mkx141,1987-03-02,1987,3,day,1.233,0.348888723071438
+epa1987mkx142,1987-03-02,1987,3,night,6.586,0.8800128383667718
+epa1987mkx143,1987-03-03,1987,3,night,0,0
+epa1987mkx144,1987-03-04,1987,3,day,1.775,0.443262987458695
+epa1987mkx145,1987-03-04,1987,3,day,0,0
+epa1987mkx146,1987-03-06,1987,3,day,18.214,1.2836177863656437
+epa1987mkx147,1987-03-07,1987,3,night,49.028,1.6992131411739222
+epa1987mkx148,1987-03-09,1987,3,night,0,0
+epa1987mkx39,1987-01-16,1987,1,day,64.049,1.8132406245283492
+epa1987mkx40,1987-01-16,1987,1,NA,4.846,0.7668588110214176
+epa1987mkx41,1987-01-16,1987,1,night,10.994,1.0789640445017605
+epa1987mkx42,1987-01-20,1987,1,day,1.946,0.46923274250661207
+epa1987mkx43,1987-01-21,1987,1,night,1.169,0.3362595520141933
+epa1987mkx44,1987-01-22,1987,1,night,11.714,1.1042822070944383
+epa1987mkx45,1987-01-22,1987,1,NA,282.234,2.4521453857623565
+epa1987mkx46,1987-01-22,1987,1,day,0.852,0.2676409823459155
+epa1987mkx47,1987-01-22,1987,1,night,6.625,0.8822398480188234
+epa1987mkx48,1987-01-23,1987,1,NA,0,0
+epa1987mkx49,1987-01-23,1987,1,night,1.334,0.36810085170935136
+epa1987mkx50,1987-01-24,1987,1,night,0.208,0.082066934285113
+epa1987mkx51,1987-01-24,1987,1,day,76.211,1.8876791772656054
+epa1987mkx52,1987-01-24,1987,1,night,33.049,1.5321043614511731
+epa1987mkx53,1987-01-25,1987,1,night,0,0
+epa1987mkx54,1987-01-25,1987,1,NA,7.239,0.9158745028576916
+epa1987mkx55,1987-01-25,1987,1,day,5.683,0.8249714611236934
+epa1987mkx56,1987-01-25,1987,1,day,2.838,0.5841049703994527
+epa1987mkx57,1987-01-25,1987,1,night,0.458,0.1637575239819558
+epa1987mkx58,1987-01-26,1987,1,night,0.363,0.13449585583467355
+epa1987mkx59,1987-01-26,1987,1,day,65.872,1.8252443122230024
+epa1987mkx60,1987-01-26,1987,1,day,2.387,0.5298151966446303
+epa1987mkx61,1987-01-26,1987,1,day,4.297,0.7240299729355977
+epa1987mkx62,1987-01-26,1987,1,night,4.064,0.7044936970092988
+epa1987mkx63,1987-01-27,1987,1,night,3.42,0.6454222693490919
+epa1987mkx64,1987-01-28,1987,1,day,0,0
+epa1987mkx65,1987-01-28,1987,1,NA,0,0
+epa1987mkx66,1987-01-29,1987,1,NA,0.323,0.12155984418750096
+epa1987mkx67,1987-01-29,1987,1,day,2.195,0.504470862494419
+epa1987mkx68,1987-01-29,1987,1,day,54.412,1.7436038255486155
+epa1987mkx69,1987-01-29,1987,1,day,0.394,0.14426277376199068
+epa1987mkx70,1987-01-29,1987,1,night,0.291,0.11092624226642028
+epa1987mkx71,1987-01-30,1987,1,night,0.294,0.11193427633268159
+epa1987mkx72,1987-01-30,1987,1,day,0,0
+epa1987mkx73,1987-01-30,1987,1,day,0,0
+epa1987mkx74,1987-01-30,1987,1,day,0.542,0.1880843737149382
+epa1987mkx75,1987-01-30,1987,1,night,2.641,0.5612206789339438
+epa1987mkx76,1987-01-31,1987,1,night,0,0
+epa1987mkx77,1987-01-31,1987,1,day,18.619,1.2926768671851159
+epa1987mkx78,1987-01-31,1987,1,NA,0,0
+epa1987mkx79,1987-02-01,1987,2,night,0.982,0.2971036501492565
+epa1987mkx80,1987-02-01,1987,2,night,0,0
+epa1987mkx81,1987-02-01,1987,2,NA,0.474,0.16849748352303268
+epa1987mkx82,1987-02-01,1987,2,day,3.047,0.6071332043915667
+epa1987mkx83,1987-02-01,1987,2,day,0,0
+epa1987mkx84,1987-02-01,1987,2,day,0,0
+epa1987mkx85,1987-02-02,1987,2,night,0,0
+epa1987mkx86,1987-02-02,1987,2,day,3.588,0.6616234092292301
+epa1987mkx87,1987-02-02,1987,2,day,0,0
+epa1987mkx88,1987-02-02,1987,2,night,0,0
+epa1987mkx89,1987-02-03,1987,2,day,0,0
+epa1987mkx90,1987-02-03,1987,2,day,0,0
+epa1987mkx91,1987-02-03,1987,2,NA,0,0
+epa1987mkx92,1987-02-03,1987,2,night,0,0
+epa1987mkx93,1987-02-04,1987,2,night,0,0
+epa1987mkx94,1987-02-04,1987,2,day,108.764,2.040459925132668
+epa1987mkx95,1987-02-14,1987,2,night,0,0
+epa1987mkx96,1987-02-15,1987,2,night,1.29,0.359835482339888
+epa1987mkx97,1987-02-15,1987,2,NA,0.926,0.28465628278851574
+epa1987mkx98,1987-02-15,1987,2,day,0,0
+epa1987mkx99,1987-02-17,1987,2,day,0.625,0.21085336531489318
+epa1987yun255,1987-02-25,1987,2,night,0,0
+epa1987yun256,1987-02-25,1987,2,night,0,0
+epa1987yun257,1987-02-25,1987,2,day,0,0
+epa1987yun258,1987-02-25,1987,2,day,0,0
+epa1987yun259,1987-02-26,1987,2,day,0,0
+epa1987yun260,1987-02-26,1987,2,night,0,0
+epa1987yun261,1987-02-26,1987,2,night,0,0
+epa1987yun262,1987-02-26,1987,2,night,0,0
+epa1987yun263,1987-02-27,1987,2,day,0,0
+epa1987yun264,1987-02-27,1987,2,day,0,0
+epa1987yun265,1987-02-27,1987,2,day,0,0
+epa1987yun266,1987-02-27,1987,2,night,0,0
+epa1987yun267,1987-02-28,1987,2,night,0,0
+epa1987yun268,1987-02-28,1987,2,night,0,0
+epa1987yun269,1987-02-28,1987,2,day,0,0
+epa1987yun289,1987-04-12,1987,4,day,0,0
+epa1987yun290,1987-04-13,1987,4,day,0,0
+epa1987yun291,1987-04-13,1987,4,night,0,0
+epa1987yun292,1987-04-15,1987,4,night,0,0
+epa1987yun293,1987-04-16,1987,4,night,0,0
+epa1988ikt1,1988-03-25,1988,3,day,81.836,1.9182191194359306
+epa1988ikt100,1988-01-15,1988,1,night,13.571,1.1634893581926986
+epa1988ikt101,1988-01-16,1988,1,night,86.358,1.9413026826076103
+epa1988ikt102,1988-01-16,1988,1,day,194.525,2.291202294560029
+epa1988ikt103,1988-01-16,1988,1,day,17.815,1.2745042226558831
+epa1988ikt104,1988-01-17,1988,1,night,4.633,0.7507397512353507
+epa1988ikt105,1988-01-17,1988,1,night,4.734,0.7584576886104657
+epa1988ikt106,1988-01-18,1988,1,day,3.67,0.6693168805661122
+epa1988ikt107,1988-01-18,1988,1,night,0.995,0.2999429000227671
+epa1988ikt108,1988-01-19,1988,1,day,0.034,0.014520538757923713
+epa1988ikt109,1988-01-19,1988,1,night,3.081,0.6107665947732709
+epa1988ikt110,1988-01-20,1988,1,night,5.096,0.7850449583315441
+epa1988ikt111,1988-01-21,1988,1,night,0,0
+epa1988ikt112,1988-01-21,1988,1,day,0.118,0.04844180355040444
+epa1988ikt113,1988-01-22,1988,1,night,0.053,0.02242837118548649
+epa1988ikt114,1988-01-22,1988,1,day,0.173,0.06929801211552926
+epa1988ikt115,1988-01-23,1988,1,day,0.066,0.027757204690553483
+epa1988ikt116,1988-01-24,1988,1,night,28.97,1.4766867429456447
+epa1988ikt117,1988-01-24,1988,1,day,23.602,1.390970414162616
+epa1988ikt118,1988-01-25,1988,1,night,0.127,0.05192391604610654
+epa1988ikt119,1988-02-03,1988,2,NA,0.252,0.09760432887441087
+epa1988ikt120,1988-02-11,1988,2,night,38.281,1.594182535351007
+epa1988ikt121,1988-02-11,1988,2,day,6.73,0.888179493918325
+epa1988ikt122,1988-02-12,1988,2,night,0.621,0.20978301484851494
+epa1988ikt123,1988-02-13,1988,2,night,0,0
+epa1988ikt124,1988-02-22,1988,2,day,57.276,1.7654897346294345
+epa1988ikt125,1988-02-28,1988,2,NA,16.346,1.2391993420543015
+epa1988ikt126,1988-02-28,1988,2,night,32.729,1.5280034653836918
+epa1988ikt127,1988-03-01,1988,3,day,37.684,1.5875313746175501
+epa1988ikt128,1988-03-02,1988,3,day,251.165,2.401684807210481
+epa1988ikt129,1988-03-05,1988,3,day,34.684,1.5524735306973434
+epa1988ikt130,1988-03-07,1988,3,day,0,0
+epa1988ikt131,1988-04-08,1988,4,night,0.184,0.07335170238690095
+epa1988ikt132,1988-04-09,1988,4,day,0.09,0.03742649794062367
+epa1988ikt133,1988-04-09,1988,4,day,0,0
+epa1988ikt134,1988-04-10,1988,4,day,0,0
+epa1988ikt135,1988-04-10,1988,4,day,0.369,0.13640344813399
+epa1988ikt136,1988-04-11,1988,4,NA,0,0
+epa1988ikt137,1988-04-11,1988,4,day,0,0
+epa1988ikt138,1988-04-12,1988,4,day,0.44,0.15836249209524964
+epa1988ikt139,1988-04-12,1988,4,night,2.512,0.545554507234065
+epa1988ikt140,1988-04-15,1988,4,day,0,0
+epa1988ikt2,1988-03-26,1988,3,day,112.534,2.05512593907255
+epa1988ikt3,1988-03-28,1988,3,day,78.884,1.9024598029992956
+epa1988ikt4,1988-04-01,1988,4,day,7215.052,3.858299654139164
+epa1988ikt70,1987-12-19,1987,12,NA,0,0
+epa1988ikt71,1987-12-20,1987,12,NA,0.065,0.027349607774756507
+epa1988ikt72,1987-12-20,1987,12,day,27.318,1.4520625774204234
+epa1988ikt73,1987-12-21,1987,12,night,12.475,1.1295287738587763
+epa1988ikt74,1987-12-23,1987,12,day,0.4,0.146128035678238
+epa1988ikt75,1987-12-23,1987,12,day,0.396,0.14488541828714224
+epa1988ikt76,1987-12-24,1987,12,night,0.019,0.008174184006426354
+epa1988ikt77,1987-12-24,1987,12,night,1.017,0.3047058982127654
+epa1988ikt78,1987-12-27,1987,12,NA,0,0
+epa1988ikt79,1987-12-28,1987,12,night,2.464,0.5395778833453091
+epa1988ikt80,1987-12-28,1987,12,day,0,0
+epa1988ikt81,1988-01-02,1988,1,day,4.639,0.7512020945883532
+epa1988ikt82,1988-01-02,1988,1,day,995.93,2.9986646651511677
+epa1988ikt83,1988-01-03,1988,1,NA,13.247,1.1537234243016807
+epa1988ikt84,1988-01-04,1988,1,day,0.119,0.04883008652835004
+epa1988ikt85,1988-01-05,1988,1,night,8.651,0.9845723156216323
+epa1988ikt86,1988-01-05,1988,1,day,0.007,0.003029470553617962
+epa1988ikt87,1988-01-07,1988,1,NA,6.503,0.875234946450165
+epa1988ikt88,1988-01-07,1988,1,NA,543.207,2.7357641237021393
+epa1988ikt89,1988-01-08,1988,1,night,6.513,0.8758133888397577
+epa1988ikt90,1988-01-09,1988,1,day,4.94,0.7737864449811935
+epa1988ikt91,1988-01-10,1988,1,night,0.449,0.1610683854711746
+epa1988ikt92,1988-01-10,1988,1,day,1.108,0.32387060654050903
+epa1988ikt93,1988-01-11,1988,1,night,0.034,0.014520538757923713
+epa1988ikt94,1988-01-11,1988,1,night,14.87,1.2005769267548483
+epa1988ikt95,1988-01-13,1988,1,day,35.186,1.5585405788545965
+epa1988ikt96,1988-01-13,1988,1,day,9.553,1.0233759381395624
+epa1988ikt97,1988-01-14,1988,1,night,2.028,0.4811558708280351
+epa1988ikt98,1988-01-14,1988,1,day,0.424,0.15350998930083753
+epa1988ikt99,1988-01-14,1988,1,night,0,0
+epa1988mkx174,1988-02-05,1988,2,day,3.169,0.6200318951262974
+epa1988mkx175,1988-02-05,1988,2,night,0.364,0.13481437032046006
+epa1988mkx176,1988-02-06,1988,2,night,0,0
+epa1988mkx177,1988-02-07,1988,2,day,0,0
+epa1988mkx178,1988-02-07,1988,2,day,8.046,0.9564565834098998
+epa1988mkx179,1988-02-07,1988,2,NA,0,0
+epa1988mkx180,1988-02-11,1988,2,night,0,0
+epa1988mkx181,1988-02-11,1988,2,day,0,0
+epa1988mkx182,1988-02-12,1988,2,night,0,0
+epa1988mkx183,1988-02-12,1988,2,NA,1.554,0.4072208929273965
+epa1988mkx184,1988-02-12,1988,2,day,0,0
+epa1988mkx185,1988-02-12,1988,2,night,0.259,0.10002573010786256
+epa1988mkx186,1988-02-13,1988,2,night,0,0
+epa1988mkx187,1988-02-22,1988,2,night,11.757,1.105748555526993
+epa1988mkx188,1988-02-22,1988,2,night,0,0
+epa1988mkx189,1988-02-22,1988,2,day,7.216,0.9146604305892219
+epa1988mkx190,1988-02-22,1988,2,day,0,0
+epa1988mkx191,1988-02-22,1988,2,day,0,0
+epa1988mkx192,1988-02-27,1988,2,day,0,0
+epa1988mkx193,1988-02-27,1988,2,night,0,0
+epa1988mkx194,1988-02-28,1988,2,day,0,0
+epa1988mkx195,1988-03-01,1988,3,day,9.294,1.0125841639141508
+epa1988mkx196,1988-03-01,1988,3,night,0,0
+epa1988mkx197,1988-03-03,1988,3,day,0.548,0.1897709563468738
+epa1988mkx198,1988-03-03,1988,3,day,5.076,0.7836177651907488
+epa1988mkx199,1988-03-03,1988,3,day,13.368,1.157396319323229
+epa1988mkx200,1988-03-03,1988,3,night,8.117,0.9598519547996054
+epa1988mkx201,1988-03-04,1988,3,night,0,0
+epa1988mkx202,1988-03-04,1988,3,night,8.548,0.979912410334717
+epa1988mkx203,1988-03-05,1988,3,night,63.06,1.806586934327803
+epa1988mkx204,1988-03-05,1988,3,NA,0,0
+epa1988mkx205,1988-03-07,1988,3,night,0,0
+epa1988mkx206,1988-03-07,1988,3,day,0,0
+epa1988mkx207,1988-03-07,1988,3,day,19.495,1.3116479226525206
+epa1988mkx208,1988-03-08,1988,3,NA,0,0
+epa1988mkx209,1988-03-08,1988,3,day,1.473,0.3932241163612973
+epa1988mkx210,1988-03-17,1988,3,day,86.452,1.9417697461077947
+epa1988mkx211,1988-03-21,1988,3,day,33.78,1.5413295776666938
+epa1988mkx212,1988-03-24,1988,3,day,0,0
+epa1988mkx213,1988-03-24,1988,3,day,71.394,1.8597025734503079
+epa1988mkx214,1988-03-25,1988,3,day,75.029,1.880979278302905
+epa1988mkx215,1988-03-27,1988,3,night,150.718,2.1810371090459864
+epa1988mkx216,1988-03-27,1988,3,night,288.858,2.4621852912788422
+epa1988mkx217,1988-03-29,1988,3,day,13.51,1.161667412437736
+epa1988mkx218,1988-03-31,1988,3,night,494.053,2.694651696661595
+epa1988mkx219,1988-03-31,1988,3,night,55.512,1.7521406775614556
+epa1988mkx220,1988-04-08,1988,4,day,0,0
+epa1988mkx221,1988-04-08,1988,4,night,0,0
+epa1988mkx222,1988-04-09,1988,4,NA,0,0
+epa1988mkx223,1988-04-10,1988,4,day,0,0
+epa1988mkx224,1988-04-11,1988,4,day,0,0
+epa1988mkx225,1988-04-12,1988,4,day,0,0
+epa1988mtt1,1988-02-13,1988,2,day,72.775,1.8679092181719972
+epa1988mtt10,1988-02-15,1988,2,day,4.556,0.744762237065578
+epa1988mtt11,1988-02-15,1988,2,night,0.807,0.25695815256093185
+epa1988mtt12,1988-02-16,1988,2,night,8.516,0.9784544333652286
+epa1988mtt13,1988-02-16,1988,2,NA,21.48,1.3517963068970236
+epa1988mtt14,1988-02-16,1988,2,night,0.995,0.2999429000227671
+epa1988mtt15,1988-02-17,1988,2,NA,1.713,0.4334497937615961
+epa1988mtt16,1988-02-17,1988,2,day,9.907,1.037705313135537
+epa1988mtt17,1988-02-17,1988,2,day,30.12,1.4930395883176515
+epa1988mtt18,1988-02-17,1988,2,day,200.232,2.303697043572633
+epa1988mtt19,1988-02-17,1988,2,night,3.657,0.6681062379327314
+epa1988mtt2,1988-02-13,1988,2,night,1.376,0.37584643630915593
+epa1988mtt20,1988-02-18,1988,2,NA,5.057,0.7822575736633017
+epa1988mtt21,1988-02-18,1988,2,day,0,0
+epa1988mtt22,1988-02-18,1988,2,day,39.319,1.605509752111388
+epa1988mtt23,1988-02-18,1988,2,night,47.11,1.682235356902564
+epa1988mtt24,1988-02-19,1988,2,night,12.775,1.1390916075238227
+epa1988mtt25,1988-02-19,1988,2,day,0.975,0.29556709996247904
+epa1988mtt26,1988-02-19,1988,2,day,0,0
+epa1988mtt27,1988-02-21,1988,2,day,21.231,1.346958998673529
+epa1988mtt28,1988-02-21,1988,2,day,0,0
+epa1988mtt29,1988-02-21,1988,2,day,1.349,0.3708830167776059
+epa1988mtt3,1988-02-14,1988,2,NA,0,0
+epa1988mtt30,1988-02-21,1988,2,night,0.92,0.28330122870354957
+epa1988mtt31,1988-02-22,1988,2,night,6.861,0.8954777962757146
+epa1988mtt32,1988-02-22,1988,2,day,18.542,1.2909690089485168
+epa1988mtt33,1988-02-22,1988,2,day,40.117,1.6140214199226428
+epa1988mtt34,1988-02-22,1988,2,night,5.289,0.7985815947285477
+epa1988mtt35,1988-02-23,1988,2,night,26.105,1.433049411616613
+epa1988mtt36,1988-02-23,1988,2,NA,13.647,1.1657486815594142
+epa1988mtt37,1988-02-23,1988,2,day,2.697,0.5678494505731069
+epa1988mtt38,1988-01-31,1988,1,day,6.528,0.8766796104192005
+epa1988mtt39,1988-02-01,1988,2,day,0,0
+epa1988mtt4,1988-02-14,1988,2,day,0,0
+epa1988mtt40,1988-02-01,1988,2,day,0,0
+epa1988mtt41,1988-01-31,1988,1,night,0,0
+epa1988mtt42,1988-02-02,1988,2,day,0,0
+epa1988mtt43,1988-02-02,1988,2,day,3.774,0.6788824146707357
+epa1988mtt44,1988-02-03,1988,2,day,3.057,0.6082050077043262
+epa1988mtt45,1988-02-03,1988,2,day,0,0
+epa1988mtt46,1988-02-04,1988,2,day,11.597,1.100267129357602
+epa1988mtt47,1988-02-05,1988,2,day,0,0
+epa1988mtt48,1988-02-05,1988,2,day,0,0
+epa1988mtt49,1988-02-06,1988,2,NA,1.962,0.47158505418518964
+epa1988mtt5,1988-02-14,1988,2,day,5.426,0.8079407212154993
+epa1988mtt50,1988-02-06,1988,2,day,0,0
+epa1988mtt51,1988-02-06,1988,2,day,3.748,0.6765107102825536
+epa1988mtt52,1988-02-07,1988,2,NA,0,0
+epa1988mtt53,1988-02-07,1988,2,day,14.255,1.1834122119784258
+epa1988mtt54,1988-02-07,1988,2,day,19.544,1.3126850063887592
+epa1988mtt55,1988-02-11,1988,2,NA,0,0
+epa1988mtt56,1988-02-12,1988,2,day,0,0
+epa1988mtt57,1988-02-12,1988,2,day,0,0
+epa1988mtt58,1988-02-13,1988,2,night,0,0
+epa1988mtt59,1988-02-13,1988,2,day,0,0
+epa1988mtt6,1988-02-14,1988,2,day,26.855,1.4449031627954616
+epa1988mtt60,1988-02-13,1988,2,day,0,0
+epa1988mtt61,1988-02-14,1988,2,night,0,0
+epa1988mtt62,1988-02-14,1988,2,NA,0,0
+epa1988mtt63,1988-02-14,1988,2,day,0,0
+epa1988mtt64,1988-02-14,1988,2,day,0,0
+epa1988mtt65,1988-02-14,1988,2,night,0,0
+epa1988mtt66,1988-02-14,1988,2,night,0,0
+epa1988mtt67,1988-02-15,1988,2,night,0,0
+epa1988mtt68,1988-02-15,1988,2,day,0,0
+epa1988mtt69,1988-02-15,1988,2,day,0,0
+epa1988mtt7,1988-02-14,1988,2,night,0.454,0.162564406523019
+epa1988mtt70,1988-02-15,1988,2,night,0,0
+epa1988mtt71,1988-02-16,1988,2,night,0,0
+epa1988mtt72,1988-02-16,1988,2,night,0,0
+epa1988mtt73,1988-02-17,1988,2,night,0,0
+epa1988mtt74,1988-02-17,1988,2,day,0,0
+epa1988mtt75,1988-02-17,1988,2,day,0,0
+epa1988mtt76,1988-02-18,1988,2,NA,0,0
+epa1988mtt77,1988-02-18,1988,2,day,0,0
+epa1988mtt78,1988-02-18,1988,2,day,0,0
+epa1988mtt79,1988-02-18,1988,2,night,0,0
+epa1988mtt8,1988-02-15,1988,2,NA,12.12,1.1179338350396415
+epa1988mtt80,1988-02-19,1988,2,night,0,0
+epa1988mtt81,1988-02-19,1988,2,day,0,0
+epa1988mtt82,1988-02-20,1988,2,night,0,0
+epa1988mtt83,1988-02-20,1988,2,NA,0,0
+epa1988mtt84,1988-02-20,1988,2,day,0,0
+epa1988mtt85,1988-02-20,1988,2,day,0,0
+epa1988mtt86,1988-02-20,1988,2,day,0,0
+epa1988mtt87,1988-02-24,1988,2,day,0,0
+epa1988mtt88,1988-02-27,1988,2,night,0,0
+epa1988mtt89,1988-02-28,1988,2,night,0,0
+epa1988mtt9,1988-02-15,1988,2,day,10.01,1.0417873189717517
+epa1988mtt90,1988-02-28,1988,2,day,0,0
+epa1988smt18,1987-12-09,1987,12,day,0,0
+epa1988smt19,1987-12-15,1987,12,day,0,0
+epa1988smt20,1987-12-15,1987,12,night,0,0
+epa1988smt21,1987-12-16,1987,12,day,0,0
+epa1988smt22,1987-12-16,1987,12,day,0,0
+epa1988smt23,1987-12-16,1987,12,night,0,0
+epa1988smt24,1987-12-17,1987,12,night,0,0
+epa1988smt25,1987-12-17,1987,12,day,0,0
+epa1988smt26,1987-12-18,1987,12,day,0,0
+epa1988smt27,1987-12-20,1987,12,night,0,0
+epa1988smt28,1988-01-09,1988,1,day,0,0
+epa1988smt29,1988-01-09,1988,1,day,0,0
+epa1988smt30,1988-01-10,1988,1,night,0,0
+epa1988smt31,1988-01-10,1988,1,day,0,0
+epa1988smt32,1988-01-14,1988,1,day,0,0
+epa1988smt33,1988-01-21,1988,1,night,0,0
+epa1988smt34,1988-01-21,1988,1,day,0,0
+epa1988smt35,1988-01-21,1988,1,night,0,0
+epa1988smt36,1988-01-22,1988,1,day,0,0
+epa1988smt37,1988-01-23,1988,1,day,0,0
+epa1988smt38,1988-01-23,1988,1,night,2.16,0.49968708261840383
+epa1988smt39,1988-01-24,1988,1,day,10.573,1.0634459531230331
+epa1988smt40,1988-01-25,1988,1,night,0,0
+epa1988yun1,1988-01-09,1988,1,day,0,0
+epa1988yun10,1988-01-22,1988,1,night,0,0
+epa1988yun11,1988-01-23,1988,1,day,0,0
+epa1988yun12,1988-01-23,1988,1,night,0,0
+epa1988yun13,1988-01-24,1988,1,day,0,0
+epa1988yun14,1988-01-25,1988,1,night,0,0
+epa1988yun15,1988-02-03,1988,2,night,0,0
+epa1988yun16,1988-02-08,1988,2,night,6.551,0.8780044702680252
+epa1988yun17,1988-02-09,1988,2,night,0,0
+epa1988yun18,1988-02-09,1988,2,night,0,0
+epa1988yun19,1988-02-11,1988,2,day,0,0
+epa1988yun2,1988-01-09,1988,1,day,0,0
+epa1988yun20,1988-02-11,1988,2,day,0,0
+epa1988yun21,1988-02-12,1988,2,night,0,0
+epa1988yun22,1988-02-12,1988,2,day,0,0
+epa1988yun23,1988-02-13,1988,2,night,0,0
+epa1988yun24,1988-02-21,1988,2,day,0,0
+epa1988yun25,1988-02-22,1988,2,night,8.511,0.9782261816745259
+epa1988yun26,1988-02-27,1988,2,day,0,0
+epa1988yun27,1988-02-27,1988,2,day,0,0
+epa1988yun270,1987-12-14,1987,12,night,0,0
+epa1988yun271,1987-12-14,1987,12,night,0,0
+epa1988yun272,1987-12-15,1987,12,night,0,0
+epa1988yun273,1987-12-15,1987,12,day,0,0
+epa1988yun274,1987-12-16,1987,12,day,0,0
+epa1988yun275,1987-12-16,1987,12,day,0,0
+epa1988yun276,1987-12-18,1987,12,night,0,0
+epa1988yun277,1987-12-18,1987,12,night,0,0
+epa1988yun278,1987-12-19,1987,12,night,0,0
+epa1988yun279,1987-12-19,1987,12,day,0,0
+epa1988yun28,1988-02-27,1988,2,day,0,0
+epa1988yun280,1987-12-20,1987,12,day,0,0
+epa1988yun281,1987-12-20,1987,12,day,0,0
+epa1988yun282,1987-12-21,1987,12,night,0,0
+epa1988yun283,1987-12-21,1987,12,night,0,0
+epa1988yun284,1987-12-21,1987,12,night,0,0
+epa1988yun285,1987-12-22,1987,12,day,0,0
+epa1988yun286,1987-12-22,1987,12,day,0,0
+epa1988yun287,1987-12-23,1987,12,day,0,0
+epa1988yun288,1987-12-23,1987,12,night,0,0
+epa1988yun29,1988-02-27,1988,2,night,0,0
+epa1988yun294,1988-02-25,1988,2,night,0,0
+epa1988yun295,1988-02-25,1988,2,day,0,0
+epa1988yun296,1988-02-25,1988,2,day,0,0
+epa1988yun297,1988-02-26,1988,2,day,0,0
+epa1988yun298,1988-02-26,1988,2,night,0,0
+epa1988yun299,1988-02-26,1988,2,night,0,0
+epa1988yun3,1988-01-10,1988,1,night,0,0
+epa1988yun30,1988-02-28,1988,2,night,0,0
+epa1988yun300,1988-02-26,1988,2,day,0,0
+epa1988yun301,1988-02-26,1988,2,day,0,0
+epa1988yun302,1988-02-27,1988,2,night,0,0
+epa1988yun303,1988-02-27,1988,2,night,0,0
+epa1988yun304,1988-02-27,1988,2,night,0,0
+epa1988yun305,1988-02-27,1988,2,day,0,0
+epa1988yun306,1988-02-27,1988,2,day,0,0
+epa1988yun307,1988-02-28,1988,2,day,0,0
+epa1988yun308,1988-02-28,1988,2,night,0,0
+epa1988yun309,1988-02-28,1988,2,night,0,0
+epa1988yun31,1988-02-28,1988,2,night,0,0
+epa1988yun310,1988-02-28,1988,2,night,0,0
+epa1988yun311,1988-03-01,1988,3,day,0,0
+epa1988yun312,1988-03-01,1988,3,day,0,0
+epa1988yun313,1988-03-01,1988,3,night,0,0
+epa1988yun314,1988-03-01,1988,3,night,0,0
+epa1988yun315,1988-03-02,1988,3,night,0,0
+epa1988yun316,1988-03-02,1988,3,day,0,0
+epa1988yun32,1988-02-28,1988,2,day,0,0
+epa1988yun33,1988-02-28,1988,2,day,0,0
+epa1988yun34,1988-02-28,1988,2,day,0,0
+epa1988yun35,1988-03-01,1988,3,day,0,0
+epa1988yun36,1988-03-01,1988,3,day,0,0
+epa1988yun37,1988-03-01,1988,3,night,0,0
+epa1988yun38,1988-03-03,1988,3,day,11.567,1.0992316150808819
+epa1988yun39,1988-03-03,1988,3,day,11.567,1.0992316150808819
+epa1988yun4,1988-01-10,1988,1,day,0,0
+epa1988yun40,1988-03-03,1988,3,night,0,0
+epa1988yun41,1988-03-04,1988,3,night,0,0
+epa1988yun42,1988-03-04,1988,3,night,0,0
+epa1988yun43,1988-03-05,1988,3,night,0,0
+epa1988yun44,1988-03-05,1988,3,night,0,0
+epa1988yun45,1988-03-05,1988,3,day,12.053,1.1157103380123794
+epa1988yun46,1988-03-07,1988,3,night,0,0
+epa1988yun47,1988-03-07,1988,3,day,25.152,1.4175049075723125
+epa1988yun48,1988-03-07,1988,3,day,12.576,1.1327718293096194
+epa1988yun49,1988-03-07,1988,3,day,0,0
+epa1988yun5,1988-01-11,1988,1,night,0,0
+epa1988yun50,1988-03-07,1988,3,night,0,0
+epa1988yun51,1988-03-08,1988,3,day,0,0
+epa1988yun52,1988-04-08,1988,4,day,0,0
+epa1988yun53,1988-04-08,1988,4,day,0,0
+epa1988yun54,1988-04-09,1988,4,night,0,0
+epa1988yun55,1988-04-09,1988,4,day,0,0
+epa1988yun56,1988-04-10,1988,4,day,0,0
+epa1988yun57,1988-04-10,1988,4,day,0,0
+epa1988yun58,1988-04-11,1988,4,night,0,0
+epa1988yun59,1988-04-11,1988,4,day,0,0
+epa1988yun6,1988-01-21,1988,1,night,0,0
+epa1988yun60,1988-04-12,1988,4,day,0,0
+epa1988yun61,1988-04-12,1988,4,day,0,0
+epa1988yun62,1988-04-13,1988,4,day,0,0
+epa1988yun7,1988-01-21,1988,1,day,0,0
+epa1988yun8,1988-01-21,1988,1,night,0,0
+epa1988yun9,1988-01-22,1988,1,day,0,0
+epa1989ikt1,1989-04-02,1989,4,night,32.497,1.525005913252151
+epa1989ikt10,1989-03-29,1989,3,day,0.101,0.041787318971751766
+epa1989ikt11,1989-03-29,1989,3,night,7.59,0.9339931638312423
+epa1989ikt2,1989-04-07,1989,4,day,0,0
+epa1989ikt3,1989-02-17,1989,2,day,0,0
+epa1989ikt4,1989-02-17,1989,2,night,0,0
+epa1989ikt5,1989-02-20,1989,2,day,0.152,0.06145247908719321
+epa1989ikt6,1989-02-20,1989,2,night,0.283,0.10822665637492848
+epa1989ikt7,1989-02-21,1989,2,day,13.809,1.1705257331231822
+epa1989ikt8,1989-02-21,1989,2,night,0.327,0.1228709228644355
+epa1989ikt9,1989-03-26,1989,3,day,18.312,1.285827252753274
+epa1989mkx1,1989-03-22,1989,3,night,98.536,1.9979801839985945
+epa1989mkx10,1989-04-06,1989,4,day,0,0
+epa1989mkx101,1989-02-14,1989,2,day,0,0
+epa1989mkx102,1989-02-17,1989,2,day,0,0
+epa1989mkx103,1989-02-18,1989,2,night,0,0
+epa1989mkx104,1989-02-18,1989,2,day,0,0
+epa1989mkx105,1989-02-23,1989,2,day,4.544,0.7438232216037504
+epa1989mkx106,1989-02-27,1989,2,day,0,0
+epa1989mkx107,1989-02-27,1989,2,night,0,0
+epa1989mkx108,1989-03-02,1989,3,day,0,0
+epa1989mkx109,1989-03-03,1989,3,day,0,0
+epa1989mkx11,1989-04-06,1989,4,night,2.573,0.5530330162024399
+epa1989mkx110,1989-03-05,1989,3,day,0,0
+epa1989mkx111,1989-03-07,1989,3,night,3.694,0.6715430852625738
+epa1989mkx112,1989-03-07,1989,3,night,414.933,2.6190233785263923
+epa1989mkx113,1989-03-09,1989,3,NA,1.791,0.44575983648863104
+epa1989mkx114,1989-03-10,1989,3,day,0,0
+epa1989mkx115,1989-03-11,1989,3,night,0,0
+epa1989mkx116,1989-03-13,1989,3,night,70.954,1.8570549418395583
+epa1989mkx117,1989-03-13,1989,3,night,0,0
+epa1989mkx118,1989-03-17,1989,3,day,0,0
+epa1989mkx119,1989-03-18,1989,3,night,47.12,1.682325618667807
+epa1989mkx12,1989-04-07,1989,4,day,0,0
+epa1989mkx13,1989-04-07,1989,4,day,0,0
+epa1989mkx14,1989-04-08,1989,4,day,0,0
+epa1989mkx15,1989-04-08,1989,4,night,0,0
+epa1989mkx2,1989-03-23,1989,3,NA,0,0
+epa1989mkx3,1989-03-23,1989,3,night,11.817,1.1077863843159532
+epa1989mkx4,1989-03-24,1989,3,day,10.818,1.0725439856436476
+epa1989mkx5,1989-03-24,1989,3,day,0,0
+epa1989mkx6,1989-04-02,1989,4,day,0,0
+epa1989mkx7,1989-04-03,1989,4,day,0,0
+epa1989mkx8,1989-04-03,1989,4,night,13.554,1.162982370585433
+epa1989mkx9,1989-04-04,1989,4,day,0,0
+epa1989mtt100,1989-01-01,1989,1,night,0,0
+epa1989mtt101,1989-01-03,1989,1,day,0,0
+epa1989mtt102,1989-01-03,1989,1,night,0,0
+epa1989mtt103,1989-01-04,1989,1,NA,0,0
+epa1989mtt104,1989-01-04,1989,1,day,0,0
+epa1989mtt105,1989-01-04,1989,1,night,0,0
+epa1989mtt106,1989-01-05,1989,1,night,0,0
+epa1989mtt107,1989-02-07,1989,2,night,0,0
+epa1989mtt108,1989-02-08,1989,2,NA,12.872,1.1421390801321352
+epa1989mtt109,1989-02-08,1989,2,day,159.874,2.2064858603268536
+epa1989mtt110,1989-02-08,1989,2,day,7.149,0.911104317804036
+epa1989mtt111,1989-02-09,1989,2,night,97.57,1.9937447565544621
+epa1989mtt112,1989-02-09,1989,2,day,14.507,1.1905277868873534
+epa1989mtt113,1989-02-10,1989,2,NA,2.238,0.5102768444173549
+epa1989mtt114,1989-02-10,1989,2,day,3.066,0.6091673743020197
+epa1989mtt115,1989-02-10,1989,2,day,15.33,1.212986184736668
+epa1989mtt116,1989-02-10,1989,2,night,0,0
+epa1989mtt117,1989-02-11,1989,2,night,8.591,0.9818638909913504
+epa1989mtt118,1989-02-11,1989,2,day,127.618,2.1093017520534896
+epa1989mtt119,1989-02-11,1989,2,day,15.563,1.2191390018598007
+epa1989mtt120,1989-02-11,1989,2,night,0,0
+epa1989mtt121,1989-02-12,1989,2,night,0,0
+epa1989mtt122,1989-02-12,1989,2,day,6.751,0.8893577368889315
+epa1989mtt123,1989-02-12,1989,2,day,173.86,2.2426904740523734
+epa1989mtt124,1989-02-12,1989,2,day,0,0
+epa1989mtt125,1989-02-12,1989,2,night,8.411,0.9736357734174075
+epa1989mtt126,1989-02-13,1989,2,NA,20.52,1.3328422669943516
+epa1989mtt127,1989-02-13,1989,2,day,9.634,1.0266966559781598
+epa1989mtt128,1989-02-13,1989,2,day,0,0
+epa1989mtt129,1989-02-13,1989,2,night,0,0
+epa1989mtt130,1989-02-14,1989,2,night,1182.354,3.0731146831305725
+epa1989mtt131,1989-02-14,1989,2,day,62.005,1.7993750159019077
+epa1989mtt132,1989-02-14,1989,2,day,6.527,0.876621916034273
+epa1989mtt133,1989-02-14,1989,2,night,2.894,0.5903959471840129
+epa1989mtt134,1989-02-15,1989,2,NA,0,0
+epa1989mtt135,1989-02-15,1989,2,day,0,0
+epa1989mtt136,1989-02-15,1989,2,night,0,0
+epa1989mtt137,1989-02-16,1989,2,night,0,0
+epa1989mtt138,1989-02-16,1989,2,day,6.747,0.8891335559667239
+epa1989mtt139,1989-02-16,1989,2,day,0,0
+epa1989mtt140,1989-02-16,1989,2,NA,0,0
+epa1989mtt141,1989-02-17,1989,2,NA,22.991,1.3800483507369252
+epa1989mtt142,1989-02-17,1989,2,day,3.431,0.6465017500316119
+epa1989mtt143,1989-02-17,1989,2,night,6.087,0.8504624327615166
+epa1989mtt144,1989-02-24,1989,2,day,58.911,1.7775065686449032
+epa1989mtt145,1989-03-03,1989,3,night,293.422,2.4689702585242412
+epa1989mtt146,1989-03-04,1989,3,night,79.741,1.9070941239669443
+epa1989mtt147,1989-03-05,1989,3,night,147.776,2.1725328780631794
+epa1989mtt148,1989-03-05,1989,3,NA,0,0
+epa1989mtt149,1989-03-12,1989,3,day,0,0
+epa1989mtt150,1989-03-12,1989,3,day,21.959,1.3609529680491148
+epa1989mtt151,1989-03-12,1989,3,night,80.338,1.9102934894400652
+epa1989mtt152,1989-03-13,1989,3,NA,3.04,0.6063813651106049
+epa1989mtt153,1989-03-13,1989,3,day,73.183,1.87030439244771
+epa1989mtt154,1989-03-13,1989,3,night,7.489,0.9288565335819108
+epa1989mtt155,1989-03-14,1989,3,night,0,0
+epa1989mtt91,1988-12-28,1988,12,day,0,0
+epa1989mtt92,1988-12-28,1988,12,day,0,0
+epa1989mtt93,1988-12-29,1988,12,night,0,0
+epa1989mtt94,1988-12-29,1988,12,day,0,0
+epa1989mtt95,1988-12-29,1988,12,day,0,0
+epa1989mtt96,1988-12-31,1988,12,day,0,0
+epa1989mtt97,1988-12-31,1988,12,day,0,0
+epa1989mtt98,1988-12-31,1988,12,NA,0,0
+epa1989mtt99,1988-12-31,1988,12,night,0,0
+epa1989smt1,1989-02-14,1989,2,day,0,0
+epa1989smt10,1989-03-12,1989,3,night,251.698,2.4026018046725817
+epa1989smt11,1989-03-26,1989,3,day,1.023,0.30599588277080464
+epa1989smt12,1989-03-26,1989,3,night,78.061,1.8979623031986457
+epa1989smt13,1989-03-29,1989,3,day,0,0
+epa1989smt14,1989-03-29,1989,3,night,172.406,2.2390641203709296
+epa1989smt15,1989-04-07,1989,4,day,1.319,0.36530074863798734
+epa1989smt16,1989-04-07,1989,4,night,3.509,0.6540802353065707
+epa1989smt17,1989-04-02,1989,4,night,28.625,1.4716583590181602
+epa1989smt2,1989-02-17,1989,2,night,0,0
+epa1989smt3,1989-02-20,1989,2,day,0,0
+epa1989smt4,1989-02-20,1989,2,night,2.897,0.5907304057926903
+epa1989smt5,1989-02-21,1989,2,day,1.957,0.47085132452611766
+epa1989smt6,1989-02-21,1989,2,night,12.259,1.1225107706103201
+epa1989smt7,1989-03-07,1989,3,day,274.117,2.439517427096636
+epa1989smt8,1989-03-07,1989,3,night,55.073,1.748753792206785
+epa1989smt9,1989-03-12,1989,3,day,11.495,1.0967362604624689
+epa1989yun63,1989-03-12,1989,3,day,NA,NA
+epa1989yun64,1989-03-12,1989,3,night,NA,NA
+epa1989yun65,1989-03-12,1989,3,day,NA,NA
+epa1989yun66,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1a,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1aicom,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1b,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1c,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1d,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1e,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1f,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1g,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1h,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1i,1989-03-12,1989,3,day,NA,NA
+epa1989yuv1j,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1jrcom,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1k,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1l,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1m,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1n,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1o,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1p,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1q,1989-03-12,1989,3,night,NA,NA
+epa1989yuv1r,1989-03-12,1989,3,night,NA,NA
+epa1990mkx149,1990-01-26,1990,1,day,0,0
+epa1990mkx150,1990-01-26,1990,1,day,30.4,1.4969296480732148
+epa1990mkx151,1990-01-27,1990,1,NA,0,0
+epa1990mkx152,1990-01-27,1990,1,day,0,0
+epa1990mkx153,1990-01-27,1990,1,day,0,0
+epa1990mkx154,1990-01-27,1990,1,NA,0,0
+epa1990mkx155,1990-01-28,1990,1,night,0,0
+epa1990mkx156,1990-01-28,1990,1,day,0,0
+epa1990mkx157,1990-01-28,1990,1,day,0,0
+epa1990mkx158,1990-01-28,1990,1,night,0,0
+epa1990mkx159,1990-01-29,1990,1,night,0,0
+epa1990mkx16,1990-02-16,1990,2,day,0,0
+epa1990mkx160,1990-01-29,1990,1,day,0,0
+epa1990mkx161,1990-01-29,1990,1,day,0,0
+epa1990mkx162,1990-01-30,1990,1,day,0,0
+epa1990mkx163,1990-01-30,1990,1,day,0,0
+epa1990mkx164,1990-01-30,1990,1,day,0,0
+epa1990mkx165,1990-01-30,1990,1,night,0,0
+epa1990mkx166,1990-01-31,1990,1,night,0,0
+epa1990mkx167,1990-01-31,1990,1,NA,0,0
+epa1990mkx168,1990-01-31,1990,1,day,0,0
+epa1990mkx169,1990-01-31,1990,1,night,0,0
+epa1990mkx17,1990-02-16,1990,2,night,0,0
+epa1990mkx170,1990-02-01,1990,2,night,0,0
+epa1990mkx171,1990-02-01,1990,2,NA,0,0
+epa1990mkx172,1990-02-01,1990,2,night,0,0
+epa1990mkx173,1990-02-02,1990,2,day,0,0
+epa1990mkx18,1990-02-17,1990,2,night,8.79,0.9907826918031378
+epa1990mkx19,1990-02-17,1990,2,NA,1.365,0.3738311450738304
+epa1990mkx20,1990-02-17,1990,2,day,0,0
+epa1990mkx21,1990-02-17,1990,2,night,0,0
+epa1990mkx22,1990-02-18,1990,2,day,0,0
+epa1990mkx226,1990-03-16,1990,3,day,0,0
+epa1990mkx227,1990-02-08,1990,2,NA,0,0
+epa1990mkx228,1990-03-15,1990,3,day,2.704,0.5686709780098967
+epa1990mkx229,1990-02-07,1990,2,night,0,0
+epa1990mkx23,1990-02-18,1990,2,day,2.127,0.49512788124293317
+epa1990mkx230,1990-03-16,1990,3,night,0,0
+epa1990mkx231,1990-03-15,1990,3,night,0.599,0.2038484637462347
+epa1990mkx232,1990-03-13,1990,3,night,0,0
+epa1990mkx233,1990-03-10,1990,3,day,0,0
+epa1990mkx234,1990-03-12,1990,3,day,0,0
+epa1990mkx235,1990-03-06,1990,3,night,0,0
+epa1990mkx236,1990-03-14,1990,3,night,0,0
+epa1990mkx237,1990-03-16,1990,3,day,0,0
+epa1990mkx238,1990-02-07,1990,2,night,0,0
+epa1990mkx239,1990-03-15,1990,3,day,0,0
+epa1990mkx24,1990-02-18,1990,2,day,0,0
+epa1990mkx240,1990-03-14,1990,3,night,4.685,0.7547304690237535
+epa1990mkx241,1990-03-15,1990,3,night,7.194,0.9134959596171235
+epa1990mkx242,1990-03-12,1990,3,night,0.552,0.19089171692216964
+epa1990mkx243,1990-03-15,1990,3,day,0,0
+epa1990mkx244,1990-03-12,1990,3,day,45.446,1.6669483178403244
+epa1990mkx245,1990-02-16,1990,2,NA,0,0
+epa1990mkx246,1990-02-24,1990,2,NA,3.525,0.6556185835412222
+epa1990mkx247,1990-02-08,1990,2,night,0,0
+epa1990mkx248,1990-02-09,1990,2,night,0.328,0.1231980750319987
+epa1990mkx249,1990-03-16,1990,3,night,7.98,0.9532763366673044
+epa1990mkx25,1990-02-19,1990,2,night,0,0
+epa1990mkx250,1990-02-16,1990,2,day,0,0
+epa1990mkx251,1990-02-25,1990,2,day,220.474,2.3453227494335938
+epa1990mkx252,1990-02-07,1990,2,night,0,0
+epa1990mkx253,1990-02-06,1990,2,NA,0,0
+epa1990mkx254,1990-02-06,1990,2,night,0.312,0.11793383503964149
+epa1990mkx255,1990-02-07,1990,2,night,0,0
+epa1990mkx256,1990-02-07,1990,2,night,0,0
+epa1990mkx257,1990-02-05,1990,2,day,0.695,0.22916970253910093
+epa1990mkx258,1990-02-06,1990,2,night,0,0
+epa1990mkx259,1990-02-07,1990,2,day,0,0
+epa1990mkx26,1990-02-19,1990,2,day,7.663,0.9376683143990051
+epa1990mkx260,1990-02-05,1990,2,day,0,0
+epa1990mkx261,1990-02-07,1990,2,day,0,0
+epa1990mkx262,1990-02-06,1990,2,day,0,0
+epa1990mkx263,1990-02-06,1990,2,night,0,0
+epa1990mkx264,1990-02-06,1990,2,day,0.704,0.2314695904306813
+epa1990mkx265,1990-02-05,1990,2,NA,0,0
+epa1990mkx266,1990-02-05,1990,2,night,0,0
+epa1990mkx267,1990-03-14,1990,3,night,0,0
+epa1990mkx268,1990-03-06,1990,3,night,17.218,1.2605006977390882
+epa1990mkx269,1990-02-06,1990,2,night,0.312,0.11793383503964149
+epa1990mkx27,1990-02-19,1990,2,day,1.083,0.3186892699477459
+epa1990mkx270,1990-03-15,1990,3,night,23.38,1.387033701282363
+epa1990mkx271,1990-03-06,1990,3,night,247.443,2.3952267647998027
+epa1990mkx272,1990-02-06,1990,2,day,0,0
+epa1990mkx273,1990-02-08,1990,2,day,0,0
+epa1990mkx274,1990-02-06,1990,2,night,0,0
+epa1990mkx28,1990-02-19,1990,2,day,5.596,0.8192806469724812
+epa1990mkx29,1990-02-19,1990,2,night,1.053,0.31238894937059186
+epa1990mkx30,1990-02-21,1990,2,day,0,0
+epa1990mkx31,1990-02-21,1990,2,day,15.781,1.2248178373950402
+epa1990mkx32,1990-02-21,1990,2,day,4.013,0.7000977046130538
+epa1990mkx33,1990-02-23,1990,2,night,0,0
+epa1990mkx34,1990-02-24,1990,2,night,0.473,0.16820274684263087
+epa1990mkx35,1990-02-24,1990,2,night,0.466,0.16613397030510912
+epa1990mkx36,1990-02-27,1990,2,day,3.21,0.6242820958356683
+epa1990mkx37,1990-02-28,1990,2,night,0,0
+epa1990mkxx38,1990-03-16,1990,3,day,133.29,2.128043673826457
+epa1992xxx10,1992-03-14,1992,3,day,0,0
+epa1992xxx11,1992-03-14,1992,3,day,0,0
+epa1992xxx12,1992-03-14,1992,3,day,0,0
+epa1992xxx13,1992-03-14,1992,3,day,0,0
+epa1992xxx14,1992-03-14,1992,3,day,0,0
+epa1992xxx15,1992-03-14,1992,3,day,0,0
+epa1992xxx16,1992-03-14,1992,3,day,0,0
+epa1992xxx17,1992-03-14,1992,3,day,0,0
+epa1992xxx18,1992-03-14,1992,3,day,0,0
+epa1992xxx19,1992-03-25,1992,3,day,86.47,1.9418591265253735
+epa1992xxx20,1992-03-27,1992,3,day,276.159,2.442728985713524
+epa1992xxx21,1992-03-30,1992,3,day,0,0
+epa1992xxx7,1992-02-15,1992,2,day,0,0
+epa1992xxx8,1992-02-15,1992,2,day,0,0
+epa1992xxx9,1992-02-15,1992,2,day,0,0
+epa1993saf211,1993-01-17,1993,1,night,5.007,0.7786576319473553
+epa1993saf212,1993-01-18,1993,1,night,0.798,0.25478968739721
+epa1993saf213,1993-01-20,1993,1,night,0,0
+epa1993saf214,1993-01-21,1993,1,night,0,0
+epa1993saf215,1993-01-21,1993,1,day,13.745,1.1686447768878168
+epa1993saf216,1993-01-22,1993,1,night,0,0
+epa1993saf217,1993-01-22,1993,1,day,0,0
+epa1993saf218,1993-01-23,1993,1,night,0,0
+epa1993saf219,1993-01-23,1993,1,day,0,0
+epa1993saf220,1993-01-23,1993,1,night,59.513,1.7818486841032959
+epa1993saf221,1993-01-24,1993,1,day,32.658,1.527088306091094
+epa1993saf222,1993-01-24,1993,1,night,0,0
+epa1993saf223,1993-01-25,1993,1,day,270.582,2.433900982251589
+epa1993saf224,1993-01-25,1993,1,night,1.717,0.4340896384178908
+epa1993saf225,1993-01-26,1993,1,night,8.027,0.955543442459743
+epa1993saf226,1993-01-26,1993,1,day,14.642,1.1942922815519725
+epa1993saf227,1993-01-27,1993,1,day,69.891,1.850591102623985
+epa1993saf228,1993-01-27,1993,1,day,135.561,2.1353266883416784
+epa1993saf229,1993-01-29,1993,1,night,4.387,0.731346975545955
+epa1993saf230,1993-01-30,1993,1,day,0,0
+epa1993saf231,1993-01-30,1993,1,night,0,0
+epa1993saf232,1993-01-31,1993,1,day,0,0
+epa1993saf233,1993-02-01,1993,2,day,0,0
+epa1993saf234,1993-02-02,1993,2,day,0,0
+epa1993saf235,1993-02-03,1993,2,day,0,0
+epa1993saf236,1993-02-04,1993,2,night,0,0
+epa1993saf237,1993-02-05,1993,2,night,0,0
+epa1993saf238,1993-02-05,1993,2,day,0,0
+epa1993saf239,1993-02-12,1993,2,day,0,0
+epa1993saf240,1993-02-13,1993,2,day,0,0
+epa1993saf241,1993-02-13,1993,2,night,0,0
+epa1993saf242,1993-02-13,1993,2,day,0,0
+epa1993saf243,1993-02-14,1993,2,night,0,0
+epa1993saf244,1993-02-14,1993,2,night,0,0
+epa1993saf245,1993-02-15,1993,2,night,0,0
+epa1993saf246,1993-02-15,1993,2,day,0,0
+epa1993saf247,1993-02-16,1993,2,night,0,0
+epa1993saf248,1993-02-16,1993,2,night,0,0
+epa1993saf249,1993-02-16,1993,2,day,0,0
+epa1993saf250,1993-02-16,1993,2,night,0,0
+epa1993saf251,1993-02-17,1993,2,night,0,0
+epa1993saf252,1993-02-17,1993,2,day,0,0
+epa1993saf253,1993-02-18,1993,2,night,0,0
+epa1993saf254,1993-02-19,1993,2,night,0,0
+epa1993saf255,1993-02-20,1993,2,night,0,0
+epa1993saf256,1993-02-21,1993,2,night,0,0
+epa1994xxx22,1994-03-07,1994,3,day,NA,NA
+epa1994xxx23,1994-03-07,1994,3,night,NA,NA
+epa1994xxx24,1994-03-07,1994,3,night,NA,NA
+epa1994xxx25,1994-03-08,1994,3,night,NA,NA
+epa1994xxx26,1994-03-08,1994,3,day,NA,NA
+epa1994xxx27,1994-03-08,1994,3,day,NA,NA
+epa1994xxx28,1994-03-08,1994,3,day,NA,NA
+epa1994xxx29,1994-03-08,1994,3,day,NA,NA
+epa1994xxx30,1994-03-09,1994,3,night,NA,NA
+epa1994xxx31,1994-03-09,1994,3,night,NA,NA
+epa1994xxx32,1994-03-09,1994,3,night,NA,NA
+epa1994xxx33,1994-03-11,1994,3,day,NA,NA
+epa1994xxx34,1994-03-12,1994,3,night,NA,NA
+epa1994xxx35,1994-03-12,1994,3,night,NA,NA
+epa1994xxx36,1994-03-12,1994,3,day,NA,NA
+epa1995bon113,1994-12-12,1994,12,night,0,0
+epa1995bon114,1994-12-16,1994,12,day,206.755,2.3175514846644716
+epa1995bon115,1994-12-16,1994,12,night,0,0
+epa1995bon116,1994-12-17,1994,12,night,0,0
+epa1995bon117,1994-12-17,1994,12,day,0,0
+epa1995bon118,1994-12-17,1994,12,day,5.673,0.8243211248507712
+epa1995bon119,1994-12-17,1994,12,night,5.366,0.8038666342849843
+epa1995bon120,1994-12-18,1994,12,night,6.672,0.8849085941626071
+epa1995bon121,1994-12-18,1994,12,day,3.272,0.6306312440205
+epa1995bon122,1994-12-18,1994,12,day,0,0
+epa1995bon123,1994-12-18,1994,12,night,0,0
+epa1995bon124,1994-12-19,1994,12,night,0,0
+epa1995bon125,1994-12-19,1994,12,day,8.74,0.9885589568786155
+epa1995bon126,1994-12-19,1994,12,day,0,0
+epa1995bon127,1994-12-19,1994,12,night,1.209,0.34419571587143494
+epa1995bon128,1994-12-20,1994,12,night,0,0
+epa1995bon129,1994-12-20,1994,12,day,0,0
+epa1995bon130,1994-12-20,1994,12,day,0,0
+epa1995bon131,1994-12-20,1994,12,night,0,0
+epa1995bon132,1994-12-20,1994,12,night,0,0
+epa1995bon133,1994-12-21,1994,12,night,0,0
+epa1995bon134,1994-12-21,1994,12,day,0,0
+epa1995bon135,1994-12-21,1994,12,day,0,0
+epa1995bon136,1994-12-21,1994,12,night,0,0
+epa1995bon137,1994-12-22,1994,12,night,0,0
+epa1995bon138,1994-12-22,1994,12,night,4.216,0.7173375827238638
+epa1995bon139,1994-12-22,1994,12,day,0,0
+epa1995bon140,1994-12-22,1994,12,day,0,0
+epa1995bon141,1994-12-22,1994,12,night,1.84,0.45331834004703764
+epa1995bon142,1994-12-23,1994,12,night,0,0
+epa1995bon143,1994-12-23,1994,12,night,0,0
+epa1995bon144,1994-12-23,1994,12,day,8.799,0.9911817576678732
+epa1995bon145,1994-12-23,1994,12,day,0,0
+epa1995bon146,1994-12-23,1994,12,night,0,0
+epa1995bon147,1994-12-24,1994,12,night,0,0
+epa1995bon148,1994-12-24,1994,12,day,0,0
+epa1995bon149,1994-12-24,1994,12,day,0,0
+epa1995bon150,1994-12-24,1994,12,day,0,0
+epa1995bon151,1994-12-26,1994,12,day,0,0
+epa1995bon152,1994-12-26,1994,12,day,0.968,0.2940250940953227
+epa1995bon153,1994-12-28,1994,12,night,10.562,1.0630329650390966
+epa1995bon154,1994-12-28,1994,12,night,0.792,0.2533380053261064
+epa1995bon155,1994-12-28,1994,12,night,0,0
+epa1995bon156,1994-12-28,1994,12,day,0,0
+epa1995bon157,1994-12-28,1994,12,day,0,0
+epa1995bon158,1994-12-28,1994,12,day,0,0
+epa1995bon159,1994-12-28,1994,12,day,0,0
+epa1995bon160,1994-12-28,1994,12,night,0,0
+epa1995bon161,1994-12-29,1994,12,night,10.854,1.0738649229029993
+epa1995bon162,1995-01-02,1995,1,night,0,0
+epa1995bon163,1995-01-02,1995,1,night,0,0
+epa1995bon164,1995-01-02,1995,1,night,0,0
+epa1995bon165,1995-01-02,1995,1,day,0,0
+epa1995bon166,1995-01-02,1995,1,day,0,0
+epa1995bon167,1995-01-02,1995,1,day,0,0
+epa1995bon168,1995-01-02,1995,1,day,0.268,0.10311925354571388
+epa1995bon169,1995-01-05,1995,1,day,0,0
+epa1995bon170,1995-01-05,1995,1,day,0,0
+epa1995bon171,1995-01-05,1995,1,day,0,0
+epa1995bon172,1995-01-05,1995,1,night,0,0
+epa1995bon173,1995-01-06,1995,1,night,1.572,0.41027096425218446
+epa1995bon174,1995-01-06,1995,1,night,0,0
+epa1995bon175,1995-01-06,1995,1,night,0,0
+epa1995bon176,1995-01-06,1995,1,day,0.236,0.09201847075279702
+epa1995bon177,1995-01-10,1995,1,day,233.894,2.3708719235457147
+epa1995bon178,1995-01-10,1995,1,night,0,0
+epa1995bon179,1995-01-11,1995,1,night,0,0
+epa1995bon180,1995-01-11,1995,1,day,0,0
+epa1995bon181,1995-01-11,1995,1,night,0,0
+epa1995bon182,1995-01-12,1995,1,night,0,0
+epa1995bon183,1995-01-12,1995,1,day,0,0
+epa1995bon184,1995-01-13,1995,1,night,0,0
+epa1995bon185,1995-01-13,1995,1,day,4.315,0.7255032688593156
+epa1995bon186,1995-01-13,1995,1,night,0,0
+epa1995bon187,1995-01-14,1995,1,night,0,0
+epa1995bon188,1995-01-14,1995,1,day,0,0
+epa1995bon189,1995-01-14,1995,1,night,0,0
+epa1995saf1,1995-01-10,1995,1,day,0.945,0.28891960566172653
+epa1995saf10,1995-01-14,1995,1,day,0.291,0.11092624226642028
+epa1995saf11,1995-01-14,1995,1,night,0.139,0.05652372407910037
+epa1995saf12,1994-12-29,1994,12,night,0.01,0.004321373782642578
+epa1995saf13,1994-12-30,1994,12,day,0.148,0.05994188806195468
+epa1995saf14,1995-01-03,1995,1,night,0.52,0.18184358794477254
+epa1995saf2,1995-01-10,1995,1,night,0.425,0.15381486434452904
+epa1995saf3,1995-01-11,1995,1,night,0.148,0.05994188806195468
+epa1995saf4,1995-01-11,1995,1,day,0.312,0.11793383503964149
+epa1995saf5,1995-01-12,1995,1,night,0.124,0.05076631123304232
+epa1995saf6,1995-01-12,1995,1,day,0.19,0.07554696139253074
+epa1995saf7,1995-01-12,1995,1,night,0.052,0.02201573981772028
+epa1995saf8,1995-01-13,1995,1,day,3.307,0.6341748717626001
+epa1995saf9,1995-01-14,1995,1,night,0.759,0.24526583945746122
+epa1995xxx37,1995-03-11,1995,3,night,NA,NA
+epa1995xxx38,1995-03-11,1995,3,night,NA,NA
+epa1995xxx39,1995-03-11,1995,3,day,NA,NA
+epa1995xxx40,1995-03-11,1995,3,night,NA,NA
+epa1995xxx41,1995-03-12,1995,3,day,NA,NA
+epa1995xxx42,1995-03-12,1995,3,night,NA,NA
+epa1995xxx43,1995-03-13,1995,3,night,NA,NA
+epa1995xxx44,1995-03-13,1995,3,night,NA,NA
+epa1995xxx45,1995-03-14,1995,3,day,NA,NA
+epa1995xxx46,1995-03-14,1995,3,night,NA,NA
+epa1995xxx47,1995-03-15,1995,3,night,NA,NA
+epa1995xxx48,1995-03-22,1995,3,day,NA,NA
+epa1995xxx49,1995-03-22,1995,3,night,NA,NA
+epa1995xxx50,1995-03-22,1995,3,night,NA,NA
+epa1995xxx51,1995-03-23,1995,3,night,NA,NA
+epa1996bro1,1996-01-30,1996,1,day,0.13,0.05307844348341968
+epa1996bro10,1996-02-07,1996,2,day,0,0
+epa1996bro11,1996-02-07,1996,2,night,0.099,0.04099769242349056
+epa1996bro12,1996-02-08,1996,2,night,6.585,0.8799555851227493
+epa1996bro13,1996-02-09,1996,2,day,0,0
+epa1996bro14,1996-02-13,1996,2,day,0,0
+epa1996bro15,1996-02-13,1996,2,night,3.9,0.6901960800285137
+epa1996bro16,1996-02-14,1996,2,day,0,0
+epa1996bro17,1996-02-14,1996,2,day,0.178,0.07114529045108281
+epa1996bro18,1996-02-14,1996,2,day,1.657,0.42439155441027754
+epa1996bro19,1996-02-15,1996,2,day,0,0
+epa1996bro2,1996-01-31,1996,1,night,2.051,0.4844422076424071
+epa1996bro20,1996-02-15,1996,2,day,0,0
+epa1996bro21,1996-02-15,1996,2,night,0.04,0.01703333929878037
+epa1996bro22,1996-02-23,1996,2,night,0.086,0.03582982525282818
+epa1996bro23,1996-02-23,1996,2,night,0.029,0.012415374762432893
+epa1996bro24,1996-02-24,1996,2,day,0,0
+epa1996bro25,1996-02-24,1996,2,night,19.815,1.3183764152227961
+epa1996bro26,1996-02-24,1996,2,night,0.379,0.13956426617584974
+epa1996bro27,1996-02-25,1996,2,day,0,0
+epa1996bro28,1996-02-25,1996,2,night,0.044,0.01870049866624337
+epa1996bro29,1996-02-26,1996,2,day,0.134,0.05461305455688774
+epa1996bro3,1996-01-31,1996,1,night,0.093,0.03862016194970278
+epa1996bro30,1996-02-27,1996,2,day,0.612,0.20736503746907187
+epa1996bro31,1996-02-28,1996,2,night,3.053,0.6077766037416931
+epa1996bro32,1996-02-29,1996,2,day,0.174,0.069668096911595645
+epa1996bro33,1996-02-29,1996,2,night,1.143,0.33102217104182863
+epa1996bro34,1996-02-29,1996,2,night,0.324,0.12188798510368115
+epa1996bro35,1996-03-01,1996,3,day,0,0
+epa1996bro36,1996-03-01,1996,3,night,0.219,0.08600370561838196
+epa1996bro37,1996-03-02,1996,3,day,0,0
+epa1996bro38,1996-03-03,1996,3,day,0,0
+epa1996bro39,1996-03-04,1996,3,day,0.22,0.08635983067474821
+epa1996bro4,1996-02-01,1996,2,day,8.471,0.9763958366118798
+epa1996bro40,1996-03-04,1996,3,night,0.341,0.1274287778515989
+epa1996bro41,1996-03-05,1996,3,day,0,0
+epa1996bro42,1996-03-05,1996,3,day,0,0
+epa1996bro43,1996-03-05,1996,3,night,0.099,0.04099769242349056
+epa1996bro44,1996-03-05,1996,3,night,0.099,0.04099769242349056
+epa1996bro45,1996-03-06,1996,3,day,0,0
+epa1996bro46,1996-03-06,1996,3,night,0,0
+epa1996bro47,1996-03-06,1996,3,night,0.033,0.014100321519620544
+epa1996bro48,1996-03-12,1996,3,day,0,0
+epa1996bro49,1996-03-12,1996,3,day,0,0
+epa1996bro5,1996-02-01,1996,2,day,0.159,0.064083435963596
+epa1996bro50,1996-03-13,1996,3,day,0,0
+epa1996bro51,1996-03-13,1996,3,night,0,0
+epa1996bro52,1996-03-13,1996,3,day,3.065,0.609060549930087
+epa1996bro53,1996-03-14,1996,3,day,6.892,0.8971870765801535
+epa1996bro54,1996-03-14,1996,3,night,0.391,0.14332712999204641
+epa1996bro55,1996-03-16,1996,3,day,0.909,0.2808059283936668
+epa1996bro56,1996-03-16,1996,3,night,0,0
+epa1996bro57,1996-03-16,1996,3,day,0,0
+epa1996bro58,1996-03-17,1996,3,day,0,0
+epa1996bro59,1996-03-19,1996,3,night,0,0
+epa1996bro6,1996-02-01,1996,2,day,0.159,0.064083435963596
+epa1996bro60,1996-03-20,1996,3,night,0,0
+epa1996bro61,1996-03-20,1996,3,night,0,0
+epa1996bro62,1996-03-21,1996,3,day,0,0
+epa1996bro63,1996-03-21,1996,3,day,0,0
+epa1996bro64,1996-03-21,1996,3,night,0,0
+epa1996bro65,1996-03-21,1996,3,day,0,0
+epa1996bro66,1996-03-22,1996,3,day,0,0
+epa1996bro7,1996-02-02,1996,2,night,0,0
+epa1996bro8,1996-02-06,1996,2,day,0.083,0.03462845662532034
+epa1996bro9,1996-02-07,1996,2,day,0,0
+epa1996xxx1,1996-04-14,1996,4,day,1791.915,3.253559700657555
+epa1996xxx2,1996-04-14,1996,4,night,1585.215,3.200362052455282
+epa1996xxx5,1995-10-15,1995,10,day,0.005,0.0021660617565076304
+epa1996xxx6,1995-10-15,1995,10,day,1.197,0.34183005692051033
+epa1997bon190,1997-05-08,1997,5,day,NA,NA
+epa1997bon191,1997-05-08,1997,5,night,NA,NA
+epa1997bon192,1997-05-09,1997,5,day,NA,NA
+epa1997bon193,1997-05-09,1997,5,day,NA,NA
+epa1997bon194,1997-05-10,1997,5,night,NA,NA
+epa1997bon195,1997-05-10,1997,5,day,NA,NA
+epa1997bon196,1997-05-11,1997,5,night,NA,NA
+epa1997bon197,1997-05-11,1997,5,day,NA,NA
+epa1997bon198,1997-05-11,1997,5,day,NA,NA
+epa1997bon199,1997-05-11,1997,5,night,NA,NA
+epa1997bon200,1997-05-12,1997,5,night,NA,NA
+epa1997bon201,1997-05-12,1997,5,day,NA,NA
+epa1997bon202,1997-05-13,1997,5,night,NA,NA
+epa1997bon203,1997-05-13,1997,5,day,NA,NA
+epa1997bon204,1997-05-13,1997,5,day,NA,NA
+epa1997bon205,1997-05-14,1997,5,night,NA,NA
+epa1997bon206,1997-05-14,1997,5,night,NA,NA
+epa1997bon207,1997-05-15,1997,5,night,NA,NA
+epa1997bon208,1997-05-15,1997,5,day,NA,NA
+epa1997bon209,1997-05-16,1997,5,night,NA,NA
+epa1997bon210,1997-05-16,1997,5,day,NA,NA
+epa1997saf15,1997-01-01,1997,1,day,0,0
+epa1997saf16,1997-05-08,1997,5,night,NA,NA
+epa1997saf17,1997-05-09,1997,5,night,NA,NA
+epa1997saf18,1997-05-09,1997,5,day,NA,NA
+epa1997saf19,1997-05-09,1997,5,night,NA,NA
+epa1997saf20,1997-05-10,1997,5,day,NA,NA
+epa1997saf21,1997-05-10,1997,5,day,NA,NA
+epa1997saf22,1997-05-11,1997,5,night,NA,NA
+epa1997saf23,1997-05-11,1997,5,day,NA,NA
+epa1997saf24,1997-05-11,1997,5,day,NA,NA
+epa1997saf25,1997-05-12,1997,5,night,NA,NA
+epa1997saf26,1997-05-12,1997,5,day,NA,NA
+epa1997saf27,1997-05-12,1997,5,night,NA,NA
+epa1997saf28,1997-05-13,1997,5,night,NA,NA
+epa1997saf29,1997-05-15,1997,5,night,NA,NA
+epa1997saf30,1997-05-15,1997,5,day,NA,NA
+epa1997saf31,1997-05-15,1997,5,day,NA,NA
+epa1997saf32,1997-05-17,1997,5,night,NA,NA
+epa1997saf33,1997-05-17,1997,5,day,NA,NA
+epa1997saf34,1997-05-17,1997,5,day,NA,NA
+epa1997saf35,1997-05-18,1997,5,night,NA,NA
+epa1997saf36,1997-05-21,1997,5,day,NA,NA
+epa1997saf37,1997-05-22,1997,5,day,NA,NA
+epa1997saf38,1997-05-22,1997,5,day,NA,NA
+epa1997xxx3,1996-12-15,1996,12,day,180.162,2.2580671065801914
+epa1997xxx4,1996-12-15,1996,12,night,142.297,2.156237098303249
+epa1998saf27,1997-12-25,1997,12,day,1.034,0.30835094858672574
+epa1998saf28,1997-12-29,1997,12,day,0,0
+epa1998saf29,1997-12-30,1997,12,night,0,0
+epa1998saf30,1997-12-31,1997,12,day,0,0
+epa1998saf31,1998-01-01,1998,1,day,0,0
+epa1998saf32,1998-01-01,1998,1,day,0,0
+epa1998saf33,1998-01-02,1998,1,day,0,0
+epa1998saf34,1998-01-02,1998,1,day,2.051,0.4844422076424071
+epa1998saf35,1998-01-02,1998,1,night,0,0
+epa1998saf36,1998-01-03,1998,1,night,4.459,0.7371130943059612
+epa1998saf37,1998-01-03,1998,1,day,5.56,0.8169038393756602
+epa1998saf38,1998-01-03,1998,1,day,0,0
+epa1998saf39,1998-01-03,1998,1,night,0,0
+epa1998saf40,1998-01-04,1998,1,night,0,0
+epa1998saf41,1998-01-04,1998,1,night,0,0
+epa1998saf42,1998-01-04,1998,1,day,0,0
+epa1998saf43,1998-01-04,1998,1,day,0,0
+epa1998saf44,1998-01-04,1998,1,day,0,0
+epa1998saf45,1998-01-04,1998,1,night,1.278,0.3575537197430816
+epa1998saf46,1998-01-05,1998,1,night,4.061,0.7042363373087877
+epa1998saf47,1998-01-05,1998,1,night,0,0
+epa1998saf48,1998-01-05,1998,1,day,0,0
+epa1998saf49,1998-01-06,1998,1,night,3.272,0.6306312440205
+epa1998saf50,1998-01-06,1998,1,night,1.327,0.3667963832867299
+epa1998saf51,1998-01-06,1998,1,night,0,0
+epa1998saf52,1998-01-06,1998,1,day,2.347,0.5246557123577771
+epa1998saf53,1998-01-06,1998,1,day,2.02,0.48000694295715063
+epa1998saf54,1998-01-06,1998,1,day,2.245,0.5112147011363881
+epa1998saf55,1998-01-06,1998,1,night,1.239,0.3500540935790302
+epa1998saf56,1998-01-07,1998,1,night,0,0
+epa1998saf57,1998-01-07,1998,1,night,0,0
+epa1998saf58,1998-01-07,1998,1,night,1.484,0.3951515915045426
+epa1998saf59,1998-01-08,1998,1,night,0,0
+epa1998saf60,1998-01-08,1998,1,night,0,0
+epa1998saf61,1998-01-08,1998,1,day,0,0
+epa1998saf62,1998-01-08,1998,1,day,0,0
+epa1998saf63,1998-01-08,1998,1,day,0,0
+epa1998saf64,1998-01-08,1998,1,night,0,0
+epa1998saf65,1998-01-09,1998,1,night,0,0
+epa1998saf66,1998-01-09,1998,1,night,0,0
+epa1998saf67,1998-01-12,1998,1,day,0,0
+epa1998saf68,1998-01-12,1998,1,day,0,0
+epa1998saf69,1998-01-12,1998,1,night,14.917,1.2018612162772624
+epa1998saf70,1998-01-13,1998,1,night,2.111,0.4929000111087034
+epa1998saf71,1998-01-13,1998,1,day,0,0
+epa1998saf72,1998-01-13,1998,1,day,3.001,0.6021685513789972
+epa1998saf73,1998-01-13,1998,1,night,37.524,1.585731374188159
+epa1998saf75,1998-01-14,1998,1,night,70.35,1.8533939774506658
+epa1998saf76,1998-01-14,1998,1,night,3.256,0.6290016192869918
+epa1998saf77,1998-01-14,1998,1,day,6.602,0.880927865267085
+epa1998saf78,1998-01-14,1998,1,day,2.4,0.5314789170422551
+epa1998saf79,1998-01-14,1998,1,day,8.179,0.9627953698572332
+epa1998saf80,1998-01-14,1998,1,night,9.078,1.00337435401975
+epa1998saf81,1998-01-15,1998,1,night,3.276,0.6310376965367404
+epa1998saf82,1998-01-15,1998,1,night,0,0
+epa1998saf83,1998-01-16,1998,1,day,0,0
+epa1998saf84,1998-01-17,1998,1,night,0.198,0.07845681805329255
+epa1998saf85,1998-01-17,1998,1,day,0,0
+epa1998saf86,1998-01-18,1998,1,day,0,0
+epa1998saf87,1998-01-20,1998,1,NA,0,0
+epa1998saf88,1998-01-21,1998,1,NA,0,0
+epa1998saf89,1998-01-24,1998,1,NA,0,0
+epa1998saf90,1998-01-24,1998,1,NA,0,0
+epa1998saf91,1998-01-24,1998,1,NA,0,0
+epa1998saf92,1998-01-24,1998,1,NA,0,0
+epa1998saf93,1998-01-26,1998,1,NA,0,0
+epa1998saf94,1998-01-26,1998,1,NA,0,0
+epa1998saf95,1998-01-27,1998,1,NA,0,0
+epa1998saf96,1998-01-27,1998,1,NA,0,0
+epa1998saf97,1998-01-27,1998,1,NA,0,0
+epa1998saf98,1998-01-27,1998,1,NA,0,0
+epa1998saf99,1998-01-27,1998,1,NA,0,0
+epa2001pol1,2001-04-18,2001,4,night,NA,NA
+epa2001pol10,2001-04-27,2001,4,day,NA,NA
+epa2001pol11,2001-04-29,2001,4,night,NA,NA
+epa2001pol12,2001-04-29,2001,4,night,NA,NA
+epa2001pol13,2001-04-30,2001,4,night,NA,NA
+epa2001pol14,2001-04-01,2001,4,night,NA,NA
+epa2001pol15,2001-04-01,2001,4,day,NA,NA
+epa2001pol16,2001-04-18,2001,4,night,NA,NA
+epa2001pol17,2001-04-18,2001,4,day,NA,NA
+epa2001pol18,2001-04-18,2001,4,night,NA,NA
+epa2001pol19,2001-04-19,2001,4,night,NA,NA
+epa2001pol2,2001-04-19,2001,4,night,NA,NA
+epa2001pol20,2001-04-19,2001,4,NA,NA,NA
+epa2001pol21,2001-04-19,2001,4,day,NA,NA
+epa2001pol22,2001-04-20,2001,4,NA,NA,NA
+epa2001pol23,2001-04-20,2001,4,day,NA,NA
+epa2001pol24,2001-04-20,2001,4,NA,NA,NA
+epa2001pol25,2001-04-21,2001,4,day,NA,NA
+epa2001pol26,2001-04-24,2001,4,night,NA,NA
+epa2001pol27,2001-04-24,2001,4,night,NA,NA
+epa2001pol28,2001-04-25,2001,4,night,NA,NA
+epa2001pol29,2001-04-25,2001,4,day,NA,NA
+epa2001pol3,2001-04-19,2001,4,night,NA,NA
+epa2001pol30,2001-04-26,2001,4,night,NA,NA
+epa2001pol31,2001-04-28,2001,4,day,NA,NA
+epa2001pol32,2001-04-28,2001,4,night,NA,NA
+epa2001pol33,2001-04-29,2001,4,day,NA,NA
+epa2001pol34,2001-04-29,2001,4,night,NA,NA
+epa2001pol35,2001-04-30,2001,4,night,NA,NA
+epa2001pol36,2001-05-01,2001,5,night,NA,NA
+epa2001pol37,2001-05-01,2001,5,night,NA,NA
+epa2001pol38,2001-05-01,2001,5,day,NA,NA
+epa2001pol39,2001-05-03,2001,5,day,NA,NA
+epa2001pol4,2001-04-19,2001,4,night,NA,NA
+epa2001pol40,2001-05-03,2001,5,day,NA,NA
+epa2001pol5,2001-04-19,2001,4,night,NA,NA
+epa2001pol6,2001-04-20,2001,4,day,NA,NA
+epa2001pol7,2001-04-21,2001,4,day,NA,NA
+epa2001pol8,2001-04-26,2001,4,night,NA,NA
+epa2001pol9,2001-04-27,2001,4,day,NA,NA
+epa2001saf1,2001-04-18,2001,4,night,0,0
+epa2001saf10,2001-04-19,2001,4,night,0,0
+epa2001saf11,2001-04-22,2001,4,day,0,0
+epa2001saf12,2001-04-22,2001,4,day,0,0
+epa2001saf13,2001-04-22,2001,4,night,0,0
+epa2001saf14,2001-04-23,2001,4,night,0,0
+epa2001saf15,2001-04-23,2001,4,night,0,0
+epa2001saf16,2001-04-23,2001,4,day,0,0
+epa2001saf17,2001-04-23,2001,4,day,0,0
+epa2001saf18,2001-04-23,2001,4,night,0,0
+epa2001saf19,2001-04-24,2001,4,night,0,0
+epa2001saf2,2001-04-18,2001,4,day,0,0
+epa2001saf20,2001-04-24,2001,4,night,0,0
+epa2001saf21,2001-04-24,2001,4,day,0,0
+epa2001saf22,2001-04-24,2001,4,day,0,0
+epa2001saf23,2001-04-24,2001,4,day,0,0
+epa2001saf24,2001-04-25,2001,4,night,0,0
+epa2001saf25,2001-04-25,2001,4,day,0,0
+epa2001saf26,2001-04-25,2001,4,day,0,0
+epa2001saf3,2001-04-18,2001,4,day,0,0
+epa2001saf4,2001-04-18,2001,4,day,0,0
+epa2001saf5,2001-04-18,2001,4,night,0,0
+epa2001saf6,2001-04-19,2001,4,night,0,0
+epa2001saf7,2001-04-19,2001,4,night,0,0
+epa2001saf8,2001-04-19,2001,4,day,0,0
+epa2001saf9,2001-04-19,2001,4,day,0,0
+epa2003dei1,2003-04-07,2003,4,night,0,0
+epa2003dei2,2003-04-08,2003,4,day,0,0
+epa2003dei3,2003-04-08,2003,4,night,0,0
+epa2003dei4,2003-04-09,2003,4,day,0,0
+epa2003dei5,2003-04-13,2003,4,night,0,0
+epa2003dei6,2003-04-14,2003,4,day,0,0
+epa2003dei7,2003-04-14,2003,4,day,0,0
+epa2003dei8,2003-04-15,2003,4,day,0,0
+epa2003dei9,2003-04-16,2003,4,night,0,0
+fru1996ana1,1995-12-03,1995,12,day,0,0
+fru1996ana13,1995-12-06,1995,12,night,0,0
+fru1996ana138,1995-12-18,1995,12,day,0,0
+fru1996ana140,1995-12-18,1995,12,night,0,0
+fru1996ana142,1995-12-18,1995,12,night,14.582,1.1926232000129464
+fru1996ana144,1995-12-18,1995,12,day,65.765,1.8245488533382037
+fru1996ana15,1995-12-06,1995,12,day,0,0
+fru1996ana156a,1995-12-19,1995,12,night,14.563,1.1920933174520343
+fru1996ana156b,1995-12-19,1995,12,night,0,0
+fru1996ana156c,1995-12-19,1995,12,night,14.563,1.1920933174520343
+fru1996ana156d,1995-12-19,1995,12,night,0,0
+fru1996ana156e,1995-12-19,1995,12,night,14.563,1.1920933174520343
+fru1996ana156f,1995-12-19,1995,12,night,0,0
+fru1996ana156g,1995-12-19,1995,12,night,0,0
+fru1996ana157,1995-12-19,1995,12,night,0,0
+fru1996ana159,1995-12-19,1995,12,day,32.84,1.529430354366986
+fru1996ana160,1995-12-19,1995,12,day,32.84,1.529430354366986
+fru1996ana161,1995-12-19,1995,12,day,0,0
+fru1996ana162,1995-12-19,1995,12,day,0,0
+fru1996ana163,1995-12-19,1995,12,day,0,0
+fru1996ana164,1995-12-19,1995,12,day,0,0
+fru1996ana168a,1995-12-20,1995,12,day,0,0
+fru1996ana168b,1995-12-20,1995,12,day,0,0
+fru1996ana168c,1995-12-20,1995,12,day,0,0
+fru1996ana168d,1995-12-20,1995,12,day,131.267,2.1214515032532413
+fru1996ana17,1995-12-06,1995,12,day,0,0
+fru1996ana177a,1995-12-21,1995,12,day,229.523,2.3627142628048934
+fru1996ana177b,1995-12-21,1995,12,day,0,0
+fru1996ana177c,1995-12-21,1995,12,day,32.783,1.5286982131922022
+fru1996ana177d,1995-12-21,1995,12,day,32.783,1.5286982131922022
+fru1996ana177e,1995-12-21,1995,12,day,0,0
+fru1996ana178a,1995-12-21,1995,12,day,0,0
+fru1996ana178b,1995-12-21,1995,12,day,0,0
+fru1996ana178c,1995-12-21,1995,12,day,0,0
+fru1996ana178d,1995-12-21,1995,12,day,0,0
+fru1996ana178e,1995-12-21,1995,12,day,0,0
+fru1996ana186,1995-12-23,1995,12,day,0,0
+fru1996ana187,1995-12-23,1995,12,day,0,0
+fru1996ana189,1995-12-23,1995,12,day,262.165,2.4202281291759036
+fru1996ana19,1995-12-06,1995,12,day,68.382,1.8412468146245875
+fru1996ana191,1995-12-26,1995,12,day,0,0
+fru1996ana193,1995-12-26,1995,12,day,0,0
+fru1996ana195,1995-12-26,1995,12,day,0,0
+fru1996ana197,1995-12-28,1995,12,day,0,0
+fru1996ana198,1995-12-28,1995,12,day,0,0
+fru1996ana20,1995-12-07,1995,12,day,34.024,1.5443657442238565
+fru1996ana201,1995-12-28,1995,12,day,0,0
+fru1996ana203,1995-12-29,1995,12,day,0,0
+fru1996ana204,1995-12-29,1995,12,day,32.946,1.5307886068853545
+fru1996ana206,1995-12-29,1995,12,day,0,0
+fru1996ana208,1995-12-29,1995,12,day,0,0
+fru1996ana210,1995-12-29,1995,12,day,0,0
+fru1996ana211,1995-12-30,1995,12,night,14.639,1.1942089796466815
+fru1996ana213,1995-12-30,1995,12,day,0,0
+fru1996ana215,1995-12-31,1995,12,day,0,0
+fru1996ana217,1996-01-02,1996,1,night,0,0
+fru1996ana219,1996-01-02,1996,1,day,0,0
+fru1996ana220,1996-01-03,1996,1,day,0,0
+fru1996ana221,1996-01-03,1996,1,day,0,0
+fru1996ana223a,1996-01-03,1996,1,day,66.733,1.8308003116109666
+fru1996ana223b,1996-01-03,1996,1,day,0,0
+fru1996ana223c,1996-01-03,1996,1,day,133.508,2.1287481152122765
+fru1996ana223d,1996-01-03,1996,1,day,0,0
+fru1996ana223e,1996-01-03,1996,1,day,0,0
+fru1996ana224a,1996-01-03,1996,1,day,33.366,1.5361289857589207
+fru1996ana224b,1996-01-03,1996,1,day,0,0
+fru1996ana224c,1996-01-03,1996,1,day,33.366,1.5361289857589207
+fru1996ana224d,1996-01-03,1996,1,day,0,0
+fru1996ana224e,1996-01-03,1996,1,day,0,0
+fru1996ana225a,1996-01-03,1996,1,day,0,0
+fru1996ana225b,1996-01-03,1996,1,day,0,0
+fru1996ana225c,1996-01-03,1996,1,day,0,0
+fru1996ana225d,1996-01-03,1996,1,day,0,0
+fru1996ana225e,1996-01-03,1996,1,day,0,0
+fru1996ana226a,1996-01-03,1996,1,day,0,0
+fru1996ana226b,1996-01-03,1996,1,day,0,0
+fru1996ana226c,1996-01-03,1996,1,day,0,0
+fru1996ana226d,1996-01-03,1996,1,day,33.366,1.5361289857589207
+fru1996ana226e,1996-01-03,1996,1,day,0,0
+fru1996ana227a,1996-01-04,1996,1,day,0,0
+fru1996ana227b,1996-01-04,1996,1,night,237.632,2.3777286811263307
+fru1996ana227c,1996-01-04,1996,1,night,0,0
+fru1996ana227d,1996-01-04,1996,1,night,0,0
+fru1996ana227e,1996-01-04,1996,1,night,0,0
+fru1996ana24,1995-12-07,1995,12,day,0,0
+fru1996ana26,1995-12-08,1995,12,day,0,0
+fru1996ana29,1995-12-08,1995,12,day,0,0
+fru1996ana3,1995-12-04,1995,12,day,0,0
+fru1996ana31,1995-12-09,1995,12,day,101.208,2.00948489012237
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+fru1996ana36a,1995-12-10,1995,12,day,67.174,1.8336187762775167
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+gho1991pry102,1991-03-10,1991,3,night,8.789,0.9907383285075373
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+his1930dis-top304,1930-01-21,1930,1,day,0,0
+his1930dis-top305,1930-01-21,1930,1,night,3.018,0.6040099324122303
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+his1930dis-top311,1930-01-24,1930,1,night,0.881,0.27438879555037887
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+his1930dis-ver319c,1930-01-29,1930,1,night,NA,NA
+his1930dis-ver319com,1930-01-29,1930,1,night,NA,NA
+his1930dis-ver319d,1930-01-29,1930,1,night,NA,NA
+his1930dis-ver319e,1930-01-29,1930,1,night,NA,NA
+his1930dis-ver319f,1930-01-29,1930,1,night,NA,NA
+his1930dis-ver320a,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver320b,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver320c,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver320com,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver320d,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver320e,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver320f,1930-01-30,1930,1,day,NA,NA
+his1930dis-ver321a,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver321b,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver321c,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver321com,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver321d,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver321e,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver321f,1930-01-30,1930,1,night,NA,NA
+his1930dis-ver322a,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver322b,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver322c,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver322com,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver322d,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver322e,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver322f,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver323a,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver323b,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver323c,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver323com,1930-01-31,1930,1,day,NA,NA
+his1930dis-ver323d,1930-01-31,1930,1,day,NA,NA
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+his1930dis-ver324a,1930-02-01,1930,2,day,NA,NA
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+his1930dis-ver324c,1930-02-01,1930,2,day,NA,NA
+his1930dis-ver324com,1930-02-01,1930,2,day,NA,NA
+his1930dis-ver324d,1930-02-01,1930,2,day,NA,NA
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+his1930dis-ver324f,1930-02-01,1930,2,day,NA,NA
+his1930dis-ver325a,1930-02-01,1930,2,day,NA,NA
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+his1930dis-ver325d,1930-02-01,1930,2,day,NA,NA
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+his1930dis-ver326a,1930-02-02,1930,2,night,NA,NA
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+his1932dis-str836b,1932-02-27,1932,2,day,NA,NA
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+his1932dis-top716,1931-11-01,1931,11,night,NA,NA
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+his1932dis-top735a,1931-11-22,1931,11,night,0.14,0.056904851336472634
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+his1932dis-top804,1932-01-11,1932,1,night,0.896,0.2778383330020474
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+his1932dis-top813a,1932-01-16,1932,1,night,10.363,1.0554930066778456
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+his1933dis-str1000a,1932-11-01,1932,11,day,0,0
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+his1934dis-ver1271com,1934-01-19,1934,1,night,NA,NA
+his1934dis-ver1271d,1934-01-19,1934,1,night,NA,NA
+his1934dis-ver1271e,1934-01-19,1934,1,night,NA,NA
+his1934dis-ver1271f,1934-01-19,1934,1,night,NA,NA
+his1934dis-ver1273a,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1273b,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1273c,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1273com,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1273d,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1273e,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1273f,1934-01-20,1934,1,night,NA,NA
+his1934dis-ver1276a,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1276b,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1276c,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1276com,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1276d,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1276e,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1276f,1934-01-22,1934,1,day,NA,NA
+his1934dis-ver1277a,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1277b,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1277c,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1277com,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1277d,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1277e,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1277f,1934-01-23,1934,1,night,NA,NA
+his1934dis-ver1312a,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1312b,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1312c,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1312com,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1312d,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1312e,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1312f,1934-03-10,1934,3,day,NA,NA
+his1934dis-ver1313a,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1313b,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1313c,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1313com,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1313d,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1313e,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1313f,1934-03-11,1934,3,day,NA,NA
+his1934dis-ver1314a,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1314b,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1314c,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1314com,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1314d,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1314e,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1314f,1934-03-11,1934,3,night,NA,NA
+his1934dis-ver1315a,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1315b,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1315c,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1315com,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1315d,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1315e,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1315f,1934-03-12,1934,3,day,NA,NA
+his1934dis-ver1316a,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1316b,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1316c,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1316com,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1316d,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1316e,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1316f,1934-03-12,1934,3,night,NA,NA
+his1934dis-ver1317a,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1317b,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1317c,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1317com,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1317d,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1317e,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1317f,1934-03-13,1934,3,day,NA,NA
+his1934dis-ver1318a,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1318b,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1318c,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1318com,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1318d,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1318e,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1318f,1934-03-13,1934,3,night,NA,NA
+his1934dis-ver1319a,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1319b,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1319c,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1319com,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1319d,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1319e,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1319f,1934-03-14,1934,3,day,NA,NA
+his1934dis-ver1320a,1934-03-14,1934,3,night,NA,NA
+his1934dis-ver1320b,1934-03-14,1934,3,night,NA,NA
+his1934dis-ver1320c,1934-03-14,1934,3,night,NA,NA
+his1934dis-ver1320com,1934-03-14,1934,3,night,NA,NA
+his1934dis-ver1320d,1934-03-14,1934,3,night,NA,NA
+his1934dis-ver1320e,1934-03-14,1934,3,night,NA,NA
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+his1935dis-str1453a,1934-11-01,1934,11,night,0,0
+his1935dis-str1453b,1934-11-01,1934,11,night,NA,NA
+his1935dis-str1455a,1934-11-02,1934,11,night,0,0
+his1935dis-str1455b,1934-11-02,1934,11,night,NA,NA
+his1935dis-str1460a,1934-11-04,1934,11,night,0,0
+his1935dis-str1460b,1934-11-04,1934,11,night,NA,NA
+his1935dis-str1462a,1934-11-05,1934,11,night,0,0
+his1935dis-str1462b,1934-11-05,1934,11,night,NA,NA
+his1935dis-str1464a,1934-11-06,1934,11,night,0,0
+his1935dis-str1464b,1934-11-06,1934,11,night,NA,NA
+his1935dis-str1465a,1934-11-07,1934,11,day,0,0
+his1935dis-str1465b,1934-11-07,1934,11,day,NA,NA
+his1935dis-str1466a,1934-11-08,1934,11,day,0,0
+his1935dis-str1466b,1934-11-08,1934,11,day,NA,NA
+his1935dis-str1467a,1934-11-10,1934,11,day,0,0
+his1935dis-str1467b,1934-11-10,1934,11,day,NA,NA
+his1935dis-str1469a,1934-11-11,1934,11,day,0,0
+his1935dis-str1469b,1934-11-11,1934,11,day,NA,NA
+his1935dis-str1472a,1934-11-13,1934,11,night,0,0
+his1935dis-str1472b,1934-11-13,1934,11,night,NA,NA
+his1935dis-str1473a,1934-11-13,1934,11,night,0,0
+his1935dis-str1473b,1934-11-13,1934,11,night,NA,NA
+his1935dis-str1474a,1934-11-14,1934,11,day,0,0
+his1935dis-str1474b,1934-11-14,1934,11,day,NA,NA
+his1935dis-str1475a,1934-11-14,1934,11,night,0,0
+his1935dis-str1475b,1934-11-14,1934,11,night,NA,NA
+his1935dis-str1476a,1934-11-15,1934,11,night,0,0
+his1935dis-str1476b,1934-11-15,1934,11,night,0,0
+his1935dis-str1491a,1935-01-21,1935,1,day,0,0
+his1935dis-str1491b,1935-01-21,1935,1,day,NA,NA
+his1935dis-str1493a,1935-01-22,1935,1,night,0,0
+his1935dis-str1493b,1935-01-22,1935,1,night,NA,NA
+his1935dis-str1495a,1935-01-23,1935,1,night,0,0
+his1935dis-str1495b,1935-01-23,1935,1,night,NA,NA
+his1935dis-str1519a,1935-02-15,1935,2,night,0.381,0.1401936785786313
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+his1935dis-str1537a,1935-02-24,1935,2,night,289.811,2.463610829796868
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+his1935dis-str1541a,1935-02-26,1935,2,night,27.279,1.4514640479056058
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+his1935dis-str1549a,1935-03-02,1935,3,night,0.133,0.05422990986339725
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+his1935dis-top1456,1934-11-03,1934,11,day,0,0
+his1935dis-top1458,1934-11-04,1934,11,day,0,0
+his1935dis-top1461,1934-11-05,1934,11,day,0,0
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+his1935dis-top1466a,1934-11-09,1934,11,day,0,0
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+his1935dis-top1472a,1934-11-15,1934,11,night,0,0
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+his1935dis-top1492,1935-01-23,1935,1,night,0.23,0.08990511143939793
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+his1935dis-top1495a,1935-01-25,1935,1,night,0,0
+his1935dis-top1496,1935-01-26,1935,1,night,0.207,0.08170727009734924
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+his1935dis-top1501,1935-02-05,1935,2,night,7.622,0.9356080185178692
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+his1935dis-top1503,1935-02-05,1935,2,day,0,0
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+his1935dis-top1505,1935-02-07,1935,2,day,0.513,0.17983892802318668
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+his1935dis-ver1472a,1934-11-14,1934,11,night,NA,NA
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+his1935dis-ver1475a,1934-11-16,1934,11,night,NA,NA
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+his1935dis-ver1476a,1934-11-17,1934,11,day,NA,NA
+his1935dis-ver1476b,1934-11-17,1934,11,day,NA,NA
+his1935dis-ver1476c,1934-11-17,1934,11,day,NA,NA
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+his1936dis-str1646b,1936-01-17,1936,1,day,NA,NA
+his1936dis-str1647a,1936-01-18,1936,1,day,NA,NA
+his1936dis-str1647b,1936-01-18,1936,1,day,NA,NA
+his1936dis-str1648a,1936-01-18,1936,1,day,NA,NA
+his1936dis-str1648b,1936-01-18,1936,1,day,NA,NA
+his1936dis-str1651a,1936-01-22,1936,1,day,NA,NA
+his1936dis-str1651b,1936-01-22,1936,1,day,NA,NA
+his1936dis-str1651com,1936-01-22,1936,1,day,NA,NA
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+his1936dis-str1652b,1936-01-23,1936,1,day,NA,NA
+his1936dis-str1653a,1936-01-23,1936,1,day,NA,NA
+his1936dis-str1653b,1936-01-23,1936,1,day,NA,NA
+his1936dis-str1654a,1936-01-25,1936,1,day,NA,NA
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+his1936dis-str1699a,1936-03-14,1936,3,day,0.685,0.22659990520735743
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+his1936dis-top1630,1935-11-27,1935,11,night,NA,NA
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+his1936dis-top1665,1936-01-30,1936,1,night,0,0
+his1936dis-top1666,1936-01-31,1936,1,day,0,0
+his1936dis-top1667,1936-01-31,1936,1,night,0,0
+his1936dis-top1668,1936-02-01,1936,2,day,0.526,0.18355453361886165
+his1936dis-top1669,1936-02-01,1936,2,day,0,0
+his1936dis-top1670,1936-02-02,1936,2,day,0,0
+his1936dis-top1671,1936-02-02,1936,2,night,0,0
+his1936dis-top1672,1936-02-03,1936,2,day,3.385,0.6419695977020593
+his1936dis-top1673,1936-02-03,1936,2,day,71.316,1.8592343960904982
+his1936dis-top1674,1936-02-05,1936,2,day,0.399,0.14581771449182762
+his1936dis-top1675,1936-02-05,1936,2,night,14.08,1.1784013415337553
+his1936dis-top1698,1936-03-14,1936,3,day,0,0
+his1936dis-top1701,1936-03-15,1936,3,night,1.124,0.3271545124094315
+his1936dis-top1702,1936-03-16,1936,3,day,0,0
+his1936dis-top1703,1936-03-16,1936,3,night,0,0
+his1936dis-top1707,1936-03-18,1936,3,night,0,0
+his1936dis-top1710,1936-03-20,1936,3,day,1.487,0.395675785269936
+his1936dis-top1711,1936-03-20,1936,3,night,3.46,0.6493348587121419
+his1936dis-top1713,1936-03-21,1936,3,night,0,0
+his1936dis-top1714,1936-03-22,1936,3,day,0,0
+his1936dis-top1715,1936-03-22,1936,3,night,0,0
+his1936dis-top1719,1936-03-24,1936,3,night,1.299,0.36153897126927903
+his1936dis-top1720,1936-03-25,1936,3,day,149.018,2.1761433712668397
+his1936dis-top1721,1936-03-26,1936,3,day,2.81,0.5809249756756193
+his1936dis-top1722,1936-03-27,1936,3,day,0,0
+his1936wsc-top898,1936-01-04,1936,1,night,0,0
+his1936wsc-top899,1936-01-06,1936,1,night,0,0
+his1936wsc-top900,1936-01-07,1936,1,night,5.472,0.8110385086042159
+his1936wsc-top901,1936-01-08,1936,1,night,15.641,1.2211794205984978
+his1936wsc-top902,1936-01-17,1936,1,night,0.296,0.11260500153457453
+his1936wsc-top904,1936-01-24,1936,1,night,5.302,0.7994783988379808
+his1936wsc-top906,1936-01-27,1936,1,night,3.521,0.6552345070342942
+his1936wsc-top907,1936-02-08,1936,2,night,0,0
+his1936wsc-top908,1936-02-09,1936,2,day,1658.222,3.2199044974919637
+his1936wsc-top909,1936-02-10,1936,2,day,5.037,0.7808211758534729
+his1936wsc-top910,1936-02-11,1936,2,night,0.566,0.19479175772192453
+his1936wsc-top911,1936-02-12,1936,2,day,22.397,1.369160175060954
+his1936wsc-top912,1936-02-22,1936,2,night,1.988,0.4753805931433612
+his1936wsc-top914,1936-02-25,1936,2,day,0,0
+his1936wsc-top915,1936-02-29,1936,2,night,194.654,2.2914887311539993
+his1936wsc-top917,1936-03-04,1936,3,night,2.677,0.5654936298688624
+his1936wsc-top918,1936-03-18,1936,3,night,0.722,0.23603314711763593
+his1937dis-bot1981,1937-03-06,1937,3,day,NA,NA
+his1937dis-bot1982,1937-03-06,1937,3,night,NA,NA
+his1937dis-str1891a,1936-11-25,1936,11,night,NA,NA
+his1937dis-str1891b,1936-11-25,1936,11,night,NA,NA
+his1937dis-str1892a,1936-11-26,1936,11,day,NA,NA
+his1937dis-str1892b,1936-11-26,1936,11,day,NA,NA
+his1937dis-str1907a,1936-11-30,1936,11,day,NA,NA
+his1937dis-str1907b,1936-11-30,1936,11,day,NA,NA
+his1937dis-str1912a,1936-12-01,1936,12,day,NA,NA
+his1937dis-str1912b,1936-12-01,1936,12,day,NA,NA
+his1937dis-str1913a,1936-12-01,1936,12,day,NA,NA
+his1937dis-str1913b,1936-12-01,1936,12,day,NA,NA
+his1937dis-str1979a,1937-03-05,1937,3,night,1.583,0.41212440617331725
+his1937dis-str1979b,1937-03-05,1937,3,night,NA,NA
+his1937dis-str2004a,1937-03-17,1937,3,night,385.74,2.5874190928924996
+his1937dis-str2004b,1937-03-17,1937,3,night,NA,NA
+his1937dis-top1860,1936-11-06,1936,11,night,0,0
+his1937dis-top1880,1936-11-21,1936,11,night,NA,NA
+his1937dis-top1881,1936-11-22,1936,11,night,NA,NA
+his1937dis-top1882,1936-11-22,1936,11,day,NA,NA
+his1937dis-top1884,1936-11-22,1936,11,day,NA,NA
+his1937dis-top1885,1936-11-22,1936,11,night,NA,NA
+his1937dis-top1886,1936-11-23,1936,11,night,NA,NA
+his1937dis-top1887,1936-11-23,1936,11,day,NA,NA
+his1937dis-top1888,1936-11-23,1936,11,day,NA,NA
+his1937dis-top1889,1936-11-23,1936,11,day,NA,NA
+his1937dis-top1890,1936-11-23,1936,11,night,NA,NA
+his1937dis-top1893,1936-11-26,1936,11,day,NA,NA
+his1937dis-top1894,1936-11-26,1936,11,day,NA,NA
+his1937dis-top1895,1936-11-26,1936,11,night,NA,NA
+his1937dis-top1896,1936-11-27,1936,11,night,NA,NA
+his1937dis-top1898,1936-11-27,1936,11,day,NA,NA
+his1937dis-top1899,1936-11-27,1936,11,day,NA,NA
+his1937dis-top1900,1936-11-27,1936,11,night,NA,NA
+his1937dis-top1901,1936-11-28,1936,11,night,NA,NA
+his1937dis-top1904,1936-11-29,1936,11,day,NA,NA
+his1937dis-top1905,1936-11-29,1936,11,day,NA,NA
+his1937dis-top1906,1936-11-29,1936,11,night,NA,NA
+his1937dis-top1908,1936-11-30,1936,11,day,NA,NA
+his1937dis-top1909,1936-11-30,1936,11,day,NA,NA
+his1937dis-top1911,1936-11-30,1936,11,night,NA,NA
+his1937dis-top1917,1936-12-02,1936,12,day,2.715,0.5699588180965941
+his1937dis-top1947,1937-01-03,1937,1,night,0.281,0.10754912974468635
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+his1937dis-top1979a,1937-03-03,1937,3,night,1.524,0.4020893505720967
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+his1937dis-top1992,1937-03-11,1937,3,night,4.912,0.7717344253867693
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+his1937dis-top1998,1937-03-14,1937,3,night,3.583,0.6611498572447866
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+his1937dis-top2000,1937-03-15,1937,3,night,2.611,0.5576274884268266
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+his1937dis-top2004,1937-03-17,1937,3,night,391.277,2.5935928453053982
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+his1937dis-top2007,1937-03-19,1937,3,night,1.373,0.375297738217339
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+his1937dis-top2009,1937-03-20,1937,3,night,0.274,0.10516942799933163
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+his1937dis-ver2010a,1937-03-21,1937,3,night,NA,NA
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+his1937dis-ver2010c,1937-03-21,1937,3,night,NA,NA
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+his1937dis-ver2010fcom,1937-03-21,1937,3,night,NA,NA
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+his1937dis-ver2012c,1937-03-22,1937,3,night,NA,NA
+his1937dis-ver2012com,1937-03-22,1937,3,night,NA,NA
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+his1937dis-ver2014a,1937-03-23,1937,3,night,NA,NA
+his1937dis-ver2014b,1937-03-23,1937,3,night,NA,NA
+his1937dis-ver2014c,1937-03-23,1937,3,night,NA,NA
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+his1938dis-str2209b,1938-01-27,1938,1,night,NA,NA
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+his1938dis-str2261a,1938-03-01,1938,3,night,0,0
+his1938dis-str2261b,1938-03-01,1938,3,night,NA,NA
+his1938dis-str2271a,1938-03-05,1938,3,night,0.289,0.11025291735340301
+his1938dis-str2271b,1938-03-05,1938,3,night,NA,NA
+his1938dis-str2274a,1938-03-06,1938,3,night,0.645,0.2161659022859931
+his1938dis-str2274b,1938-03-06,1938,3,night,NA,NA
+his1938dis-str2277a,1938-03-07,1938,3,night,0,0
+his1938dis-str2277b,1938-03-07,1938,3,night,NA,NA
+his1938dis-str2280a,1938-03-08,1938,3,night,0,0
+his1938dis-str2280b,1938-03-08,1938,3,night,NA,NA
+his1938dis-str2283a,1938-03-09,1938,3,night,0.554,0.19145101446489546
+his1938dis-str2283b,1938-03-09,1938,3,night,NA,NA
+his1938dis-str2285a,1938-03-10,1938,3,day,0,0
+his1938dis-str2285b,1938-03-10,1938,3,day,NA,NA
+his1938dis-str2286a,1938-03-11,1938,3,night,0,0
+his1938dis-str2286b,1938-03-11,1938,3,night,NA,NA
+his1938dis-str2288a,1938-03-12,1938,3,night,0,0
+his1938dis-str2288b,1938-03-12,1938,3,night,NA,NA
+his1938dis-str2289a,1938-03-13,1938,3,night,NA,NA
+his1938dis-str2289b,1938-03-13,1938,3,night,NA,NA
+his1938dis-str2291a,1938-03-20,1938,3,night,NA,NA
+his1938dis-str2291b,1938-03-20,1938,3,night,NA,NA
+his1938dis-str2292a,1938-03-21,1938,3,night,NA,NA
+his1938dis-str2292b,1938-03-21,1938,3,night,NA,NA
+his1938dis-str2294a,1938-03-23,1938,3,day,0,0
+his1938dis-str2294b,1938-03-23,1938,3,day,NA,NA
+his1938dis-str2295a,1938-03-23,1938,3,night,20.108,1.324447085505086
+his1938dis-str2295b,1938-03-23,1938,3,night,NA,NA
+his1938dis-str2296a,1938-03-23,1938,3,night,1.297,0.36116099519502604
+his1938dis-str2296b,1938-03-23,1938,3,night,NA,NA
+his1938dis-str2297a,1938-03-25,1938,3,day,8.393,0.9728043223365777
+his1938dis-str2297b,1938-03-25,1938,3,day,NA,NA
+his1938dis-str2298a,1938-03-25,1938,3,night,0,0
+his1938dis-str2298b,1938-03-25,1938,3,night,NA,NA
+his1938dis-str2300a,1938-03-26,1938,3,night,0,0
+his1938dis-str2300b,1938-03-26,1938,3,night,0,0
+his1938dis-str2300c,1938-03-26,1938,3,night,NA,NA
+his1938dis-top2087,1937-11-17,1937,11,night,NA,NA
+his1938dis-top2088,1937-11-18,1937,11,night,NA,NA
+his1938dis-top2089,1937-11-20,1937,11,night,0,0
+his1938dis-top2090,1937-11-21,1937,11,day,0,0
+his1938dis-top2091,1937-11-21,1937,11,night,0.25,0.09691001300805642
+his1938dis-top2092,1937-11-22,1937,11,day,0,0
+his1938dis-top2093,1937-11-22,1937,11,night,0.735,0.23929947912689248
+his1938dis-top2094,1937-11-23,1937,11,night,0.438,0.15775888604686378
+his1938dis-top2095,1937-11-24,1937,11,day,2.048,0.48401496266756294
+his1938dis-top2097,1937-11-24,1937,11,night,0.458,0.1637575239819558
+his1938dis-top2099,1937-11-25,1937,11,day,0,0
+his1938dis-top2100,1937-11-25,1937,11,night,2.882,0.589055531052344
+his1938dis-top2101,1937-11-26,1937,11,day,0.68,0.22530928172586287
+his1938dis-top2102,1937-11-26,1937,11,day,0,0
+his1938dis-top2103,1937-11-26,1937,11,night,0,0
+his1938dis-top2104,1937-11-27,1937,11,day,0.439,0.15806079393660522
+his1938dis-top2105,1937-11-27,1937,11,day,0,0
+his1938dis-top2107,1937-11-28,1937,11,day,0,0
+his1938dis-top2109a,1937-11-28,1937,11,night,1.694,0.43039759138696676
+his1938dis-top2109b,1937-11-28,1937,11,night,0,0
+his1938dis-top2111,1937-11-29,1937,11,day,2.785,0.5780658838360916
+his1938dis-top2113,1937-11-29,1937,11,night,4.001,0.6990568545476677
+his1938dis-top2115,1937-11-30,1937,11,day,1.141,0.33061666729443834
+his1938dis-top2116,1937-11-30,1937,11,night,0,0
+his1938dis-top2117,1937-12-01,1937,12,day,0,0
+his1938dis-top2118,1937-12-01,1937,12,day,0,0
+his1938dis-top2119,1937-12-01,1937,12,night,0.499,0.1758016328482795
+his1938dis-top2121,1937-12-02,1937,12,day,0,0
+his1938dis-top2122,1937-12-01,1937,12,night,0.101,0.041787318971751766
+his1938dis-top2123,1937-12-03,1937,12,day,0,0
+his1938dis-top2124,1937-12-03,1937,12,day,0,0
+his1938dis-top2125,1937-12-03,1937,12,night,0,0
+his1938dis-top2126,1937-12-04,1937,12,day,0.405,0.1476763242410987
+his1938dis-top2127,1937-12-04,1937,12,day,0,0
+his1938dis-top2128,1937-12-04,1937,12,night,1.071,0.31618009889345255
+his1938dis-top2129,1937-12-05,1937,12,day,0,0
+his1938dis-top2130,1937-12-05,1937,12,day,0,0
+his1938dis-top2131,1937-12-05,1937,12,night,0,0
+his1938dis-top2132,1937-12-06,1937,12,day,0,0
+his1938dis-top2133,1937-12-06,1937,12,day,0,0
+his1938dis-top2135,1937-12-07,1937,12,day,0,0
+his1938dis-top2136,1937-12-07,1937,12,day,0,0
+his1938dis-top2137,1937-12-07,1937,12,night,0.138,0.05614226205905233
+his1938dis-top2138,1937-12-08,1937,12,day,0,0
+his1938dis-top2140,1937-12-09,1937,12,day,0,0
+his1938dis-top2141,1937-12-09,1937,12,night,0,0
+his1938dis-top2148,1937-12-15,1937,12,night,NA,NA
+his1938dis-top2153,1937-12-29,1937,12,night,NA,NA
+his1938dis-top2158,1938-01-05,1938,1,night,0,0
+his1938dis-top2159,1938-01-06,1938,1,day,0,0
+his1938dis-top2161,1938-01-07,1938,1,day,0,0
+his1938dis-top2163,1938-01-08,1938,1,day,0,0
+his1938dis-top2164,1938-01-08,1938,1,night,0,0
+his1938dis-top2165,1938-01-09,1938,1,day,0,0
+his1938dis-top2169,1938-01-11,1938,1,day,1.029,0.307282047033346
+his1938dis-top2170,1938-01-11,1938,1,day,63.803,1.8115951116312
+his1938dis-top2171,1938-01-11,1938,1,night,4.269,0.7217281985727882
+his1938dis-top2172,1938-01-12,1938,1,day,4.389,0.7315081835960254
+his1938dis-top2173,1938-01-12,1938,1,day,0,0
+his1938dis-top2175,1938-01-13,1938,1,day,1.427,0.38506977633193473
+his1938dis-top2176,1938-01-13,1938,1,day,0,0
+his1938dis-top2177,1938-01-13,1938,1,night,0,0
+his1938dis-top2178,1938-01-14,1938,1,day,0,0
+his1938dis-top2179,1938-01-14,1938,1,day,0,0
+his1938dis-top2181,1938-01-15,1938,1,day,0,0
+his1938dis-top2182,1938-01-15,1938,1,day,0.631,0.21245396104027578
+his1938dis-top2184,1938-01-16,1938,1,day,0,0
+his1938dis-top2185,1938-01-16,1938,1,day,6.56,0.8785217955012066
+his1938dis-top2186,1938-01-16,1938,1,night,0,0
+his1938dis-top2187,1938-01-17,1938,1,day,1.024,0.3062105081677615
+his1938dis-top2188,1938-01-17,1938,1,day,2.417,0.5336449787987628
+his1938dis-top2190,1938-01-18,1938,1,day,0,0
+his1938dis-top2191,1938-01-18,1938,1,day,0,0
+his1938dis-top2193,1938-01-19,1938,1,day,0,0
+his1938dis-top2194,1938-01-19,1938,1,day,1.435,0.38649896555065316
+his1938dis-top2196,1938-01-20,1938,1,day,0.758,0.24501887073775308
+his1938dis-top2197,1938-01-20,1938,1,day,1.623,0.4187982905903536
+his1938dis-top2199,1938-01-21,1938,1,day,336.467,2.528231310741532
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+his1938dis-top2203,1938-01-23,1938,1,day,0,0
+his1938dis-top2205,1938-01-24,1938,1,day,NA,NA
+his1938dis-top2206,1938-01-24,1938,1,night,NA,NA
+his1938dis-top2213,1938-02-08,1938,2,night,NA,NA
+his1938dis-top2223,1938-02-16,1938,2,day,NA,NA
+his1938dis-top2224,1938-02-16,1938,2,night,0,0
+his1938dis-top2225,1938-02-17,1938,2,day,0,0
+his1938dis-top2228,1938-02-18,1938,2,day,8.634,0.9838066419784153
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+his1938dis-top2231,1938-02-19,1938,2,day,0,0
+his1938dis-top2233,1938-02-20,1938,2,day,0,0
+his1938dis-top2234,1938-02-20,1938,2,day,0.574,0.1970047280230457
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+his1938dis-top2237,1938-02-21,1938,2,day,0,0
+his1938dis-top2238,1938-02-21,1938,2,night,0,0
+his1938dis-top2239,1938-02-22,1938,2,day,0,0
+his1938dis-top2240,1938-02-22,1938,2,day,3.375,0.640978057358332
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+his1938dis-top2243,1938-02-23,1938,2,day,0,0
+his1938dis-top2244a,1938-02-23,1938,2,night,NA,NA
+his1938dis-top2244b,1938-02-23,1938,2,night,NA,NA
+his1938dis-top2245,1938-02-24,1938,2,day,0,0
+his1938dis-top2246,1938-02-24,1938,2,day,0,0
+his1938dis-top2247,1938-02-24,1938,2,night,0.716,0.2345172835126866
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+his1938dis-top2253,1938-02-26,1938,2,night,3.474,0.6506959797606109
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+his1938dis-top2267,1938-03-04,1938,3,day,0.705,0.23172438332851655
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+his1939dis-bot2584,1939-02-22,1939,2,night,NA,NA
+his1939dis-bot2622,1939-03-14,1939,3,night,NA,NA
+his1939dis-bot2624,1939-03-16,1939,3,night,NA,NA
+his1939dis-bot2625,1939-03-17,1939,3,night,NA,NA
+his1939dis-str2478a,1938-11-02,1938,11,day,3.41,0.6444385894678386
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+his1939dis-str2481b,1938-11-03,1938,11,day,NA,NA
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+his1939dis-str2482b,1938-11-05,1938,11,day,NA,NA
+his1939dis-str2484a,1938-11-07,1938,11,night,NA,NA
+his1939dis-str2484b,1938-11-07,1938,11,night,NA,NA
+his1939dis-str2485a,1938-11-08,1938,11,day,NA,NA
+his1939dis-str2485b,1938-11-08,1938,11,day,NA,NA
+his1939dis-str2504a,1938-12-06,1938,12,night,0.111,0.04571405894086761
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+his1939dis-str2507b,1938-12-07,1938,12,night,NA,NA
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+his1939dis-str2519b,1938-12-15,1938,12,day,NA,NA
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+his1939dis-str2522b,1938-12-17,1938,12,day,NA,NA
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+his1939dis-str2565a,1939-01-27,1939,1,night,0,0
+his1939dis-str2565b,1939-01-27,1939,1,night,NA,NA
+his1939dis-str2567a,1939-01-28,1939,1,night,152.358,2.1857064361074374
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+his1939dis-str2569b,1939-01-29,1939,1,night,NA,NA
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+his1939dis-str2573a,1939-02-02,1939,2,night,NA,NA
+his1939dis-str2573b,1939-02-02,1939,2,night,NA,NA
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+his1939dis-str2574b,1939-02-03,1939,2,night,NA,NA
+his1939dis-str2575a,1939-02-04,1939,2,night,NA,NA
+his1939dis-str2575b,1939-02-04,1939,2,night,NA,NA
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+his1939dis-str2576b,1939-02-05,1939,2,night,NA,NA
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+his1939dis-str2607b,1939-03-07,1939,3,night,NA,NA
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+his1939dis-str2608b,1939-03-07,1939,3,night,NA,NA
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+his1939dis-str2610b,1939-03-08,1939,3,night,NA,NA
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+his1939dis-str2612b,1939-03-09,1939,3,night,NA,NA
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+his1939dis-str2614b,1939-03-10,1939,3,night,NA,NA
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+his1939dis-str2618b,1939-03-12,1939,3,night,NA,NA
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+his1939dis-str2620b,1939-03-13,1939,3,night,NA,NA
+his1939dis-str2623a,1939-03-15,1939,3,night,NA,NA
+his1939dis-str2623b,1939-03-15,1939,3,night,NA,NA
+his1939dis-top2477,1938-11-01,1938,11,day,27.55,1.455606112581867
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+his1939dis-top2498,1938-12-03,1938,12,night,0.142,0.05766610390982921
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+his1939dis-top2518,1938-12-12,1938,12,day,NA,NA
+his1939dis-top2519a,1938-12-12,1938,12,day,NA,NA
+his1939dis-top2519b,1938-12-12,1938,12,day,NA,NA
+his1939dis-top2520,1938-12-13,1938,12,day,NA,NA
+his1939dis-top2521a,1938-12-13,1938,12,day,NA,NA
+his1939dis-top2521b,1938-12-13,1938,12,day,NA,NA
+his1939dis-top2522a,1938-12-14,1938,12,day,NA,NA
+his1939dis-top2522b,1938-12-14,1938,12,day,NA,NA
+his1939dis-top2523a,1938-12-15,1938,12,day,NA,NA
+his1939dis-top2523b,1938-12-15,1938,12,day,NA,NA
+his1939dis-top2524,1938-12-16,1938,12,day,NA,NA
+his1939dis-top2526,1939-01-07,1939,1,night,NA,NA
+his1939dis-top2527,1939-01-08,1939,1,night,NA,NA
+his1939dis-top2528,1939-01-09,1939,1,night,NA,NA
+his1939dis-top2534,1939-01-16,1939,1,day,0,0
+his1939dis-top2535,1939-01-16,1939,1,night,0,0
+his1939dis-top2536,1939-01-16,1939,1,night,0,0
+his1939dis-top2537,1939-01-17,1939,1,night,0.186,0.07408468902824378
+his1939dis-top2538,1939-01-18,1939,1,day,0,0
+his1939dis-top2539,1939-01-18,1939,1,night,0,0
+his1939dis-top2540,1939-01-19,1939,1,day,0,0
+his1939dis-top2541,1939-01-19,1939,1,night,0.466,0.16613397030510912
+his1939dis-top2542,1939-01-20,1939,1,day,0,0
+his1939dis-top2543,1939-01-20,1939,1,night,0,0
+his1939dis-top2544,1939-01-21,1939,1,day,0,0
+his1939dis-top2545,1939-01-21,1939,1,night,0,0
+his1939dis-top2546,1939-01-22,1939,1,day,0,0
+his1939dis-top2547,1939-01-22,1939,1,day,100.83,2.007875743767585
+his1939dis-top2548,1939-01-23,1939,1,day,0.313,0.11826472608947933
+his1939dis-top2549,1939-01-23,1939,1,day,0,0
+his1939dis-top2550,1939-01-23,1939,1,night,0,0
+his1939dis-top2551,1939-01-24,1939,1,day,0,0
+his1939dis-top2552,1939-01-24,1939,1,day,0,0
+his1939dis-top2553,1939-01-24,1939,1,night,0.379,0.13956426617584974
+his1939dis-top2554,1939-01-25,1939,1,day,0,0
+his1939dis-top2555,1939-01-25,1939,1,day,0,0
+his1939dis-top2556,1939-01-25,1939,1,night,NA,NA
+his1939dis-top2557,1939-01-26,1939,1,day,0.345,0.12872228433842678
+his1939dis-top2558,1939-01-26,1939,1,day,7.381,0.923295840655504
+his1939dis-top2559,1939-01-27,1939,1,day,4.834,0.7659664247857139
+his1939dis-top2561,1939-01-27,1939,1,night,0.368,0.13608609738409738
+his1939dis-top2562,1939-01-28,1939,1,day,10.461,1.0592225125296897
+his1939dis-top2563,1939-01-28,1939,1,day,0,0
+his1939dis-top2564,1939-01-29,1939,1,day,0,0
+his1939dis-top2565a,1939-01-29,1939,1,night,0,0
+his1939dis-top2566,1939-01-30,1939,1,day,0,0
+his1939dis-top2567a,1939-01-30,1939,1,night,155.287,2.193922854774983
+his1939dis-top2568,1939-01-31,1939,1,day,0,0
+his1939dis-top2569a,1939-01-31,1939,1,night,0,0
+his1939dis-top2570,1939-02-01,1939,2,day,0,0
+his1939dis-top2572,1939-02-02,1939,2,day,0,0
+his1939dis-top2578,1939-02-16,1939,2,night,NA,NA
+his1939dis-top2580,1939-02-18,1939,2,day,NA,NA
+his1939dis-top2581,1939-02-19,1939,2,day,NA,NA
+his1939dis-top2587,1939-02-25,1939,2,day,0,0
+his1939dis-top2589,1939-02-26,1939,2,day,1.266,0.35525990552737846
+his1939dis-top2591,1939-02-27,1939,2,day,0,0
+his1939dis-top2593,1939-02-28,1939,2,day,1.595,0.4141373621844766
+his1939dis-top2594,1939-02-28,1939,2,day,5.213,0.793301353613115
+his1939dis-top2595,1939-03-01,1939,3,day,0,0
+his1939dis-top2599,1939-03-03,1939,3,day,5.821,0.8338480495311481
+his1939dis-top2601,1939-03-04,1939,3,day,0,0
+his1939dis-top2603,1939-03-04,1939,3,night,53.263,1.7345038006203661
+his1939dis-top2604,1939-03-05,1939,3,day,1.249,0.3519894554356322
+his1939dis-top2609,1939-03-08,1939,3,day,0,0
+his1939dis-top2611,1939-03-09,1939,3,day,0,0
+his1939dis-top2613,1939-03-10,1939,3,day,0,0
+his1939dis-top2617,1939-03-12,1939,3,day,0,0
+his1939dis-ver2492a,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2492b,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2492c,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2492com,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2492d,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2492e,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2492f,1938-11-28,1938,11,day,NA,NA
+his1939dis-ver2494a,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2494b,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2494c,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2494com,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2494d,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2494e,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2494f,1938-11-30,1938,11,day,NA,NA
+his1939dis-ver2495a,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2495b,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2495c,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2495com,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2495d,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2495e,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2495f,1938-12-01,1938,12,day,NA,NA
+his1939dis-ver2496a,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2496b,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2496c,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2496com,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2496d,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2496e,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2496f,1938-12-02,1938,12,night,NA,NA
+his1939dis-ver2498a,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2498b,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2498c,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2498com,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2498d,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2498e,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2498f,1938-12-03,1938,12,night,NA,NA
+his1939dis-ver2501a,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2501b,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2501c,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2501com,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2501d,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2501e,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2501f,1938-12-05,1938,12,day,NA,NA
+his1939dis-ver2530a,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2530b,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2530c,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2530com,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2530d,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2530e,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2530f,1939-01-11,1939,1,night,NA,NA
+his1939dis-ver2531a,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2531b,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2531c,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2531com,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2531d,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2531e,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2531f,1939-01-12,1939,1,day,NA,NA
+his1939dis-ver2532a,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2532b,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2532c,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2532com,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2532d,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2532e,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2532f,1939-01-13,1939,1,night,NA,NA
+his1939dis-ver2533a,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2533b,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2533c,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2533com,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2533d,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2533e,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2533f,1939-01-15,1939,1,day,NA,NA
+his1939dis-ver2535a,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2535b,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2535c,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2535com,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2535d,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2535e,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2535f,1939-01-16,1939,1,day,NA,NA
+his1939dis-ver2538a,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2538b,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2538c,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2538com,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2538d,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2538e,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2538f,1939-01-18,1939,1,day,NA,NA
+his1939dis-ver2541a,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2541b,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2541c,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2541com,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2541d,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2541e,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2541f,1939-01-19,1939,1,night,NA,NA
+his1939dis-ver2543a,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2543b,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2543c,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2543com,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2543d,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2543e,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2543f,1939-01-20,1939,1,day,NA,NA
+his1939dis-ver2545a,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2545b,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2545c,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2545com,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2545d,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2545e,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2545f,1939-01-21,1939,1,day,NA,NA
+his1939dis-ver2547a,1939-01-22,1939,1,day,NA,NA
+his1939dis-ver2547b,1939-01-22,1939,1,day,NA,NA
+his1939dis-ver2547c,1939-01-22,1939,1,day,NA,NA
+his1939dis-ver2547com,1939-01-22,1939,1,day,NA,NA
+his1939dis-ver2547d,1939-01-22,1939,1,day,NA,NA
+his1939dis-ver2547e,1939-01-22,1939,1,day,NA,NA
+his1939dis-ver2547f,1939-01-22,1939,1,day,NA,NA
+his1951dis-bot2742,1951-01-15,1951,1,NA,NA,NA
+his1951dis-top2736,1950-11-10,1950,11,night,NA,NA
+his1951dis-top2737,1951-01-11,1951,1,night,NA,NA
+his1951dis-top2738,1951-01-12,1951,1,night,NA,NA
+his1951dis-top2739,1951-01-13,1951,1,night,0,0
+his1951dis-top2740,1951-01-14,1951,1,night,0,0
+his1951dis-top2741,1951-01-15,1951,1,night,3.707,0.6727441983065992
+his1951dis-top2742,1951-01-15,1951,1,NA,NA,NA
+his1951dis-top2743,1951-01-16,1951,1,night,0,0
+his1951dis-top2744,1951-01-17,1951,1,day,0,0
+his1951dis-top2745,1951-01-17,1951,1,night,0,0
+his1951dis-top2747,1951-01-18,1951,1,day,8.522,0.9787281771384918
+his1951dis-top2748,1951-01-18,1951,1,night,0,0
+his1951dis-top2749,1951-01-19,1951,1,day,0,0
+his1951dis-top2750,1951-01-19,1951,1,day,0,0
+his1951dis-top2751,1951-01-19,1951,1,night,2.046,0.4837298990000237
+his1951dis-top2752,1951-01-20,1951,1,day,0,0
+his1951dis-top2753,1951-01-20,1951,1,day,0,0
+his1951dis-top2754,1951-01-20,1951,1,night,0,0
+his1951dis-top2755,1951-01-21,1951,1,day,0,0
+his1951dis-top2756,1951-01-21,1951,1,day,0,0
+his1951dis-top2757,1951-01-21,1951,1,day,0,0
+his1951dis-top2758,1951-01-22,1951,1,day,0,0
+his1951dis-top2759,1951-01-22,1951,1,day,0,0
+his1951dis-top2760,1951-01-22,1951,1,night,0,0
+his1951dis-top2761,1951-01-23,1951,1,day,0,0
+his1951dis-top2762,1951-01-23,1951,1,day,0,0
+his1951dis-top2763,1951-01-23,1951,1,night,21.669,1.355432362470549
+his1951dis-top2764,1951-01-24,1951,1,day,0,0
+his1951dis-top2765,1951-01-24,1951,1,day,0,0
+his1951dis-top2767,1951-01-25,1951,1,day,0,0
+his1951dis-top2768,1951-01-25,1951,1,night,0,0
+his1951dis-top2769,1951-01-26,1951,1,night,0,0
+his1951dis-top2770,1951-01-27,1951,1,night,NA,NA
+his1951dis-top2771,1951-01-28,1951,1,night,NA,NA
+his1951dis-top2802,1951-02-10,1951,2,night,0,0
+his1951dis-top2803,1951-02-11,1951,2,night,0,0
+his1951dis-top2804,1951-02-12,1951,2,night,0,0
+his1951dis-top2805,1951-02-13,1951,2,night,0,0
+his1951dis-top2806,1951-02-14,1951,2,night,0,0
+his1951dis-top2809,1951-02-17,1951,2,night,0.515,0.18041263283832384
+his1951dis-top2810,1951-02-18,1951,2,night,0,0
+his1951dis-top2811,1951-02-19,1951,2,night,0,0
+his1951dis-top2812,1951-02-20,1951,2,night,0.59,0.20139712432045143
+his1951dis-top2814,1951-02-22,1951,2,night,6.045,0.8478809974453753
+his1951dis-top2815,1951-02-23,1951,2,night,2.72,0.5705429398818975
+his1951dis-top2816,1951-02-24,1951,2,night,0.543,0.1883659260631483
+his1951dis-ver2727a,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2727b,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2727c,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2727com,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2727d,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2727e,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2727f,1950-11-11,1950,11,night,NA,NA
+his1951dis-ver2736a,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2736b,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2736c,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2736com,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2736d,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2736e,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2736f,1950-11-10,1950,11,night,NA,NA
+his1951dis-ver2738a,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2738b,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2738c,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2738com,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2738d,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2738e,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2738f,1950-11-12,1950,11,night,NA,NA
+his1951dis-ver2739a,1950-11-13,1950,11,night,NA,NA
+his1951dis-ver2739b,1950-11-13,1950,11,night,NA,NA
+his1951dis-ver2739c,1950-11-13,1950,11,night,NA,NA
+his1951dis-ver2739com,1950-11-13,1950,11,night,NA,NA
+his1951dis-ver2739d,1950-11-13,1950,11,night,NA,NA
+his1951dis-ver2739e,1950-11-13,1950,11,night,NA,NA
+his1951dis-ver2740a,1950-11-14,1950,11,night,NA,NA
+his1951dis-ver2740b,1950-11-14,1950,11,night,NA,NA
+his1951dis-ver2740c,1950-11-14,1950,11,night,NA,NA
+his1951dis-ver2740com,1950-11-14,1950,11,night,NA,NA
+his1951dis-ver2740d,1950-11-14,1950,11,night,NA,NA
+his1951dis-ver2740e,1950-11-14,1950,11,night,NA,NA
+his1951dis-ver2741a,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2741b,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2741c,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2741com,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2741d,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2741e,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2741f,1950-11-15,1950,11,night,NA,NA
+his1951dis-ver2743a,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2743b,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2743c,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2743com,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2743d,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2743e,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2743f,1950-11-16,1950,11,night,NA,NA
+his1951dis-ver2768a,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2768b,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2768c,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2768com,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2768d,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2768e,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2768f,1950-11-25,1950,11,day,NA,NA
+his1951dis-ver2802a,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2802b,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2802c,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2802com,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2802d,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2802e,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2802f,1951-02-10,1951,2,night,NA,NA
+his1951dis-ver2803a,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2803b,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2803c,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2803com,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2803d,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2803e,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2803f,1951-02-11,1951,2,night,NA,NA
+his1951dis-ver2804a,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2804b,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2804c,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2804com,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2804d,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2804e,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2804f,1951-02-12,1951,2,night,NA,NA
+his1951dis-ver2805a,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2805b,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2805c,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2805com,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2805d,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2805e,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2805f,1951-02-13,1951,2,night,NA,NA
+his1951dis-ver2806a,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2806b,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2806c,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2806com,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2806d,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2806e,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2806f,1951-02-14,1951,2,night,NA,NA
+his1951dis-ver2807a,1951-02-15,1951,2,night,NA,NA
+his1951dis-ver2807b,1951-02-15,1951,2,night,NA,NA
+his1951dis-ver2807c,1951-02-15,1951,2,night,NA,NA
+his1951dis-ver2807com,1951-02-15,1951,2,night,NA,NA
+his1951dis-ver2808a,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2808b,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2808c,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2808com,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2808d,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2808e,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2808f,1951-02-17,1951,2,day,NA,NA
+his1951dis-ver2810a,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2810b,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2810c,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2810com,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2810d,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2810e,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2810f,1951-02-18,1951,2,night,NA,NA
+his1951dis-ver2812a,1951-02-20,1951,2,night,NA,NA
+his1951dis-ver2812b,1951-02-20,1951,2,night,NA,NA
+his1951dis-ver2812c,1951-02-20,1951,2,night,NA,NA
+his1951dis-ver2812com,1951-02-20,1951,2,night,NA,NA
+his1951dis-ver2812d,1951-02-20,1951,2,night,NA,NA
+his1951dis-ver2812e,1951-02-20,1951,2,night,NA,NA
+his1951dis-ver2812f,1951-02-20,1951,2,night,NA,NA
+jap1984sib1,1983-12-14,1983,12,day,0,0
+jap1984sib10,1984-01-19,1984,1,night,12.689,1.1363717234923234
+jap1984sib11,1984-01-22,1984,1,night,0,0
+jap1984sib12,1984-01-18,1984,1,NA,0,0
+jap1984sib13,1984-01-09,1984,1,night,0,0
+jap1984sib2,1984-01-28,1984,1,day,0,0
+jap1984sib3,1983-12-16,1983,12,day,0,0
+jap1984sib4,1984-01-27,1984,1,night,0,0
+jap1984sib5,1983-12-19,1983,12,day,0.746,0.24204423936955088
+jap1984sib6,1983-12-26,1983,12,night,0.247,0.09586645347854257
+jap1984sib7,1984-01-13,1984,1,day,11.17,1.085290578230065
+jap1984sib8,1983-12-21,1983,12,day,0,0
+jap1984sib9,1984-01-17,1984,1,day,5.325,0.8010605298478556
+lan1983tuc,1983-03-28,1983,3,NA,1964.414,3.2934540452853
+lan1984tuc,1983-12-15,1983,12,NA,6.945,0.9000939015433984
+loe1983bon,1983-01-01,1983,1,NA,34.639,1.5519255096023565
+loe1988bon,1988-02-14,1988,2,NA,28.527,1.4702193240517294
+loe1989bon,1989-02-14,1989,2,NA,17.587,1.2692092989389354
+nis1991kai102,1991-01-22,1991,1,NA,NA,NA
+nis1991kai104,1991-01-22,1991,1,NA,NA,NA
+nis1991kai106,1991-01-22,1991,1,NA,NA,NA
+nis1991kai108,1991-01-23,1991,1,NA,NA,NA
+nis1991kai110,1991-01-23,1991,1,NA,NA,NA
+nis1991kai114,1991-01-23,1991,1,NA,NA,NA
+nis1991kai116,1991-01-26,1991,1,NA,NA,NA
+nis1991kai118,1991-01-26,1991,1,NA,NA,NA
+nis1991kai120,1991-01-26,1991,1,NA,NA,NA
+nis1991kai122,1991-01-26,1991,1,NA,NA,NA
+nis1991kai124,1991-01-27,1991,1,NA,NA,NA
+nis1991kai126,1991-01-27,1991,1,NA,NA,NA
+nis1991kai128,1991-01-27,1991,1,NA,NA,NA
+nis1991kai129,1991-01-27,1991,1,NA,NA,NA
+nis1991kai132,1991-01-29,1991,1,NA,NA,NA
+nis1991kai138,1991-01-31,1991,1,NA,NA,NA
+nis1991kai140,1991-01-31,1991,1,NA,NA,NA
+nis1991kai141,1991-01-31,1991,1,NA,NA,NA
+nis1991kai142,1991-01-31,1991,1,NA,NA,NA
+nis1991kai143,1991-02-01,1991,2,NA,NA,NA
+nis1991kai148,1991-02-01,1991,2,NA,NA,NA
+nis1991kai150,1991-02-02,1991,2,NA,NA,NA
+nis1991kai23,1990-12-22,1990,12,NA,NA,NA
+nis1991kai25,1990-12-22,1990,12,NA,NA,NA
+nis1991kai27,1990-12-22,1990,12,NA,NA,NA
+nis1991kai29,1990-12-23,1990,12,NA,NA,NA
+nis1991kai31,1990-12-23,1990,12,NA,NA,NA
+nis1991kai33,1990-12-23,1990,12,NA,NA,NA
+nis1991kai35,1990-12-24,1990,12,NA,NA,NA
+nis1991kai37,1990-12-24,1990,12,NA,NA,NA
+nis1991kai39,1990-12-26,1990,12,NA,NA,NA
+nis1991kai41,1990-12-26,1990,12,NA,NA,NA
+nis1991kai43,1990-12-26,1990,12,NA,NA,NA
+nis1991kai45,1990-12-27,1990,12,NA,NA,NA
+nis1991kai47,1990-12-27,1990,12,NA,NA,NA
+nis1991kai49,1990-12-27,1990,12,NA,NA,NA
+nis1991kai51,1990-12-27,1990,12,NA,NA,NA
+nis1991kai53,1990-12-27,1990,12,NA,NA,NA
+nis1991kai55,1990-12-28,1990,12,NA,NA,NA
+nis1991kai57,1990-12-28,1990,12,NA,NA,NA
+nis1991kai58,1990-12-28,1990,12,NA,NA,NA
+nis1991kai84,1991-01-19,1991,1,NA,NA,NA
+nis1991kai86,1991-01-20,1991,1,NA,NA,NA
+nis1991kai87,1991-01-20,1991,1,NA,NA,NA
+nis1991kai89,1991-01-20,1991,1,NA,NA,NA
+nis1991kai91,1991-01-21,1991,1,NA,NA,NA
+nis1991kai93,1991-01-21,1991,1,NA,NA,NA
+nis1991kai95,1991-01-21,1991,1,NA,NA,NA
+nis1991kai97,1991-01-21,1991,1,NA,NA,NA
+nis1991kai99,1991-01-22,1991,1,NA,NA,NA
+nis1995ver1a,1994-12-23,1994,12,day,NA,NA
+nis1995ver1b,1994-12-23,1994,12,day,NA,NA
+nis1995ver1c,1994-12-23,1994,12,day,NA,NA
+nis1995ver1com,1994-12-23,1994,12,day,NA,NA
+nis1995ver1d,1994-12-23,1994,12,day,NA,NA
+nis1995ver2a,1994-12-22,1994,12,day,NA,NA
+nis1995ver2b,1994-12-22,1994,12,day,NA,NA
+nis1995ver2c,1994-12-22,1994,12,day,NA,NA
+nis1995ver2com,1994-12-22,1994,12,day,NA,NA
+nis1995ver2d,1994-12-22,1994,12,day,NA,NA
+nis1995ver3a,1994-12-22,1994,12,day,NA,NA
+nis1995ver3b,1994-12-22,1994,12,day,NA,NA
+nis1995ver3c,1994-12-22,1994,12,day,NA,NA
+nis1995ver3com,1994-12-22,1994,12,day,NA,NA
+nis1995ver3d,1994-12-22,1994,12,day,NA,NA
+nis1995ver4a,1994-12-23,1994,12,day,NA,NA
+nis1995ver4b,1994-12-23,1994,12,day,NA,NA
+nis1995ver4c,1994-12-23,1994,12,day,NA,NA
+nis1995ver4com,1994-12-23,1994,12,day,NA,NA
+nis1995ver4d,1994-12-23,1994,12,day,NA,NA
+nis1995ver5a,1994-12-23,1994,12,day,NA,NA
+nis1995ver5b,1994-12-23,1994,12,day,NA,NA
+nis1995ver5c,1994-12-23,1994,12,day,NA,NA
+nis1995ver5com,1994-12-23,1994,12,day,NA,NA
+nis1995ver5d,1994-12-23,1994,12,day,NA,NA
+nis1995ver6a,1994-12-24,1994,12,day,NA,NA
+nis1995ver6b,1994-12-24,1994,12,day,NA,NA
+nis1995ver6c,1994-12-24,1994,12,day,NA,NA
+nis1995ver6com,1994-12-24,1994,12,day,NA,NA
+nis1995ver6d,1994-12-24,1994,12,day,NA,NA
+nis1995ver7a,1994-12-24,1994,12,day,NA,NA
+nis1995ver7b,1994-12-24,1994,12,day,NA,NA
+nis1995ver7c,1994-12-24,1994,12,day,NA,NA
+nis1995ver7com,1994-12-24,1994,12,day,NA,NA
+nis1995ver7d,1994-12-24,1994,12,day,NA,NA
+nis1995ver8a,1994-12-23,1994,12,day,NA,NA
+nis1995ver8b,1994-12-23,1994,12,day,NA,NA
+nis1995ver8c,1994-12-23,1994,12,day,NA,NA
+nis1995ver8com,1994-12-23,1994,12,day,NA,NA
+nis1995ver8d,1994-12-23,1994,12,day,NA,NA
+pal1993lte128,1993-01-13,1993,1,day,6.09,0.8506462351830666
+pal1993lte136,1993-01-13,1993,1,night,0,0
+pal1993lte140,1993-01-13,1993,1,day,9.682,1.0286525736331185
+pal1993lte149,1993-01-13,1993,1,day,0.116,0.04766419460155998
+pal1993lte158,1993-01-13,1993,1,day,3.917,0.6917002082901615
+pal1993lte166,1993-01-14,1993,1,day,0.171,0.06855689507236314
+pal1993lte172,1993-01-14,1993,1,night,20.007,1.3223640354391095
+pal1993lte18,1993-01-09,1993,1,day,83.589,1.9273138908179257
+pal1993lte27,1993-01-09,1993,1,day,63.806,1.8116152164610486
+pal1993lte33,1993-01-09,1993,1,day,0.291,0.11092624226642028
+pal1993lte43,1993-01-10,1993,1,night,0.055,0.023252459633711444
+pal1993lte50,1993-01-10,1993,1,day,0.054,0.022840610876527823
+pal1993lte59,1993-01-10,1993,1,day,0.133,0.05422990986339725
+pal1993lte643,1993-01-25,1993,1,day,0.067,0.028164419424469872
+pal1993lte644,1993-01-25,1993,1,day,353.344,2.549425083382416
+pal1993lte646,1993-01-26,1993,1,day,0.146,0.05918461763137114
+pal1993lte66,1993-01-10,1993,1,day,5.374,0.8044120591377139
+pal1993lte677,1993-01-27,1993,1,day,0.274,0.10516942799933163
+pal1993lte706,1993-01-28,1993,1,day,21.789,1.3577252684006318
+pal1993lte718,1993-01-29,1993,1,night,1083.132,3.0350821635506273
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+pal1993lte74,1993-01-11,1993,1,day,2.451,0.5379449592914869
+pal1993lte742,1993-01-29,1993,1,day,4.204,0.7163372878895486
+pal1993lte774,1993-01-30,1993,1,day,4.798,0.7632782110189786
+pal1993lte784,1993-01-31,1993,1,day,2.083,0.48897352472650824
+pal1993lte790,1993-01-31,1993,1,night,42.633,1.6398150740607282
+pal1993lte803,1993-01-31,1993,1,day,6.197,0.8571515026874931
+pal1993lte812,1993-01-31,1993,1,day,25.027,1.415424112076535
+pal1993lte822,1993-02-01,1993,2,day,4.818,0.7647737169110403
+pal1993lte83,1993-01-11,1993,1,night,0,0
+pal1993lte830,1993-02-01,1993,2,night,0,0
+pal1993lte838,1993-02-01,1993,2,day,140.787,2.151636413589778
+pal1993lte848,1993-02-01,1993,2,day,0.248,0.09621458534640517
+pal1993lte853,1993-02-01,1993,2,day,653.168,2.815689295882931
+pal1993lte863,1993-02-02,1993,2,night,0.576,0.19755621315353653
+pal1993lte870,1993-02-02,1993,2,day,0.545,0.18892848376085342
+pal1993lte880,1993-02-02,1993,2,day,9.297,1.012710712741787
+pal1993lte888,1993-02-03,1993,2,night,0,0
+pal1993lte894,1993-02-03,1993,2,night,0,0
+pal1993lte900,1993-02-03,1993,2,day,280.018,2.4487341385894528
+pal1993lte918,1993-02-05,1993,2,day,455.495,2.6594360250591422
+pal1994lte101,1994-01-15,1994,1,day,2.881,0.5889436427400149
+pal1994lte108,1994-01-16,1994,1,day,0,0
+pal1994lte117,1994-01-16,1994,1,day,0.575,0.19728055812561932
+pal1994lte12,1994-01-12,1994,1,day,0.058,0.024485667699166973
+pal1994lte128,1994-01-17,1994,1,day,1.833,0.45224657452043726
+pal1994lte137,1994-01-17,1994,1,day,14.819,1.1991790260511266
+pal1994lte151,1994-01-18,1994,1,day,42.464,1.6381296920256645
+pal1994lte164,1994-01-18,1994,1,day,38.9,1.6009728956867482
+pal1994lte171,1994-01-19,1994,1,day,0.044,0.01870049866624337
+pal1994lte177,1994-01-19,1994,1,day,0.229,0.08955188288645412
+pal1994lte187,1994-01-19,1994,1,day,0.104,0.04296907339318013
+pal1994lte194,1994-01-20,1994,1,day,0.059,0.024895960107484977
+pal1994lte201,1994-01-20,1994,1,day,5.327,0.8011978344591488
+pal1994lte213,1994-01-21,1994,1,day,32.803,1.5289552454146031
+pal1994lte225,1994-01-21,1994,1,day,0.12,0.04921802267018165
+pal1994lte232,1994-01-21,1994,1,day,50.179,1.7090917958394674
+pal1994lte239,1994-01-22,1994,1,day,12.114,1.1177351793304966
+pal1994lte252,1994-01-22,1994,1,day,1.585,0.4124605474299613
+pal1994lte264,1994-01-22,1994,1,day,2.306,0.5193028492354287
+pal1994lte27,1994-01-12,1994,1,day,8.904,0.9958106316760428
+pal1994lte273,1994-01-23,1994,1,day,27.369,1.4528440272961625
+pal1994lte308,1994-01-25,1994,1,day,1.555,0.4073909044707315
+pal1994lte319,1994-01-25,1994,1,day,0.39,0.1430148002540951
+pal1994lte34,1994-01-13,1994,1,day,0.135,0.05499586152914153
+pal1994lte341,1994-01-26,1994,1,day,3.346,0.6380897219845058
+pal1994lte350,1994-01-26,1994,1,day,9.577,1.0243625043532822
+pal1994lte362,1994-01-27,1994,1,day,0.296,0.11260500153457453
+pal1994lte376,1994-01-27,1994,1,day,7.659,0.9374677396433775
+pal1994lte390,1994-01-28,1994,1,day,18.286,1.2852421624254151
+pal1994lte407,1994-01-29,1994,1,day,3.925,0.6924062348336305
+pal1994lte411,1994-01-29,1994,1,day,7.118,0.9094490469812666
+pal1994lte417,1994-01-31,1994,1,day,0.237,0.09236969962912069
+pal1994lte432,1994-01-31,1994,1,day,0.304,0.11527759139590141
+pal1994lte441,1994-01-31,1994,1,day,27.627,1.4567758380072107
+pal1994lte449,1994-02-01,1994,2,day,0.496,0.17493159352844254
+pal1994lte456,1994-02-01,1994,2,day,15.549,1.2187717559604923
+pal1994lte46,1994-01-13,1994,1,day,0.411,0.14952701375434782
+pal1994lte469,1994-02-01,1994,2,day,20.41,1.3306166672944384
+pal1994lte474,1994-02-02,1994,2,day,6.235,0.8594385354550562
+pal1994lte486,1994-02-02,1994,2,day,1.535,0.4039779636693548
+pal1994lte497,1994-02-02,1994,2,day,2.231,0.509336958017644
+pal1994lte502,1994-02-03,1994,2,day,1.831,0.451939869365103
+pal1994lte511,1994-02-03,1994,2,day,8.752,0.9890936926103255
+pal1994lte52,1994-01-14,1994,1,day,0.19,0.07554696139253074
+pal1994lte522,1994-02-03,1994,2,day,1.782,0.4443571256560276
+pal1994lte527,1994-02-04,1994,2,day,2.99,0.6009728956867483
+pal1994lte537,1994-02-04,1994,2,day,3.063,0.6088468223264116
+pal1994lte547,1994-02-04,1994,2,day,2.134,0.4960989921325713
+pal1994lte553,1994-02-06,1994,2,day,0.042,0.017867718963505686
+pal1994lte564,1994-02-06,1994,2,day,1.851,0.45499721730945997
+pal1994lte63,1994-01-14,1994,1,day,5.455,0.8098962466024391
+pal1994lte70,1994-01-14,1994,1,day,7.038,0.9051480018560161
+pal1994lte77,1994-01-14,1994,1,day,7.2,0.9138138523837167
+pal1994lte81,1994-01-15,1994,1,day,0.154,0.06220580881971259
+pal1994lte94,1994-01-15,1994,1,day,2.687,0.5666731376061165
+pal1995lte1003,1995-01-27,1995,1,day,0.909,0.2808059283936668
+pal1995lte1011,1995-01-27,1995,1,day,1.447,0.3886339693517892
+pal1995lte1024,1995-01-27,1995,1,day,1.39,0.37839790094813763
+pal1995lte1027,1995-01-28,1995,1,day,0,0
+pal1995lte1034,1995-01-28,1995,1,day,0.08,0.03342375548694973
+pal1995lte1041,1995-01-28,1995,1,day,0.184,0.07335170238690095
+pal1995lte1049,1995-01-28,1995,1,day,0.39,0.1430148002540951
+pal1995lte1058,1995-01-29,1995,1,day,3.895,0.6897526961391566
+pal1995lte1063,1995-01-29,1995,1,night,0.055,0.023252459633711444
+pal1995lte1073,1995-01-29,1995,1,day,5.323,0.8009231818132183
+pal1995lte1081,1995-01-29,1995,1,day,5.171,0.7903555464143864
+pal1995lte1092,1995-01-29,1995,1,day,5.036,0.7807492311035522
+pal1995lte1100,1995-01-30,1995,1,day,3.548,0.6578204560156972
+pal1995lte1115,1995-01-30,1995,1,day,6.506,0.8754085600770636
+pal1995lte1123,1995-01-30,1995,1,day,4.088,0.7065471026403575
+pal1995lte1134,1995-01-31,1995,1,day,4.784,0.7622282842864744
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+pal1995lte1169,1995-01-31,1995,1,day,0.136,0.05537833137500011
+pal1995lte1180,1995-02-01,1995,2,day,0.307,0.11627558758054428
+pal1995lte1183,1995-02-01,1995,2,night,0.22,0.08635983067474821
+pal1995lte1193,1995-02-01,1995,2,day,0.344,0.12839926871780638
+pal1995lte1211,1995-02-02,1995,2,day,0.1,0.04139268515822507
+pal1995lte1216,1995-02-02,1995,2,day,0,0
+pal1995lte1226,1995-02-03,1995,2,day,0.233,0.09096307659573168
+pal1995lte1236,1995-02-03,1995,2,day,0.028,0.011993114659256938
+pal1995lte1252,1995-02-04,1995,2,day,0.444,0.15956719323362029
+pal1995lte1313,1995-02-06,1995,2,night,3.939,0.6936390261615483
+pal1995lte145,1995-01-08,1995,1,day,0.29,0.11058971029924897
+pal1995lte158,1995-01-08,1995,1,day,2.122,0.4944328987263987
+pal1995lte170,1995-01-09,1995,1,day,21.402,1.3502867928962083
+pal1995lte174,1995-01-09,1995,1,day,0.029,0.012415374762432893
+pal1995lte183,1995-01-09,1995,1,day,0,0
+pal1995lte193,1995-01-09,1995,1,day,0.449,0.1610683854711746
+pal1995lte203,1995-01-09,1995,1,day,0.378,0.13924921757160705
+pal1995lte212,1995-01-10,1995,1,day,0.122,0.04999285692014256
+pal1995lte229,1995-01-10,1995,1,day,0,0
+pal1995lte237,1995-01-10,1995,1,day,0.042,0.017867718963505686
+pal1995lte248,1995-01-10,1995,1,day,10.043,1.043087072166329
+pal1995lte258,1995-01-10,1995,1,day,6.175,0.8558219054060299
+pal1995lte272,1995-01-11,1995,1,day,0.077,0.03221570329798157
+pal1995lte286,1995-01-11,1995,1,day,0.071,0.029789470831855614
+pal1995lte298,1995-01-11,1995,1,day,0.602,0.2046625117482188
+pal1995lte308,1995-01-12,1995,1,day,0.842,0.26528962586083016
+pal1995lte448,1995-01-15,1995,1,day,0.023,0.009875633712160119
+pal1995lte542,1995-01-16,1995,1,day,0.056,0.02366391819779347
+pal1995lte568,1995-01-17,1995,1,day,0,0
+pal1995lte577,1995-01-17,1995,1,day,0.037,0.01577875638904093
+pal1995lte585,1995-01-17,1995,1,day,0.096,0.039810554148350386
+pal1995lte592,1995-01-18,1995,1,day,0.173,0.06929801211552926
+pal1995lte603,1995-01-18,1995,1,day,0.189,0.0751818546186916
+pal1995lte612,1995-01-18,1995,1,day,0.312,0.11793383503964149
+pal1995lte949,1995-01-26,1995,1,night,1.703,0.4318460456987255
+pal1995lte960,1995-01-26,1995,1,day,1.544,0.4055171069763763
+pal1995lte971,1995-01-26,1995,1,day,0.54,0.18752072083646307
+pal1995lte980,1995-01-26,1995,1,day,0.029,0.012415374762432893
+pal1995lte988,1995-01-27,1995,1,day,0,0
+pal1995lte995,1995-01-27,1995,1,day,0.115,0.04727486738417948
+pal1996lte100,1996-01-11,1996,1,night,0.588,0.20085049809107747
+pal1996lte1002,1996-02-04,1996,2,day,7.088,0.9078411425829447
+pal1996lte1014,1996-02-06,1996,2,day,2.223,0.5082603055123345
+pal1996lte1023,1996-02-07,1996,2,day,1.897,0.4619484952037618
+pal1996lte1035,1996-02-07,1996,2,day,0,0
+pal1996lte109,1996-01-11,1996,1,day,17.515,1.2675237163854614
+pal1996lte119,1996-01-11,1996,1,day,9.22,1.0094508957986938
+pal1996lte123,1996-01-11,1996,1,day,4.507,0.7409150764812825
+pal1996lte135,1996-01-12,1996,1,day,7.645,0.9367649976099414
+pal1996lte139,1996-01-12,1996,1,night,1.238,0.3498600821923312
+pal1996lte142,1996-01-12,1996,1,day,1.99,0.4756711883244297
+pal1996lte155,1996-01-12,1996,1,day,10.55,1.0625819842281632
+pal1996lte164,1996-01-12,1996,1,day,7.437,0.9261880491072061
+pal1996lte173,1996-01-12,1996,1,day,4.206,0.7165041637732169
+pal1996lte186,1996-01-13,1996,1,day,0.266,0.1024337056813363
+pal1996lte195,1996-01-13,1996,1,day,37,1.5797835966168101
+pal1996lte204,1996-01-13,1996,1,day,3.788,0.680154141734373
+pal1996lte213,1996-01-14,1996,1,day,2.227,0.5087989654039049
+pal1996lte254,1996-01-15,1996,1,day,18.895,1.298743943482407
+pal1996lte34,1996-01-09,1996,1,day,16.679,1.2474576958148116
+pal1996lte46,1996-01-09,1996,1,day,95.246,1.9833826892036466
+pal1996lte465,1996-01-19,1996,1,day,2.049,0.4841574243653807
+pal1996lte470,1996-01-19,1996,1,day,0.401,0.1464381352857746
+pal1996lte510,1996-01-21,1996,1,day,477.724,2.680085200630664
+pal1996lte517,1996-01-21,1996,1,day,375.631,2.5759160633568476
+pal1996lte525,1996-01-21,1996,1,day,181.339,2.2608795686731247
+pal1996lte535,1996-01-22,1996,1,day,1.808,0.4483971034577676
+pal1996lte544,1996-01-22,1996,1,day,2.429,0.5351674851149442
+pal1996lte56,1996-01-10,1996,1,day,460.155,2.663846921768411
+pal1996lte601,1996-01-23,1996,1,day,181.4,2.2610248339923973
+pal1996lte604,1996-01-24,1996,1,day,25.822,1.4284911581755186
+pal1996lte606,1996-01-24,1996,1,night,1.168,0.33605927786634937
+pal1996lte66,1996-01-10,1996,1,night,0.447,0.1604685311190375
+pal1996lte674,1996-01-26,1996,1,night,3.652,0.6676397060564108
+pal1996lte688,1996-01-26,1996,1,day,4.22,0.7176705030022621
+pal1996lte697,1996-01-26,1996,1,day,3.708,0.6728364541713971
+pal1996lte706,1996-01-26,1996,1,day,2.453,0.5381965783494542
+pal1996lte714,1996-01-27,1996,1,night,13.917,1.1736814897284589
+pal1996lte720,1996-01-27,1996,1,night,1.955,0.47055748521727414
+pal1996lte730,1996-01-27,1996,1,day,8.61,0.9827233876685453
+pal1996lte742,1996-01-27,1996,1,day,0.768,0.24748226067705428
+pal1996lte751,1996-01-27,1996,1,day,3.915,0.6915235221681544
+pal1996lte766,1996-01-28,1996,1,day,26.568,1.4404052601039532
+pal1996lte775,1996-01-28,1996,1,day,0.615,0.2081725266671217
+pal1996lte787,1996-01-28,1996,1,day,59.739,1.7834676374660632
+pal1996lte796,1996-01-28,1996,1,day,7.191,0.9133369259326232
+pal1996lte805,1996-01-29,1996,1,day,14.607,1.193319430374477
+pal1996lte814,1996-01-29,1996,1,night,11.643,1.1018501379381256
+pal1996lte82,1996-01-10,1996,1,day,1.05,0.31175386105575426
+pal1996lte825,1996-01-29,1996,1,day,28.536,1.470351679345818
+pal1996lte834,1996-01-29,1996,1,day,6.827,0.8935953338198832
+pal1996lte846,1996-01-29,1996,1,day,4.096,0.707229419327294
+pal1996lte853,1996-01-30,1996,1,night,0.185,0.07371835034612269
+pal1996lte865,1996-01-30,1996,1,day,4.306,0.7247672456463103
+pal1996lte874,1996-01-30,1996,1,day,4.29,0.7234556720351858
+pal1996lte883,1996-01-30,1996,1,day,4.049,0.7032053706954863
+pal1996lte892,1996-01-31,1996,1,day,1.185,0.33945144130644067
+pal1996lte897,1996-01-31,1996,1,night,0.158,0.06370855939141737
+pal1996lte906,1996-01-31,1996,1,day,0.129,0.05269394192496786
+pal1996lte915,1996-01-31,1996,1,day,0.432,0.1559430179718367
+pal1996lte92,1996-01-10,1996,1,day,1.202,0.34281731463573295
+pal1996lte926,1996-01-31,1996,1,day,0.301,0.11427729656158624
+pal1996lte934,1996-01-31,1996,1,day,0.166,0.06669855042299526
+pal1996lte943,1996-02-01,1996,2,night,0.247,0.09586645347854257
+pal1996lte983,1996-02-03,1996,2,day,14.806,1.1988219776032059
+pal1996lte993,1996-02-04,1996,2,day,8.764,0.989627770745151
+pal1997lte1000,1997-02-02,1997,2,night,0.187,0.07445071895459124
+pal1997lte1011,1997-02-02,1997,2,day,0,0
+pal1997lte1020,1997-02-02,1997,2,day,1.521,0.4015728456764459
+pal1997lte1029,1997-02-02,1997,2,day,13.445,1.1597175461802158
+pal1997lte1038,1997-02-02,1997,2,day,0.267,0.10277661488344131
+pal1997lte1043,1997-02-03,1997,2,night,0.023,0.009875633712160119
+pal1997lte1054,1997-02-03,1997,2,day,0.253,0.09795107099415004
+pal1997lte1063,1997-02-03,1997,2,day,0.129,0.05269394192496786
+pal1997lte1072,1997-02-03,1997,2,day,0.197,0.0780941504064107
+pal1997lte1078,1997-02-04,1997,2,night,0.296,0.11260500153457453
+pal1997lte1090,1997-02-04,1997,2,day,4.34,0.7275412570285564
+pal1997lte1100,1997-02-05,1997,2,day,3.52,0.6551384348113821
+pal1997lte1108,1997-02-06,1997,2,day,2.615,0.5581083016305497
+pal1997lte1116,1997-02-07,1997,2,day,0.068,0.028571252692537637
+pal1997lte1123,1997-02-07,1997,2,day,6.929,0.8992184178513728
+pal1997lte1131,1997-02-07,1997,2,day,0.31,0.11727129565576427
+pal1997lte1135,1997-02-08,1997,2,night,13.531,1.1622955027727522
+pal1997lte1145,1997-02-08,1997,2,day,253.185,2.4051499183287697
+pal1997lte116,1997-01-12,1997,1,day,19.692,1.315802469737045
+pal1997lte1222,1997-02-10,1997,2,day,149.259,2.1768404942074056
+pal1997lte1236,1997-02-10,1997,2,day,180.12,2.2579664095081773
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+pal1997lte1249,1997-02-11,1997,2,night,12.941,1.1442939271985308
+pal1997lte1260,1997-02-11,1997,2,day,0.112,0.0461047872460387
+pal1997lte1266,1997-02-11,1997,2,day,182.817,2.264385673887848
+pal1997lte127,1997-01-12,1997,1,day,1888.08,3.276250350099107
+pal1997lte1276,1997-02-12,1997,2,night,130.817,2.11997142338463
+pal1997lte135,1997-01-12,1997,1,day,71.274,1.858982091384046
+pal1997lte143,1997-01-13,1997,1,day,87.766,1.9482466500135878
+pal1997lte149,1997-01-13,1997,1,day,73.353,1.871298496155363
+pal1997lte157,1997-01-13,1997,1,day,44.369,1.6567592068774755
+pal1997lte165,1997-01-13,1997,1,day,11.938,1.1118671468044696
+pal1997lte177,1997-01-14,1997,1,day,2.878,0.5886078047426866
+pal1997lte183,1997-01-14,1997,1,night,0,0
+pal1997lte187,1997-01-14,1997,1,night,0.105,0.04336227802112949
+pal1997lte303,1997-01-16,1997,1,day,102.714,2.0158373842806574
+pal1997lte314,1997-01-16,1997,1,day,16.667,1.2471628090393312
+pal1997lte324,1997-01-16,1997,1,day,17.603,1.2695829860666132
+pal1997lte333,1997-01-16,1997,1,day,9.117,1.005051749993996
+pal1997lte341,1997-01-17,1997,1,night,36.841,1.5779626047050046
+pal1997lte352,1997-01-17,1997,1,day,11.031,1.0803017267939117
+pal1997lte361,1997-01-17,1997,1,day,12.958,1.1448231939898936
+pal1997lte371,1997-01-17,1997,1,day,4.636,0.7509709844373188
+pal1997lte379,1997-01-18,1997,1,night,0.049,0.02077548819355783
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+pal1997lte389,1997-01-18,1997,1,day,1.113,0.32489949705231336
+pal1997lte409,1997-01-19,1997,1,night,0.479,0.16996817399689249
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+pal1997lte691,1997-01-25,1997,1,day,19.354,1.3086497701813844
+pal1997lte693,1997-01-26,1997,1,day,62.649,1.8037915847706087
+pal1997lte727,1997-01-27,1997,1,day,12.3,1.1238516409670858
+pal1997lte738,1997-01-27,1997,1,day,0.4,0.146128035678238
+pal1997lte748,1997-01-27,1997,1,day,2.635,0.5605044151950567
+pal1997lte757,1997-01-27,1997,1,day,0.639,0.21457895357049908
+pal1997lte766,1997-01-28,1997,1,day,53.979,1.740196836299892
+pal1997lte771,1997-01-28,1997,1,night,7.384,0.9234512696396514
+pal1997lte782,1997-01-28,1997,1,day,0.972,0.29490691060519236
+pal1997lte791,1997-01-28,1997,1,day,0.743,0.24129738710999313
+pal1997lte800,1997-01-28,1997,1,day,5.082,0.7840464158081133
+pal1997lte807,1997-01-29,1997,1,day,0.028,0.011993114659256938
+pal1997lte818,1997-01-29,1997,1,day,0.534,0.18582535961296215
+pal1997lte827,1997-01-29,1997,1,day,0.432,0.1559430179718367
+pal1997lte836,1997-01-29,1997,1,day,4.351,0.7284349509742548
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+pal1997lte856,1997-01-30,1997,1,day,86.123,1.9401328215343767
+pal1997lte865,1997-01-30,1997,1,day,3.308,0.6342756946259439
+pal1997lte875,1997-01-30,1997,1,day,134.705,2.132595849372379
+pal1997lte884,1997-01-30,1997,1,day,84.684,1.9328997325364334
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+pal1997lte986,1997-02-01,1997,2,day,37.766,1.5884509914238125
+pal1997lte994,1997-02-01,1997,2,day,1.806,0.4480876666923411
+pal1998lte100,1998-02-01,1998,2,day,37.601,1.5865985556783981
+pal1998lte116,1998-02-02,1998,2,day,2.219,0.5077209766856134
+pal1998lte118,1998-02-02,1998,2,night,0.605,0.20547503674089088
+pal1998lte121,1998-02-02,1998,2,day,1.261,0.3543005623453597
+pal1998lte133,1998-02-02,1998,2,day,7.173,0.9123814989188004
+pal1998lte142,1998-02-02,1998,2,day,0.104,0.04296907339318013
+pal1998lte15,1998-01-29,1998,1,day,6.454,0.8723893884178209
+pal1998lte151,1998-02-02,1998,2,day,86.298,1.9410042941189325
+pal1998lte156,1998-02-03,1998,2,night,0.37,0.1367205671564068
+pal1998lte158,1998-02-03,1998,2,day,3.472,0.6505017948783668
+pal1998lte160,1998-02-03,1998,2,day,886.772,2.9483014434182775
+pal1998lte168,1998-02-03,1998,2,day,18.893,1.2987002826316714
+pal1998lte177,1998-02-03,1998,2,day,1.097,0.32159843046534387
+pal1998lte180,1998-02-03,1998,2,day,35.868,1.5666495788178658
+pal1998lte189,1998-02-03,1998,2,day,78.039,1.897841436928392
+pal1998lte194,1998-02-04,1998,2,day,22.248,1.3663855969539465
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+pal1998lte211,1998-02-04,1998,2,day,1.398,0.3798491787628298
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+pal1998lte275,1998-02-06,1998,2,day,0.759,0.24526583945746122
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+pal1998lte289,1998-02-07,1998,2,day,75.967,1.8863045590703655
+pal1998lte298,1998-02-07,1998,2,day,18.278,1.2850619759351616
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+pal1998lte313,1998-02-08,1998,2,day,0.077,0.03221570329798157
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+pal1998lte338,1998-02-08,1998,2,day,2230.398,3.348577039485041
+pal1998lte343,1998-02-09,1998,2,day,1.712,0.4332896851950257
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+pal1998lte362,1998-02-10,1998,2,day,4.445,0.7359978840917938
+pal1998lte37,1998-01-30,1998,1,night,31.873,1.5168393397370286
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+pal1998lte389,1998-02-11,1998,2,day,0.6,0.20411998265592482
+pal1998lte400,1998-02-11,1998,2,day,2.502,0.5443161417474274
+pal1998lte404,1998-02-12,1998,2,day,0.144,0.05842602445700535
+pal1998lte414,1998-02-12,1998,2,day,1.312,0.3639878297484915
+pal1998lte420,1998-02-13,1998,2,day,137.507,2.141471722747408
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+pal1999lte1016,1999-02-03,1999,2,day,14.65,1.1945143418824673
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+pal1999lte1233,1999-02-10,1999,2,night,0.106,0.04375512696867949
+pal1999lte1235,1999-02-10,1999,2,night,13.854,1.1718434195795786
+pal1999lte1244,1999-02-10,1999,2,day,1.593,0.4138025167693515
+pal1999lte1251,1999-02-10,1999,2,day,0.401,0.1464381352857746
+pal1999lte1258,1999-02-10,1999,2,day,2.167,0.5006480633719119
+pal1999lte1265,1999-02-10,1999,2,day,0.824,0.26102483399239734
+pal1999lte1267,1999-02-11,1999,2,night,2.613,0.5578679615680222
+pal1999lte128,1999-01-12,1999,1,day,1.082,0.3184807251745173
+pal1999lte1283,1999-02-11,1999,2,day,76.647,1.8901246808164802
+pal1999lte152,1999-01-12,1999,1,day,4.95,0.7745169657285496
+pal1999lte168,1999-01-13,1999,1,day,0.217,0.08529057823006501
+pal1999lte176,1999-01-13,1999,1,night,0.049,0.02077548819355783
+pal1999lte22,1999-01-09,1999,1,day,10.17,1.048053173115609
+pal1999lte252,1999-01-15,1999,1,day,0.979,0.2964457942063963
+pal1999lte258,1999-01-15,1999,1,day,1.358,0.37254380075907034
+pal1999lte277,1999-01-16,1999,1,day,0,0
+pal1999lte284,1999-01-16,1999,1,day,0.523,0.18269990333604263
+pal1999lte305,1999-01-16,1999,1,day,3.15,0.6180480967120927
+pal1999lte322,1999-01-16,1999,1,day,3.343,0.637789829362229
+pal1999lte334,1999-01-17,1999,1,day,2.947,0.5962671263955154
+pal1999lte34,1999-01-09,1999,1,day,0.119,0.04883008652835004
+pal1999lte355,1999-01-17,1999,1,day,0.515,0.18041263283832384
+pal1999lte372,1999-01-17,1999,1,day,2.058,0.48543748107630125
+pal1999lte402,1999-01-18,1999,1,day,5.214,0.7933712489189557
+pal1999lte405,1999-01-19,1999,1,day,21.105,1.3444905192418928
+pal1999lte408,1999-01-19,1999,1,day,21.443,1.3510809094630156
+pal1999lte429,1999-01-19,1999,1,day,19.299,1.3074746435694118
+pal1999lte43,1999-01-10,1999,1,day,0.125,0.05115252244738129
+pal1999lte446,1999-01-19,1999,1,day,1.176,0.3376588910261423
+pal1999lte456,1999-01-20,1999,1,night,1.74,0.437750562820388
+pal1999lte475,1999-01-20,1999,1,day,2.173,0.5014700721004123
+pal1999lte502,1999-01-21,1999,1,night,0.045,0.01911629044707278
+pal1999lte509,1999-01-21,1999,1,day,3.309,0.6343764940883678
+pal1999lte524,1999-01-21,1999,1,day,1.714,0.4336098433237182
+pal1999lte550,1999-01-23,1999,1,day,0.619,0.20924684875337374
+pal1999lte552,1999-01-23,1999,1,day,0,0
+pal1999lte65,1999-01-10,1999,1,day,1.272,0.3564083270389813
+pal1999lte712,1999-01-26,1999,1,night,0,0
+pal1999lte733,1999-01-26,1999,1,day,46.02,1.6722826247889206
+pal1999lte749,1999-01-27,1999,1,day,1.811,0.44886084560744066
+pal1999lte75,1999-01-11,1999,1,day,0.052,0.02201573981772028
+pal1999lte758,1999-01-27,1999,1,night,0.076,0.0318122713303704
+pal1999lte784,1999-01-27,1999,1,day,9.912,1.0379043573124038
+pal1999lte801,1999-01-28,1999,1,day,1.433,0.38614210893081846
+pal1999lte810,1999-01-28,1999,1,night,3.213,0.6245914591268478
+pal1999lte831,1999-01-28,1999,1,day,5.326,0.801129187579704
+pal1999lte84,1999-01-11,1999,1,day,1.537,0.4043204672217307
+pal1999lte847,1999-01-28,1999,1,day,30.122,1.4930674983772287
+pal1999lte853,1999-01-29,1999,1,day,12.739,1.1379551235323606
+pal1999lte870,1999-01-29,1999,1,day,46.448,1.6762179110252784
+pal1999lte882,1999-01-29,1999,1,day,0.139,0.05652372407910037
+pal1999lte890,1999-01-30,1999,1,night,0,0
+pal1999lte905,1999-02-01,1999,2,day,0.095,0.03941411917613713
+pal1999lte907,1999-02-01,1999,2,day,0.177,0.0707764628434347
+pal1999lte915,1999-02-01,1999,2,day,1.832,0.4520932490177314
+pal1999lte924,1999-02-01,1999,2,day,0.029,0.012415374762432893
+pal1999lte948,1999-02-02,1999,2,day,10.077,1.044422155711843
+pal1999lte969,1999-02-02,1999,2,day,6.301,0.8633823484407877
+pal1999lte979,1999-02-02,1999,2,day,6.02,0.8463371121298052
+pal1999lte993,1999-02-03,1999,2,night,16.92,1.2533380053261065
+pal2000lte113,2000-01-10,2000,1,day,2.516,0.5460488664017342
+pal2000lte124,2000-01-10,2000,1,day,2.842,0.5845573605256749
+pal2000lte132,2000-01-11,2000,1,day,1.527,0.40260524191991476
+pal2000lte144,2000-01-11,2000,1,day,0.06,0.02530586526477026
+pal2000lte160,2000-01-11,2000,1,day,0.105,0.04336227802112949
+pal2000lte173,2000-01-12,2000,1,day,0.203,0.08026562733984477
+pal2000lte179,2000-01-12,2000,1,day,0.066,0.027757204690553483
+pal2000lte200,2000-01-12,2000,1,day,0.057,0.024074987307426243
+pal2000lte221,2000-01-13,2000,1,night,0.012,0.005180512503780314
+pal2000lte232,2000-01-13,2000,1,day,1.263,0.35468455395472853
+pal2000lte250,2000-01-13,2000,1,day,2.801,0.5798978696031039
+pal2000lte272,2000-01-13,2000,1,day,7.316,0.9199144806594315
+pal2000lte285,2000-01-13,2000,1,day,1.256,0.35333909531130475
+pal2000lte294,2000-01-14,2000,1,day,7.059,0.9062811557721531
+pal2000lte308,2000-01-14,2000,1,day,3.414,0.6448323288256362
+pal2000lte327,2000-01-14,2000,1,day,1.541,0.40500466505036936
+pal2000lte338,2000-01-14,2000,1,day,2.291,0.5173278822943735
+pal2000lte346,2000-01-15,2000,1,day,2.768,0.5761108941208397
+pal2000lte357,2000-01-15,2000,1,day,7.508,0.9298274812306914
+pal2000lte374,2000-01-15,2000,1,day,3.039,0.6062738531699883
+pal2000lte386,2000-01-15,2000,1,day,3.273,0.6307328928171965
+pal2000lte398,2000-01-16,2000,1,day,0.865,0.2706788361447064
+pal2000lte404,2000-01-16,2000,1,day,1.833,0.45224657452043726
+pal2000lte425,2000-01-16,2000,1,day,1.036,0.30877777366472114
+pal2000lte441,2000-01-16,2000,1,day,1.567,0.40942586867144337
+pal2000lte457,2000-01-17,2000,1,day,0.954,0.29092455938275424
+pal2000lte477,2000-01-18,2000,1,day,1.683,0.42862067267193893
+pal2000lte485,2000-01-19,2000,1,day,2.147,0.4978967429132203
+pal2000lte494,2000-01-19,2000,1,day,1.061,0.31407799177921286
+pal2000lte507,2000-01-19,2000,1,day,8.852,0.9935244031670656
+pal2000lte518,2000-01-20,2000,1,day,3.08,0.6106601630898799
+pal2000lte521,2000-01-20,2000,1,day,1.902,0.4626974081017171
+pal2000lte532,2000-01-20,2000,1,day,1.742,0.4380674504534938
+pal2000lte547,2000-01-20,2000,1,day,10.147,1.047158001128309
+pal2000lte559,2000-01-20,2000,1,day,11.646,1.1019531774771996
+pal2000lte571,2000-01-21,2000,1,day,2.166,0.5005109105263371
+pal2000lte577,2000-01-21,2000,1,day,5.229,0.794418330874141
+pal2000lte589,2000-01-21,2000,1,day,1.858,0.4560622244549514
+pal2000lte624,2000-01-22,2000,1,day,0.456,0.16316137497701835
+pal2000lte640,2000-01-23,2000,1,day,0.476,0.16908635748702275
+pal2000lte653,2000-01-24,2000,1,day,18.054,1.2799861608815726
+pal2000lte663,2000-01-24,2000,1,day,0,0
+pal2000lte673,2000-01-24,2000,1,day,0.068,0.028571252692537637
+pal2000lte68,2000-01-09,2000,1,day,2.549,0.5501059993475927
+pal2000lte82,2000-01-10,2000,1,night,1.2,0.3424226808222063
+pal2000lte85,2000-01-10,2000,1,day,1.806,0.4480876666923411
+pal2000lte97,2000-01-10,2000,1,day,21.639,1.3548572394741183
+pal2001lte110,2001-01-10,2001,1,day,1.072,0.31638975107319545
+pal2001lte122,2001-01-11,2001,1,day,0.101,0.041787318971751766
+pal2001lte129,2001-01-11,2001,1,day,0.271,0.10414555055400815
+pal2001lte147,2001-01-11,2001,1,day,19.636,1.3146255192012706
+pal2001lte159,2001-01-11,2001,1,day,6.308,0.8637985386805003
+pal2001lte171,2001-01-12,2001,1,day,1.569,0.40976410426634613
+pal2001lte196,2001-01-12,2001,1,day,2.298,0.5182506513084999
+pal2001lte207,2001-01-12,2001,1,day,2.907,0.5918434112247845
+pal2001lte216,2001-01-13,2001,1,day,0.748,0.2425414282983842
+pal2001lte252,2001-01-14,2001,1,day,2.605,0.5569052690554478
+pal2001lte256,2001-01-14,2001,1,night,1.138,0.3300077008727592
+pal2001lte262,2001-01-14,2001,1,day,1.313,0.3641756327706194
+pal2001lte275,2001-01-14,2001,1,day,0.89,0.2764618041732442
+pal2001lte290,2001-01-14,2001,1,day,2.359,0.5262100038416643
+pal2001lte301,2001-01-14,2001,1,day,5.291,0.7987196851850062
+pal2001lte313,2001-01-15,2001,1,day,3.863,0.6869042695681773
+pal2001lte319,2001-01-15,2001,1,day,1.69,0.42975228000240795
+pal2001lte337,2001-01-15,2001,1,day,0.92,0.28330122870354957
+pal2001lte364,2001-01-16,2001,1,day,4.291,0.7235377615320568
+pal2001lte369,2001-01-16,2001,1,night,0.053,0.02242837118548649
+pal2001lte374,2001-01-16,2001,1,day,0.533,0.18554215485437514
+pal2001lte400,2001-01-16,2001,1,day,0.13,0.05307844348341968
+pal2001lte413,2001-01-16,2001,1,day,0.614,0.20790353038605158
+pal2001lte424,2001-01-17,2001,1,day,8.393,0.9728043223365777
+pal2001lte432,2001-01-17,2001,1,day,2.7,0.568201724066995
+pal2001lte445,2001-01-17,2001,1,day,1.695,0.4305587695227575
+pal2001lte459,2001-01-17,2001,1,day,1.14,0.33041377334919075
+pal2001lte477,2001-01-18,2001,1,day,6.446,0.8719230318823734
+pal2001lte490,2001-01-18,2001,1,day,2.369,0.5275010109811202
+pal2001lte507,2001-01-18,2001,1,day,1.892,0.46119828862249324
+pal2001lte514,2001-01-18,2001,1,day,1.083,0.3186892699477459
+pal2001lte529,2001-01-19,2001,1,day,0.764,0.24649858079580092
+pal2001lte535,2001-01-19,2001,1,day,0.031,0.01325866528351651
+pal2001lte554,2001-01-19,2001,1,day,5.535,0.8152455919165631
+pal2001lte568,2001-01-19,2001,1,day,0.955,0.2911467617318856
+pal2001lte582,2001-01-20,2001,1,day,0.727,0.2372923375674587
+pal2001lte595,2001-01-20,2001,1,day,0.156,0.06295783408451021
+pal2001lte608,2001-01-20,2001,1,day,0.127,0.05192391604610654
+pal2001lte625,2001-01-20,2001,1,day,0.153,0.06182930729469903
+pal2001lte638,2001-01-21,2001,1,day,0.029,0.012415374762432893
+pal2001lte644,2001-01-21,2001,1,night,0.14,0.056904851336472634
+pal2001lte669,2001-01-21,2001,1,day,0,0
+pal2001lte677,2001-01-21,2001,1,day,0.053,0.02242837118548649
+pal2001lte681,2001-01-22,2001,1,night,11.361,1.0920536064254753
+pal2001lte697,2001-01-23,2001,1,day,0,0
+pal2001lte709,2001-01-23,2001,1,day,1.332,0.36772854608697647
+pal2001lte722,2001-01-23,2001,1,day,8.492,0.9773577295453014
+pal2001lte733,2001-01-24,2001,1,night,0.504,0.17724783625562346
+pal2001lte739,2001-01-24,2001,1,day,3.99,0.69810054562339
+pal2002lte111,2002-01-09,2002,1,day,2.618,0.5584685625237948
+pal2002lte117,2002-01-09,2002,1,day,1.746,0.4387005329007363
+pal2002lte131,2002-01-09,2002,1,day,24.468,1.4059948411918042
+pal2002lte151,2002-01-10,2002,1,day,0.438,0.15775888604686378
+pal2002lte156,2002-01-10,2002,1,night,2.033,0.48187241031066347
+pal2002lte163,2002-01-10,2002,1,day,2.148,0.49803472368702695
+pal2002lte181,2002-01-10,2002,1,day,0,0
+pal2002lte193,2002-01-11,2002,1,night,0.123,0.050379756261457784
+pal2002lte195,2002-01-11,2002,1,day,1.325,0.36642295722597273
+pal2002lte209,2002-01-11,2002,1,day,3.698,0.6719130124415871
+pal2002lte220,2002-01-12,2002,1,day,3.001,0.6021685513789972
+pal2002lte225,2002-01-12,2002,1,night,0.184,0.07335170238690095
+pal2002lte230,2002-01-12,2002,1,day,0.639,0.21457895357049908
+pal2002lte255,2002-01-12,2002,1,day,43.658,1.6498992693755152
+pal2002lte267,2002-01-12,2002,1,day,19.969,1.3215777196957983
+pal2002lte275,2002-01-13,2002,1,day,50.722,1.7136753099471456
+pal2002lte280,2002-01-13,2002,1,night,78.311,1.8993334257538863
+pal2002lte298,2002-01-13,2002,1,day,10.494,1.0604711927976789
+pal2002lte308,2002-01-14,2002,1,day,405.401,2.608954767912545
+pal2002lte316,2002-01-15,2002,1,day,0.722,0.23603314711763593
+pal2002lte320,2002-01-15,2002,1,night,6.048,0.8480658954039915
+pal2002lte336,2002-01-15,2002,1,day,1.355,0.371990911464915
+pal2002lte347,2002-01-15,2002,1,day,2.681,0.5659658174466666
+pal2002lte358,2002-01-15,2002,1,day,21.203,1.346411658929061
+pal2002lte370,2002-01-16,2002,1,night,21.242,1.3471738364132073
+pal2002lte375,2002-01-16,2002,1,day,1.825,0.45101845215545733
+pal2002lte391,2002-01-16,2002,1,day,1.074,0.3168087520530221
+pal2002lte403,2002-01-16,2002,1,day,8.788,0.9906939606797516
+pal2002lte409,2002-01-17,2002,1,day,3.23,0.6263403673750424
+pal2002lte423,2002-01-17,2002,1,day,1.938,0.4680517914542377
+pal2002lte434,2002-01-17,2002,1,day,0.122,0.04999285692014256
+pal2002lte440,2002-01-18,2002,1,day,65.088,1.820122609154337
+pal2002lte454,2002-01-19,2002,1,day,90.42,1.961041216953312
+pal2002lte461,2002-01-20,2002,1,day,79.71,1.9069273473089559
+pal2002lte476,2002-01-20,2002,1,day,23.719,1.3930308975144152
+pal2002lte493,2002-01-21,2002,1,day,0.616,0.20844135643856737
+pal2002lte507,2002-01-21,2002,1,day,0.135,0.05499586152914153
+pal2002lte519,2002-01-21,2002,1,day,1538.659,3.187424544981322
+pal2002lte528,2002-01-21,2002,1,day,58.593,1.7751952490034177
+pal2002lte533,2002-01-22,2002,1,day,52.661,1.7296587617096666
+pal2002lte560,2002-01-22,2002,1,day,0.71,0.23299611039215382
+pal2002lte576,2002-01-23,2002,1,day,1.447,0.3886339693517892
+pal2002lte580,2002-01-23,2002,1,night,0.336,0.1258064581395269
+pal2002lte585,2002-01-23,2002,1,day,0.914,0.2819419334408248
+pal2002lte601,2002-01-23,2002,1,day,9.18,1.00774777800074
+pal2002lte613,2002-01-24,2002,1,day,1.495,0.3970705499594087
+pal2002lte617,2002-01-24,2002,1,day,9.501,1.0212306584797024
+pal2002lte631,2002-01-24,2002,1,day,82.145,1.9198361376481425
+pal2002lte687,2002-01-26,2002,1,night,0.865,0.2706788361447064
+pal2002lte81,2002-01-08,2002,1,day,45.457,1.667051161436834
+pal2002lte88,2002-01-08,2002,1,day,7.68,0.9385197251764918
+pal2003lte10,2003-01-06,2003,1,night,11.465,1.0956922828397921
+pal2003lte100,2003-01-09,2003,1,night,0.967,0.29380435991933673
+pal2003lte115,2003-01-09,2003,1,day,10.112,1.0457922327295592
+pal2003lte124,2003-01-09,2003,1,day,5.056,0.7821858664920164
+pal2003lte132,2003-01-10,2003,1,night,0,0
+pal2003lte145,2003-01-10,2003,1,day,3.808,0.681964458994683
+pal2003lte152,2003-01-11,2003,1,night,0.456,0.16316137497701835
+pal2003lte157,2003-01-11,2003,1,day,0,0
+pal2003lte170,2003-01-11,2003,1,day,1.774,0.44310645673726606
+pal2003lte197,2003-01-12,2003,1,day,72.523,1.8664232194997117
+pal2003lte208,2003-01-13,2003,1,day,73.772,1.8737389973023864
+pal2003lte212,2003-01-13,2003,1,day,0.17,0.06818586174616162
+pal2003lte220,2003-01-13,2003,1,day,22.898,1.3783615567929717
+pal2003lte227,2003-01-13,2003,1,day,81.475,1.9163223242173815
+pal2003lte232,2003-01-13,2003,1,day,107.535,2.035569810587602
+pal2003lte239,2003-01-14,2003,1,day,0.112,0.0461047872460387
+pal2003lte247,2003-01-14,2003,1,day,12.97,1.145196406114182
+pal2003lte25,2003-01-06,2003,1,day,0.789,0.252610340567373
+pal2003lte255,2003-01-14,2003,1,day,1.973,0.4731949092049378
+pal2003lte259,2003-01-14,2003,1,day,9.996,1.041234731171431
+pal2003lte35,2003-01-07,2003,1,day,18.105,1.2811470420244278
+pal2003lte40,2003-01-07,2003,1,night,5.459,0.8101652845431496
+pal2003lte417,2003-01-19,2003,1,day,1.85,0.4548448600085102
+pal2003lte432,2003-01-20,2003,1,day,27.957,1.4617535661254555
+pal2003lte436,2003-01-20,2003,1,night,5.895,0.8385342705118686
+pal2003lte443,2003-01-20,2003,1,day,2.919,0.5931752634781026
+pal2003lte450,2003-01-20,2003,1,day,141.843,2.154858962703634
+pal2003lte458,2003-01-20,2003,1,day,13.668,1.1663709012885652
+pal2003lte468,2003-01-20,2003,1,day,5.394,0.8057726319356692
+pal2003lte475,2003-01-21,2003,1,night,5.619,0.8207923810882037
+pal2003lte486,2003-01-21,2003,1,day,0.559,0.19284611518884173
+pal2003lte494,2003-01-21,2003,1,day,1.282,0.35831564008219585
+pal2003lte502,2003-01-22,2003,1,day,47.228,1.6832992522396597
+pal2003lte507,2003-01-22,2003,1,day,0.092,0.038222638368718455
+pal2003lte518,2003-01-22,2003,1,day,38.632,1.5980459892417618
+pal2003lte527,2003-01-22,2003,1,day,52.445,1.7279070814066968
+pal2003lte536,2003-01-22,2003,1,day,43.362,1.6470111174902862
+pal2003lte545,2003-01-23,2003,1,night,0.031,0.01325866528351651
+pal2003lte559,2003-01-23,2003,1,day,0.24,0.09342168516223506
+pal2003lte567,2003-01-24,2003,1,day,0,0
+pal2003lte575,2003-01-24,2003,1,day,4.025,0.7011360660925265
+pal2003lte588,2003-01-26,2003,1,day,0,0
+pal2003lte596,2003-01-26,2003,1,day,102.106,2.0132839387177732
+pal2003lte602,2003-01-27,2003,1,day,0.281,0.10754912974468635
+pal2003lte604,2003-01-27,2003,1,night,2.29,0.5171958979499743
+pal2003lte609,2003-01-27,2003,1,day,0.189,0.0751818546186916
+pal2003lte615,2003-01-27,2003,1,day,26.283,1.4358921230156538
+pal2003lte645,2003-01-29,2003,1,day,0.041,0.017450729510536125
+pal2003lte654,2003-01-29,2003,1,day,7.984,0.9534697432534014
+pal2003lte663,2003-01-29,2003,1,day,62.643,1.8037506432048647
+pal2003lte671,2003-01-29,2003,1,day,3.574,0.6602961602707297
+pal2003lte678,2003-01-30,2003,1,night,0.108,0.04453976039241098
+pal2003lte689,2003-01-30,2003,1,day,24.297,1.4030690207529772
+pal2003lte699,2003-01-30,2003,1,day,5.2,0.7923916894982539
+pal2003lte709,2003-01-31,2003,1,day,0.4,0.146128035678238
+pal2003lte714,2003-01-31,2003,1,night,1.916,0.464787519645937
+pal2003lte722,2003-02-01,2003,2,night,12.124,1.11806622171401
+pal2003lte732,2003-02-01,2003,2,day,56.317,1.7582834511331593
+pal2003lte75,2003-01-08,2003,1,day,23.433,1.3879767949904371
+pal2003lte84,2003-01-08,2003,1,day,21.935,1.3604987444680012
+pal2003lte95,2003-01-09,2003,1,day,4.059,0.7040646794085674
+pal2004lte114,2004-01-10,2004,1,day,2.297,0.518118947143153
+pal2004lte123,2004-01-11,2004,1,day,3.343,0.637789829362229
+pal2004lte128,2004-01-11,2004,1,night,0,0
+pal2004lte144,2004-01-12,2004,1,day,0,0
+pal2004lte148,2004-01-12,2004,1,night,1.197,0.34183005692051033
+pal2004lte160,2004-01-12,2004,1,day,16.371,1.2398248202882673
+pal2004lte175,2004-01-12,2004,1,day,21.362,1.3495106431374833
+pal2004lte185,2004-01-13,2004,1,day,0.627,0.2113875529368588
+pal2004lte189,2004-01-13,2004,1,night,1.699,0.4312028845565166
+pal2004lte205,2004-01-13,2004,1,day,9.436,1.0185340704281831
+pal2004lte215,2004-01-13,2004,1,day,3.367,0.6401831919213401
+pal2004lte225,2004-01-14,2004,1,day,6.167,0.8553374044695403
+pal2004lte23,2004-01-08,2004,1,day,0,0
+pal2004lte230,2004-01-14,2004,1,day,5.648,0.8226910107760546
+pal2004lte243,2004-01-14,2004,1,day,0.304,0.11527759139590141
+pal2004lte251,2004-01-15,2004,1,day,0.088,0.03662889536216113
+pal2004lte255,2004-01-15,2004,1,night,0.236,0.09201847075279702
+pal2004lte31,2004-01-08,2004,1,day,1.121,0.3265406685165618
+pal2004lte315,2004-01-16,2004,1,night,0.033,0.014100321519620544
+pal2004lte338,2004-01-18,2004,1,day,0,0
+pal2004lte353,2004-01-18,2004,1,day,0.638,0.21431389742439963
+pal2004lte364,2004-01-18,2004,1,day,7.936,0.9511431601075526
+pal2004lte375,2004-01-19,2004,1,day,0.232,0.09061070782840663
+pal2004lte380,2004-01-19,2004,1,night,0,0
+pal2004lte395,2004-01-19,2004,1,day,1.433,0.38614210893081846
+pal2004lte407,2004-01-19,2004,1,day,4.7,0.7558748556724915
+pal2004lte418,2004-01-20,2004,1,day,0,0
+pal2004lte423,2004-01-20,2004,1,night,0,0
+pal2004lte443,2004-01-21,2004,1,night,0,0
+pal2004lte447,2004-01-21,2004,1,night,0.018,0.007747778000739948
+pal2004lte453,2004-01-21,2004,1,day,0.095,0.03941411917613713
+pal2004lte468,2004-01-21,2004,1,day,11.777,1.1064288947934113
+pal2004lte481,2004-01-22,2004,1,day,6.474,0.8735530935136189
+pal2004lte491,2004-01-22,2004,1,day,32.54,1.525563058270067
+pal2004lte495,2004-01-23,2004,1,day,3.617,0.6643598745511412
+pal2004lte50,2004-01-08,2004,1,day,23.963,1.3972967767296853
+pal2004lte502,2004-01-23,2004,1,day,53.962,1.7400625277843937
+pal2004lte509,2004-01-24,2004,1,day,8.916,0.9963365180957838
+pal2004lte540,2004-01-26,2004,1,day,1.335,0.36828688490213096
+pal2004lte549,2004-01-26,2004,1,day,0.171,0.06855689507236314
+pal2004lte559,2004-01-27,2004,1,day,18.782,1.2962701975267967
+pal2004lte563,2004-01-27,2004,1,night,0.064,0.0269416279590294
+pal2004lte573,2004-01-27,2004,1,day,5.994,0.8447256279732259
+pal2004lte585,2004-01-28,2004,1,day,9.104,1.0044933375472742
+pal2004lte59,2004-01-09,2004,1,day,0,0
+pal2004lte590,2004-01-28,2004,1,night,0,0
+pal2004lte605,2004-01-28,2004,1,day,24.338,1.4037723318085513
+pal2004lte616,2004-01-28,2004,1,day,17.292,1.2622611927086442
+pal2004lte62,2004-01-09,2004,1,day,15.113,1.2071764070951623
+pal2004lte627,2004-01-29,2004,1,day,0.032,0.013679697291192559
+pal2004lte632,2004-01-29,2004,1,night,2.173,0.5014700721004123
+pal2004lte648,2004-01-29,2004,1,day,1.1,0.3222192947339193
+pal2004lte652,2004-01-30,2004,1,night,0.057,0.024074987307426243
+pal2004lte667,2004-01-30,2004,1,day,2.966,0.5983527098692838
+pal2004lte674,2004-01-30,2004,1,day,2.823,0.582404298019028
+pal2004lte681,2004-01-31,2004,1,day,0.668,0.22219604630171996
+pal2004lte71,2004-01-09,2004,1,day,2.97,0.5987905067631151
+pal2004lte82,2004-01-09,2004,1,day,0.208,0.082066934285113
+pal2004lte93,2004-01-10,2004,1,day,0.04,0.01703333929878037
+pal2004lte99,2004-01-10,2004,1,night,0,0
+pal2005lte100,2005-01-08,2005,1,day,0.022,0.009450895798693935
+pal2005lte109,2005-01-08,2005,1,day,1.982,0.4745076391169758
+pal2005lte123,2005-01-08,2005,1,day,12.708,1.1369740957578294
+pal2005lte129,2005-01-09,2005,1,night,0.116,0.04766419460155998
+pal2005lte13,2005-01-05,2005,1,day,0,0
+pal2005lte139,2005-01-09,2005,1,day,4.829,0.7655940553194449
+pal2005lte149,2005-01-09,2005,1,day,0.615,0.2081725266671217
+pal2005lte156,2005-01-10,2005,1,night,0.556,0.19200959265367015
+pal2005lte168,2005-01-10,2005,1,day,4.207,0.7165875776756924
+pal2005lte189,2005-01-10,2005,1,day,6.289,0.8626679502285879
+pal2005lte209,2005-01-11,2005,1,day,0.257,0.09933527768595779
+pal2005lte21,2005-01-05,2005,1,day,0.283,0.10822665637492848
+pal2005lte216,2005-01-12,2005,1,night,0.16,0.06445798922691845
+pal2005lte232,2005-01-13,2005,1,day,2.725,0.5711262770843116
+pal2005lte236,2005-01-13,2005,1,night,3.95,0.6946051989335688
+pal2005lte29,2005-01-06,2005,1,day,6.337,0.8655185190747741
+pal2005lte312,2005-01-15,2005,1,day,13.026,1.1469338345627638
+pal2005lte326,2005-01-17,2005,1,night,2.727,0.5713593927538396
+pal2005lte330,2005-01-17,2005,1,day,0.182,0.07261747654523654
+pal2005lte340,2005-01-17,2005,1,day,0.997,0.3003780648707025
+pal2005lte350,2005-01-17,2005,1,day,13.398,1.1583021692280226
+pal2005lte361,2005-01-17,2005,1,day,11.074,1.0818511715174541
+pal2005lte369,2005-01-18,2005,1,day,0,0
+pal2005lte373,2005-01-18,2005,1,night,0.025,0.010723865391773066
+pal2005lte391,2005-01-18,2005,1,day,7.059,0.9062811557721531
+pal2005lte396,2005-01-19,2005,1,night,0,0
+pal2005lte401,2005-01-19,2005,1,day,0,0
+pal2005lte41,2005-01-06,2005,1,day,32.119,1.5200772151587842
+pal2005lte420,2005-01-19,2005,1,day,6.546,0.8777168008649767
+pal2005lte430,2005-01-20,2005,1,day,1.666,0.42586014507784037
+pal2005lte435,2005-01-20,2005,1,night,1.607,0.41614103116832896
+pal2005lte446,2005-01-20,2005,1,day,1.569,0.40976410426634613
+pal2005lte457,2005-01-20,2005,1,day,2.038,0.4825877695267675
+pal2005lte467,2005-01-20,2005,1,day,21.373,1.349724222634397
+pal2005lte475,2005-01-21,2005,1,day,0.467,0.1664301138432827
+pal2005lte482,2005-01-21,2005,1,day,0.05,0.021189299069938092
+pal2005lte491,2005-01-23,2005,1,day,0.057,0.024074987307426243
+pal2005lte498,2005-01-23,2005,1,day,0.041,0.017450729510536125
+pal2005lte50,2005-01-06,2005,1,day,0.73,0.23804610312879543
+pal2005lte503,2005-01-24,2005,1,night,1.919,0.4652340949880144
+pal2005lte512,2005-01-24,2005,1,day,22.053,1.3627274502751994
+pal2005lte519,2005-01-24,2005,1,day,7.7,0.9395192526186185
+pal2005lte533,2005-01-27,2005,1,day,1.58,0.41161970596323016
+pal2005lte537,2005-01-27,2005,1,night,0.439,0.15806079393660522
+pal2005lte549,2005-01-27,2005,1,day,2.799,0.5796692935547203
+pal2005lte560,2005-01-27,2005,1,day,3.946,0.6942541120252784
+pal2005lte570,2005-01-27,2005,1,day,4.069,0.7049222912234018
+pal2005lte578,2005-01-28,2005,1,night,0.059,0.024895960107484977
+pal2005lte590,2005-01-28,2005,1,day,1.994,0.4762517960070335
+pal2005lte60,2005-01-06,2005,1,day,0.126,0.05153839051532738
+pal2005lte600,2005-01-28,2005,1,day,1.168,0.33605927786634937
+pal2005lte608,2005-01-29,2005,1,day,6.866,0.8957539420737279
+pal2005lte617,2005-01-30,2005,1,day,7.162,0.911796590437252
+pal2005lte621,2005-01-31,2005,1,night,63.156,1.8072372787162365
+pal2005lte67,2005-01-07,2005,1,night,6.527,0.876621916034273
+pal2005lte82,2005-01-07,2005,1,day,0.796,0.2543063323312856
+pal2005lte98,2005-01-07,2005,1,day,1.815,0.449478399187365
+pal2006lte105,2006-01-09,2006,1,day,0,0
+pal2006lte117,2006-01-11,2006,1,day,0.352,0.13097669160561706
+pal2006lte121,2006-01-11,2006,1,day,0.334,0.12515582958053018
+pal2006lte132,2006-01-11,2006,1,day,14.958,1.2029784607370653
+pal2006lte142,2006-01-11,2006,1,day,2.739,0.5727554651542196
+pal2006lte153,2006-01-12,2006,1,day,3.115,0.6143698395482887
+pal2006lte164,2006-01-12,2006,1,night,2.05,0.4842998393467858
+pal2006lte175,2006-01-13,2006,1,night,39.873,1.611436515265544
+pal2006lte187,2006-01-13,2006,1,day,1.102,0.32263271169222346
+pal2006lte19,2006-01-07,2006,1,day,4.982,0.7768464086952993
+pal2006lte195,2006-01-13,2006,1,day,0.571,0.1961761850399733
+pal2006lte280,2006-01-15,2006,1,day,0.646,0.216429830876251
+pal2006lte29,2006-01-08,2006,1,day,0.108,0.04453976039241098
+pal2006lte290,2006-01-15,2006,1,day,2.076,0.48798633112939344
+pal2006lte298,2006-01-16,2006,1,night,1.689,0.4295908022233016
+pal2006lte310,2006-01-16,2006,1,day,11.255,1.0883133155880969
+pal2006lte327,2006-01-16,2006,1,day,14.021,1.1766988461185717
+pal2006lte331,2006-01-17,2006,1,day,0.159,0.064083435963596
+pal2006lte339,2006-01-17,2006,1,day,1.297,0.36116099519502604
+pal2006lte352,2006-01-17,2006,1,day,2.661,0.5635997288815311
+pal2006lte362,2006-01-17,2006,1,day,4.772,0.7613263224214566
+pal2006lte37,2006-01-08,2006,1,night,0.038,0.01619735351243906
+pal2006lte372,2006-01-17,2006,1,day,1.09,0.320146286111054
+pal2006lte449,2006-01-19,2006,1,night,3.894,0.6896639650157704
+pal2006lte460,2006-01-19,2006,1,day,0.305,0.11561051167429974
+pal2006lte478,2006-01-20,2006,1,day,1.506,0.39898106665813127
+pal2006lte48,2006-01-08,2006,1,day,1.141,0.33061666729443834
+pal2006lte482,2006-01-20,2006,1,night,2.518,0.5462958351214424
+pal2006lte490,2006-01-20,2006,1,day,0.828,0.2619761913978126
+pal2006lte503,2006-01-20,2006,1,day,6.876,0.8963057074660806
+pal2006lte513,2006-01-20,2006,1,day,0.868,0.2713768718940745
+pal2006lte521,2006-01-21,2006,1,day,10.79,1.0715138050950892
+pal2006lte530,2006-01-21,2006,1,night,3.637,0.6662370958958045
+pal2006lte534,2006-01-21,2006,1,day,0.148,0.05994188806195468
+pal2006lte550,2006-01-22,2006,1,day,0.095,0.03941411917613713
+pal2006lte565,2006-01-23,2006,1,day,0,0
+pal2006lte572,2006-01-23,2006,1,night,0.215,0.08457627793433102
+pal2006lte584,2006-01-23,2006,1,day,0.061,0.025715383901340642
+pal2006lte596,2006-01-24,2006,1,day,0.041,0.017450729510536125
+pal2006lte603,2006-01-24,2006,1,night,1.307,0.3630475945210935
+pal2006lte617,2006-01-24,2006,1,day,0.219,0.08600370561838196
+pal2006lte62,2006-01-08,2006,1,day,0.022,0.009450895798693935
+pal2006lte627,2006-01-24,2006,1,day,1.137,0.3298045221640694
+pal2006lte638,2006-01-24,2006,1,day,3.49,0.6522463410033232
+pal2006lte646,2006-01-25,2006,1,night,1.052,0.3121773564397787
+pal2006lte655,2006-01-26,2006,1,day,0.864,0.27044590801796253
+pal2006lte659,2006-01-26,2006,1,night,0.304,0.11527759139590141
+pal2006lte664,2006-01-26,2006,1,night,0.45,0.16136800223497488
+pal2006lte675,2006-01-26,2006,1,day,0.929,0.2853322276438845
+pal2006lte682,2006-01-26,2006,1,day,0.162,0.06520612805431189
+pal2006lte692,2006-01-27,2006,1,day,0.234,0.09131515969722287
+pal2006lte708,2006-01-29,2006,1,day,0.137,0.05576046468773478
+pal2006lte710,2006-01-29,2006,1,day,0.118,0.04844180355040444
+pal2006lte72,2006-01-08,2006,1,day,0.733,0.238798562713917
+pal2006lte775,2006-01-31,2006,1,day,0.64,0.2148438480476979
+pal2006lte779,2006-02-01,2006,2,day,20.55,1.3334472744967505
+pal2006lte789,2006-02-01,2006,2,day,1.276,0.3571722577230335
+pal2006lte8,2006-01-07,2006,1,day,13.896,1.1730696636372675
+pal2006lte81,2006-01-09,2006,1,day,0,0
+pal2006lte94,2006-01-09,2006,1,day,0.139,0.05652372407910037
+pal2007lte105,2007-01-12,2007,1,day,0.976,0.2957869402516093
+pal2007lte114,2007-01-12,2007,1,night,0.674,0.22375545365724117
+pal2007lte128,2007-01-12,2007,1,day,0.846,0.26623169668989327
+pal2007lte134,2007-01-13,2007,1,day,0.696,0.22942584792069498
+pal2007lte139,2007-01-13,2007,1,night,0.029,0.012415374762432893
+pal2007lte152,2007-01-13,2007,1,day,1.114,0.32510498297140744
+pal2007lte163,2007-01-13,2007,1,day,1.617,0.41780372263988097
+pal2007lte173,2007-01-13,2007,1,day,0.371,0.13703745478951265
+pal2007lte18,2007-01-08,2007,1,day,3.349,0.638389407665336
+pal2007lte181,2007-01-14,2007,1,night,0.368,0.13608609738409738
+pal2007lte193,2007-01-14,2007,1,day,25.868,1.429235339626655
+pal2007lte209,2007-01-15,2007,1,day,0.273,0.10482840365365544
+pal2007lte28,2007-01-08,2007,1,day,5.551,0.8163075994319398
+pal2007lte309,2007-01-18,2007,1,day,2.293,0.5175917307119078
+pal2007lte317,2007-01-18,2007,1,night,0.997,0.3003780648707025
+pal2007lte329,2007-01-18,2007,1,day,4.201,0.716086853774832
+pal2007lte341,2007-01-18,2007,1,day,0.5,0.17609125905568124
+pal2007lte351,2007-01-18,2007,1,day,69.866,1.8504379198935357
+pal2007lte359,2007-01-19,2007,1,night,0.219,0.08600370561838196
+pal2007lte371,2007-01-19,2007,1,day,29.419,1.4831449328721287
+pal2007lte381,2007-01-19,2007,1,day,16.339,1.2390240466000937
+pal2007lte391,2007-01-19,2007,1,day,40.612,1.6192185898157772
+pal2007lte400,2007-01-21,2007,1,day,4.273,0.7220577713314641
+pal2007lte406,2007-01-21,2007,1,night,146.597,2.1690775302736993
+pal2007lte41,2007-01-09,2007,1,day,18.936,1.2996380249790755
+pal2007lte415,2007-01-21,2007,1,day,0.178,0.07114529045108281
+pal2007lte422,2007-01-21,2007,1,day,3.472,0.6505017948783668
+pal2007lte431,2007-01-21,2007,1,day,0.377,0.13893394025692368
+pal2007lte436,2007-01-22,2007,1,night,0.157,0.06333335895174956
+pal2007lte445,2007-01-22,2007,1,day,0.949,0.28981183911762143
+pal2007lte453,2007-01-22,2007,1,day,10.119,1.0460657302306877
+pal2007lte460,2007-01-23,2007,1,day,4.403,0.7326349675391957
+pal2007lte466,2007-01-23,2007,1,day,0.057,0.024074987307426243
+pal2007lte472,2007-01-23,2007,1,day,3.852,0.6859207921945354
+pal2007lte478,2007-01-23,2007,1,day,5.945,0.8416722500736344
+pal2007lte483,2007-01-24,2007,1,day,1.057,0.313234291694724
+pal2007lte488,2007-01-24,2007,1,night,0.12,0.04921802267018165
+pal2007lte501,2007-01-24,2007,1,day,4.507,0.7409150764812825
+pal2007lte507,2007-01-25,2007,1,day,0.296,0.11260500153457453
+pal2007lte511,2007-01-25,2007,1,night,0.133,0.05422990986339725
+pal2007lte519,2007-01-25,2007,1,day,1.245,0.351216345339342
+pal2007lte52,2007-01-09,2007,1,day,0.084,0.03502928220236815
+pal2007lte527,2007-01-25,2007,1,day,0.856,0.2685779718828432
+pal2007lte533,2007-01-26,2007,1,day,0.778,0.24993175663419492
+pal2007lte544,2007-01-26,2007,1,day,0.026,0.011147360775797477
+pal2007lte554,2007-01-26,2007,1,day,0.026,0.011147360775797477
+pal2007lte569,2007-01-28,2007,1,day,14.801,1.1986845730771434
+pal2007lte577,2007-01-28,2007,1,day,0.126,0.05153839051532738
+pal2007lte581,2007-01-29,2007,1,night,8.494,0.9774492273823412
+pal2007lte594,2007-01-29,2007,1,day,17.426,1.2654310668267126
+pal2007lte60,2007-01-10,2007,1,night,0,0
+pal2007lte604,2007-01-29,2007,1,day,264.687,2.4243703050072405
+pal2007lte614,2007-01-30,2007,1,day,2.142,0.4972061807039545
+pal2007lte619,2007-01-30,2007,1,day,7.028,0.9046073638154478
+pal2007lte632,2007-01-30,2007,1,day,289.052,2.462475864411336
+pal2007lte636,2007-01-30,2007,1,day,23.071,1.3814941328782981
+pal2007lte722,2007-02-01,2007,2,day,5.69,0.8254261177678232
+pal2007lte724,2007-02-01,2007,2,day,0.434,0.1565491513317813
+pal2007lte728,2007-02-01,2007,2,day,3.819,0.6829569263012084
+pal2007lte73,2007-01-10,2007,1,day,0.145,0.0588054866759068
+pal2007lte730,2007-02-01,2007,2,day,3.386,0.642068627341504
+pal2007lte733,2007-02-02,2007,2,day,19.571,1.3132554041850373
+pal2007lte743,2007-02-02,2007,2,day,0.216,0.08493357493671612
+pal2007lte752,2007-02-02,2007,2,day,9.48,1.0203612826477078
+pal2007lte759,2007-02-03,2007,2,night,0.923,0.2839792842384799
+pal2007lte770,2007-02-03,2007,2,day,0.121,0.049605612594973154
+pal2007lte776,2007-02-04,2007,2,day,0.159,0.064083435963596
+pal2007lte785,2007-02-04,2007,2,day,8.307,0.9688097139128582
+pal2007lte792,2007-02-04,2007,2,day,1.398,0.3798491787628298
+pal2007lte8,2007-01-08,2007,1,day,3.445,0.6478717653062325
+pal2007lte83,2007-01-10,2007,1,day,2.585,0.5544891600038189
+pal2007lte93,2007-01-10,2007,1,day,0.201,0.07954300740290607
+pal2008lte107,2008-01-09,2008,1,night,0,0
+pal2008lte12,2008-01-06,2008,1,day,30.172,1.4937646675671452
+pal2008lte121,2008-01-09,2008,1,day,7.146,0.9109444057499789
+pal2008lte136,2008-01-10,2008,1,day,3.783,0.6797003808719642
+pal2008lte144,2008-01-10,2008,1,day,0.39,0.1430148002540951
+pal2008lte150,2008-01-11,2008,1,night,0.026,0.011147360775797477
+pal2008lte157,2008-01-11,2008,1,day,0.027,0.011570443597278159
+pal2008lte168,2008-01-11,2008,1,day,2.406,0.5322446436265823
+pal2008lte185,2008-01-12,2008,1,day,4.378,0.7306207978872828
+pal2008lte202,2008-01-12,2008,1,day,0.096,0.039810554148350386
+pal2008lte206,2008-01-13,2008,1,night,5.851,0.8357539675193834
+pal2008lte213,2008-01-13,2008,1,day,0.054,0.022840610876527823
+pal2008lte228,2008-01-13,2008,1,day,3.615,0.6641717053619309
+pal2008lte23,2008-01-06,2008,1,day,78.833,1.902182449689005
+pal2008lte238,2008-01-13,2008,1,day,14.435,1.1885066338181143
+pal2008lte34,2008-01-07,2008,1,day,79.737,1.9070726079966205
+pal2008lte346,2008-01-16,2008,1,day,1.907,0.46344503177042773
+pal2008lte356,2008-01-16,2008,1,day,70.154,1.8521993194462767
+pal2008lte367,2008-01-16,2008,1,day,128.478,2.112195982392011
+pal2008lte376,2008-01-16,2008,1,day,145.68,2.1663709012885652
+pal2008lte385,2008-01-17,2008,1,night,41.041,1.6236730377395712
+pal2008lte397,2008-01-17,2008,1,day,37.603,1.586621056817303
+pal2008lte407,2008-01-17,2008,1,day,0.195,0.0773679052841565
+pal2008lte417,2008-01-17,2008,1,day,2.735,0.5722906061514175
+pal2008lte426,2008-01-18,2008,1,night,0.03,0.012837224705172217
+pal2008lte435,2008-01-18,2008,1,day,0.283,0.10822665637492848
+pal2008lte445,2008-01-19,2008,1,night,0,0
+pal2008lte45,2008-01-07,2008,1,day,84.922,1.9341043775134725
+pal2008lte457,2008-01-19,2008,1,day,2.587,0.5547313766759666
+pal2008lte465,2008-01-19,2008,1,day,2.801,0.5798978696031039
+pal2008lte475,2008-01-19,2008,1,day,146.833,2.1697713902035023
+pal2008lte482,2008-01-20,2008,1,night,24.634,1.4088163799799345
+pal2008lte488,2008-01-20,2008,1,day,14.269,1.1838105950981346
+pal2008lte501,2008-01-20,2008,1,day,293.265,2.4687386100072013
+pal2008lte513,2008-01-20,2008,1,day,90.723,1.9624782508348164
+pal2008lte525,2008-01-22,2008,1,day,39.594,1.6084618473837275
+pal2008lte529,2008-01-22,2008,1,night,0,0
+pal2008lte546,2008-01-23,2008,1,night,0,0
+pal2008lte548,2008-01-23,2008,1,night,4.632,0.7506626461340558
+pal2008lte558,2008-01-23,2008,1,day,0.287,0.10957854690438665
+pal2008lte56,2008-01-07,2008,1,day,10.13,1.0464951643347082
+pal2008lte568,2008-01-23,2008,1,day,4.967,0.775756037844098
+pal2008lte578,2008-01-24,2008,1,day,7.39,0.9237619608287003
+pal2008lte586,2008-01-24,2008,1,night,0.042,0.017867718963505686
+pal2008lte599,2008-01-24,2008,1,day,0,0
+pal2008lte621,2008-01-26,2008,1,day,1.748,0.4390167283875128
+pal2008lte625,2008-01-26,2008,1,night,0.054,0.022840610876527823
+pal2008lte633,2008-01-27,2008,1,day,45.502,1.6674716318208143
+pal2008lte640,2008-01-27,2008,1,day,0.504,0.17724783625562346
+pal2008lte649,2008-01-28,2008,1,night,0.06,0.02530586526477026
+pal2008lte658,2008-01-28,2008,1,day,98.636,1.9984162839708757
+pal2008lte66,2008-01-07,2008,1,day,3.437,0.6470894287165548
+pal2008lte665,2008-01-28,2008,1,day,2.084,0.4891143693789194
+pal2008lte669,2008-01-29,2008,1,day,1.404,0.3809344633307019
+pal2008lte677,2008-01-29,2008,1,day,0.103,0.04257551244019059
+pal2008lte690,2008-01-30,2008,1,day,62.39,1.802020751771976
+pal2008lte699,2008-01-31,2008,1,day,0.932,0.28600712207947454
+pal2008lte708,2008-01-31,2008,1,day,1041.955,3.018265570484736
+pal2008lte715,2008-01-31,2008,1,day,36.57,1.5748411950633847
+pal2008lte719,2008-02-01,2008,2,day,0.105,0.04336227802112949
+pal2008lte727,2008-02-02,2008,2,day,211.314,2.326978632518709
+pal2008lte74,2008-01-08,2008,1,night,0.029,0.012415374762432893
+pal2008lte8,2008-01-05,2008,1,day,2.378,0.5286596452349899
+pal2008lte87,2008-01-08,2008,1,day,6.293,0.8629062135629981
+pal2008lte98,2008-01-08,2008,1,day,0.567,0.1950689964685901
+pal2009lte141,2009-01-11,2009,1,day,0.042,0.017867718963505686
+pal2009lte152,2009-01-12,2009,1,day,0.2,0.07918124604762482
+pal2009lte168,2009-01-12,2009,1,day,1.546,0.4058583993176366
+pal2009lte169,2009-01-12,2009,1,day,36.625,1.5754765086018998
+pal2009lte182,2009-01-13,2009,1,day,0.38,0.13987908640123647
+pal2009lte192,2009-01-13,2009,1,day,2.425,0.5346605758284444
+pal2009lte209,2009-01-14,2009,1,day,93.829,1.9769411708189513
+pal2009lte218,2009-01-14,2009,1,day,98.148,1.9962839581003302
+pal2009lte230,2009-01-15,2009,1,day,1.219,0.3461573022320084
+pal2009lte246,2009-01-16,2009,1,day,2.557,0.5510838651857803
+pal2009lte260,2009-01-16,2009,1,day,4.987,0.7772092581456849
+pal2009lte262,2009-01-16,2009,1,day,2.16,0.49968708261840383
+pal2009lte281,2009-01-17,2009,1,day,1.055,0.312811826212088
+pal2009lte295,2009-01-17,2009,1,day,17.826,1.2747580543520085
+pal2009lte302,2009-01-18,2009,1,day,1.629,0.4197905861063627
+pal2009lte314,2009-01-19,2009,1,day,5.66,0.8234742291703011
+pal2009lte316,2009-01-19,2009,1,day,17.78,1.2736955879300922
+pal2009lte33,2009-01-07,2009,1,day,0.8,0.25527250510330607
+pal2009lte348,2009-01-20,2009,1,day,11.576,1.0995425286953326
+pal2009lte354,2009-01-21,2009,1,day,0.91,0.28103336724772754
+pal2009lte371,2009-01-21,2009,1,day,7.99,0.9537596917332287
+pal2009lte372,2009-01-21,2009,1,day,32.477,1.524746532301446
+pal2009lte381,2009-01-22,2009,1,day,3.312,0.6346787521786822
+pal2009lte384,2009-01-22,2009,1,night,1.566,0.4092566520389097
+pal2009lte400,2009-01-22,2009,1,day,6.764,0.8900855267163252
+pal2009lte409,2009-01-23,2009,1,night,0.202,0.0799044676667207
+pal2009lte436,2009-01-26,2009,1,day,1.008,0.30276370847298173
+pal2009lte437,2009-01-26,2009,1,day,0.764,0.24649858079580092
+pal2009lte447,2009-01-27,2009,1,day,4.024,0.7010496307291397
+pal2009lte473,2009-01-27,2009,1,day,24.265,1.4025193025742493
+pal2009lte476,2009-01-27,2009,1,day,1.895,0.461648568063455
+pal2009lte493,2009-01-28,2009,1,day,40.19,1.6147917919564174
+pal2009lte494,2009-01-28,2009,1,day,3.8,0.6812412373755872
+pal2009lte53,2009-01-08,2009,1,day,0.125,0.05115252244738129
+pal2009lte9,2009-01-06,2009,1,day,5.263,0.7967824117013078
+pal2010lte126,2010-01-10,2010,1,day,0.339,0.12678057701200898
+pal2010lte15,2010-01-06,2010,1,day,0.031,0.01325866528351651
+pal2010lte152,2010-01-11,2010,1,day,8.563,0.9805941557762202
+pal2010lte161,2010-01-11,2010,1,day,4.251,0.720242018287057
+pal2010lte172,2010-01-12,2010,1,day,0.062,0.026124516745450282
+pal2010lte187,2010-01-12,2010,1,day,5.064,0.7827591926239972
+pal2010lte196,2010-01-12,2010,1,day,1.358,0.37254380075907034
+pal2010lte206,2010-01-13,2010,1,night,11.496,1.0967710165332252
+pal2010lte213,2010-01-13,2010,1,day,0.08,0.03342375548694973
+pal2010lte226,2010-01-13,2010,1,day,0.149,0.06032002868828519
+pal2010lte233,2010-01-14,2010,1,day,0,0
+pal2010lte248,2010-01-14,2010,1,day,0.493,0.17405980772502536
+pal2010lte259,2010-01-16,2010,1,night,0.554,0.19145101446489546
+pal2010lte277,2010-01-16,2010,1,day,16.443,1.241621180791424
+pal2010lte287,2010-01-18,2010,1,day,0.522,0.18241465243455401
+pal2010lte29,2010-01-06,2010,1,day,2.755,0.5746099413401872
+pal2010lte339,2010-01-21,2010,1,day,0.404,0.14736710779378645
+pal2010lte351,2010-01-21,2010,1,day,8.139,0.9608986773266607
+pal2010lte359,2010-01-21,2010,1,day,2.405,0.532117116248804
+pal2010lte373,2010-01-22,2010,1,night,0.063,0.026533264523296733
+pal2010lte387,2010-01-24,2010,1,day,0.186,0.07408468902824378
+pal2010lte400,2010-01-26,2010,1,day,0.065,0.027349607774756507
+pal2010lte421,2010-01-28,2010,1,day,0.733,0.238798562713917
+pal2010lte57,2010-01-07,2010,1,day,1.718,0.4342494523964755
+pal2010lte68,2010-01-07,2010,1,day,1.401,0.38039216005702725
+pal2011lte106,2011-01-11,2011,1,day,15.958,1.2293746309288434
+pal2011lte16,2011-01-07,2011,1,day,7.142,0.9107310980433807
+pal2011lte160,2011-01-12,2011,1,day,0.409,0.14891099310935643
+pal2011lte162,2011-01-12,2011,1,day,0.2,0.07918124604762482
+pal2011lte173,2011-01-13,2011,1,night,0.831,0.26268834430169646
+pal2011lte189,2011-01-13,2011,1,day,0.167,0.06707085604537018
+pal2011lte211,2011-01-14,2011,1,day,0.284,0.10856502373283447
+pal2011lte231,2011-01-14,2011,1,day,2.598,0.5560611590095326
+pal2011lte232,2011-01-14,2011,1,day,14.754,1.1973908411332976
+pal2011lte240,2011-01-14,2011,1,day,2.569,0.5525465479556604
+pal2011lte252,2011-01-15,2011,1,night,13.641,1.1655707406329001
+pal2011lte263,2011-01-15,2011,1,day,698.445,2.8447535699563513
+pal2011lte274,2011-01-15,2011,1,day,4.738,0.7587605439099796
+pal2011lte281,2011-01-15,2011,1,day,4.355,0.7287594751678745
+pal2011lte288,2011-01-17,2011,1,day,0.999,0.30081279411811696
+pal2011lte321,2011-01-18,2011,1,day,30.471,1.4979105429634876
+pal2011lte327,2011-01-18,2011,1,day,10.03,1.0425755124401905
+pal2011lte336,2011-01-18,2011,1,day,2.291,0.5173278822943735
+pal2011lte337,2011-01-18,2011,1,day,5.055,0.782114147479071
+pal2011lte34,2011-01-08,2011,1,day,3.479,0.6511810624446879
+pal2011lte344,2011-01-18,2011,1,day,72.526,1.8664409398982984
+pal2011lte345,2011-01-19,2011,1,day,45.716,1.6694656497657867
+pal2011lte350,2011-01-19,2011,1,night,5.047,0.7815399686059417
+pal2011lte351,2011-01-19,2011,1,night,4.154,0.712144414214886
+pal2011lte361,2011-01-19,2011,1,day,9.08,1.0034605321095065
+pal2011lte362,2011-01-19,2011,1,day,12.995,1.1459729028021812
+pal2011lte367,2011-01-20,2011,1,day,0.657,0.21932250841933673
+pal2011lte369,2011-01-20,2011,1,day,1.136,0.32960124835651883
+pal2011lte374,2011-01-20,2011,1,day,11.555,1.0988167170489413
+pal2011lte376,2011-01-20,2011,1,day,6.262,0.8610562445768735
+pal2011lte380,2011-01-20,2011,1,day,2.009,0.47842218774008055
+pal2011lte382,2011-01-20,2011,1,day,2.132,0.49582175338590584
+pal2011lte386,2011-01-20,2011,1,day,0.689,0.2276296495710087
+pal2011lte388,2011-01-20,2011,1,day,0.995,0.2999429000227671
+pal2011lte396,2011-01-21,2011,1,day,3.754,0.6770591773921615
+pal2011lte417,2011-01-25,2011,1,night,9.894,1.0371873709371133
+pal2011lte439,2011-01-25,2011,1,day,2.962,0.5979144712025283
+pal2011lte44,2011-01-08,2011,1,day,0.735,0.23929947912689248
+pal2011lte459,2011-01-25,2011,1,day,0.857,0.2688119037397804
+pal2011lte465,2011-01-26,2011,1,night,0.073,0.030599721965951062
+pal2011lte473,2011-01-26,2011,1,day,1.714,0.4336098433237182
+pal2011lte502,2011-01-27,2011,1,day,12.647,1.1350371915496176
+pal2011lte518,2011-01-28,2011,1,night,3.291,0.6325585145326719
+pal2011lte519,2011-01-28,2011,1,night,5.586,0.818621726375889
+pal2011lte534,2011-01-28,2011,1,day,0.454,0.162564406523019
+pal2011lte535,2011-01-28,2011,1,day,0.764,0.24649858079580092
+pal2011lte544,2011-01-29,2011,1,day,36.671,1.576007148971984
+pal2011lte545,2011-01-29,2011,1,day,38.647,1.5982103308027371
+pal2011lte548,2011-01-29,2011,1,day,4.843,0.7666358863102676
+pal2011lte549,2011-01-29,2011,1,day,11.505,1.0970836960665213
+pal2011lte554,2011-01-30,2011,1,night,22.373,1.3687144590686275
+pal2011lte555,2011-01-30,2011,1,night,26.568,1.4404052601039532
+pal2011lte562,2011-01-30,2011,1,day,39.139,1.6035665485228734
+pal2011lte566,2011-01-31,2011,1,night,4.323,0.7261564661727549
+pal2011lte60,2011-01-09,2011,1,day,0.451,0.1616674124377359
+pal2011lte74,2011-01-09,2011,1,day,40.149,1.6143592854836724
+pal2011lte89,2011-01-10,2011,1,day,0.443,0.15926633109349422
+pal2011lte90,2011-01-10,2011,1,day,0.894,0.2773799746672546
+pal2012lte104,2012-01-07,2012,1,day,115.723,2.0671564412114942
+pal2012lte125,2012-01-08,2012,1,night,0,0
+pal2012lte132,2012-01-08,2012,1,day,3.957,0.695218918905151
+pal2012lte158,2012-01-09,2012,1,night,49.754,1.7054702753432998
+pal2012lte172,2012-01-09,2012,1,day,9.718,1.030113752707593
+pal2012lte183,2012-01-11,2012,1,day,93.481,1.9753444812627783
+pal2012lte199,2012-01-11,2012,1,day,33.702,1.5403545054517782
+pal2012lte222,2012-01-11,2012,1,day,0.224,0.08778141780954238
+pal2012lte227,2012-01-12,2012,1,night,0,0
+pal2012lte254,2012-01-12,2012,1,day,10.582,1.0637835605973551
+pal2012lte269,2012-01-12,2012,1,day,304.358,2.4848093024564726
+pal2012lte274,2012-01-13,2012,1,day,2765.339,3.441905399492789
+pal2012lte284,2012-01-13,2012,1,day,679.955,2.8331184130913223
+pal2012lte297,2012-01-13,2012,1,day,55.532,1.7522943502858586
+pal2012lte305,2012-01-15,2012,1,day,94.483,1.979926055736662
+pal2012lte320,2012-01-16,2012,1,day,1204.995,3.0813455072428466
+pal2012lte325,2012-01-16,2012,1,day,120.851,2.085829097679133
+pal2012lte335,2012-01-17,2012,1,day,32.674,1.527294707559675
+pal2012lte339,2012-01-17,2012,1,day,26.332,1.4366714120506496
+pal2012lte343,2012-01-18,2012,1,day,3.785,0.6798819421128623
+pal2012lte349,2012-01-18,2012,1,day,8.995,0.9997827984541356
+pal2012lte379,2012-01-20,2012,1,night,0.014,0.006037954997317177
+pal2012lte384,2012-01-20,2012,1,day,28.849,1.4749297859928023
+pal2012lte396,2012-01-22,2012,1,day,7.473,0.9280372064068829
+pal2012lte403,2012-01-23,2012,1,day,325.146,2.5134120565099543
+pal2012lte432,2012-01-23,2012,1,day,85.046,1.9347306861882174
+pal2012lte437,2012-01-24,2012,1,day,50.589,1.7125571096005605
+pal2012lte454,2012-01-24,2012,1,day,4.487,0.7393349601960794
+pal2012lte465,2012-01-24,2012,1,day,148.521,2.1747021929523007
+pal2012lte476,2012-01-25,2012,1,day,0.261,0.10071508657308166
+pal2012lte491,2012-01-25,2012,1,day,33.916,1.5430244848207617
+pal2012lte502,2012-01-26,2012,1,day,3.474,0.6506959797606109
+pal2012lte514,2012-01-26,2012,1,day,7.718,0.9404168646816652
+pal2012lte525,2012-01-27,2012,1,day,0.133,0.05422990986339725
+pal2012lte538,2012-01-27,2012,1,day,6.599,0.8807564445102105
+pal2012lte54,2012-01-06,2012,1,day,23.549,1.3900338058957593
+pal2012lte547,2012-01-28,2012,1,day,3.979,0.6971421262754596
+pal2012lte551,2012-01-28,2012,1,day,200.497,2.304268584506187
+pal2012lte575,2012-01-31,2012,1,night,0.359,0.1332194567324943
+pal2012lte65,2012-01-06,2012,1,day,55.664,1.753307228790333
+pal2012lte71,2012-01-07,2012,1,night,3.189,0.6221103603612195
+pal2013lte104,2013-01-09,2013,1,night,23.596,1.3908644843726108
+pal2013lte11,2013-01-05,2013,1,day,129.708,2.1163021694438102
+pal2013lte112,2013-01-09,2013,1,day,11.014,1.0796876276113365
+pal2013lte128,2013-01-10,2013,1,day,142.149,2.1557883185050235
+pal2013lte136,2013-01-10,2013,1,day,0.26,0.10037054511756291
+pal2013lte145,2013-01-10,2013,1,day,14.749,1.19725298299739
+pal2013lte147,2013-01-10,2013,1,day,33.724,1.5406297475490236
+pal2013lte153,2013-01-10,2013,1,day,63.592,1.8101787320662392
+pal2013lte16,2013-01-06,2013,1,day,171.931,2.237872852858373
+pal2013lte167,2013-01-11,2013,1,day,87.935,1.949072709400645
+pal2013lte171,2013-01-11,2013,1,day,27.519,1.4551342931960993
+pal2013lte185,2013-01-12,2013,1,night,19.46,1.3109056293761414
+pal2013lte196,2013-01-12,2013,1,day,28.437,1.4688935478586151
+pal2013lte202,2013-01-12,2013,1,day,7.79,0.9439888750737718
+pal2013lte214,2013-01-14,2013,1,night,0.836,0.2638726768652236
+pal2013lte221,2013-01-14,2013,1,day,32.435,1.524201327535919
+pal2013lte227,2013-01-15,2013,1,day,128.673,2.112849558414903
+pal2013lte241,2013-01-15,2013,1,day,0.251,0.0972573096934199
+pal2013lte243,2013-01-15,2013,1,day,0.209,0.0824263008607719
+pal2013lte253,2013-01-16,2013,1,day,0.373,0.1376705372367551
+pal2013lte268,2013-01-16,2013,1,day,7.58,0.9334872878487055
+pal2013lte278,2013-01-17,2013,1,day,307.424,2.489148165299866
+pal2013lte286,2013-01-17,2013,1,day,0.093,0.03862016194970278
+pal2013lte295,2013-01-18,2013,1,day,0.215,0.08457627793433102
+pal2013lte307,2013-01-20,2013,1,day,42.3,1.6364878963533653
+pal2013lte314,2013-01-21,2013,1,night,0.185,0.07371835034612269
+pal2013lte320,2013-01-21,2013,1,day,77.996,1.8976051011397037
+pal2013lte328,2013-01-21,2013,1,day,82.354,1.920926445630671
+pal2013lte330,2013-01-21,2013,1,day,16.672,1.2472857028633786
+pal2013lte334,2013-01-22,2013,1,day,57.929,1.7703290713655246
+pal2013lte340,2013-01-23,2013,1,day,5.002,0.7782959910888338
+pal2013lte345,2013-01-23,2013,1,day,2.621,0.5588285248170117
+pal2013lte360,2013-01-23,2013,1,day,1.981,0.47436197603263075
+pal2013lte368,2013-01-24,2013,1,day,4.322,0.7260748702153701
+pal2013lte385,2013-01-24,2013,1,day,1.074,0.3168087520530221
+pal2013lte395,2013-01-25,2013,1,day,285.751,2.457504941111142
+pal2013lte405,2013-01-26,2013,1,night,6.789,0.89148170383952
+pal2013lte411,2013-01-26,2013,1,day,102.698,2.015770380329833
+pal2013lte44,2013-01-06,2013,1,day,139.401,2.1473702010483113
+pal2013lte452,2013-01-30,2013,1,day,124.024,2.0969933895446906
+pal2013lte62,2013-01-07,2013,1,day,1.212,0.3447851226326606
+pal2013lte90,2013-01-08,2013,1,day,11.459,1.0954831858295406
+pal2014lte104,2014-01-11,2014,1,day,7.261,0.9170326221623933
+pal2014lte115,2014-01-11,2014,1,day,14.213,1.1822148652675355
+pal2014lte127,2014-01-12,2014,1,day,28.679,1.472449263797072
+pal2014lte134,2014-01-12,2014,1,day,2.841,0.5844443071651761
+pal2014lte151,2014-01-13,2014,1,day,15.657,1.221596785740263
+pal2014lte158,2014-01-13,2014,1,day,2.484,0.5420781463356256
+pal2014lte164,2014-01-13,2014,1,day,2.364,0.5268559871258746
+pal2014lte17,2014-01-08,2014,1,day,2.394,0.530711837981657
+pal2014lte171,2014-01-14,2014,1,day,14.815,1.1990691962517417
+pal2014lte178,2014-01-15,2014,1,day,62.245,1.8010261969112076
+pal2014lte183,2014-01-15,2014,1,day,3.035,0.6058435390580892
+pal2014lte192,2014-01-16,2014,1,day,3.065,0.609060549930087
+pal2014lte199,2014-01-16,2014,1,day,0.129,0.05269394192496786
+pal2014lte209,2014-01-17,2014,1,day,2.733,0.5720579899263045
+pal2014lte218,2014-01-17,2014,1,day,0.291,0.11092624226642028
+pal2014lte232,2014-01-20,2014,1,night,1.245,0.351216345339342
+pal2014lte239,2014-01-20,2014,1,day,82.943,1.923984486202179
+pal2014lte250,2014-01-21,2014,1,day,0.253,0.09795107099415004
+pal2014lte263,2014-01-21,2014,1,day,735.592,2.86722699774626
+pal2014lte270,2014-01-22,2014,1,day,0.851,0.2674064187529041
+pal2014lte291,2014-01-23,2014,1,day,1.046,0.3109056293761414
+pal2014lte297,2014-01-24,2014,1,night,35.719,1.5648908451763832
+pal2014lte301,2014-01-24,2014,1,day,13.084,1.1487260163981465
+pal2014lte314,2014-01-25,2014,1,day,0.312,0.11793383503964149
+pal2014lte321,2014-01-27,2014,1,day,0.864,0.27044590801796253
+pal2014lte33,2014-01-08,2014,1,day,1.153,0.3330440298234872
+pal2014lte46,2014-01-09,2014,1,day,60.048,1.785671440546395
+pal2014lte58,2014-01-09,2014,1,day,0.824,0.26102483399239734
+pal2014lte71,2014-01-10,2014,1,day,0.111,0.04571405894086761
+pal2014lte78,2014-01-10,2014,1,day,0.155,0.06258198422816312
+pal2014lte9,2014-01-07,2014,1,day,0.363,0.13449585583467355
+pal2014lte90,2014-01-10,2014,1,day,54.648,1.7454495603226186
+pal2014lte98,2014-01-11,2014,1,day,0.172,0.06892761168207182
+pal2015lte116,2015-01-11,2015,1,night,0.138,0.05614226205905233
+pal2015lte124,2015-01-11,2015,1,day,44.714,1.6600492239566549
+pal2015lte131,2015-01-12,2015,1,day,1.337,0.36865871239222686
+pal2015lte147,2015-01-12,2015,1,day,1.584,0.4122925093230465
+pal2015lte148,2015-01-12,2015,1,day,22.102,1.3636495795183228
+pal2015lte168,2015-01-13,2015,1,night,0.043,0.018284308426530838
+pal2015lte174,2015-01-13,2015,1,day,1.861,0.4565178578052626
+pal2015lte205,2015-01-15,2015,1,day,3.407,0.6441430505099189
+pal2015lte21,2015-01-06,2015,1,day,0.035,0.014940349792936524
+pal2015lte215,2015-01-15,2015,1,day,5.514,0.8138477542288544
+pal2015lte226,2015-01-16,2015,1,day,3.801,0.6813317059691658
+pal2015lte231,2015-01-16,2015,1,day,7.467,0.9277295597716544
+pal2015lte232,2015-01-16,2015,1,day,0.363,0.13449585583467355
+pal2015lte243,2015-01-18,2015,1,day,4.49,0.7395723444500919
+pal2015lte250,2015-01-19,2015,1,day,7.37,0.92272545799326
+pal2015lte259,2015-01-20,2015,1,day,1.912,0.4641913706409996
+pal2015lte270,2015-01-21,2015,1,day,1.994,0.4762517960070335
+pal2015lte289,2015-01-22,2015,1,day,3.408,0.6442415858437286
+pal2015lte302,2015-01-22,2015,1,day,4.621,0.7498135852929378
+pal2015lte320,2015-01-23,2015,1,day,4.313,0.7253398159097372
+pal2015lte331,2015-01-23,2015,1,day,13.992,1.1758595735437032
+pal2015lte34,2015-01-07,2015,1,day,6.346,0.8660509240092749
+pal2015lte345,2015-01-24,2015,1,day,42.906,1.64252387306301
+pal2015lte356,2015-01-24,2015,1,day,117.803,2.074827407539076
+pal2015lte362,2015-01-25,2015,1,day,20.72,1.3368598209168092
+pal2015lte369,2015-01-25,2015,1,day,155.985,2.1958581573235865
+pal2015lte371,2015-01-25,2015,1,day,930.2,2.969042967305813
+pal2015lte381,2015-01-26,2015,1,day,148.268,2.173966713867183
+pal2015lte382,2015-01-26,2015,1,day,604.132,2.7818501194767338
+pal2015lte408,2015-01-27,2015,1,day,133.261,2.127949877564115
+pal2015lte427,2015-01-28,2015,1,night,0,0
+pal2015lte433,2015-01-29,2015,1,day,5.012,0.7790189719148706
+pal2015lte45,2015-01-08,2015,1,day,0.043,0.018284308426530838
+pal2015lte64,2015-01-09,2015,1,day,0,0
+pal2015lte84,2015-01-10,2015,1,day,0,0
+pal2015lte9,2015-01-05,2015,1,day,8.345,0.9705793057148508
+pal2015lte96,2015-01-10,2015,1,day,0.088,0.03662889536216113
+pal2016lte100,2016-01-17,2016,1,night,17.655,1.2707952533768478
+pal2016lte110,2016-01-17,2016,1,day,0.983,0.29732271420530265
+pal2016lte121,2016-01-17,2016,1,day,45.743,1.669716582288874
+pal2016lte135,2016-01-18,2016,1,night,8.047,0.9565045903166975
+pal2016lte154,2016-01-18,2016,1,day,4.538,0.7433529514095557
+pal2016lte16,2016-01-11,2016,1,day,91.002,1.9637972684273244
+pal2016lte163,2016-01-19,2016,1,day,0.585,0.2000292665537703
+pal2016lte184,2016-01-20,2016,1,day,9.056,1.0024252646779013
+pal2016lte196,2016-01-21,2016,1,day,2.401,0.5316066319327222
+pal2016lte205,2016-01-21,2016,1,day,2.797,0.5794405971397971
+pal2016lte221,2016-01-22,2016,1,day,7.448,0.9267539051897371
+pal2016lte235,2016-01-22,2016,1,day,2.703,0.5685537120494426
+pal2016lte25,2016-01-12,2016,1,day,9.972,1.0402857989324918
+pal2016lte250,2016-01-25,2016,1,day,3.883,0.6886867242841235
+pal2016lte257,2016-01-25,2016,1,day,5.976,0.8436064719245108
+pal2016lte269,2016-01-25,2016,1,day,10.525,1.0616409340616857
+pal2016lte276,2016-01-26,2016,1,day,5.481,0.811642021453151
+pal2016lte285,2016-01-27,2016,1,day,1.319,0.36530074863798734
+pal2016lte293,2016-01-27,2016,1,day,0.336,0.1258064581395269
+pal2016lte299,2016-01-28,2016,1,day,0.377,0.13893394025692368
+pal2016lte309,2016-01-28,2016,1,day,0.96,0.292256071356476
+pal2016lte32,2016-01-12,2016,1,day,4.554,0.7446058754142388
+pal2016lte323,2016-01-30,2016,1,day,0.723,0.23628527744802844
+pal2016lte395,2016-02-07,2016,2,night,168.224,2.2284619563886485
+pal2016lte415,2016-02-07,2016,2,day,40.788,1.6210515860335737
+pal2016lte446,2016-02-08,2016,2,day,21.01,1.342620042553348
+pal2016lte60,2016-01-15,2016,1,night,17.79,1.2739267801005256
+pal2016lte74,2016-01-15,2016,1,day,0.137,0.05576046468773478
+pal2016lte82,2016-01-16,2016,1,day,42.946,1.6429193513782805
+par1990bon,1990-02-01,1990,2,NA,NA,NA
+pol1981jaz100a,1981-03-10,1981,3,NA,0,0
+pol1981jaz100b,1981-03-10,1981,3,NA,NA,NA
+pol1981jaz100com,1981-03-10,1981,3,NA,0,0
+pol1981jaz102,1981-03-11,1981,3,NA,0,0
+pol1981jaz103,1981-03-11,1981,3,NA,0,0
+pol1981jaz104a,1981-03-11,1981,3,NA,0,0
+pol1981jaz104b,1981-03-11,1981,3,NA,NA,NA
+pol1981jaz104com,1981-03-11,1981,3,NA,0,0
+pol1981jaz105,1981-03-11,1981,3,NA,0,0
+pol1981jaz108a,1981-03-12,1981,3,NA,0,0
+pol1981jaz108b,1981-03-12,1981,3,NA,NA,NA
+pol1981jaz108com,1981-03-12,1981,3,NA,0,0
+pol1981jaz11,1981-02-16,1981,2,NA,0,0
+pol1981jaz14,1981-02-17,1981,2,NA,0,0
+pol1981jaz16,1981-02-18,1981,2,NA,0,0
+pol1981jaz17a,1981-02-18,1981,2,NA,0,0
+pol1981jaz17b,1981-02-18,1981,2,NA,NA,NA
+pol1981jaz17com,1981-02-18,1981,2,NA,0,0
+pol1981jaz20a,1981-02-18,1981,2,NA,0,0
+pol1981jaz20b,1981-02-18,1981,2,NA,NA,NA
+pol1981jaz20com,1981-02-18,1981,2,NA,0,0
+pol1981jaz22a,1981-02-19,1981,2,NA,0,0
+pol1981jaz22b,1981-02-19,1981,2,NA,NA,NA
+pol1981jaz22com,1981-02-19,1981,2,NA,0,0
+pol1981jaz25a,1981-02-19,1981,2,NA,0,0
+pol1981jaz25b,1981-02-19,1981,2,NA,NA,NA
+pol1981jaz25com,1981-02-19,1981,2,NA,0,0
+pol1981jaz27a,1981-02-20,1981,2,NA,0,0
+pol1981jaz27b,1981-02-20,1981,2,NA,NA,NA
+pol1981jaz27com,1981-02-20,1981,2,NA,0,0
+pol1981jaz31a,1981-02-20,1981,2,NA,0,0
+pol1981jaz31b,1981-02-20,1981,2,NA,NA,NA
+pol1981jaz31com,1981-02-20,1981,2,NA,0,0
+pol1981jaz32a,1981-02-21,1981,2,NA,0,0
+pol1981jaz32b,1981-02-21,1981,2,NA,NA,NA
+pol1981jaz32com,1981-02-21,1981,2,NA,0,0
+pol1981jaz34a,1981-02-21,1981,2,NA,0,0
+pol1981jaz34b,1981-02-21,1981,2,NA,NA,NA
+pol1981jaz34com,1981-02-21,1981,2,NA,0,0
+pol1981jaz36a,1981-02-22,1981,2,NA,0,0
+pol1981jaz36b,1981-02-22,1981,2,NA,NA,NA
+pol1981jaz36com,1981-02-22,1981,2,NA,0,0
+pol1981jaz38a,1981-02-22,1981,2,NA,0,0
+pol1981jaz38b,1981-02-22,1981,2,NA,NA,NA
+pol1981jaz38com,1981-02-22,1981,2,NA,0,0
+pol1981jaz4,1981-02-15,1981,2,NA,0,0
+pol1981jaz40a,1981-02-23,1981,2,NA,0,0
+pol1981jaz40b,1981-02-23,1981,2,NA,NA,NA
+pol1981jaz40com,1981-02-23,1981,2,NA,0,0
+pol1981jaz44a,1981-02-23,1981,2,NA,0,0
+pol1981jaz44b,1981-02-23,1981,2,NA,NA,NA
+pol1981jaz44com,1981-02-23,1981,2,NA,0,0
+pol1981jaz46a,1981-02-24,1981,2,NA,0,0
+pol1981jaz46b,1981-02-21,1981,2,NA,NA,NA
+pol1981jaz46com,1981-02-21,1981,2,NA,0,0
+pol1981jaz47,1981-02-24,1981,2,NA,0,0
+pol1981jaz49a,1981-02-25,1981,2,NA,0,0
+pol1981jaz49b,1981-02-25,1981,2,NA,NA,NA
+pol1981jaz49com,1981-02-25,1981,2,NA,0,0
+pol1981jaz51,1981-02-25,1981,2,NA,0,0
+pol1981jaz53,1981-02-25,1981,2,NA,0,0
+pol1981jaz55a,1981-02-26,1981,2,NA,0,0
+pol1981jaz55b,1981-02-26,1981,2,NA,NA,NA
+pol1981jaz55com,1981-02-26,1981,2,NA,0,0
+pol1981jaz56,1981-02-26,1981,2,NA,0,0
+pol1981jaz58a,1981-02-26,1981,2,NA,0,0
+pol1981jaz58b,1981-02-26,1981,2,NA,NA,NA
+pol1981jaz58com,1981-02-26,1981,2,NA,0,0
+pol1981jaz6,1981-02-15,1981,2,NA,0,0
+pol1981jaz60a,1981-02-27,1981,2,NA,0,0
+pol1981jaz60b,1981-02-27,1981,2,NA,NA,NA
+pol1981jaz60com,1981-02-27,1981,2,NA,0,0
+pol1981jaz61,1981-02-27,1981,2,NA,0,0
+pol1981jaz62a,1981-02-27,1981,2,NA,0,0
+pol1981jaz62b,1981-02-27,1981,2,NA,NA,NA
+pol1981jaz62com,1981-02-27,1981,2,NA,0,0
+pol1981jaz65a,1981-02-28,1981,2,NA,0,0
+pol1981jaz65b,1981-02-28,1981,2,NA,NA,NA
+pol1981jaz65com,1981-02-28,1981,2,NA,0,0
+pol1981jaz66,1981-02-28,1981,2,NA,0,0
+pol1981jaz67a,1981-02-28,1981,2,NA,0,0
+pol1981jaz67b,1981-02-28,1981,2,NA,NA,NA
+pol1981jaz67com,1981-02-28,1981,2,NA,0,0
+pol1981jaz68a,1981-03-04,1981,3,NA,0,0
+pol1981jaz68b,1981-03-04,1981,3,NA,NA,NA
+pol1981jaz68com,1981-03-04,1981,3,NA,0,0
+pol1981jaz69,1981-03-04,1981,3,NA,0,0
+pol1981jaz70a,1981-03-04,1981,3,NA,0,0
+pol1981jaz70b,1981-03-04,1981,3,NA,NA,NA
+pol1981jaz70com,1981-03-04,1981,3,NA,0,0
+pol1981jaz71,1981-03-05,1981,3,NA,0,0
+pol1981jaz72,1981-03-05,1981,3,NA,0,0
+pol1981jaz73a,1981-03-05,1981,3,NA,0,0
+pol1981jaz73b,1981-03-05,1981,3,NA,NA,NA
+pol1981jaz73com,1981-03-05,1981,3,NA,0,0
+pol1981jaz76,1981-03-05,1981,3,NA,0,0
+pol1981jaz77a,1981-03-05,1981,3,NA,0,0
+pol1981jaz77b,1981-03-05,1981,3,NA,NA,NA
+pol1981jaz77com,1981-03-05,1981,3,NA,0,0
+pol1981jaz79a,1981-03-06,1981,3,NA,0,0
+pol1981jaz79b,1981-03-06,1981,3,NA,NA,NA
+pol1981jaz79com,1981-03-06,1981,3,NA,0,0
+pol1981jaz8,1981-02-16,1981,2,NA,0,0
+pol1981jaz81a,1981-03-06,1981,3,NA,0,0
+pol1981jaz81b,1981-03-06,1981,3,NA,NA,NA
+pol1981jaz81com,1981-03-06,1981,3,NA,0,0
+pol1981jaz82,1981-03-06,1981,3,NA,0,0
+pol1981jaz84a,1981-03-06,1981,3,NA,157.136,2.1990307492591423
+pol1981jaz84b,1981-03-06,1981,3,NA,NA,NA
+pol1981jaz84com,1981-03-06,1981,3,NA,164.279,2.2182176765388335
+pol1981jaz85a,1981-03-07,1981,3,NA,0,0
+pol1981jaz85b,1981-03-07,1981,3,NA,NA,NA
+pol1981jaz85com,1981-03-07,1981,3,NA,0,0
+pol1981jaz87a,1981-03-07,1981,3,NA,0,0
+pol1981jaz87b,1981-03-07,1981,3,NA,NA,NA
+pol1981jaz87com,1981-03-07,1981,3,NA,0,0
+pol1981jaz88a,1981-03-08,1981,3,NA,0,0
+pol1981jaz88b,1981-03-08,1981,3,NA,NA,NA
+pol1981jaz88com,1981-03-08,1981,3,NA,0,0
+pol1981jaz89,1981-03-09,1981,3,NA,0,0
+pol1981jaz90a,1981-03-09,1981,3,NA,0,0
+pol1981jaz90b,1981-03-09,1981,3,NA,NA,NA
+pol1981jaz90com,1981-03-09,1981,3,NA,0,0
+pol1981jaz92,1981-03-09,1981,3,NA,0,0
+pol1981jaz94a,1981-03-09,1981,3,NA,0,0
+pol1981jaz94b,1981-03-09,1981,3,NA,NA,NA
+pol1981jaz94com,1981-03-09,1981,3,NA,0,0
+pol1981jaz96,1981-03-10,1981,3,NA,0,0
+pol1981jaz97a,1981-03-10,1981,3,NA,0,0
+pol1981jaz97b,1981-03-10,1981,3,NA,NA,NA
+pol1981jaz97com,1981-03-10,1981,3,NA,0,0
+pol1981jaz98,1981-03-10,1981,3,NA,0,0
+pol1984wit10,1983-12-10,1983,12,NA,2.447,0.5374412834079476
+pol1984wit102,1983-12-24,1983,12,NA,1.744,0.43838410703471403
+pol1984wit104,1983-12-24,1983,12,NA,1.769,0.4423229557455745
+pol1984wit107,1983-12-25,1983,12,NA,3.559,0.6588695922019623
+pol1984wit111,1983-12-25,1983,12,NA,0.436,0.15715443990628153
+pol1984wit113,1983-12-26,1983,12,NA,0,0
+pol1984wit116,1983-12-26,1983,12,NA,0.5,0.17609125905568124
+pol1984wit119,1983-12-27,1983,12,NA,0,0
+pol1984wit12,1983-12-11,1983,12,NA,1.813,0.4491697321652009
+pol1984wit121,1983-12-27,1983,12,NA,4.232,0.7186677353162109
+pol1984wit123,1983-12-28,1983,12,NA,0.813,0.25839780409550867
+pol1984wit127,1983-12-28,1983,12,NA,1.434,0.3863205738940462
+pol1984wit129,1983-12-29,1983,12,NA,1.053,0.31238894937059186
+pol1984wit131,1983-12-29,1983,12,NA,0,0
+pol1984wit134,1983-12-30,1983,12,NA,0,0
+pol1984wit137,1983-12-30,1983,12,NA,0,0
+pol1984wit140,1983-12-31,1983,12,NA,0,0
+pol1984wit142,1983-12-31,1983,12,NA,0.851,0.2674064187529041
+pol1984wit145,1983-12-31,1983,12,NA,0,0
+pol1984wit150,1984-01-01,1984,1,NA,2.625,0.5593080109070125
+pol1984wit152,1984-01-01,1984,1,NA,1.301,0.36191661866864344
+pol1984wit154,1984-01-02,1984,1,NA,1.839,0.4531653925258572
+pol1984wit156,1984-01-02,1984,1,NA,56.768,1.761687331986636
+pol1984wit159,1984-01-03,1984,1,NA,0,0
+pol1984wit162,1984-01-03,1984,1,NA,6.266,0.8612953935266958
+pol1984wit165,1984-01-04,1984,1,NA,18.304,1.2856473089007294
+pol1984wit168,1984-01-04,1984,1,NA,14.395,1.1873796932392169
+pol1984wit17,1983-12-11,1983,12,NA,0.412,0.14983469671578495
+pol1984wit170,1984-01-05,1984,1,NA,0,0
+pol1984wit173,1984-01-05,1984,1,NA,7.451,0.9269081017054358
+pol1984wit176,1984-01-06,1984,1,NA,24.805,1.4117038634059706
+pol1984wit184,1984-01-06,1984,1,NA,3.669,0.6692238739308058
+pol1984wit200,1984-01-07,1984,1,NA,272.488,2.436938275283285
+pol1984wit208,1984-01-07,1984,1,NA,104.913,2.024949269661401
+pol1984wit210,1984-01-08,1984,1,NA,1.355,0.371990911464915
+pol1984wit213,1984-01-08,1984,1,NA,1.854,0.45545396877862815
+pol1984wit217,1984-01-08,1984,1,NA,0,0
+pol1984wit23,1983-12-12,1983,12,NA,8.908,0.9959859979137994
+pol1984wit29,1983-12-12,1983,12,NA,2.404,0.5319895514125502
+pol1984wit3,1983-12-10,1983,12,NA,1.268,0.35564305022086895
+pol1984wit39,1983-12-13,1983,12,NA,1.738,0.4374334437979712
+pol1984wit41,1983-12-13,1983,12,NA,0.936,0.28690535297237485
+pol1984wit52,1983-12-14,1983,12,NA,12.529,1.1312656967800316
+pol1984wit55,1983-12-14,1983,12,NA,4.518,0.7417816961431667
+pol1984wit58,1983-12-15,1983,12,NA,3.261,0.6295115342004532
+pol1984wit60,1983-12-15,1983,12,NA,0.472,0.16790781000148003
+pol1984wit62,1983-12-16,1983,12,NA,1.533,0.40363518979054797
+pol1984wit64,1983-12-16,1983,12,NA,0.541,0.1878026387184193
+pol1984wit67,1983-12-17,1983,12,NA,0.443,0.15926633109349422
+pol1984wit70,1983-12-17,1983,12,NA,0,0
+pol1984wit73,1983-12-18,1983,12,NA,2.08,0.48855071650044424
+pol1984wit75,1983-12-18,1983,12,NA,0,0
+pol1984wit77,1983-12-19,1983,12,NA,0,0
+pol1984wit79,1983-12-19,1983,12,NA,0.327,0.1228709228644355
+pol1984wit8,1983-12-10,1983,12,NA,2.566,0.5521813388393357
+pol1984wit81,1983-12-20,1983,12,NA,0,0
+pol1984wit83,1983-12-20,1983,12,NA,0,0
+pol1984wit86,1983-12-21,1983,12,NA,0,0
+pol1984wit88,1983-12-21,1983,12,NA,0,0
+pol1984wit91,1983-12-22,1983,12,NA,7.384,0.9234512696396514
+pol1984wit93,1983-12-22,1983,12,NA,2.763,0.5755342183198644
+pol1984wit96,1983-12-23,1983,12,NA,407.65,2.611351503254819
+pol1984wit99,1983-12-23,1983,12,NA,2895.407,3.4618595884072367
+vsi1976xxx10,1975-11-22,1975,11,day,0,0
+vsi1976xxx100,1976-01-04,1976,1,night,NA,NA
+vsi1976xxx103,1976-01-07,1976,1,night,NA,NA
+vsi1976xxx105,1976-01-08,1976,1,night,NA,NA
+vsi1976xxx107,1976-01-09,1976,1,night,NA,NA
+vsi1976xxx11,1975-11-22,1975,11,day,0,0
+vsi1976xxx110,1976-01-23,1976,1,day,0,0
+vsi1976xxx113,1976-01-24,1976,1,day,0,0
+vsi1976xxx115,1976-01-25,1976,1,night,0,0
+vsi1976xxx116,1976-01-25,1976,1,day,0,0
+vsi1976xxx12,1975-11-23,1975,11,day,0,0
+vsi1976xxx120,1976-01-26,1976,1,day,0,0
+vsi1976xxx121,1976-01-26,1976,1,day,0.348,0.129689892199301
+vsi1976xxx123,1976-01-26,1976,1,day,0,0
+vsi1976xxx126,1976-01-27,1976,1,day,0,0
+vsi1976xxx137,1976-01-30,1976,1,day,3.473,0.6505988981726569
+vsi1976xxx138,1976-01-31,1976,1,day,36.093,1.5692919595285866
+vsi1976xxx139,1976-01-31,1976,1,day,61.081,1.792958703916757
+vsi1976xxx140,1976-01-31,1976,1,day,0.59,0.20139712432045143
+vsi1976xxx141,1976-02-01,1976,2,day,4.884,0.7696726640554925
+vsi1976xxx142,1976-02-01,1976,2,day,0.448,0.1607685618611281
+vsi1976xxx143,1976-02-01,1976,2,day,155.027,2.1931997582030687
+vsi1976xxx144,1976-02-01,1976,2,day,2.935,0.5949447366950833
+vsi1976xxx145,1976-02-02,1976,2,day,NA,NA
+vsi1976xxx147,1976-02-02,1976,2,day,0.43,0.1553360374650618
+vsi1976xxx148,1976-02-02,1976,2,day,1.51,0.3996737214810381
+vsi1976xxx149,1976-02-03,1976,2,day,NA,NA
+vsi1976xxx151,1976-02-04,1976,2,day,19.418,1.310013199479453
+vsi1976xxx153,1976-02-04,1976,2,day,0,0
+vsi1976xxx154,1976-02-04,1976,2,day,3.171,0.6202401898458313
+vsi1976xxx155,1976-02-04,1976,2,day,6.659,0.8841720695239128
+vsi1976xxx158,1976-02-05,1976,2,day,1.328,0.3669829759778509
+vsi1976xxx159,1976-02-05,1976,2,day,0,0
+vsi1976xxx160,1976-02-05,1976,2,day,0.046,0.01953168453125545
+vsi1976xxx161,1976-02-06,1976,2,night,495.718,2.696109898185341
+vsi1976xxx162,1976-02-06,1976,2,night,207.478,2.3190602320545697
+vsi1976xxx163,1976-02-06,1976,2,day,1.446,0.38845645270026663
+vsi1976xxx164,1976-02-06,1976,2,day,2.239,0.510410948010177
+vsi1976xxx165,1976-02-06,1976,2,day,0,0
+vsi1976xxx166,1976-02-06,1976,2,day,5.487,0.8120438979302267
+vsi1976xxx167,1976-02-06,1976,2,day,0,0
+vsi1976xxx168a,1976-02-06,1976,2,day,0,0
+vsi1976xxx168aa,1976-02-09,1976,2,day,45.654,1.6688888850268873
+vsi1976xxx168ab,1976-02-09,1976,2,day,NA,NA
+vsi1976xxx168ac,1976-02-10,1976,2,night,634.42,2.8030608802313575
+vsi1976xxx168ad,1976-02-10,1976,2,night,5417.366,3.7338683374287505
+vsi1976xxx168ae,1976-02-10,1976,2,night,NA,NA
+vsi1976xxx168af,1976-02-10,1976,2,night,4.049,0.7032053706954863
+vsi1976xxx168ag,1976-02-10,1976,2,night,6.022,0.8464608251293324
+vsi1976xxx168ah,1976-02-10,1976,2,day,217.886,2.3402179848790987
+vsi1976xxx168ai,1976-02-10,1976,2,day,NA,NA
+vsi1976xxx168aj,1976-02-10,1976,2,day,0,0
+vsi1976xxx168bb,1976-02-06,1976,2,day,54.59,1.74499667403856
+vsi1976xxx168bc,1976-02-06,1976,2,day,27.666,1.457367096534791
+vsi1976xxx168bcom,1976-02-07,1976,2,night,2647.182,3.422947829287119
+vsi1976xxx168bd,1976-02-07,1976,2,night,NA,NA
+vsi1976xxx168be,1976-02-07,1976,2,night,1918.832,3.2832632262737524
+vsi1976xxx168bf,1976-02-07,1976,2,day,NA,NA
+vsi1976xxx168ccom,1976-02-07,1976,2,day,991.659,2.9968000844951326
+vsi1976xxx168cg,1976-02-07,1976,2,day,0.097,0.04020662757471112
+vsi1976xxx168ch,1976-02-07,1976,2,day,32.097,1.519788629954198
+vsi1976xxx168ci,1976-02-07,1976,2,day,956.152,2.9809809111513594
+vsi1976xxx168dcom,1976-02-08,1976,2,day,12977.418,4.11322175750728
+vsi1976xxx168do,1976-02-08,1976,2,day,11445.849,4.058685953903418
+vsi1976xxx168dp,1976-02-08,1976,2,day,35.705,1.5647252284074704
+vsi1976xxx168dq,1976-02-08,1976,2,day,0,0
+vsi1976xxx168dr,1976-02-08,1976,2,day,NA,NA
+vsi1976xxx168ds,1976-02-08,1976,2,day,NA,NA
+vsi1976xxx168j,1976-02-07,1976,2,day,0,0
+vsi1976xxx168k,1976-02-07,1976,2,day,83.229,1.9254616446062958
+vsi1976xxx168l,1976-02-08,1976,2,night,138.591,2.1448574184566427
+vsi1976xxx168m,1976-02-08,1976,2,night,NA,NA
+vsi1976xxx168n,1976-02-08,1976,2,day,34.53,1.5505952074893279
+vsi1976xxx168t,1976-02-08,1976,2,day,NA,NA
+vsi1976xxx168u,1976-02-08,1976,2,day,NA,NA
+vsi1976xxx168v,1976-02-09,1976,2,night,4.805,0.7638022240745926
+vsi1976xxx168w,1976-02-09,1976,2,night,NA,NA
+vsi1976xxx168x,1976-02-09,1976,2,day,NA,NA
+vsi1976xxx168y,1976-02-09,1976,2,day,0,0
+vsi1976xxx168z,1976-02-09,1976,2,day,13047.548,4.115562187484895
+vsi1976xxx169,1976-02-11,1976,2,night,30.645,1.5003051000105654
+vsi1976xxx170,1976-02-11,1976,2,day,4.007,0.6995775913989091
+vsi1976xxx173,1976-02-11,1976,2,day,1077.647,3.0328793399239027
+vsi1976xxx176,1976-02-12,1976,2,day,0.881,0.27438879555037887
+vsi1976xxx183,1976-02-13,1976,2,night,0.241,0.09377178149872986
+vsi1976xxx185,1976-02-13,1976,2,day,0.098,0.04060234011407314
+vsi1976xxx186,1976-02-13,1976,2,day,0.091,0.037824750588341866
+vsi1976xxx189,1976-02-13,1976,2,night,29.964,1.490857058784643
+vsi1976xxx190,1976-02-14,1976,2,night,6.366,0.8672317145188941
+vsi1976xxx193,1976-02-14,1976,2,day,3.704,0.6724673130680822
+vsi1976xxx195,1976-02-14,1976,2,day,0.484,0.17143390094300825
+vsi1976xxx20,1975-11-27,1975,11,day,0,0
+vsi1976xxx21,1975-11-28,1975,11,day,0,0
+vsi1976xxx217,1976-02-24,1976,2,night,0,0
+vsi1976xxx218,1976-02-24,1976,2,night,0.015,0.006466042249231681
+vsi1976xxx219,1976-02-24,1976,2,day,20.449,1.3314070492197811
+vsi1976xxx22,1975-11-28,1975,11,day,0,0
+vsi1976xxx223,1976-02-24,1976,2,day,0,0
+vsi1976xxx225,1976-02-25,1976,2,day,7.965,0.9525502938982017
+vsi1976xxx226,1976-02-25,1976,2,day,0.188,0.07481644064517472
+vsi1976xxx23,1975-11-29,1975,11,day,0.145,0.0588054866759068
+vsi1976xxx239,1976-02-28,1976,2,day,3.036,0.6059511575648728
+vsi1976xxx240,1976-02-29,1976,2,night,0.69,0.22788670461367355
+vsi1976xxx241a,1976-02-29,1976,2,night,0.271,0.10414555055400815
+vsi1976xxx241b,1976-02-29,1976,2,day,1.062,0.3142886609474978
+vsi1976xxx243,1976-02-29,1976,2,day,0,0
+vsi1976xxx244,1976-03-01,1976,3,night,0.028,0.011993114659256938
+vsi1976xxx254,1976-03-03,1976,3,night,0.195,0.0773679052841565
+vsi1976xxx25a,1975-11-30,1975,11,night,1121.881,3.050333733303523
+vsi1976xxx25b,1975-11-30,1975,11,day,60.763,1.7907283827260423
+vsi1976xxx25c,1975-11-30,1975,11,day,0.57,0.19589965240923368
+vsi1976xxx25d,1975-11-30,1975,11,day,18.967,1.3003128179343697
+vsi1976xxx25e,1975-12-01,1975,12,night,0,0
+vsi1976xxx25f,1975-12-01,1975,12,day,0,0
+vsi1976xxx25g,1975-12-01,1975,12,day,0,0
+vsi1976xxx25h,1975-12-01,1975,12,day,0,0
+vsi1976xxx25i,1975-12-02,1975,12,night,0.142,0.05766610390982921
+vsi1976xxx267,1976-03-22,1976,3,day,0,0
+vsi1976xxx26a,1975-12-02,1975,12,day,0,0
+vsi1976xxx26b,1975-12-02,1975,12,day,0,0
+vsi1976xxx26c,1975-12-02,1975,12,day,0,0
+vsi1976xxx26d,1975-12-03,1975,12,night,0,0
+vsi1976xxx26e,1975-12-03,1975,12,day,0,0
+vsi1976xxx26f,1975-12-03,1975,12,day,0.362,0.13417710757676637
+vsi1976xxx26g,1975-12-03,1975,12,day,258.786,2.4146157430165545
+vsi1976xxx26h,1975-12-04,1975,12,night,180.626,2.2591780184597536
+vsi1976xxx26i,1975-12-04,1975,12,day,342.807,2.536314714789733
+vsi1976xxx26j,1975-12-04,1975,12,day,3.066,0.6091673743020197
+vsi1976xxx26k,1975-12-04,1975,12,day,371.094,2.570652667097857
+vsi1976xxx270,1976-03-23,1976,3,night,0.12,0.04921802267018165
+vsi1976xxx276,1976-03-24,1976,3,day,0,0
+vsi1976xxx278,1976-03-25,1976,3,night,0.749,0.24278980947867654
+vsi1976xxx27a,1975-12-05,1975,12,day,302.521,2.4821887444017516
+vsi1976xxx27b,1975-12-05,1975,12,day,7.519,0.9303886182443218
+vsi1976xxx27c,1975-12-05,1975,12,day,1.021,0.30556631351530394
+vsi1976xxx27d,1975-12-06,1975,12,day,7918.263,3.8986847661947377
+vsi1976xxx27e,1975-12-06,1975,12,day,7.904,0.9495851513266519
+vsi1976xxx27f,1975-12-06,1975,12,day,1036.153,3.0158428278976053
+vsi1976xxx27g,1975-12-07,1975,12,night,1.314,0.3643633546157308
+vsi1976xxx27h,1975-12-07,1975,12,day,0.212,0.0835026198302674
+vsi1976xxx27i,1975-12-07,1975,12,day,0.768,0.24748226067705428
+vsi1976xxx27j,1975-12-07,1975,12,day,0.185,0.07371835034612269
+vsi1976xxx283,1976-03-26,1976,3,night,8.615,0.9829492885744987
+vsi1976xxx285,1976-03-26,1976,3,day,0.072,0.03019478535675124
+vsi1976xxx287,1976-03-26,1976,3,day,1587.336,3.2009423793948586
+vsi1976xxx29,1975-12-09,1975,12,day,0.058,0.024485667699166973
+vsi1976xxx292,1976-03-27,1976,3,day,0.476,0.16908635748702275
+vsi1976xxx298,1976-03-29,1976,3,day,0,0
+vsi1976xxx30,1975-12-09,1975,12,day,0.196,0.07773117965239201
+vsi1976xxx310,1976-04-02,1976,4,day,0.125,0.05115252244738129
+vsi1976xxx312,1976-04-03,1976,4,night,10.494,1.0604711927976789
+vsi1976xxx315,1976-04-03,1976,4,day,233.444,2.3700391225505126
+vsi1976xxx320,1976-04-04,1976,4,night,209.629,2.3235181658661315
+vsi1976xxx321,1976-04-04,1976,4,day,50.344,1.7104896997514287
+vsi1976xxx322,1976-04-04,1976,4,night,0.292,0.11126251365906528
+vsi1976xxx324,1976-04-05,1976,4,night,2073.191,3.316848745508532
+vsi1976xxx326,1976-04-05,1976,4,day,4.051,0.7033773685123496
+vsi1976xxx327,1976-04-05,1976,4,night,188.796,2.2782870553187067
+vsi1976xxx330,1976-04-07,1976,4,day,24.296,1.403051852588134
+vsi1976xxx332,1976-04-07,1976,4,night,125.625,2.102519458368337
+vsi1976xxx335,1976-04-09,1976,4,day,3851.66,3.585760683905497
+vsi1976xxx336,1976-04-10,1976,4,night,3899.977,3.591173389736931
+vsi1976xxx337,1976-04-10,1976,4,day,33.682,1.5401041338998749
+vsi1976xxx338,1976-04-10,1976,4,day,7.578,0.9333860419030544
+vsi1976xxx341,1976-04-11,1976,4,day,1.797,0.44669246637152715
+vsi1976xxx346,1976-04-12,1976,4,day,0.23,0.08990511143939793
+vsi1976xxx349,1976-04-15,1976,4,day,NA,NA
+vsi1976xxx35,1975-12-11,1975,12,day,0.088,0.03662889536216113
+vsi1976xxx354,1976-04-15,1976,4,night,9.25,1.0107238653917732
+vsi1976xxx356,1976-04-17,1976,4,night,1.342,0.3695868907363443
+vsi1976xxx357,1976-04-17,1976,4,day,150.219,2.1796063617785886
+vsi1976xxx358,1976-04-18,1976,4,night,0,0
+vsi1976xxx359,1976-04-18,1976,4,day,0,0
+vsi1976xxx36,1975-12-11,1975,12,day,1.294,0.3605934135652489
+vsi1976xxx360,1976-04-19,1976,4,night,0.135,0.05499586152914153
+vsi1976xxx361,1976-04-19,1976,4,day,0.558,0.19256745333654568
+vsi1976xxx362,1976-04-20,1976,4,night,313.787,2.498016788665289
+vsi1976xxx364,1976-04-20,1976,4,night,429.057,2.6335260210328175
+vsi1976xxx365,1976-04-21,1976,4,night,329.639,2.519354078775168
+vsi1976xxx366,1976-04-21,1976,4,night,186.238,2.2723939935235378
+vsi1976xxx367,1976-04-21,1976,4,night,597.534,2.7770888257415636
+vsi1976xxx368,1976-04-21,1976,4,night,56.969,1.763195808595236
+vsi1976xxx369,1976-04-21,1976,4,night,855.837,2.9328982119658176
+vsi1976xxx37,1975-12-12,1975,12,day,1.272,0.3564083270389813
+vsi1976xxx370,1976-04-21,1976,4,night,142.323,2.1563158901290844
+vsi1976xxx371,1976-04-21,1976,4,night,1837.116,3.264372915412569
+vsi1976xxx372,1976-04-21,1976,4,night,223.319,2.3508660602333817
+vsi1976xxx373,1976-04-21,1976,4,night,700.011,2.8457248328052027
+vsi1976xxx374,1976-04-21,1976,4,night,239.215,2.3806001230245517
+vsi1976xxx376,1976-04-21,1976,4,day,717.407,2.8563705553498973
+vsi1976xxx377,1976-04-21,1976,4,night,308.026,2.489995020465296
+vsi1976xxx378,1976-04-21,1976,4,night,359.17,2.556507535865375
+vsi1976xxx379,1976-04-22,1976,4,night,614.289,2.7890791509372015
+vsi1976xxx381,1976-04-22,1976,4,night,3248.742,3.5118488833099484
+vsi1976xxx383,1976-05-07,1976,5,day,NA,NA
+vsi1976xxx386,1976-05-09,1976,5,day,NA,NA
+vsi1976xxx388,1976-05-10,1976,5,night,NA,NA
+vsi1976xxx389,1976-05-11,1976,5,day,NA,NA
+vsi1976xxx390,1976-05-11,1976,5,night,NA,NA
+vsi1976xxx391,1976-05-12,1976,5,night,NA,NA
+vsi1976xxx392,1976-05-12,1976,5,day,NA,NA
+vsi1976xxx394,1976-05-13,1976,5,day,NA,NA
+vsi1976xxx395,1976-05-13,1976,5,day,NA,NA
+vsi1976xxx397,1976-05-14,1976,5,night,NA,NA
+vsi1976xxx4,1975-11-19,1975,11,day,0,0
+vsi1976xxx400,1976-05-14,1976,5,day,NA,NA
+vsi1976xxx401,1976-05-14,1976,5,day,NA,NA
+vsi1976xxx402,1976-05-15,1976,5,day,NA,NA
+vsi1976xxx403,1976-05-15,1976,5,night,NA,NA
+vsi1976xxx43,1975-12-13,1975,12,day,2.387,0.5298151966446303
+vsi1976xxx44,1975-12-13,1975,12,night,0.717,0.2347702951609165
+vsi1976xxx45,1975-12-14,1975,12,day,0.135,0.05499586152914153
+vsi1976xxx46,1975-12-14,1975,12,day,1.741,0.4379090355394984
+vsi1976xxx47,1975-12-14,1975,12,night,144.943,2.1641832693529555
+vsi1976xxx48,1975-12-15,1975,12,day,0.531,0.18497519069826104
+vsi1976xxx5,1975-11-19,1975,11,night,0,0
+vsi1976xxx50,1975-12-22,1975,12,day,1.717,0.4340896384178908
+vsi1976xxx52,1975-12-22,1975,12,day,36.337,1.5721394197783738
+vsi1976xxx53,1975-12-23,1975,12,night,355.049,2.551509770347099
+vsi1976xxx54,1975-12-23,1975,12,day,181.44,2.261120063568901
+vsi1976xxx55,1975-12-23,1975,12,day,77.318,1.893861588396023
+vsi1976xxx58,1975-12-23,1975,12,day,28.089,1.46372879165109
+vsi1976xxx59,1975-12-24,1975,12,night,55.988,1.7557834156296432
+vsi1976xxx6,1975-11-20,1975,11,day,0,0
+vsi1976xxx60,1975-12-24,1975,12,day,6.299,0.863263363650481
+vsi1976xxx61,1975-12-24,1975,12,day,0,0
+vsi1976xxx62,1975-12-24,1975,12,day,0,0
+vsi1976xxx63,1975-12-26,1975,12,day,46.502,1.6767118953233557
+vsi1976xxx64,1975-12-26,1975,12,day,1.277,0.3573630306151428
+vsi1976xxx65,1975-12-26,1975,12,day,0.584,0.19975517725347472
+vsi1976xxx66,1975-12-27,1975,12,day,0.377,0.13893394025692368
+vsi1976xxx68,1975-12-27,1975,12,day,0,0
+vsi1976xxx69,1975-12-27,1975,12,day,13.375,1.157607853361668
+vsi1976xxx7,1975-11-20,1975,11,day,0,0
+vsi1976xxx70,1975-12-27,1975,12,night,0.566,0.19479175772192453
+vsi1976xxx71,1975-12-28,1975,12,day,0,0
+vsi1976xxx73,1975-12-28,1975,12,day,0,0
+vsi1976xxx74,1975-12-28,1975,12,day,0.214,0.08421868673923877
+vsi1976xxx75,1975-12-30,1975,12,day,20566.105,4.313173165248068
+vsi1976xxx8,1975-11-21,1975,11,day,0,0
+vsi1976xxx80,1975-12-31,1975,12,day,22.907,1.3785250815449142
+vsi1976xxx81,1975-12-31,1975,12,day,0.032,0.013679697291192559
+vsi1976xxx82,1975-12-31,1975,12,day,0.487,0.17231096852195424
+vsi1976xxx86,1976-01-01,1976,1,day,0.022,0.009450895798693935
+vsi1976xxx87,1976-01-01,1976,1,day,0.05,0.021189299069938092
+vsi1976xxx88,1976-01-01,1976,1,day,0.016,0.006893707947900456
+vsi1976xxx89,1976-01-01,1976,1,day,5.033,0.7805333253164044
+vsi1976xxx9,1975-11-21,1975,11,day,0,0
+vsi1976xxx90,1976-01-01,1976,1,day,0.01,0.004321373782642578
+vsi1976xxx91,1976-01-01,1976,1,day,0.033,0.014100321519620544
+vsi1976xxx93,1976-01-02,1976,1,day,0.231,0.09025805293131633
+vsi1976xxx95,1976-01-02,1976,1,day,0.224,0.08778141780954238
+vsi1976xxx98,1976-01-03,1976,1,night,NA,NA
+vsi1978xxx102,1977-11-29,1977,11,day,0.11,0.04532297878665747
+vsi1978xxx103,1977-11-29,1977,11,day,0.046,0.01953168453125545
+vsi1978xxx104,1977-11-29,1977,11,day,32.069,1.5194210621332878
+vsi1978xxx107,1977-11-30,1977,11,day,0,0
+vsi1978xxx108,1977-11-30,1977,11,day,0,0
+vsi1978xxx109,1977-11-30,1977,11,day,0.156,0.06295783408451021
+vsi1978xxx110,1977-12-01,1977,12,day,1.313,0.3641756327706194
+vsi1978xxx114,1977-12-02,1977,12,day,1306.124,3.116316788775129
+vsi1978xxx115,1977-12-02,1977,12,day,0.348,0.129689892199301
+vsi1978xxx116,1977-12-02,1977,12,day,0.07,0.029383777685209667
+vsi1978xxx117,1977-12-02,1977,12,day,1.387,0.37785241900675454
+vsi1978xxx122,1977-12-04,1977,12,day,0.09,0.03742649794062367
+vsi1978xxx123,1977-12-04,1977,12,day,0,0
+vsi1978xxx124,1977-12-05,1977,12,day,1.386,0.3776704393343231
+vsi1978xxx125,1977-12-05,1977,12,day,1.55,0.4065401804339551
+vsi1978xxx127,1977-12-05,1977,12,day,0.094,0.039017321997412
+vsi1978xxx128,1977-12-05,1977,12,day,61.531,1.7960953740030619
+vsi1978xxx129,1977-12-05,1977,12,day,0,0
+vsi1978xxx13,1977-11-06,1977,11,day,0.51,0.17897694729316943
+vsi1978xxx136,1977-12-07,1977,12,day,0.066,0.027757204690553483
+vsi1978xxx15,1977-11-07,1977,11,day,0.077,0.03221570329798157
+vsi1978xxx158,1978-01-05,1978,1,night,7.279,0.9179778825929082
+vsi1978xxx163,1978-01-06,1978,1,day,0.731,0.23829706787539381
+vsi1978xxx166,1978-01-06,1978,1,day,0.145,0.0588054866759068
+vsi1978xxx167,1978-01-06,1978,1,day,0.471,0.16761267272753014
+vsi1978xxx169,1978-01-06,1978,1,day,0.875,0.27300127206373764
+vsi1978xxx170,1978-01-07,1978,1,day,30.46,1.497758718287268
+vsi1978xxx171,1978-01-07,1978,1,day,0.218,0.08564728829685654
+vsi1978xxx174,1978-01-07,1978,1,day,16.975,1.2546688990549202
+vsi1978xxx177,1978-01-07,1978,1,day,0.527,0.1838390370564212
+vsi1978xxx182,1978-01-08,1978,1,day,0.073,0.030599721965951062
+vsi1978xxx183,1978-01-08,1978,1,day,6.017,0.8461514765288155
+vsi1978xxx184,1978-01-08,1978,1,day,1.271,0.35621713421973517
+vsi1978xxx185,1978-01-08,1978,1,day,0.618,0.20897851727625347
+vsi1978xxx188,1978-01-09,1978,1,day,6.513,0.8758133888397577
+vsi1978xxx189a,1978-01-10,1978,1,day,38.05,1.5916210382133191
+vsi1978xxx189b,1978-01-10,1978,1,day,18.218,1.2837081890474653
+vsi1978xxx191,1978-01-10,1978,1,day,6.179,0.8560639533330998
+vsi1978xxx206,1978-01-13,1978,1,day,0.058,0.024485667699166973
+vsi1978xxx208,1978-01-13,1978,1,day,75.23,1.8821259197700317
+vsi1978xxx209,1978-01-13,1978,1,day,0.382,0.14050804303817965
+vsi1978xxx210,1978-01-14,1978,1,day,3.042,0.6065963091792852
+vsi1978xxx213,1978-01-14,1978,1,day,0.273,0.10482840365365544
+vsi1978xxx225,1978-01-17,1978,1,day,54.173,1.741726599111325
+vsi1978xxx226,1978-01-17,1978,1,day,34.025,1.5443781439578121
+vsi1978xxx227,1978-01-17,1978,1,day,0.192,0.07627625540421759
+vsi1978xxx228,1978-01-18,1978,1,day,1.972,0.4730488050885377
+vsi1978xxx229,1978-01-18,1978,1,night,0.716,0.2345172835126866
+vsi1978xxx232,1978-01-18,1978,1,day,3.828,0.6837672614253115
+vsi1978xxx233,1978-01-18,1978,1,day,41.918,1.632639475428074
+vsi1978xxx234,1978-01-18,1978,1,day,207.167,2.3184118834174483
+vsi1978xxx236,1978-01-18,1978,1,day,0.046,0.01953168453125545
+vsi1978xxx237,1978-01-18,1978,1,day,0.09,0.03742649794062367
+vsi1978xxx238,1978-01-19,1978,1,night,6.021,0.8463989730346749
+vsi1978xxx239,1978-01-19,1978,1,day,1.942,0.46864266839151136
+vsi1978xxx240,1978-01-19,1978,1,day,92.924,1.972776579867508
+vsi1978xxx241,1978-01-19,1978,1,day,94.924,1.981927280408841
+vsi1978xxx242,1978-01-19,1978,1,day,0.387,0.14207646107328487
+vsi1978xxx243,1978-01-19,1978,1,day,0.507,0.17811325231463185
+vsi1978xxx244,1978-01-19,1978,1,day,9.555,1.0234582376436752
+vsi1978xxx253a,1978-01-21,1978,1,day,17.359,1.2638490218374718
+vsi1978xxx253b,1978-01-21,1978,1,day,2.985,0.6004283257321312
+vsi1978xxx253c,1978-01-21,1978,1,day,28.225,1.4657545198338777
+vsi1978xxx253d,1978-01-21,1978,1,day,0.141,0.05728564441821465
+vsi1978xxx253e,1978-01-21,1978,1,day,0.937,0.28712962071911086
+vsi1978xxx253f,1978-01-21,1978,1,day,1.044,0.3104808914626751
+vsi1978xxx253g,1978-01-21,1978,1,day,5.955,0.8422971343280652
+vsi1978xxx255a,1978-01-22,1978,1,night,3.711,0.6731131042382337
+vsi1978xxx255b,1978-01-22,1978,1,night,14.933,1.2022975561387996
+vsi1978xxx256a,1978-01-22,1978,1,night,1.501,0.3981136917305025
+vsi1978xxx256b,1978-01-22,1978,1,night,0.101,0.041787318971751766
+vsi1978xxx257a,1978-01-22,1978,1,day,6.185,0.8564267724702445
+vsi1978xxx257b,1978-01-22,1978,1,day,52.4,1.7275412570285564
+vsi1978xxx257c,1978-01-22,1978,1,day,0.835,0.2636360685881081
+vsi1978xxx257d,1978-01-22,1978,1,day,4.311,0.7251763014191371
+vsi1978xxx257e,1978-01-22,1978,1,day,5.408,0.8067225030761813
+vsi1978xxx257f,1978-01-22,1978,1,day,170.252,2.233635652187189
+vsi1978xxx258a,1978-01-22,1978,1,day,24.058,1.3989464049013067
+vsi1978xxx258b,1978-01-22,1978,1,day,NA,NA
+vsi1978xxx258c,1978-01-22,1978,1,day,6.248,0.8602181846687567
+vsi1978xxx259a,1978-01-23,1978,1,day,27.42,1.453624073591451
+vsi1978xxx259b,1978-01-23,1978,1,day,13.115,1.149680882482938
+vsi1978xxx259c,1978-01-23,1978,1,day,6.786,0.8913143993821431
+vsi1978xxx259d,1978-01-23,1978,1,day,0.407,0.14829409743474573
+vsi1978xxx259e,1978-01-23,1978,1,day,0.951,0.29025726939451807
+vsi1978xxx259f,1978-01-23,1978,1,day,0.635,0.21351775699630487
+vsi1978xxx261a,1978-01-23,1978,1,day,171.518,2.2368344147301245
+vsi1978xxx261b,1978-01-23,1978,1,day,294.889,2.471128820084258
+vsi1978xxx263,1978-01-23,1978,1,day,55.733,1.753835749349954
+vsi1978xxx265a,1978-01-24,1978,1,night,0.037,0.01577875638904093
+vsi1978xxx265b,1978-01-24,1978,1,night,1.386,0.3776704393343231
+vsi1978xxx265c,1978-01-24,1978,1,night,0.032,0.013679697291192559
+vsi1978xxx265d,1978-01-24,1978,1,night,43.225,1.6456678415817607
+vsi1978xxx266a,1978-01-24,1978,1,day,6.932,0.899382705533265
+vsi1978xxx266b,1978-01-24,1978,1,day,16.524,1.2436332445061378
+vsi1978xxx266c,1978-01-24,1978,1,day,0.891,0.2766915288450397
+vsi1978xxx266com,1978-01-24,1978,1,day,64.612,1.8169832762228597
+vsi1978xxx266d,1978-01-24,1978,1,day,14.043,1.1773344555057002
+vsi1978xxx266e,1978-01-24,1978,1,day,8.197,0.9636461864848433
+vsi1978xxx266f,1978-01-24,1978,1,day,NA,NA
+vsi1978xxx266g,1978-01-24,1978,1,day,NA,NA
+vsi1978xxx266h,1978-01-24,1978,1,day,0.074,0.031004281363536824
+vsi1978xxx269,1978-01-24,1978,1,day,33.868,1.5424270373136277
+vsi1978xxx270,1978-01-24,1978,1,day,47.116,1.6822895162128295
+vsi1978xxx272a,1978-01-24,1978,1,day,11.766,1.1060548400937864
+vsi1978xxx272b,1978-01-25,1978,1,night,NA,NA
+vsi1978xxx272c,1978-01-25,1978,1,night,0.33,0.12385164096708583
+vsi1978xxx272com,1978-01-25,1978,1,night,80.921,1.9133952450457106
+vsi1978xxx272d,1978-01-25,1978,1,night,0.401,0.1464381352857746
+vsi1978xxx272e,1978-01-25,1978,1,night,56.498,1.7596527385318903
+vsi1978xxx273a,1978-01-25,1978,1,day,1.725,0.4353665066126613
+vsi1978xxx273b,1978-01-25,1978,1,day,NA,NA
+vsi1978xxx273c,1978-01-25,1978,1,day,14.844,1.1998648337322553
+vsi1978xxx273com,1978-01-25,1978,1,day,66.539,1.8295546260699431
+vsi1978xxx273d,1978-01-25,1978,1,day,0.12,0.04921802267018165
+vsi1978xxx273e,1978-01-25,1978,1,day,33.383,1.536343767249352
+vsi1978xxx273f,1978-01-25,1978,1,day,1.738,0.4374334437979712
+vsi1978xxx273g,1978-01-25,1978,1,day,1.39,0.37839790094813763
+vsi1978xxx276a,1978-01-25,1978,1,day,8.111,0.9595660466379277
+vsi1978xxx276b,1978-01-25,1978,1,day,NA,NA
+vsi1978xxx276c,1978-01-25,1978,1,day,3.747,0.67641923171836
+vsi1978xxx276com,1978-01-25,1978,1,day,55.499,1.7520407611234161
+vsi1978xxx276d,1978-01-25,1978,1,day,19.063,1.3023958731525664
+vsi1978xxx276e,1978-01-25,1978,1,day,1.503,0.39846084960822326
+vsi1978xxx276f,1978-01-25,1978,1,day,1.219,0.3461573022320084
+vsi1978xxx277,1978-01-26,1978,1,night,2.466,0.5398285583778982
+vsi1978xxx279a,1978-01-26,1978,1,day,2.803,0.5801263254115825
+vsi1978xxx279b,1978-01-26,1978,1,day,1.69,0.42975228000240795
+vsi1978xxx279c,1978-01-26,1978,1,day,5.868,0.8368302864888789
+vsi1978xxx279com,1978-01-26,1978,1,day,38.909,1.6010708458014895
+vsi1978xxx279d,1978-01-26,1978,1,day,1.517,0.4008832155483627
+vsi1978xxx279e,1978-01-26,1978,1,day,NA,NA
+vsi1978xxx279f,1978-01-26,1978,1,day,0.024,0.010299956639811961
+vsi1978xxx279g,1978-01-26,1978,1,day,23.467,1.3885807219746578
+vsi1978xxx280a,1978-01-26,1978,1,day,0.012,0.005180512503780314
+vsi1978xxx280b,1978-01-26,1978,1,day,0.553,0.19117145572855848
+vsi1978xxx280c,1978-01-26,1978,1,day,0.496,0.17493159352844254
+vsi1978xxx280d,1978-01-26,1978,1,day,0.03,0.012837224705172217
+vsi1978xxx281,1978-01-26,1978,1,day,50.351,1.710548905387108
+vsi1978xxx282,1978-01-26,1978,1,day,22.544,1.3718802490915545
+vsi1978xxx283a,1978-01-27,1978,1,night,0.81,0.2576785748691845
+vsi1978xxx283b,1978-01-27,1978,1,night,0.03,0.012837224705172217
+vsi1978xxx283c,1978-01-27,1978,1,night,0.021,0.009025742086910206
+vsi1978xxx283com,1978-01-27,1978,1,night,4.088,0.7065471026403575
+vsi1978xxx283d,1978-01-27,1978,1,night,2.151,0.49844840317399963
+vsi1978xxx283e,1978-01-27,1978,1,night,0.019,0.008174184006426354
+vsi1978xxx283f,1978-01-27,1978,1,night,0.126,0.05153839051532738
+vsi1978xxx284a,1978-01-27,1978,1,night,0.17,0.06818586174616162
+vsi1978xxx284b,1978-01-27,1978,1,night,0.102,0.04218159451576627
+vsi1978xxx284c,1978-01-27,1978,1,night,0.276,0.10585067438514352
+vsi1978xxx284d,1978-01-27,1978,1,night,0.297,0.11293997608408006
+vsi1978xxx284e,1978-01-27,1978,1,night,NA,NA
+vsi1978xxx319,1978-02-05,1978,2,day,2.255,0.5125509929042107
+vsi1978xxx326,1978-02-06,1978,2,day,0,0
+vsi1978xxx327,1978-02-07,1978,2,night,0,0
+vsi1978xxx329,1978-02-07,1978,2,day,5.662,0.8236046283551579
+vsi1978xxx330,1978-02-07,1978,2,day,508.163,2.7068568366947643
+vsi1978xxx331,1978-02-07,1978,2,day,0.45,0.16136800223497488
+vsi1978xxx332,1978-02-08,1978,2,night,0.485,0.17172645365323114
+vsi1978xxx333,1978-02-08,1978,2,day,0.21,0.08278537031645007
+vsi1978xxx334,1978-02-08,1978,2,day,1.056,0.31302311032323815
+vsi1978xxx335,1978-02-08,1978,2,day,NA,NA
+vsi1978xxx336,1978-02-08,1978,2,day,11.656,1.1022964661536014
+vsi1978xxx337,1978-02-08,1978,2,day,9.705,1.0295866716304571
+vsi1978xxx339,1978-02-08,1978,2,day,27.54,1.455453968778628
+vsi1978xxx340a,1978-02-08,1978,2,day,94.318,1.9791749212313186
+vsi1978xxx341b,1978-02-08,1978,2,day,NA,NA
+vsi1978xxx342c,1978-02-08,1978,2,day,NA,NA
+vsi1978xxx343d,1978-02-08,1978,2,day,NA,NA
+vsi1978xxx344com,1978-02-09,1978,2,day,120.117,2.083205105059057
+vsi1978xxx344e,1978-02-09,1978,2,night,NA,NA
+vsi1978xxx345,1978-02-09,1978,2,night,27.497,1.4547991423936564
+vsi1978xxx346,1978-02-09,1978,2,day,8.133,0.9606134576479087
+vsi1978xxx347,1978-02-10,1978,2,night,1.823,0.45071087814691935
+vsi1978xxx349,1978-02-10,1978,2,day,0.059,0.024895960107484977
+vsi1978xxx35,1977-11-11,1977,11,day,0.066,0.027757204690553483
+vsi1978xxx355,1978-02-10,1978,2,day,3.376,0.6410773133253743
+vsi1978xxx359,1978-02-12,1978,2,day,2.005,0.4778444763387583
+vsi1978xxx360,1978-02-12,1978,2,day,0.893,0.27715061396379675
+vsi1978xxx361,1978-02-13,1978,2,night,0.089,0.03702787975577494
+vsi1978xxx364,1978-03-03,1978,3,day,0.094,0.039017321997412
+vsi1978xxx365a,1978-03-03,1978,3,day,131.187,2.121188746330585
+vsi1978xxx365b,1978-03-03,1978,3,night,14.555,1.1918700154447222
+vsi1978xxx365c,1978-03-03,1978,3,night,6.369,0.8674085565227913
+vsi1978xxx365com,1978-03-03,1978,3,night,105.479,2.0272639637990637
+vsi1978xxx365d,1978-03-03,1978,3,night,98.394,1.9973601686474194
+vsi1978xxx366,1978-03-04,1978,3,day,1.656,0.42422807069597984
+vsi1978xxx367,1978-03-04,1978,3,day,0.182,0.07261747654523654
+vsi1978xxx369,1978-03-04,1978,3,day,NA,NA
+vsi1978xxx370,1978-03-04,1978,3,day,0.119,0.04883008652835004
+vsi1978xxx371,1978-03-05,1978,3,night,0.248,0.09621458534640517
+vsi1978xxx373a,1978-03-05,1978,3,day,0.407,0.14829409743474573
+vsi1978xxx373b,1978-03-05,1978,3,night,NA,NA
+vsi1978xxx373com,1978-03-05,1978,3,day,0.557,0.19228861256812027
+vsi1978xxx376a,1978-03-05,1978,3,day,0,0
+vsi1978xxx376b,1978-03-05,1978,3,day,NA,NA
+vsi1978xxx376com,1978-03-05,1978,3,day,1.531,0.4032921451582542
+vsi1978xxx378,1978-03-06,1978,3,night,1.897,0.4619484952037618
+vsi1978xxx382,1978-03-06,1978,3,day,0.143,0.05804623039528174
+vsi1978xxx383a,1978-03-06,1978,3,day,0.25,0.09691001300805642
+vsi1978xxx385,1978-03-07,1978,3,day,0.11,0.04532297878665747
+vsi1978xxx386,1978-03-07,1978,3,day,2.368,0.5273720828276118
+vsi1978xxx388a,1978-03-07,1978,3,day,0.432,0.1559430179718367
+vsi1978xxx388b,1978-03-07,1978,3,day,565.776,2.7534114518643364
+vsi1978xxx388c,1978-03-07,1978,3,day,136.624,2.1386941763317813
+vsi1978xxx388com,1978-03-07,1978,3,day,701.107,2.846403302968044
+vsi1978xxx388d,1978-03-07,1978,3,day,89.483,1.9565669913630959
+vsi1978xxx389,1978-03-07,1978,3,day,519.672,2.7165642233978815
+vsi1978xxx390,1978-03-08,1978,3,night,1.615,0.4174716932032931
+vsi1978xxx391,1978-03-08,1978,3,day,0.492,0.17376882313665
+vsi1978xxx392,1978-03-08,1978,3,day,18.308,1.2857372901466544
+vsi1978xxx402,1978-03-09,1978,3,day,2.386,0.5296869537729165
+vsi1978xxx407,1978-03-11,1978,3,day,269.465,2.432111072435803
+vsi1978xxx409,1978-03-11,1978,3,day,0.298,0.11327469246435044
+vsi1978xxx411,1978-03-11,1978,3,day,1.406,0.3812956230038259
+vsi1978xxx412a,1978-03-11,1978,3,night,18.604,1.292344693840592
+vsi1978xxx412b,1978-03-12,1978,3,night,1.393,0.3789426986134373
+vsi1978xxx414,1978-03-12,1978,3,night,26.156,1.4338658000023534
+vsi1978xxx415,1978-03-12,1978,3,day,3.073,0.6099144100859978
+vsi1978xxx417,1978-03-12,1978,3,day,1634.284,3.2135931875296597
+vsi1978xxx418,1978-03-12,1978,3,day,0.045,0.01911629044707278
+vsi1978xxx419,1978-03-12,1978,3,day,347.685,2.5424332649631642
+vsi1978xxx420,1978-03-12,1978,3,night,223.403,2.3510286586212747
+vsi1978xxx421a,1978-03-13,1978,3,night,5.36,0.8034571156484139
+vsi1978xxx421b,1978-03-13,1978,3,day,NA,NA
+vsi1978xxx421com,1978-03-13,1978,3,night,5.804,0.8327643049405314
+vsi1978xxx422,1978-03-13,1978,3,day,0.09,0.03742649794062367
+vsi1978xxx423,1978-03-13,1978,3,day,0.14,0.056904851336472634
+vsi1978xxx425,1978-03-13,1978,3,day,0.053,0.02242837118548649
+vsi1978xxx427,1978-03-14,1978,3,day,308.309,2.4903925569041534
+vsi1978xxx428,1978-03-14,1978,3,day,0.132,0.05384642685225267
+vsi1978xxx430,1978-03-14,1978,3,day,0.097,0.04020662757471112
+vsi1978xxx432,1978-03-14,1978,3,day,17.46,1.2662316966898932
+vsi1978xxx434a,1978-03-16,1978,3,day,0.053,0.02242837118548649
+vsi1978xxx434b,1978-03-16,1978,3,day,NA,NA
+vsi1978xxx434com,1978-03-16,1978,3,day,0.342,0.12775251583297328
+vsi1978xxx435,1978-03-17,1978,3,night,286.193,2.4581738502693504
+vsi1978xxx436,1978-03-17,1978,3,night,0.77,0.24797326636180664
+vsi1978xxx437,1978-03-17,1978,3,day,25.323,1.4203353837716624
+vsi1978xxx438,1978-03-17,1978,3,day,3.739,0.6756867086994012
+vsi1978xxx439,1978-03-18,1978,3,night,3.721,0.6740340004312549
+vsi1978xxx444,1978-03-19,1978,3,night,0.188,0.07481644064517472
+vsi1978xxx445,1978-03-19,1978,3,night,0.285,0.1089031276673133
+vsi1978xxx446,1978-03-19,1978,3,day,0.313,0.11826472608947933
+vsi1978xxx448,1978-03-19,1978,3,day,22.699,1.3747300209618132
+vsi1978xxx449,1978-03-20,1978,3,night,7.04,0.9052560487484512
+vsi1978xxx450,1978-03-20,1978,3,night,0.773,0.24870873560091783
+vsi1978xxx451,1978-03-20,1978,3,day,25.464,1.4226554878724527
+vsi1978xxx452,1978-03-20,1978,3,day,1.93,0.46686762035410945
+vsi1978xxx453,1978-03-21,1978,3,night,0.631,0.21245396104027578
+vsi1978xxx455,1978-03-21,1978,3,day,90.017,1.9591225166799593
+vsi1978xxx54,1977-11-16,1977,11,day,2.581,0.554004321011903
+vsi1978xxx55,1977-11-17,1977,11,day,20.602,1.3344939617413862
+vsi1978xxx56,1977-11-17,1977,11,day,0,0
+vsi1978xxx57,1977-11-17,1977,11,day,24.27,1.4026052419199146
+vsi1978xxx58,1977-11-17,1977,11,day,0,0
+vsi1978xxx59,1977-11-18,1977,11,day,214.072,2.332583873720685
+vsi1978xxx60,1977-11-18,1977,11,day,198.057,2.2989774545044903
+vsi1978xxx61,1977-11-18,1977,11,day,17.696,1.2717486993841705
+vsi1978xxx62,1977-11-18,1977,11,day,0.937,0.28712962071911086
+vsi1978xxx63,1977-11-19,1977,11,day,431.815,2.6363023036335007
+vsi1978xxx64,1977-11-19,1977,11,day,1.527,0.40260524191991476
+vsi1978xxx65,1977-11-19,1977,11,day,0,0
+vsi1978xxx67,1977-11-19,1977,11,day,8.221,0.9647780220223761
+vsi1978xxx68,1977-11-20,1977,11,day,0.131,0.05346260492545529
+vsi1978xxx69,1977-11-20,1977,11,day,98.266,1.9968005220011271
+vsi1978xxx70,1977-11-20,1977,11,day,2.563,0.5518158223510157
+vsi1978xxx71,1977-11-20,1977,11,day,0.044,0.01870049866624337
+vsi1978xxx83,1977-11-23,1977,11,day,71.539,1.860571564237586
+vsi1978xxx85,1977-11-24,1977,11,day,0.755,0.24427712080184286
+vsi1978xxx86,1977-11-25,1977,11,day,0.235,0.09166695759568448
+vsi1978xxx88,1977-11-25,1977,11,day,0.063,0.026533264523296733
+vsi1978xxx89,1977-11-25,1977,11,day,0.519,0.18155777386278638
+vsi1978xxx9,1977-11-05,1977,11,day,0.107,0.0441476208787228
+vsi1978xxx91,1977-11-26,1977,11,day,1.008,0.30276370847298173
+vsi1978xxx92,1977-11-26,1977,11,day,7.697,0.9393694700746599
+vsi1978xxx93,1977-11-26,1977,11,day,5.278,0.7978213113640236
+vsi1978xxx94,1977-11-26,1977,11,day,11.268,1.0887737673104478
+vsi1978xxx95,1977-11-26,1977,11,day,1.072,0.31638975107319545
+vsi1978xxx98,1977-11-27,1977,11,day,12.531,1.1313298940428098
+vsi1980pol100,1980-02-15,1980,2,day,2.789,0.5785246052749933
+vsi1980pol101,1980-02-15,1980,2,day,1.811,0.44886084560744066
+vsi1980pol105,1980-02-19,1980,2,night,0,0
+vsi1980pol106,1980-02-20,1980,2,night,0,0
+vsi1980pol107,1980-02-20,1980,2,day,0,0
+vsi1980pol108,1980-02-20,1980,2,day,0,0
+vsi1980pol109,1980-02-20,1980,2,day,0,0
+vsi1980pol110,1980-02-20,1980,2,night,0,0
+vsi1980pol111,1980-02-21,1980,2,night,0,0
+vsi1980pol112,1980-02-21,1980,2,day,0,0
+vsi1980pol113,1980-02-21,1980,2,day,0,0
+vsi1980pol114,1980-02-21,1980,2,day,0,0
+vsi1980pol115,1980-02-21,1980,2,day,0,0
+vsi1980pol116,1980-02-21,1980,2,night,5.934,0.840983837320378
+vsi1980pol20,1979-12-30,1979,12,day,0,0
+vsi1980pol21,1979-12-31,1979,12,day,3.664,0.6687585417509576
+vsi1980pol23,1979-12-31,1979,12,day,0,0
+vsi1980pol24,1979-12-31,1979,12,day,11.794,1.1070063463815438
+vsi1980pol27,1980-01-01,1980,1,day,74.864,1.8800357375511858
+vsi1980pol28,1980-01-01,1980,1,day,0,0
+vsi1980pol29,1980-01-01,1980,1,day,0,0
+vsi1980pol31,1980-01-03,1980,1,day,0,0
+vsi1980pol33,1980-01-03,1980,1,day,0,0
+vsi1980pol34,1980-01-04,1980,1,day,0,0
+vsi1980pol35,1980-01-04,1980,1,day,0,0
+vsi1980pol46,1980-01-06,1980,1,day,0,0
+vsi1980pol47,1980-01-06,1980,1,day,0,0
+vsi1980pol48,1980-01-06,1980,1,day,0,0
+vsi1980pol49,1980-01-06,1980,1,day,0,0
+vsi1980pol50,1980-01-06,1980,1,day,0,0
+vsi1980pol51,1980-01-06,1980,1,day,0,0
+vsi1980pol52,1980-01-06,1980,1,day,0,0
+vsi1980pol54,1980-01-07,1980,1,day,0,0
+vsi1980pol55,1980-01-15,1980,1,day,0,0
+vsi1980pol56,1980-01-16,1980,1,day,0,0
+vsi1980pol57,1980-01-16,1980,1,day,0,0
+vsi1980pol58,1980-01-16,1980,1,day,0,0
+vsi1980pol59,1980-01-16,1980,1,day,0,0
+vsi1980pol60,1980-01-20,1980,1,day,0,0
+vsi1980pol61,1980-01-21,1980,1,day,0,0
+vsi1980pol62,1980-01-21,1980,1,day,0,0
+vsi1980pol63,1980-01-21,1980,1,day,0,0
+vsi1980pol64,1980-01-22,1980,1,day,0,0
+vsi1980pol65,1980-01-22,1980,1,day,0,0
+vsi1980pol66,1980-01-22,1980,1,day,0,0
+vsi1980pol67,1980-01-22,1980,1,day,0,0
+vsi1980pol68,1980-01-22,1980,1,day,0.9,0.2787536009528289
+vsi1980pol69,1980-01-23,1980,1,day,0,0
+vsi1980pol70,1980-01-23,1980,1,day,0,0
+vsi1980pol71,1980-01-23,1980,1,day,0,0
+vsi1980pol72,1980-01-26,1980,1,day,0,0
+vsi1980pol73,1980-01-26,1980,1,day,0,0
+vsi1980pol74,1980-01-27,1980,1,day,0,0
+vsi1980pol75,1980-01-27,1980,1,day,0,0
+vsi1980pol76,1980-01-27,1980,1,day,0,0
+vsi1980pol77,1980-01-27,1980,1,day,0,0
+vsi1980pol78,1980-01-29,1980,1,day,0,0
+vsi1980pol79,1980-01-29,1980,1,day,0,0
+vsi1980pol82,1980-02-02,1980,2,day,0,0
+vsi1980pol83,1980-02-02,1980,2,day,0,0
+vsi1980pol84,1980-02-02,1980,2,day,0,0
+vsi1980pol85,1980-02-05,1980,2,day,0,0
+vsi1980pol87,1980-02-10,1980,2,day,0,0
+vsi1980pol88,1980-02-10,1980,2,day,0,0
+vsi1980pol89,1980-02-10,1980,2,day,0,0
+vsi1980pol90,1980-02-11,1980,2,day,0,0
+vsi1980pol91,1980-02-12,1980,2,day,0,0
+vsi1980pol92,1980-02-12,1980,2,day,0,0
+vsi1980pol93,1980-02-13,1980,2,day,0,0
+vsi1980pol94,1980-02-13,1980,2,day,0,0
+vsi1980pol95,1980-02-14,1980,2,day,0,0
+vsi1980pol96,1980-02-14,1980,2,day,0.703,0.23121464796260097
+vsi1980pol97,1980-02-14,1980,2,night,0,0
+vsi1980pol98,1980-02-15,1980,2,night,3.604,0.6631353149577542
+vsi1980pol99,1980-02-15,1980,2,night,3.388,0.6422666189026736
+vsi1981mex103,1980-12-07,1980,12,day,1.837,0.45285933579585225
+vsi1981mex106,1980-12-08,1980,12,day,15.367,1.2139690824146994
+vsi1981mex135,1980-12-11,1980,12,day,2.101,0.4915017662373264
+vsi1981mex138,1980-12-12,1980,12,night,2.863,0.5869247081448203
+vsi1981mex140,1980-12-12,1980,12,day,22.931,1.3789608467708918
+vsi1981mex141,1980-12-13,1980,12,day,0.426,0.15411952551584673
+vsi1981mex142,1980-12-13,1980,12,night,3.197,0.6229389692114901
+vsi1981mex143,1980-12-13,1980,12,day,0.458,0.1637575239819558
+vsi1981mex145,1980-12-13,1980,12,day,4.314,0.7254215500742587
+vsi1981mex146,1980-12-14,1980,12,day,0.905,0.2798949800116381
+vsi1981mex147,1980-12-14,1980,12,day,0.541,0.1878026387184193
+vsi1981mex149,1980-12-14,1980,12,day,0.027,0.011570443597278159
+vsi1981mex23,1980-11-23,1980,11,day,2.323,0.521530341278711
+vsi1981mex25,1980-11-24,1980,11,day,42.852,1.6419894054365227
+vsi1981mex265,1981-01-25,1981,1,night,158.307,2.202234859257388
+vsi1981mex271,1981-01-25,1981,1,day,12.921,1.1436704334701606
+vsi1981mex276,1981-01-26,1981,1,day,13.169,1.1513392002963623
+vsi1981mex283,1981-01-27,1981,1,night,2.311,0.5199591807520684
+vsi1981mex294,1981-01-29,1981,1,night,0.31,0.11727129565576427
+vsi1981mex301,1981-01-29,1981,1,day,0.708,0.23248786635298624
+vsi1981mex307,1981-01-30,1981,1,night,7.624,0.9357087478426634
+vsi1981mex312,1981-01-30,1981,1,day,2.901,0.5911759503117914
+vsi1981mex36,1980-11-27,1980,11,day,1.065,0.3149200559924198
+vsi1981mex386,1981-02-18,1981,2,day,43.671,1.650025674648262
+vsi1981mex388,1981-02-19,1981,2,night,NA,NA
+vsi1981mex390,1981-02-19,1981,2,night,7.48,0.9283958522567138
+vsi1981mex391,1981-02-19,1981,2,day,66.482,1.8291879455248568
+vsi1981mex392,1981-02-19,1981,2,day,0.037,0.01577875638904093
+vsi1981mex393,1981-02-20,1981,2,night,7.217,0.9147132869736238
+vsi1981mex402,1981-02-21,1981,2,night,1255.319,3.099099927895968
+vsi1981mex404,1981-02-21,1981,2,day,8.392,0.9727580839035392
+vsi1981mex409,1981-02-21,1981,2,night,0.028,0.011993114659256938
+vsi1981mex68,1980-11-29,1980,11,day,6.381,0.8681152053272172
+vsi1981mex80,1980-12-02,1980,12,night,14.42,1.1880843737149382
+vsi1981mex81,1980-12-02,1980,12,night,10.201,1.0492567972322273
+vsi1981mex82,1980-12-02,1980,12,day,49.252,1.7011533511010621
+vsi1981mex83,1980-12-02,1980,12,day,57.2,1.7649229846498886
+vsi1981mex84,1980-12-02,1980,12,day,4.053,0.7035492982382305
+vsi1981mex87,1980-12-02,1980,12,day,0.068,0.028571252692537637
+vsi1981mex98,1980-12-06,1980,12,day,1.845,0.45408227073109
+vsi1981pol101,1981-01-06,1981,1,day,0,0
+vsi1981pol106,1981-01-10,1981,1,day,1.605,0.4158077276355433
+vsi1981pol108,1981-01-11,1981,1,day,0,0
+vsi1981pol110,1981-01-11,1981,1,day,2.661,0.5635997288815311
+vsi1981pol111,1981-01-12,1981,1,day,0,0
+vsi1981pol126,1981-01-19,1981,1,day,1.08,0.31806333496276157
+vsi1981pol127,1981-01-19,1981,1,day,72.706,1.867502842822796
+vsi1981pol128,1981-01-20,1981,1,day,0,0
+vsi1981pol130,1981-01-20,1981,1,day,0,0
+vsi1981pol132,1981-01-21,1981,1,day,0,0
+vsi1981pol134,1981-01-21,1981,1,day,5.188,0.7915503050273299
+vsi1981pol136,1981-01-21,1981,1,day,0,0
+vsi1981pol138,1981-01-21,1981,1,day,0,0
+vsi1981pol143,1981-01-27,1981,1,day,19.506,1.311880953037904
+vsi1981pol178,1981-02-04,1981,2,day,0,0
+vsi1981pol179,1981-02-04,1981,2,day,2.332,0.5227049927347499
+vsi1981pol185,1981-02-04,1981,2,day,0,0
+vsi1981pol186,1981-02-04,1981,2,day,0,0
+vsi1981pol191,1981-02-05,1981,2,day,0,0
+vsi1981pol193,1981-02-05,1981,2,day,7.4,0.9242792860618817
+vsi1981pol195,1981-02-05,1981,2,day,36.199,1.5705312651421313
+vsi1981pol197,1981-02-05,1981,2,day,0,0
+vsi1981pol203a,1981-02-07,1981,2,day,62.368,1.8018700002817691
+vsi1981pol203b,1981-02-07,1981,2,day,0,0
+vsi1981pol205,1981-02-08,1981,2,day,0,0
+vsi1981pol208,1981-02-08,1981,2,day,5.913,0.8396665568824334
+vsi1981pol209,1981-02-08,1981,2,day,1.892,0.46119828862249324
+vsi1981pol210,1981-02-08,1981,2,day,6.008,0.8455940931600243
+vsi1981pol211,1981-02-08,1981,2,night,0,0
+vsi1981pol213,1981-02-09,1981,2,day,1.867,0.4574276929464845
+vsi1981pol214,1981-02-09,1981,2,day,0,0
+vsi1981pol215,1981-02-09,1981,2,day,8.021,0.95525468282018
+vsi1981pol217,1981-02-09,1981,2,day,0,0
+vsi1981pol219,1981-02-11,1981,2,day,0,0
+vsi1981pol224,1981-02-12,1981,2,day,0,0
+vsi1981pol226,1981-02-12,1981,2,day,0,0
+vsi1981pol227,1981-02-12,1981,2,day,0,0
+vsi1981pol228,1981-02-12,1981,2,day,5.223,0.7939998009844705
+vsi1981pol229,1981-02-12,1981,2,day,0,0
+vsi1981pol230,1981-02-12,1981,2,day,3.07,0.6095944092252201
+vsi1981pol231,1981-02-13,1981,2,day,1.572,0.41027096425218446
+vsi1981pol232,1981-02-13,1981,2,day,2.256,0.5126843962171637
+vsi1981pol233,1981-02-13,1981,2,day,0,0
+vsi1981pol234,1981-02-13,1981,2,day,2.28,0.515873843711679
+vsi1981pol235,1981-02-13,1981,2,day,0,0
+vsi1981pol237,1981-02-13,1981,2,day,1.396,0.3794868137172738
+vsi1981pol282,1981-02-26,1981,2,day,0,0
+vsi1981pol89,1981-01-04,1981,1,day,1.988,0.4753805931433612
+vsi1981pol91,1981-01-04,1981,1,day,0,0
+vsi1981pol94,1981-01-04,1981,1,day,0,0
+vsi1981pol97,1981-01-04,1981,1,day,0,0
+vsi1981pol99,1981-01-05,1981,1,day,0,0
+vsi1981xxx107,1981-02-11,1981,2,day,14.33,1.1855421548543752
+vsi1981xxx11,1981-01-26,1981,1,day,0.364,0.13481437032046006
+vsi1981xxx116,1981-02-12,1981,2,day,0.749,0.24278980947867654
+vsi1981xxx123,1981-02-13,1981,2,day,0.205,0.08098704691088722
+vsi1981xxx125,1981-02-14,1981,2,night,0,0
+vsi1981xxx128,1981-02-14,1981,2,day,0.42,0.15228834438305647
+vsi1981xxx131,1981-02-15,1981,2,night,0.116,0.04766419460155998
+vsi1981xxx137,1981-02-15,1981,2,day,25.626,1.4253059276703188
+vsi1981xxx140,1981-02-16,1981,2,night,1.472,0.3930484664167782
+vsi1981xxx144,1981-02-16,1981,2,day,0.127,0.05192391604610654
+vsi1981xxx147,1981-02-17,1981,2,night,0.181,0.07224989761351482
+vsi1981xxx151,1981-02-17,1981,2,day,1.484,0.3951515915045426
+vsi1981xxx154,1981-02-18,1981,2,night,2.29,0.5171958979499743
+vsi1981xxx159,1981-02-18,1981,2,day,58.947,1.7777674540562844
+vsi1981xxx167,1981-02-19,1981,2,day,270.35,2.433529825915495
+vsi1981xxx169,1981-02-20,1981,2,night,0.017,0.0073209529227445565
+vsi1981xxx178,1981-02-21,1981,2,day,0.184,0.07335170238690095
+vsi1981xxx19,1981-01-30,1981,1,day,0.086,0.03582982525282818
+vsi1981xxx205,1981-02-27,1981,2,day,0.143,0.05804623039528174
+vsi1981xxx210,1981-02-28,1981,2,night,51.366,1.7190494013929223
+vsi1981xxx220,1981-03-11,1981,3,night,0,0
+vsi1981xxx221,1981-03-11,1981,3,day,0,0
+vsi1981xxx222,1981-03-11,1981,3,day,0,0
+vsi1981xxx226,1981-03-12,1981,3,night,0.069,0.028977705208777998
+vsi1981xxx227,1981-03-12,1981,3,day,49.852,1.7063080383184066
+vsi1981xxx228,1981-03-12,1981,3,day,0.35,0.13033376849500614
+vsi1981xxx229,1981-03-12,1981,3,day,0.21,0.08278537031645007
+vsi1981xxx230,1981-03-12,1981,3,day,211.425,2.327205626925103
+vsi1981xxx232,1981-03-12,1981,3,night,12.846,1.1413243272754174
+vsi1981xxx233,1981-03-13,1981,3,night,171.02,2.235578943330128
+vsi1981xxx234,1981-03-13,1981,3,day,8.835,0.9927743642553556
+vsi1981xxx236,1981-03-13,1981,3,day,0.151,0.06107532362979181
+vsi1981xxx238,1981-03-13,1981,3,day,186.707,2.2734804687038577
+vsi1981xxx239,1981-03-13,1981,3,day,0.744,0.2415464805965484
+vsi1981xxx240,1981-03-15,1981,3,day,0.25,0.09691001300805642
+vsi1981xxx241,1981-03-15,1981,3,day,0,0
+vsi1981xxx242,1981-03-15,1981,3,day,26.676,1.4421033219314563
+vsi1981xxx244,1981-03-15,1981,3,day,0.189,0.0751818546186916
+vsi1981xxx245,1981-03-15,1981,3,day,1.662,0.4252080511386563
+vsi1981xxx246,1981-03-16,1981,3,day,6.15,0.8543060418010806
+vsi1981xxx247,1981-03-16,1981,3,day,2.682,0.5660837841679959
+vsi1981xxx248,1981-03-16,1981,3,day,0.045,0.01911629044707278
+vsi1981xxx249,1981-03-17,1981,3,night,0.241,0.09377178149872986
+vsi1981xxx250,1981-03-17,1981,3,day,10.577,1.063596033291059
+vsi1981xxx251,1981-03-17,1981,3,day,7.103,0.9086458389071134
+vsi1981xxx252,1981-03-17,1981,3,day,0.242,0.09412159584056139
+vsi1981xxx253,1981-03-18,1981,3,night,15.207,1.2097026321203521
+vsi1981xxx254,1981-03-18,1981,3,day,83.58,1.9272676808108815
+vsi1981xxx255,1981-03-18,1981,3,day,0.573,0.19672872262328683
+vsi1981xxx257,1981-03-19,1981,3,day,48.134,1.6913821214972042
+vsi1981xxx258,1981-03-19,1981,3,day,0.018,0.007747778000739948
+vsi1981xxx259,1981-03-19,1981,3,day,0.141,0.05728564441821465
+vsi1981xxx260,1981-03-19,1981,3,day,1.167,0.33585891131981793
+vsi1981xxx27,1981-01-31,1981,1,day,108.251,2.0384254208597987
+vsi1981xxx34,1981-02-01,1981,2,day,0.015,0.006466042249231681
+vsi1981xxx38,1981-02-02,1981,2,night,0.163,0.06557971472844842
+vsi1981xxx42,1981-02-02,1981,2,day,41.965,1.6331148161284958
+vsi1981xxx47,1981-02-03,1981,2,night,19.218,1.3057381922083175
+vsi1981xxx51,1981-02-03,1981,2,day,0.53,0.1846914308175988
+vsi1981xxx52,1981-02-03,1981,2,day,0,0
+vsi1981xxx53,1981-02-03,1981,2,day,0,0
+vsi1981xxx58,1981-02-04,1981,2,day,0,0
+vsi1981xxx59,1981-02-04,1981,2,day,0,0
+vsi1981xxx61,1981-02-05,1981,2,night,0,0
+vsi1981xxx65,1981-02-05,1981,2,day,3.562,0.6591552809406297
+vsi1981xxx67,1981-02-05,1981,2,day,4.901,0.7709256146389991
+vsi1981xxx69,1981-02-05,1981,2,night,1.163,0.33505651943909154
+vsi1981xxx73,1981-02-06,1981,2,day,1.405,0.3811150807098506
+vsi1981xxx74,1981-02-06,1981,2,day,3.332,0.6366884479532827
+vsi1981xxx75,1981-02-06,1981,2,day,13.557,1.1630718820038193
+vsi1981xxx77,1981-02-07,1981,2,night,2.897,0.5907304057926903
+vsi1981xxx81,1981-02-07,1981,2,day,0.139,0.05652372407910037
+vsi1981xxx82,1981-02-07,1981,2,day,0.272,0.10448711131239508
+vsi1981xxx83,1981-02-08,1981,2,night,0.027,0.011570443597278159
+vsi1981xxx90,1981-02-08,1981,2,day,0,0
+vsi1981xxx91,1981-02-08,1981,2,day,0,0
+vsi1981xxx98,1981-02-10,1981,2,night,0.086,0.03582982525282818
+vsi1982jbi1010a,1982-02-10,1982,2,night,NA,NA
+vsi1982jbi1010b,1982-02-10,1982,2,night,NA,NA
+vsi1982jbi1010c,1982-02-10,1982,2,night,0.135,0.05499586152914153
+vsi1982jbi1010com,1982-02-10,1982,2,night,0.162,0.06520612805431189
+vsi1982jbi1020a,1982-02-10,1982,2,day,NA,NA
+vsi1982jbi1020b,1982-02-10,1982,2,day,NA,NA
+vsi1982jbi1020c,1982-02-10,1982,2,day,0.05,0.021189299069938092
+vsi1982jbi1020com,1982-02-10,1982,2,day,0.718,0.23502315949522348
+vsi1982jbi1028a,1982-02-10,1982,2,day,NA,NA
+vsi1982jbi1028b,1982-02-10,1982,2,day,NA,NA
+vsi1982jbi1028c,1982-02-10,1982,2,day,0.708,0.23248786635298624
+vsi1982jbi1028com,1982-02-10,1982,2,day,333.751,2.5247218830517335
+vsi1982jbi1047,1982-02-11,1982,2,day,NA,NA
+vsi1982jbi1050a,1982-02-11,1982,2,day,NA,NA
+vsi1982jbi1050b,1982-02-11,1982,2,day,0.755,0.24427712080184286
+vsi1982jbi1050c,1982-02-11,1982,2,day,34.071,1.5449481493514254
+vsi1982jbi1050com,1982-02-11,1982,2,day,39.748,1.6101062974863127
+vsi1982jbi1058a,1982-02-12,1982,2,night,NA,NA
+vsi1982jbi1058b,1982-02-12,1982,2,night,NA,NA
+vsi1982jbi1058c,1982-02-12,1982,2,night,4.994,0.777716738609626
+vsi1982jbi1058com,1982-02-12,1982,2,night,17.147,1.2588048392270133
+vsi1982jbi1065a,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1065b,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1065c,1982-02-12,1982,2,day,0.061,0.025715383901340642
+vsi1982jbi1065com,1982-02-12,1982,2,day,1.059,0.31365634661803143
+vsi1982jbi1071a,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1071b,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1071c,1982-02-12,1982,2,day,62.476,1.8026095514687366
+vsi1982jbi1071com,1982-02-12,1982,2,day,138.214,2.1436829121209073
+vsi1982jbi1073,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1081a,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1081b,1982-02-12,1982,2,day,NA,NA
+vsi1982jbi1081c,1982-02-12,1982,2,day,0.081,0.03382569395331033
+vsi1982jbi1081com,1982-02-12,1982,2,day,0.128,0.05230909964732353
+vsi1982jbi1084a,1982-02-13,1982,2,day,NA,NA
+vsi1982jbi1084b,1982-02-13,1982,2,day,NA,NA
+vsi1982jbi1084c,1982-02-13,1982,2,day,0,0
+vsi1982jbi1084com,1982-02-13,1982,2,day,0.264,0.10174707394636621
+vsi1982jbi1091a,1982-02-13,1982,2,day,NA,NA
+vsi1982jbi1091b,1982-02-13,1982,2,day,NA,NA
+vsi1982jbi1091c,1982-02-13,1982,2,day,0.025,0.010723865391773066
+vsi1982jbi1091com,1982-02-13,1982,2,day,0.051,0.02160271602824219
+vsi1982jbi1097a,1982-02-14,1982,2,night,NA,NA
+vsi1982jbi1097b,1982-02-14,1982,2,night,4.108,0.7082508885913776
+vsi1982jbi1097c,1982-02-14,1982,2,night,18.059,1.2801001100549243
+vsi1982jbi1097com,1982-02-14,1982,2,night,27.02,1.4474681309497557
+vsi1982jbi1103a,1982-02-14,1982,2,night,NA,NA
+vsi1982jbi1103b,1982-02-14,1982,2,night,0.521,0.18212921405299837
+vsi1982jbi1103c,1982-02-14,1982,2,night,2.452,0.538070787043172
+vsi1982jbi1103com,1982-02-14,1982,2,night,3.576,0.6604860157849678
+vsi1982jbi1109a,1982-02-14,1982,2,day,NA,NA
+vsi1982jbi1109b,1982-02-14,1982,2,day,NA,NA
+vsi1982jbi1109c,1982-02-14,1982,2,day,14.839,1.199727758807056
+vsi1982jbi1109com,1982-02-14,1982,2,day,16.164,1.2346185058606343
+vsi1982jbi1115a,1982-02-14,1982,2,day,NA,NA
+vsi1982jbi1115b,1982-02-14,1982,2,day,NA,NA
+vsi1982jbi1115c,1982-02-14,1982,2,day,1785.755,3.252065006091168
+vsi1982jbi1115com,1982-02-14,1982,2,day,2022.531,3.306109862063791
+vsi1982jbi1121a,1982-02-14,1982,2,day,NA,NA
+vsi1982jbi1121b,1982-02-14,1982,2,day,NA,NA
+vsi1982jbi1121c,1982-02-14,1982,2,day,8.841,0.9930392318069095
+vsi1982jbi1121com,1982-02-14,1982,2,day,10.126,1.046339055604809
+vsi1982jbi1124a,1982-02-15,1982,2,night,NA,NA
+vsi1982jbi1124b,1982-02-15,1982,2,night,NA,NA
+vsi1982jbi1124c,1982-02-15,1982,2,night,470.387,2.673377601297947
+vsi1982jbi1124com,1982-02-15,1982,2,night,520.38,2.717154367729452
+vsi1982jbi1130a,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1130b,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1130c,1982-02-15,1982,2,day,1274.492,3.105677739020485
+vsi1982jbi1130com,1982-02-15,1982,2,day,1581.034,3.1992158128239288
+vsi1982jbi1132a,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1132b,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1132c,1982-02-15,1982,2,day,391.576,2.5939237456303537
+vsi1982jbi1132com,1982-02-15,1982,2,day,438.403,2.6428630177164854
+vsi1982jbi1144a,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1144b,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1144c,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1144com,1982-02-15,1982,2,day,NA,NA
+vsi1982jbi1149a,1982-02-16,1982,2,night,NA,NA
+vsi1982jbi1149b,1982-02-16,1982,2,night,30.74,1.5016069224188293
+vsi1982jbi1149c,1982-02-16,1982,2,night,883.563,2.9467287694989985
+vsi1982jbi1149com,1982-02-16,1982,2,night,1066.894,3.0285281464193616
+vsi1982jbi1154a,1982-02-16,1982,2,day,NA,NA
+vsi1982jbi1154b,1982-02-16,1982,2,day,8.168,0.9622746046233147
+vsi1982jbi1154c,1982-02-16,1982,2,day,498.426,2.698471147872365
+vsi1982jbi1154com,1982-02-16,1982,2,day,600.038,2.778901930685939
+vsi1982jbi1160a,1982-02-16,1982,2,day,NA,NA
+vsi1982jbi1160b,1982-02-16,1982,2,day,NA,NA
+vsi1982jbi1160c,1982-02-16,1982,2,day,NA,NA
+vsi1982jbi1160com,1982-02-16,1982,2,day,NA,NA
+vsi1982jbi1167a,1982-02-17,1982,2,night,NA,NA
+vsi1982jbi1167b,1982-02-17,1982,2,night,NA,NA
+vsi1982jbi1167c,1982-02-17,1982,2,night,0.102,0.04218159451576627
+vsi1982jbi1167com,1982-02-17,1982,2,night,0.349,0.13001194967190424
+vsi1982jbi1168a,1982-02-17,1982,2,night,NA,NA
+vsi1982jbi1168b,1982-02-17,1982,2,night,NA,NA
+vsi1982jbi1168c,1982-02-17,1982,2,night,NA,NA
+vsi1982jbi1168com,1982-02-17,1982,2,night,NA,NA
+vsi1982jbi1170a,1982-02-17,1982,2,day,NA,NA
+vsi1982jbi1170b,1982-02-17,1982,2,day,NA,NA
+vsi1982jbi1170c,1982-02-17,1982,2,day,0.226,0.08849047018239622
+vsi1982jbi1170com,1982-02-17,1982,2,day,0.583,0.19948091486235592
+vsi1982jbi1174a,1982-02-18,1982,2,day,NA,NA
+vsi1982jbi1174b,1982-02-18,1982,2,day,NA,NA
+vsi1982jbi1174c,1982-02-18,1982,2,day,3.113,0.6141587095091751
+vsi1982jbi1174com,1982-02-18,1982,2,day,6.282,0.862250674597925
+vsi1982jbi1183a,1982-02-18,1982,2,day,NA,NA
+vsi1982jbi1183b,1982-02-18,1982,2,day,NA,NA
+vsi1982jbi1183c,1982-02-18,1982,2,day,100.619,2.0069749168425433
+vsi1982jbi1183com,1982-02-18,1982,2,day,111.128,2.049714075815949
+vsi1982jbi1187a,1982-02-19,1982,2,night,NA,NA
+vsi1982jbi1187b,1982-02-19,1982,2,night,NA,NA
+vsi1982jbi1187c,1982-02-19,1982,2,night,6.067,0.8492350913147225
+vsi1982jbi1187com,1982-02-19,1982,2,night,35.928,1.5673557871396187
+vsi1982jbi1194a,1982-02-19,1982,2,day,NA,NA
+vsi1982jbi1194b,1982-02-19,1982,2,day,NA,NA
+vsi1982jbi1194c,1982-02-19,1982,2,day,45.169,1.6643504680277452
+vsi1982jbi1194com,1982-02-19,1982,2,day,64.557,1.8166190712978463
+vsi1982jbi1201a,1982-02-19,1982,2,day,NA,NA
+vsi1982jbi1201b,1982-02-19,1982,2,day,NA,NA
+vsi1982jbi1201c,1982-02-19,1982,2,day,3.035,0.6058435390580892
+vsi1982jbi1201com,1982-02-19,1982,2,day,3.351,0.6385890832927169
+vsi1982jbi1207a,1982-02-19,1982,2,day,NA,NA
+vsi1982jbi1207b,1982-02-19,1982,2,day,NA,NA
+vsi1982jbi1207c,1982-02-19,1982,2,day,23.029,1.3807356974156912
+vsi1982jbi1207com,1982-02-19,1982,2,day,80.004,1.9085064649902022
+vsi1982jbi1213a,1982-02-20,1982,2,night,NA,NA
+vsi1982jbi1213b,1982-02-20,1982,2,night,NA,NA
+vsi1982jbi1213c,1982-02-20,1982,2,night,0.845,0.2659963704950792
+vsi1982jbi1213com,1982-02-20,1982,2,night,3.245,0.6278776945799714
+vsi1982jbi1218a,1982-02-20,1982,2,night,NA,NA
+vsi1982jbi1218b,1982-02-20,1982,2,night,NA,NA
+vsi1982jbi1218com,1982-02-20,1982,2,night,NA,NA
+vsi1982jbi1234a,1982-02-22,1982,2,day,NA,NA
+vsi1982jbi1234b,1982-02-22,1982,2,day,NA,NA
+vsi1982jbi1234c,1982-02-22,1982,2,day,1.538,0.40449161775868603
+vsi1982jbi1234com,1982-02-22,1982,2,day,2.427,0.5349141044298669
+vsi1982jbi1239a,1982-02-22,1982,2,day,NA,NA
+vsi1982jbi1239b,1982-02-22,1982,2,day,NA,NA
+vsi1982jbi1239c,1982-02-22,1982,2,day,1139.506,3.0570975743650015
+vsi1982jbi1239com,1982-02-22,1982,2,day,1160.698,3.0650932416686687
+vsi1982jbi1250a,1982-02-23,1982,2,night,NA,NA
+vsi1982jbi1250b,1982-02-23,1982,2,night,NA,NA
+vsi1982jbi1250c,1982-02-23,1982,2,night,32.732,1.5280420916550654
+vsi1982jbi1250com,1982-02-23,1982,2,night,183.724,2.2665233241919216
+vsi1982jbi1257a,1982-02-23,1982,2,day,NA,NA
+vsi1982jbi1257b,1982-02-23,1982,2,day,3.177,0.6208644752651211
+vsi1982jbi1257c,1982-02-23,1982,2,day,118.738,2.07823199979081
+vsi1982jbi1257com,1982-02-23,1982,2,day,134.539,2.132064277112742
+vsi1982jbi838a,1982-02-02,1982,2,night,NA,NA
+vsi1982jbi838b,1982-02-02,1982,2,night,NA,NA
+vsi1982jbi838c,1982-02-02,1982,2,night,21.574,1.3536085207086392
+vsi1982jbi838com,1982-02-02,1982,2,night,26.687,1.4422759007065877
+vsi1982jbi848a,1982-02-02,1982,2,day,NA,NA
+vsi1982jbi848b,1982-02-02,1982,2,day,NA,NA
+vsi1982jbi848c,1982-02-02,1982,2,day,554.225,2.744469012773902
+vsi1982jbi848com,1982-02-02,1982,2,day,573.2,2.7590631881604875
+vsi1982jbi857a,1982-02-02,1982,2,day,NA,NA
+vsi1982jbi857b,1982-02-02,1982,2,day,NA,NA
+vsi1982jbi857c,1982-02-02,1982,2,day,1.857,0.455910240382743
+vsi1982jbi857com,1982-02-02,1982,2,day,55.623,1.7529928754305397
+vsi1982jbi865a,1982-02-03,1982,2,night,NA,NA
+vsi1982jbi865b,1982-02-03,1982,2,night,NA,NA
+vsi1982jbi865c,1982-02-03,1982,2,night,210.701,2.3257229094764558
+vsi1982jbi865com,1982-02-03,1982,2,night,462.547,2.666093774739016
+vsi1982jbi872a,1982-02-03,1982,2,day,NA,NA
+vsi1982jbi872b,1982-02-03,1982,2,day,NA,NA
+vsi1982jbi872c,1982-02-03,1982,2,day,0.804,0.25623653320592293
+vsi1982jbi872com,1982-02-03,1982,2,day,7.217,0.9147132869736238
+vsi1982jbi880a,1982-02-03,1982,2,day,NA,NA
+vsi1982jbi880b,1982-02-03,1982,2,day,NA,NA
+vsi1982jbi880c,1982-02-03,1982,2,day,0.725,0.23678909940929294
+vsi1982jbi880com,1982-02-03,1982,2,day,3.372,0.6406801532776651
+vsi1982jbi882a,1982-02-03,1982,2,day,NA,NA
+vsi1982jbi882b,1982-02-03,1982,2,day,NA,NA
+vsi1982jbi882c,1982-02-03,1982,2,day,375.797,2.576107436348461
+vsi1982jbi882com,1982-02-03,1982,2,day,414.418,2.618485310521927
+vsi1982jbi889a,1982-02-04,1982,2,night,NA,NA
+vsi1982jbi889b,1982-02-04,1982,2,night,NA,NA
+vsi1982jbi889c,1982-02-04,1982,2,night,489.527,2.6906629171762946
+vsi1982jbi889com,1982-02-04,1982,2,night,564.346,2.7523143237466887
+vsi1982jbi896a,1982-02-04,1982,2,day,NA,NA
+vsi1982jbi896b,1982-02-04,1982,2,day,NA,NA
+vsi1982jbi896c,1982-02-04,1982,2,day,85.286,1.9359403366656924
+vsi1982jbi896com,1982-02-04,1982,2,day,116.971,2.0717752607988125
+vsi1982jbi954a,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi954b,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi954c,1982-02-07,1982,2,day,0.026,0.011147360775797477
+vsi1982jbi954com,1982-02-07,1982,2,day,0.726,0.2370407913791908
+vsi1982jbi960a,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi960b,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi960c,1982-02-07,1982,2,day,0.6,0.20411998265592482
+vsi1982jbi960com,1982-02-07,1982,2,day,1804.959,3.2567078864670336
+vsi1982jbi961a,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi961b,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi961c,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi961com,1982-02-07,1982,2,day,NA,NA
+vsi1982jbi966a,1982-02-08,1982,2,night,NA,NA
+vsi1982jbi966b,1982-02-08,1982,2,night,NA,NA
+vsi1982jbi966c,1982-02-08,1982,2,night,7.518,0.9303376357377301
+vsi1982jbi967com,1982-02-08,1982,2,night,10.589,1.0640459628644827
+vsi1982jbi967d,1982-02-08,1982,2,night,NA,NA
+vsi1982jbi967e,1982-02-08,1982,2,night,NA,NA
+vsi1982jbi967f,1982-02-08,1982,2,night,NA,NA
+vsi1982jbi969a,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi969b,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi969c,1982-02-08,1982,2,day,1.646,0.42258983985148213
+vsi1982jbi969com,1982-02-08,1982,2,day,2.799,0.5796692935547203
+vsi1982jbi975a,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi975b,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi975c,1982-02-08,1982,2,day,0.509,0.1786892397755898
+vsi1982jbi975com,1982-02-08,1982,2,day,0.647,0.21669359916975434
+vsi1982jbi976a,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi976b,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi976c,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi976com,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi982a,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi982b,1982-02-08,1982,2,day,NA,NA
+vsi1982jbi982c,1982-02-08,1982,2,day,0.034,0.014520538757923713
+vsi1982jbi982com,1982-02-08,1982,2,day,0.62,0.20951501454263097
+vsi1982jbi986a,1982-02-09,1982,2,night,NA,NA
+vsi1982jbi986b,1982-02-09,1982,2,night,NA,NA
+vsi1982jbi986c,1982-02-09,1982,2,night,0.163,0.06557971472844842
+vsi1982jbi986com,1982-02-09,1982,2,night,0.857,0.2688119037397804
+vsi1982jbi992a,1982-02-09,1982,2,day,NA,NA
+vsi1982jbi992b,1982-02-09,1982,2,day,NA,NA
+vsi1982jbi992c,1982-02-09,1982,2,day,0,0
+vsi1982jbi992com,1982-02-09,1982,2,day,0.326,0.12254352406875435
+vsi1982jbi997a,1982-02-09,1982,2,day,NA,NA
+vsi1982jbi997b,1982-02-09,1982,2,day,NA,NA
+vsi1982jbi997c,1982-02-09,1982,2,day,0.022,0.009450895798693935
+vsi1982jbi997com,1982-02-09,1982,2,day,6.5,0.8750612633917001
+vsi1984arg1,1984-01-27,1984,1,night,0.059,0.024895960107484977
+vsi1984arg10,1984-01-30,1984,1,day,0,0
+vsi1984arg100,1984-02-24,1984,2,day,0,0
+vsi1984arg101,1984-02-24,1984,2,day,34.474,1.549910161688267
+vsi1984arg102,1984-02-24,1984,2,night,0.442,0.15896526038341022
+vsi1984arg103,1984-02-25,1984,2,night,0.142,0.05766610390982921
+vsi1984arg104,1984-02-25,1984,2,day,4.798,0.7632782110189786
+vsi1984arg105,1984-02-25,1984,2,day,0,0
+vsi1984arg106,1984-02-26,1984,2,day,0,0
+vsi1984arg107,1984-02-26,1984,2,day,87.575,1.947311161005215
+vsi1984arg108,1984-02-26,1984,2,day,0,0
+vsi1984arg109,1984-02-26,1984,2,night,0,0
+vsi1984arg11,1984-01-31,1984,1,night,0.137,0.05576046468773478
+vsi1984arg110,1984-02-27,1984,2,night,0,0
+vsi1984arg113,1984-02-28,1984,2,day,0,0
+vsi1984arg115,1984-02-29,1984,2,day,0,0
+vsi1984arg116,1984-02-29,1984,2,day,0,0
+vsi1984arg117,1984-02-29,1984,2,day,0,0
+vsi1984arg118,1984-02-29,1984,2,day,0,0
+vsi1984arg119,1984-02-29,1984,2,day,0.609,0.20655604409902956
+vsi1984arg12,1984-01-31,1984,1,day,0,0
+vsi1984arg120,1984-03-01,1984,3,night,522.388,2.718823761113327
+vsi1984arg121,1984-03-01,1984,3,day,0,0
+vsi1984arg122,1984-03-01,1984,3,day,0,0
+vsi1984arg123,1984-03-02,1984,3,night,0,0
+vsi1984arg124,1984-03-02,1984,3,day,24.398,1.4047995188576539
+vsi1984arg125,1984-03-02,1984,3,day,58.929,1.777637030940181
+vsi1984arg126,1984-03-02,1984,3,day,13.149,1.1507257465861944
+vsi1984arg127,1984-03-02,1984,3,day,0,0
+vsi1984arg128,1984-03-03,1984,3,night,0.775,0.24919835739111287
+vsi1984arg129,1984-03-03,1984,3,day,20.122,1.3247350382860186
+vsi1984arg13,1984-01-31,1984,1,day,0,0
+vsi1984arg130,1984-03-03,1984,3,day,0,0
+vsi1984arg131,1984-03-04,1984,3,night,2.95,0.5965970956264602
+vsi1984arg132,1984-03-04,1984,3,night,1.313,0.3641756327706194
+vsi1984arg133,1984-03-04,1984,3,day,0,0
+vsi1984arg134,1984-03-04,1984,3,day,208.613,2.321418213679946
+vsi1984arg135,1984-03-04,1984,3,day,0,0
+vsi1984arg136,1984-03-05,1984,3,night,1.485,0.39532639306935097
+vsi1984arg137,1984-03-06,1984,3,day,37.302,1.5832214519395373
+vsi1984arg138,1984-03-06,1984,3,day,2.403,0.5318619490958094
+vsi1984arg139,1984-03-06,1984,3,night,10.921,1.0763126879768579
+vsi1984arg14,1984-01-31,1984,1,day,0,0
+vsi1984arg140,1984-03-07,1984,3,night,2.055,0.4850112145785731
+vsi1984arg141,1984-03-07,1984,3,day,3.75,0.6766936096248666
+vsi1984arg142,1984-03-07,1984,3,night,0,0
+vsi1984arg143,1984-03-09,1984,3,night,0,0
+vsi1984arg144,1984-03-09,1984,3,day,2425.991,3.385068165842613
+vsi1984arg146,1984-03-09,1984,3,day,124.96,2.1002326519645065
+vsi1984arg147,1984-03-10,1984,3,night,0,0
+vsi1984arg148,1984-03-10,1984,3,night,0.453,0.16226561429802155
+vsi1984arg149,1984-03-10,1984,3,day,1.568,0.40959501939681564
+vsi1984arg15,1984-01-31,1984,1,day,0.432,0.1559430179718367
+vsi1984arg151,1984-03-10,1984,3,night,116.708,2.07080598058163
+vsi1984arg152,1984-03-11,1984,3,night,0.707,0.23223352111473358
+vsi1984arg153,1984-03-11,1984,3,day,670.324,2.8269321735982227
+vsi1984arg154,1984-03-11,1984,3,day,277.314,2.444535053153375
+vsi1984arg155,1984-03-11,1984,3,day,0,0
+vsi1984arg156,1984-03-12,1984,3,night,2746.086,3.4388722556130666
+vsi1984arg157,1984-03-12,1984,3,night,2.849,0.585347911094591
+vsi1984arg158,1984-03-12,1984,3,day,0,0
+vsi1984arg159,1984-03-12,1984,3,day,0,0
+vsi1984arg16,1984-02-01,1984,2,night,1.162,0.3348556896172915
+vsi1984arg160,1984-03-12,1984,3,day,6.768,0.8903092168999485
+vsi1984arg161,1984-03-13,1984,3,night,6.546,0.8777168008649767
+vsi1984arg162,1984-03-13,1984,3,night,0.365,0.13513265137677483
+vsi1984arg163,1984-03-13,1984,3,day,0,0
+vsi1984arg164,1984-03-13,1984,3,day,0,0
+vsi1984arg165,1984-03-13,1984,3,day,0,0
+vsi1984arg166,1984-03-14,1984,3,night,0.743,0.24129738710999313
+vsi1984arg167,1984-03-14,1984,3,night,0.475,0.16879202031418183
+vsi1984arg168,1984-03-14,1984,3,day,0,0
+vsi1984arg169,1984-03-14,1984,3,day,0,0
+vsi1984arg170,1984-03-15,1984,3,night,0.483,0.17114115102838204
+vsi1984arg171,1984-03-15,1984,3,night,0,0
+vsi1984arg172,1984-03-15,1984,3,day,0.429,0.15503222879097023
+vsi1984arg174,1984-03-15,1984,3,day,1.71,0.4329692908744057
+vsi1984arg175,1984-03-16,1984,3,night,0.454,0.162564406523019
+vsi1984arg176,1984-03-16,1984,3,night,0,0
+vsi1984arg177,1984-03-16,1984,3,day,0,0
+vsi1984arg178,1984-04-03,1984,4,night,0.833,0.2631624649622166
+vsi1984arg179,1984-04-03,1984,4,night,2.77,0.5763413502057928
+vsi1984arg18,1984-02-01,1984,2,day,0,0
+vsi1984arg180,1984-04-03,1984,4,day,0,0
+vsi1984arg181,1984-04-03,1984,4,day,0,0
+vsi1984arg183,1984-04-04,1984,4,night,0.568,0.19534605834841964
+vsi1984arg184,1984-04-04,1984,4,night,2.834,0.5836521085420439
+vsi1984arg185,1984-04-04,1984,4,day,0,0
+vsi1984arg186,1984-04-04,1984,4,day,2.878,0.5886078047426866
+vsi1984arg187,1984-04-04,1984,4,night,0.411,0.14952701375434782
+vsi1984arg188,1984-04-05,1984,4,night,0.203,0.08026562733984477
+vsi1984arg189,1984-04-05,1984,4,day,6.539,0.8773137433122384
+vsi1984arg190,1984-04-05,1984,4,day,0,0
+vsi1984arg193,1984-04-06,1984,4,night,1.129,0.32817566143832255
+vsi1984arg195,1984-04-07,1984,4,day,0,0
+vsi1984arg196,1984-04-07,1984,4,day,1.44,0.3873898263387294
+vsi1984arg198,1984-04-08,1984,4,night,0,0
+vsi1984arg199,1984-04-08,1984,4,day,0,0
+vsi1984arg2,1984-01-28,1984,1,night,0,0
+vsi1984arg20,1984-02-01,1984,2,day,0.249,0.09656243837413554
+vsi1984arg200,1984-04-07,1984,4,day,0,0
+vsi1984arg201,1984-04-07,1984,4,night,13.094,1.1490342671612495
+vsi1984arg202,1984-04-07,1984,4,night,0,0
+vsi1984arg21,1984-02-02,1984,2,night,1.157,0.33385014510254507
+vsi1984arg22,1984-02-02,1984,2,day,0,0
+vsi1984arg23,1984-02-02,1984,2,day,0,0
+vsi1984arg24,1984-02-02,1984,2,day,0,0
+vsi1984arg26,1984-02-03,1984,2,night,0.112,0.0461047872460387
+vsi1984arg27,1984-02-03,1984,2,day,0,0
+vsi1984arg28,1984-02-03,1984,2,day,0,0
+vsi1984arg3,1984-01-28,1984,1,day,0,0
+vsi1984arg30,1984-02-04,1984,2,night,0.113,0.04649516433470831
+vsi1984arg32,1984-02-04,1984,2,day,0,0
+vsi1984arg34,1984-02-04,1984,2,day,0,0
+vsi1984arg35,1984-02-04,1984,2,night,0.283,0.10822665637492848
+vsi1984arg37,1984-02-05,1984,2,day,0,0
+vsi1984arg38,1984-02-05,1984,2,day,0,0
+vsi1984arg39,1984-02-05,1984,2,day,0,0
+vsi1984arg4,1984-01-28,1984,1,day,0,0
+vsi1984arg40,1984-02-06,1984,2,day,0,0
+vsi1984arg41,1984-02-06,1984,2,day,0,0
+vsi1984arg45,1984-02-07,1984,2,day,0,0
+vsi1984arg46,1984-02-07,1984,2,day,0,0
+vsi1984arg47,1984-02-08,1984,2,day,0,0
+vsi1984arg48,1984-02-08,1984,2,day,0,0
+vsi1984arg5,1984-01-28,1984,1,day,0.148,0.05994188806195468
+vsi1984arg50,1984-02-09,1984,2,day,0.187,0.07445071895459124
+vsi1984arg53,1984-02-10,1984,2,day,482.578,2.6844665347529704
+vsi1984arg55,1984-02-11,1984,2,day,42.127,1.6347492488076665
+vsi1984arg56,1984-02-11,1984,2,day,1.037,0.3089910290001641
+vsi1984arg57,1984-02-12,1984,2,night,0.923,0.2839792842384799
+vsi1984arg58,1984-02-12,1984,2,day,1816.897,3.259569272945985
+vsi1984arg59,1984-02-12,1984,2,day,2.88,0.5888317255942073
+vsi1984arg61,1984-02-13,1984,2,day,0,0
+vsi1984arg62,1984-02-13,1984,2,day,0,0
+vsi1984arg63,1984-02-13,1984,2,day,23.254,1.3847833732339923
+vsi1984arg64,1984-02-14,1984,2,day,0,0
+vsi1984arg65,1984-02-14,1984,2,day,0,0
+vsi1984arg66,1984-02-15,1984,2,night,0,0
+vsi1984arg67,1984-02-15,1984,2,day,0,0
+vsi1984arg68,1984-02-15,1984,2,day,0,0
+vsi1984arg7,1984-01-30,1984,1,night,0.469,0.16702179579025647
+vsi1984arg70,1984-02-16,1984,2,day,0,0
+vsi1984arg71,1984-02-16,1984,2,day,0,0
+vsi1984arg73,1984-02-17,1984,2,day,0.614,0.20790353038605158
+vsi1984arg74,1984-02-18,1984,2,night,0.131,0.05346260492545529
+vsi1984arg76,1984-02-18,1984,2,day,0.309,0.11693964655075577
+vsi1984arg78,1984-02-19,1984,2,night,0,0
+vsi1984arg79,1984-02-19,1984,2,day,1.208,0.3439990690571613
+vsi1984arg8,1984-01-30,1984,1,day,0,0
+vsi1984arg80,1984-02-19,1984,2,night,0.263,0.10140335055533072
+vsi1984arg81,1984-02-20,1984,2,day,0,0
+vsi1984arg82,1984-02-20,1984,2,day,0.316,0.11925588927793669
+vsi1984arg83,1984-02-20,1984,2,day,3.012,0.6033609243483805
+vsi1984arg84,1984-02-20,1984,2,day,0.302,0.11461098423217315
+vsi1984arg85,1984-02-21,1984,2,night,0.42,0.15228834438305647
+vsi1984arg86,1984-02-21,1984,2,day,15.328,1.212932991708014
+vsi1984arg87,1984-02-21,1984,2,day,3.248,0.6281845080734126
+vsi1984arg88,1984-02-21,1984,2,day,0,0
+vsi1984arg89,1984-02-21,1984,2,night,8.853,0.9935684827897274
+vsi1984arg90,1984-02-22,1984,2,night,5.89,0.8382192219076258
+vsi1984arg91,1984-02-22,1984,2,day,5.255,0.7962273140294387
+vsi1984arg92,1984-02-22,1984,2,day,36.225,1.5708347064242139
+vsi1984arg93,1984-02-22,1984,2,day,0,0
+vsi1984arg94,1984-02-23,1984,2,night,0.139,0.05652372407910037
+vsi1984arg95,1984-02-23,1984,2,day,503.312,2.702699302229684
+vsi1984arg96,1984-02-23,1984,2,day,0,0
+vsi1984arg97,1984-02-23,1984,2,day,0,0
+vsi1984arg98,1984-02-24,1984,2,night,0,0
+vsi1984arg99,1984-02-24,1984,2,day,0,0
+vsi1984pnx102,1983-10-24,1983,10,day,0,0
+vsi1984pnx105,1983-10-24,1983,10,day,405.73,2.609306206714763
+vsi1984pnx108,1983-10-25,1983,10,night,26.92,1.4459154139511234
+vsi1984pnx110,1983-10-25,1983,10,day,61.667,1.797038904614905
+vsi1984pnx111,1983-10-25,1983,10,day,7.428,0.9257245269360627
+vsi1984pnx114,1983-10-25,1983,10,day,0.182,0.07261747654523654
+vsi1984pnx115,1983-10-26,1983,10,day,18.21,1.2835273648616936
+vsi1984pnx117,1983-10-29,1983,10,day,0.285,0.1089031276673133
+vsi1984pnx118,1983-10-29,1983,10,day,27.657,1.4572307236872692
+vsi1984pnx119,1983-10-29,1983,10,day,0,0
+vsi1984pnx120,1983-10-29,1983,10,day,2645.069,3.422601164843372
+vsi1984pnx121,1983-10-30,1983,10,day,0.897,0.27806733088866253
+vsi1984pnx124,1983-10-31,1983,10,day,0.699,0.23019337886904556
+vsi1984pnx125,1983-10-31,1983,10,day,3.568,0.6597260952377916
+vsi1984pnx126,1983-10-31,1983,10,day,2.659,0.5633624094866074
+vsi1984pnx127,1983-10-31,1983,10,day,352.573,2.548479093448739
+vsi1984pnx128,1983-11-01,1983,11,day,911.223,2.9601010179675797
+vsi1984pnx129,1983-11-01,1983,11,day,0.897,0.27806733088866253
+vsi1984pnx130,1983-11-01,1983,11,day,769.471,2.8867562968156357
+vsi1984pnx131,1983-11-03,1983,11,day,229.755,2.3631511200939785
+vsi1984pnx133,1983-11-03,1983,11,day,21.805,1.358030076576957
+vsi1984pnx135,1983-11-03,1983,11,day,2161.02,3.3348597071239814
+vsi1984pnx137,1983-11-03,1983,11,day,9.702,1.029464946638236
+vsi1984pnx139,1983-11-03,1983,11,day,0.09,0.03742649794062367
+vsi1984pnx141,1983-11-04,1983,11,night,2.556,0.5509617522981761
+vsi1984pnx143,1983-11-04,1983,11,day,0.209,0.0824263008607719
+vsi1984pnx145,1983-11-04,1983,11,day,0,0
+vsi1984pnx147,1983-11-04,1983,11,day,0.229,0.08955188288645412
+vsi1984pnx150,1983-11-04,1983,11,day,0.383,0.14082218010931058
+vsi1984pnx152,1983-11-05,1983,11,night,4.73,0.75815462196739
+vsi1984pnx154,1983-11-05,1983,11,day,1029.04,3.012854090182812
+vsi1984pnx156,1983-11-05,1983,11,day,0.272,0.10448711131239508
+vsi1984pnx158,1983-11-05,1983,11,day,9.234,1.0100454126360985
+vsi1984pnx160,1983-11-05,1983,11,day,0.494,0.17435059747937998
+vsi1984pnx162,1983-11-05,1983,11,day,0.454,0.162564406523019
+vsi1984pnx164,1983-11-05,1983,11,day,25.133,1.417189268459015
+vsi1984pnx166,1983-11-06,1983,11,day,1.796,0.4465371670736439
+vsi1984pnx167,1983-11-06,1983,11,day,0.248,0.09621458534640517
+vsi1984pnx168,1983-11-06,1983,11,day,0.653,0.2182728535714475
+vsi1984pnx170,1983-11-06,1983,11,day,8.392,0.9727580839035392
+vsi1984pnx172,1983-11-07,1983,11,day,1.734,0.4367985102318035
+vsi1984pnx174,1983-11-07,1983,11,day,0.551,0.19061179781360496
+vsi1984pnx176,1983-11-07,1983,11,day,102.42,2.014604533436051
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+vsi1988evr138,1988-03-03,1988,3,night,0,0
+vsi1988evr139,1988-03-03,1988,3,day,190.119,2.2813038643704417
+vsi1988evr140,1988-03-03,1988,3,day,70.409,1.8537529513281956
+vsi1988evr141,1988-03-03,1988,3,day,148.854,2.1756683399059042
+vsi1988evr142,1988-03-04,1988,3,night,2.437,0.5361795321372254
+vsi1988evr145,1988-03-04,1988,3,night,15.119,1.2073380952883563
+vsi1988evr146,1988-03-05,1988,3,night,25.158,1.417604535440898
+vsi1988evr147,1988-03-05,1988,3,day,1.702,0.4316853446860118
+vsi1988evr148,1988-03-05,1988,3,day,0,0
+vsi1988evr150,1988-03-06,1988,3,night,0,0
+vsi1988evr151,1988-03-06,1988,3,night,2.02,0.48000694295715063
+vsi1988evr153,1988-03-06,1988,3,day,14.679,1.1953183601130135
+vsi1988evr154,1988-03-06,1988,3,day,5.389,0.80543288813214
+vsi1988evr155,1988-03-06,1988,3,night,24.066,1.3990850353332245
+vsi1988evr156,1988-03-07,1988,3,night,9.82,1.0342272607705507
+vsi1988evr158,1988-03-07,1988,3,night,0.518,0.18127177155946156
+vsi1988evr16,1988-01-25,1988,1,day,0,0
+vsi1988evr161,1988-03-08,1988,3,day,6.062,0.8489277132270784
+vsi1988evr162,1988-03-08,1988,3,day,962.804,2.983988724387189
+vsi1988evr163,1988-03-07,1988,3,night,0.125,0.05115252244738129
+vsi1988evr164,1988-03-08,1988,3,night,51.99,1.7241939195143297
+vsi1988evr165,1988-03-08,1988,3,night,1.05,0.31175386105575426
+vsi1988evr167,1988-03-08,1988,3,day,5.151,0.7889457270237478
+vsi1988evr18,1988-01-26,1988,1,night,0,0
+vsi1988evr19,1988-01-26,1988,1,day,0,0
+vsi1988evr20,1988-01-26,1988,1,day,0,0
+vsi1988evr21,1988-01-27,1988,1,night,1.635,0.4207806195485654
+vsi1988evr22,1988-01-27,1988,1,night,0,0
+vsi1988evr24,1988-01-27,1988,1,day,0,0
+vsi1988evr25,1988-01-27,1988,1,day,15.505,1.2176155286663304
+vsi1988evr26,1988-01-28,1988,1,night,8.035,0.9559281568969507
+vsi1988evr27,1988-01-27,1988,1,night,0,0
+vsi1988evr28,1988-01-28,1988,1,day,1373.661,3.1381956116344427
+vsi1988evr29,1988-01-28,1988,1,day,0,0
+vsi1988evr3,1988-01-20,1988,1,day,0,0
+vsi1988evr30,1988-01-31,1988,1,night,0,0
+vsi1988evr31,1988-01-31,1988,1,day,0,0
+vsi1988evr32,1988-01-31,1988,1,day,0,0
+vsi1988evr33,1988-02-01,1988,2,night,0.471,0.16761267272753014
+vsi1988evr34,1988-02-01,1988,2,night,29.944,1.4905764524029077
+vsi1988evr35,1988-02-01,1988,2,day,0,0
+vsi1988evr36,1988-02-01,1988,2,day,164.293,2.2182544620061466
+vsi1988evr37,1988-02-01,1988,2,day,57.096,1.764146231568817
+vsi1988evr38,1988-02-01,1988,2,day,108.781,2.0405271824713194
+vsi1988evr39,1988-02-02,1988,2,night,0.527,0.1838390370564212
+vsi1988evr40,1988-02-02,1988,2,night,444.22,2.6485746653089937
+vsi1988evr41,1988-02-02,1988,2,day,0.512,0.17955179116518774
+vsi1988evr42,1988-02-02,1988,2,day,140.45,2.1506029517930094
+vsi1988evr43,1988-02-02,1988,2,day,3547.5,3.5500448095649055
+vsi1988evr44,1988-02-02,1988,2,day,49.103,1.6998637307462632
+vsi1988evr45,1988-02-03,1988,2,night,154.265,2.1910735676411632
+vsi1988evr46,1988-02-03,1988,2,night,85.165,1.935330892348943
+vsi1988evr48,1988-02-03,1988,2,day,363.702,2.5619381449605654
+vsi1988evr5,1988-01-21,1988,1,day,0,0
+vsi1988evr50,1988-02-04,1988,2,night,1.68,0.42813479402878873
+vsi1988evr51,1988-02-04,1988,2,day,66.381,1.8285374520481767
+vsi1988evr52,1988-02-04,1988,2,day,145.111,2.164682913124363
+vsi1988evr53,1988-02-04,1988,2,day,592.017,2.773067143449199
+vsi1988evr54,1988-02-04,1988,2,day,256.357,2.4105359853704464
+vsi1988evr55,1988-02-05,1988,2,night,2.136,0.4963760540124008
+vsi1988evr56,1988-02-05,1988,2,night,95.907,1.9863551491000169
+vsi1988evr57,1988-02-05,1988,2,day,675.793,2.830455858191288
+vsi1988evr58,1988-02-05,1988,2,day,0.876,0.2732328340430456
+vsi1988evr59,1988-02-05,1988,2,day,290.009,2.4639064205663037
+vsi1988evr6,1988-01-21,1988,1,day,43.146,1.6448913589526197
+vsi1988evr60,1988-02-06,1988,2,night,23.748,1.3935401073479632
+vsi1988evr61,1988-02-06,1988,2,night,15.289,1.2118944233009867
+vsi1988evr63,1988-02-06,1988,2,day,3.865,0.6870828446043707
+vsi1988evr65,1988-02-07,1988,2,night,1.787,0.4451369687133042
+vsi1988evr66,1988-02-07,1988,2,day,2.486,0.5423273827739744
+vsi1988evr67,1988-02-07,1988,2,day,4.508,0.7409939315848861
+vsi1988evr68,1988-02-08,1988,2,night,103.861,2.0206139950180924
+vsi1988evr69,1988-02-08,1988,2,day,74.782,1.8795660627692994
+vsi1988evr7,1988-01-21,1988,1,day,17.062,1.256765837941647
+vsi1988evr70,1988-02-08,1988,2,day,43.613,1.6494614283444757
+vsi1988evr71,1988-02-09,1988,2,day,0,0
+vsi1988evr72,1988-02-09,1988,2,day,2.963,0.5980240723341896
+vsi1988evr73,1988-02-09,1988,2,day,0,0
+vsi1988evr74,1988-02-10,1988,2,night,52.934,1.731862630811199
+vsi1988evr75,1988-02-10,1988,2,day,1867.03,3.271383846588848
+vsi1988evr76,1988-02-10,1988,2,day,377.331,2.577871928333049
+vsi1988evr77,1988-02-10,1988,2,day,0,0
+vsi1988evr78,1988-02-11,1988,2,night,27.215,1.45048005460606
+vsi1988evr79,1988-02-11,1988,2,day,2.44,0.5365584425715302
+vsi1988evr81,1988-02-12,1988,2,day,4.467,0.7377490738915571
+vsi1988evr83,1988-02-13,1988,2,night,1.351,0.3712526291249394
+vsi1988evr85,1988-02-13,1988,2,day,2.589,0.5549734583332397
+vsi1988evr86,1988-02-14,1988,2,night,2.647,0.5619357633137813
+vsi1988evr87,1988-02-14,1988,2,day,3.172,0.6203442997544933
+vsi1988evr88,1988-02-14,1988,2,day,4.015,0.7002709373564369
+vsi1988evr89,1988-02-14,1988,2,night,379.189,2.579999547278477
+vsi1988evr9,1988-01-22,1988,1,day,0,0
+vsi1988evr91,1988-02-15,1988,2,day,111.543,2.051318487733563
+vsi1988evr92,1988-02-21,1988,2,day,1.382,0.37694175714675865
+vsi1988evr93,1988-02-21,1988,2,day,34.652,1.552083897851289
+vsi1988evr94,1988-02-21,1988,2,night,66.102,1.826735464670026
+vsi1988evr95,1988-02-22,1988,2,night,0.858,0.269045709657623
+vsi1988evr97,1988-02-22,1988,2,day,1.793,0.4460709357010051
+vsi1988evr99,1988-02-23,1988,2,day,60.61,1.7896512087934096
+vsi1988pnx100,1987-11-03,1987,11,day,0.21,0.08278537031645007
+vsi1988pnx102,1987-11-03,1987,11,day,18.274,1.2849718546478694
+vsi1988pnx104,1987-11-04,1987,11,night,45.633,1.668693355440256
+vsi1988pnx105,1987-11-04,1987,11,day,8.408,0.9734973087320632
+vsi1988pnx106,1987-11-04,1987,11,day,0.084,0.03502928220236815
+vsi1988pnx108,1987-11-04,1987,11,day,2.077,0.48812749624745844
+vsi1988pnx110,1987-11-04,1987,11,day,0.709,0.23274206272073683
+vsi1988pnx112,1987-11-04,1987,11,day,2.728,0.5714759036819438
+vsi1988pnx114,1987-11-05,1987,11,day,34.53,1.5505952074893279
+vsi1988pnx116,1987-11-05,1987,11,day,2.36,0.5263392773898441
+vsi1988pnx117,1987-11-05,1987,11,day,0.346,0.12904505988795806
+vsi1988pnx118,1987-11-05,1987,11,day,740.924,2.870359420008011
+vsi1988pnx126,1987-11-11,1987,11,day,0.802,0.25575478664304413
+vsi1988pnx127,1987-11-11,1987,11,day,0.272,0.10448711131239508
+vsi1988pnx128,1987-11-12,1987,11,day,876.109,2.9430535673192995
+vsi1988pnx129,1987-11-12,1987,11,day,0.038,0.01619735351243906
+vsi1988pnx130,1987-11-13,1987,11,day,249.909,2.399516239553891
+vsi1988pnx131,1987-11-13,1987,11,day,1.271,0.35621713421973517
+vsi1988pnx132,1987-11-14,1987,11,night,33.239,1.5345210719857247
+vsi1988pnx133,1987-11-14,1987,11,day,18.858,1.2979355063596474
+vsi1988pnx134,1987-11-14,1987,11,day,1.172,0.33685982091680927
+vsi1988pnx135,1987-11-14,1987,11,day,0.423,0.15320490008428433
+vsi1988pnx136,1987-11-15,1987,11,night,0.99,0.29885307640970665
+vsi1988pnx137,1987-11-15,1987,11,day,0.646,0.216429830876251
+vsi1988pnx140,1987-11-16,1987,11,night,11.345,1.0914910942679512
+vsi1988pnx141,1987-11-16,1987,11,day,2.099,0.49122157623928303
+vsi1988pnx142,1987-11-16,1987,11,day,6.328,0.864985460659794
+vsi1988pnx143,1987-11-16,1987,11,day,15.658,1.2216228577487898
+vsi1988pnx144,1987-11-16,1987,11,day,1.822,0.450557009418329
+vsi1988pnx146,1987-11-17,1987,11,day,1300.102,3.1143113444483923
+vsi1988pnx147,1987-11-17,1987,11,day,2.56,0.5514499979728752
+vsi1988pnx152,1987-11-17,1987,11,day,206.229,2.316450531279193
+vsi1988pnx154,1987-11-17,1987,11,day,0.408,0.14860265480609336
+vsi1988pnx156,1987-11-17,1987,11,day,0.426,0.15411952551584673
+vsi1988pnx157,1987-11-18,1987,11,day,185.432,2.2705204586115544
+vsi1988pnx158,1987-11-18,1987,11,day,0.536,0.1863912156954932
+vsi1988pnx159,1987-11-18,1987,11,day,49.521,1.703471938284396
+vsi1988pnx161,1987-11-19,1987,11,day,0.014,0.006037954997317177
+vsi1988pnx162,1987-11-19,1987,11,day,0,0
+vsi1988pnx163,1987-11-19,1987,11,day,0,0
+vsi1988pnx164,1987-11-20,1987,11,night,0.191,0.07591176148277752
+vsi1988pnx169,1987-11-20,1987,11,day,10.992,1.0788916198402232
+vsi1988pnx171,1987-11-20,1987,11,day,0.28,0.10720996964786837
+vsi1988pnx173,1987-11-20,1987,11,day,4.627,0.7502769151539926
+vsi1988pnx174,1987-11-20,1987,11,day,2.765,0.5757649805367194
+vsi1988pnx175,1987-11-21,1987,11,day,4.318,0.7257483329955482
+vsi1988pnx176,1987-11-21,1987,11,day,0.506,0.17782497186468177
+vsi1988pnx177,1987-11-21,1987,11,day,0.11,0.04532297878665747
+vsi1988pnx178,1987-11-21,1987,11,day,2.012,0.47885496752866297
+vsi1988pnx180,1987-11-22,1987,11,day,0.323,0.12155984418750096
+vsi1988pnx181,1987-11-22,1987,11,day,31.602,1.5132442430839226
+vsi1988pnx182,1987-11-22,1987,11,day,23.228,1.3843175649912642
+vsi1988pnx183,1987-11-22,1987,11,day,4.303,0.7245216271185627
+vsi1988pnx186,1987-11-30,1987,11,day,1.453,0.38969754820638575
+vsi1988pnx188,1987-12-01,1987,12,day,0.053,0.02242837118548649
+vsi1988pnx189,1987-12-01,1987,12,day,0.036,0.01535975540921423
+vsi1988pnx190,1987-12-02,1987,12,day,0.351,0.13065534902203058
+vsi1988pnx191,1987-12-02,1987,12,day,20.172,1.3257618853192303
+vsi1988pnx192,1987-12-02,1987,12,day,21.965,1.3610664497539422
+vsi1988pnx193,1987-12-02,1987,12,day,2.108,0.4924810101288767
+vsi1988pnx194,1987-12-03,1987,12,day,0.184,0.07335170238690095
+vsi1988pnx195,1987-12-03,1987,12,day,0.137,0.05576046468773478
+vsi1988pnx199,1987-12-03,1987,12,day,2.339,0.5236164190543707
+vsi1988pnx200,1987-12-03,1987,12,day,1.325,0.36642295722597273
+vsi1988pnx201,1987-12-04,1987,12,day,0.119,0.04883008652835004
+vsi1988pnx202,1987-12-05,1987,12,day,0.225,0.0881360887005513
+vsi1988pnx203,1987-12-05,1987,12,day,0.141,0.05728564441821465
+vsi1988pnx204,1987-12-05,1987,12,day,0.071,0.029789470831855614
+vsi1988pnx205,1987-12-05,1987,12,day,13.178,1.1516149720160127
+vsi1988pnx206,1987-12-05,1987,12,day,1.25,0.3521825181113625
+vsi1988pnx208,1987-12-07,1987,12,day,0.484,0.17143390094300825
+vsi1988pnx212,1987-12-09,1987,12,day,13.223,1.152991210038619
+vsi1988pnx217,1987-12-12,1987,12,day,2.012,0.47885496752866297
+vsi1988pnx91,1987-11-02,1987,11,day,0.136,0.05537833137500011
+vsi1988pnx92,1987-11-02,1987,11,day,6.157,0.8547310172139422
+vsi1988pnx93,1987-11-03,1987,11,night,216.762,2.3379820965852653
+vsi1988pnx94,1987-11-03,1987,11,day,0.445,0.15986784709256668
+vsi1988pnx96,1987-11-03,1987,11,day,36.384,1.572685768016257
+vsi1988pnx98,1987-11-03,1987,11,day,596.444,2.776297203961995
+vsi1989pnx102a,1988-10-27,1988,10,day,NA,NA
+vsi1989pnx102b,1988-10-27,1988,10,day,NA,NA
+vsi1989pnx102c,1988-10-27,1988,10,day,1.657,0.42439155441027754
+vsi1989pnx102com,1988-10-27,1988,10,day,2.425,0.5346605758284444
+vsi1989pnx104,1988-10-28,1988,10,day,1.263,0.35468455395472853
+vsi1989pnx109a,1988-10-29,1988,10,day,NA,NA
+vsi1989pnx109b,1988-10-29,1988,10,day,73.156,1.8701462957753463
+vsi1989pnx109com,1988-10-29,1988,10,day,82.739,1.9229277702992404
+vsi1989pnx113,1988-10-31,1988,10,day,6.656,0.8840019247687871
+vsi1989pnx116a,1988-11-01,1988,11,night,NA,NA
+vsi1989pnx116b,1988-11-01,1988,11,night,0.044,0.01870049866624337
+vsi1989pnx116com,1988-11-01,1988,11,night,0.048,0.02036128264770786
+vsi1989pnx119a,1988-11-03,1988,11,day,NA,NA
+vsi1989pnx119b,1988-11-03,1988,11,day,8.148,0.9613261559347108
+vsi1989pnx119com,1988-11-03,1988,11,day,9.177,1.0076197745174034
+vsi1989pnx122a,1988-11-06,1988,11,day,NA,NA
+vsi1989pnx122b,1988-11-06,1988,11,day,NA,NA
+vsi1989pnx122c,1988-11-06,1988,11,day,140.056,2.1493915641636345
+vsi1989pnx122com,1988-11-06,1988,11,day,392.12,2.594525139136006
+vsi1989pnx127a,1988-11-09,1988,11,day,NA,NA
+vsi1989pnx127b,1988-11-09,1988,11,day,138.754,2.1453642469918552
+vsi1989pnx127com,1988-11-09,1988,11,day,151.597,2.183545995635554
+vsi1989pnx131a,1988-11-11,1988,11,day,NA,NA
+vsi1989pnx131b,1988-11-11,1988,11,day,0.081,0.03382569395331033
+vsi1989pnx132a,1988-11-11,1988,11,day,NA,NA
+vsi1989pnx132b,1988-11-11,1988,11,day,NA,NA
+vsi1989pnx132c,1988-11-11,1988,11,day,1.896,0.4617985575251093
+vsi1989pnx132com,1988-11-11,1988,11,day,2.313,0.52022143588196
+vsi1989pnx134a,1988-11-12,1988,11,day,NA,NA
+vsi1989pnx134b,1988-11-12,1988,11,day,NA,NA
+vsi1989pnx134c,1988-11-12,1988,11,day,1.414,0.3827372657613304
+vsi1989pnx134com,1988-11-12,1988,11,day,2.083,0.48897352472650824
+vsi1989pnx136a,1988-11-12,1988,11,day,NA,NA
+vsi1989pnx136b,1988-11-12,1988,11,day,NA,NA
+vsi1989pnx136c,1988-11-12,1988,11,day,3.955,0.6950436588212942
+vsi1989pnx136com,1988-11-12,1988,11,day,4.748,0.7595167594621881
+vsi1989pnx139a,1988-11-13,1988,11,day,NA,NA
+vsi1989pnx139b,1988-11-13,1988,11,day,NA,NA
+vsi1989pnx139c,1988-11-13,1988,11,day,0,0
+vsi1989pnx139com,1988-11-13,1988,11,day,0.069,0.028977705208777998
+vsi1989pnx141a,1988-11-15,1988,11,day,NA,NA
+vsi1989pnx141b,1988-11-15,1988,11,day,0.112,0.0461047872460387
+vsi1989pnx141c,1988-11-15,1988,11,day,6.765,0.8901414600645774
+vsi1989pnx141com,1988-11-15,1988,11,day,8.287,0.9678754455480332
+vsi1989pnx141d,1988-11-15,1988,11,day,NA,NA
+vsi1989pnx141e,1988-11-15,1988,11,day,NA,NA
+vsi1989pnx141f,1988-11-15,1988,11,day,NA,NA
+vsi1989pnx79a,1988-10-18,1988,10,day,NA,NA
+vsi1989pnx79b,1988-10-18,1988,10,day,NA,NA
+vsi1989pnx79c,1988-10-18,1988,10,day,10.372,1.0558368510184044
+vsi1989pnx79com,1988-10-18,1988,10,day,45.058,1.663305075447098
+vsi1989pnx87a,1988-10-20,1988,10,day,NA,NA
+vsi1989pnx87b,1988-10-20,1988,10,day,NA,NA
+vsi1989pnx87c,1988-10-20,1988,10,day,1.562,0.40857912540866753
+vsi1989pnx87com,1988-10-20,1988,10,day,19.094,1.3030663979235104
+vsi1989pnx89a,1988-10-22,1988,10,day,NA,NA
+vsi1989pnx89b,1988-10-22,1988,10,day,NA,NA
+vsi1989pnx89c,1988-10-22,1988,10,day,0.566,0.19479175772192453
+vsi1989pnx89com,1988-10-22,1988,10,day,0.891,0.2766915288450397
+vsi1989pnx94a,1988-10-24,1988,10,day,NA,NA
+vsi1989pnx94b,1988-10-24,1988,10,day,NA,NA
+vsi1989pnx94c,1988-10-24,1988,10,day,4.196,0.7156691424009902
+vsi1989pnx94com,1988-10-24,1988,10,day,6.036,0.8473258307854237
+vsi1989pnx96a,1988-10-25,1988,10,day,NA,NA
+vsi1989pnx96b,1988-10-25,1988,10,day,NA,NA
+vsi1989pnx96c,1988-10-25,1988,10,day,3.779,0.679337030520794
+vsi1989pnx96com,1988-10-25,1988,10,day,4.865,0.7682680164515481
+vsi1989pnx99a,1988-10-25,1988,10,night,NA,NA
+vsi1989pnx99b,1988-10-25,1988,10,night,NA,NA
+vsi1989pnx99c,1988-10-25,1988,10,night,0.118,0.04844180355040444
+vsi1989pnx99com,1988-10-25,1988,10,night,0.178,0.07114529045108281
+vsi1990mex1,1989-12-26,1989,12,day,18.455,1.2890312351397613
+vsi1990mex10,1989-12-28,1989,12,day,17.368,1.2640618707178795
+vsi1990mex11,1989-12-28,1989,12,day,NA,NA
+vsi1990mex12,1989-12-28,1989,12,day,12.077,1.1165081237373464
+vsi1990mex13,1989-12-29,1989,12,day,0.648,0.216957207361097
+vsi1990mex16,1989-12-29,1989,12,day,4.137,0.7107095657243372
+vsi1990mex17,1989-12-30,1989,12,day,0,0
+vsi1990mex18,1989-12-30,1989,12,day,1.541,0.40500466505036936
+vsi1990mex19,1989-12-30,1989,12,day,25.167,1.4177539344041343
+vsi1990mex2,1989-12-27,1989,12,night,10.029,1.0425361367221435
+vsi1990mex20,1989-12-30,1989,12,day,0.947,0.2893659515200317
+vsi1990mex21,1989-12-30,1989,12,day,0.122,0.04999285692014256
+vsi1990mex22,1989-12-30,1989,12,day,1.099,0.3220124385824005
+vsi1990mex24,1989-12-31,1989,12,day,0.693,0.22865695810893527
+vsi1990mex25,1989-12-31,1989,12,day,0.371,0.13703745478951265
+vsi1990mex26a,1989-12-31,1989,12,day,NA,NA
+vsi1990mex26b,1989-12-31,1989,12,day,0.3,0.11394335230683678
+vsi1990mex27,1990-01-01,1990,1,day,499.013,2.6989812958457593
+vsi1990mex28,1990-01-01,1990,1,day,0,0
+vsi1990mex29,1990-01-01,1990,1,day,0.388,0.14238946611883607
+vsi1990mex3,1989-12-27,1989,12,day,14.034,1.1770745459100764
+vsi1990mex31,1990-01-02,1990,1,day,0,0
+vsi1990mex32,1990-01-02,1990,1,day,383.531,2.5849313573656456
+vsi1990mex33,1990-01-02,1990,1,day,14.968,1.203250523943296
+vsi1990mex34,1990-01-03,1990,1,day,0,0
+vsi1990mex35,1990-01-03,1990,1,day,NA,NA
+vsi1990mex36,1990-01-03,1990,1,day,0.666,0.22167499707076874
+vsi1990mex37,1990-01-03,1990,1,day,0.09,0.03742649794062367
+vsi1990mex38,1990-01-03,1990,1,day,0.553,0.19117145572855848
+vsi1990mex39,1990-01-03,1990,1,day,0,0
+vsi1990mex4,1989-12-27,1989,12,day,11.138,1.0841471331544483
+vsi1990mex40,1990-01-04,1990,1,day,66.586,1.8298567439825812
+vsi1990mex41,1990-01-04,1990,1,day,1.832,0.4520932490177314
+vsi1990mex42,1990-01-04,1990,1,day,29.243,1.4806248694621988
+vsi1990mex43,1990-01-04,1990,1,day,3.848,0.6855626111582298
+vsi1990mex46,1990-01-04,1990,1,day,325.309,2.5136290523242955
+vsi1990mex47,1990-01-04,1990,1,day,1.167,0.33585891131981793
+vsi1990mex48,1990-01-05,1990,1,day,8.151,0.9614685553507863
+vsi1990mex49,1990-01-05,1990,1,day,4.019,0.7006171956820569
+vsi1990mex5,1989-12-27,1989,12,day,0,0
+vsi1990mex50,1990-01-05,1990,1,day,9.818,1.0341469771060456
+vsi1990mex51,1990-01-05,1990,1,day,4.635,0.7508939203821253
+vsi1990mex52,1990-01-05,1990,1,day,2.608,0.5572665288699041
+vsi1990mex53,1990-01-06,1990,1,night,4.654,0.7523558041535008
+vsi1990mex54,1990-01-06,1990,1,day,1.423,0.38435341413750623
+vsi1990mex56,1990-01-06,1990,1,day,NA,NA
+vsi1990mex57,1990-01-06,1990,1,day,0,0
+vsi1990mex58,1990-01-07,1990,1,day,0,0
+vsi1990mex59,1990-01-07,1990,1,night,0.09,0.03742649794062367
+vsi1990mex6,1989-12-27,1989,12,day,5.48,0.8115750058705934
+vsi1990mex60,1990-01-07,1990,1,day,0,0
+vsi1990mex61,1990-01-07,1990,1,day,2.619,0.5585885831081994
+vsi1990mex62,1990-01-07,1990,1,day,66.682,1.8304731836026322
+vsi1990mex63,1990-01-07,1990,1,day,16.102,1.2330469020981825
+vsi1990mex65,1990-01-08,1990,1,day,107.208,2.0342593700812674
+vsi1990mex66,1990-01-08,1990,1,day,9.79,1.0330214446829107
+vsi1990mex67,1990-01-08,1990,1,day,6.881,0.8965813275057327
+vsi1990mex68,1990-01-08,1990,1,day,7.041,0.9053100621160856
+vsi1990mex69,1990-01-09,1990,1,night,0,0
+vsi1990mex7,1989-12-28,1989,12,night,0.735,0.23929947912689248
+vsi1990mex70,1990-01-09,1990,1,day,34.277,1.5474916450866656
+vsi1990mex71,1990-01-09,1990,1,day,3.94,0.6937269489236468
+vsi1990mex72,1990-01-09,1990,1,day,0,0
+vsi1990mex73,1990-01-10,1990,1,night,0.285,0.1089031276673133
+vsi1990mex74,1990-01-10,1990,1,day,3.832,0.6841269256130754
+vsi1990mex75,1990-01-10,1990,1,day,4.733,0.7583819417746753
+vsi1990mex76,1990-01-10,1990,1,day,18.099,1.2810106287214833
+vsi1990mex77,1990-01-10,1990,1,day,16.236,1.2364364854163301
+vsi1990mex78,1990-01-11,1990,1,day,13.398,1.1583021692280226
+vsi1990mex79,1990-01-11,1990,1,day,32.384,1.5235383717036524
+vsi1990mex8,1989-12-28,1989,12,day,0.7,0.2304489213782739
+vsi1990mex80,1990-01-11,1990,1,day,2.611,0.5576274884268266
+vsi1990mex81,1990-01-11,1990,1,day,9.516,1.0218505774343252
+vsi1990mex82,1990-01-11,1990,1,day,1.24,0.35024801833416286
+vsi1990mex83,1990-01-12,1990,1,day,0.214,0.08421868673923877
+vsi1990mex84,1990-01-12,1990,1,day,0,0
+vsi1990mex85,1990-01-12,1990,1,day,1.349,0.3708830167776059
+vsi1990mex86,1990-01-12,1990,1,day,9.096,1.0041493419000591
+vsi1990mex87,1990-01-12,1990,1,day,0,0
+vsi1990mex88,1990-01-13,1990,1,day,2.971,0.598899887063883
+vsi1990mex89,1990-01-13,1990,1,day,0,0
+vsi1990mex9,1989-12-28,1989,12,day,11.044,1.0807707468407217
+vsi1990mex90,1990-01-13,1990,1,day,0,0
+vsi1990mex91b,1990-01-13,1990,1,day,1.427,0.38506977633193473
+vsi1990mex92,1990-01-13,1990,1,day,13.634,1.1653630506973742
+vsi1990mex93,1990-01-14,1990,1,day,0.893,0.27715061396379675
+vsi1990mex94,1990-01-14,1990,1,day,0.322,0.12123145514962146
+vsi1990mex95,1990-01-13,1990,1,day,9.074,1.0032019465216924
+vsi1994pnx1,1994-01-18,1994,1,day,0,0
+vsi1994pnx10,1994-01-28,1994,1,day,6.295,0.8630252962294704
+vsi1994pnx11,1994-01-29,1994,1,night,1.401,0.38039216005702725
+vsi1994pnx16,1994-02-01,1994,2,day,1.059,0.31365634661803143
+vsi1994pnx18,1994-02-04,1994,2,day,7.524,0.9306434410421645
+vsi1994pnx2,1994-01-25,1994,1,day,0.212,0.0835026198302674
+vsi1994pnx21,1994-02-05,1994,2,day,0.601,0.2043913319192997
+vsi1994pnx24,1994-02-07,1994,2,day,1.711,0.4331295175804856
+vsi1994pnx33,1994-02-09,1994,2,day,16.234,1.2363860886095543
+vsi1994pnx40,1994-02-13,1994,2,day,1.293,0.36040405472993886
+vsi1994pnx42,1994-02-15,1994,2,day,3.682,0.6704314093606056
+vsi1994pnx43,1994-02-16,1994,2,night,0.043,0.018284308426530838
+vsi1994pnx45,1994-02-16,1994,2,day,166.699,2.2245304728876794
+vsi1994pnx49,1994-02-17,1994,2,day,0.311,0.11760269169008425
+vsi1994pnx50,1994-02-18,1994,2,night,0.093,0.03862016194970278
+vsi1994pnx57,1994-02-28,1994,2,day,0,0
+vsi1994pnx61,1994-03-01,1994,3,day,0.075,0.031408464251624114
+vsi1994pnx62,1994-03-02,1994,3,night,0.022,0.009450895798693935
+vsi1994pnx66,1994-03-03,1994,3,night,0.103,0.04257551244019059
+vsi1994pnx73,1994-03-07,1994,3,day,0,0
+vsi1994pnx78,1994-03-10,1994,3,day,32.841,1.5294431879386512
+vsi1994pnx79,1994-03-11,1994,3,day,8.919,0.9964678902617172
+vsi1994pnx80,1994-03-13,1994,3,day,0.351,0.13065534902203058
+vsi1995pnx17,1994-11-27,1994,11,night,2.079,0.48840968890319814
+vsi1995pnx18,1994-11-26,1994,11,day,0.058,0.024485667699166973
+vsi1995pnx19,1994-11-26,1994,11,day,0.367,0.13576851456782227
+vsi1995pnx20,1994-11-26,1994,11,day,0.135,0.05499586152914153
+vsi1995pnx21,1994-11-26,1994,11,day,0.637,0.21404867941194142
+vsi1995pnx22,1994-11-26,1994,11,day,36.638,1.575626537920978
+vsi1995pnx23,1994-11-26,1994,11,day,0,0
+vsi1995pnx24,1994-11-26,1994,11,day,2.395,0.5308397786165204
+vsi1995pnx25,1994-11-28,1994,11,day,0.032,0.013679697291192559
+vsi1995pnx26,1994-11-28,1994,11,day,0.034,0.014520538757923713
+vsi1995pnx27,1994-11-28,1994,11,day,0.345,0.12872228433842678
+vsi1995pnx28,1994-11-28,1994,11,day,0.063,0.026533264523296733
+vsi1995pnx29,1994-11-28,1994,11,day,0.026,0.011147360775797477
+vsi1995pnx30,1994-11-28,1994,11,day,0.021,0.009025742086910206
+vsi1995pnx31,1994-11-28,1994,11,night,0.284,0.10856502373283447
+vsi1995pnx32,1994-11-28,1994,11,night,0.127,0.05192391604610654
+vsi1995pnx33,1994-11-27,1994,11,day,4.723,0.757623745908389
+vsi1995pnx34,1994-11-27,1994,11,day,0.36,0.13353890837021748
+vsi1995pnx35,1994-11-29,1994,11,day,1.775,0.443262987458695
+vsi1995pnx36,1994-11-29,1994,11,day,1.875,0.4586378490256493
+vsi1995pnx37,1994-11-29,1994,11,day,100.653,2.007120200133133
+vsi1995pnx38,1994-11-29,1994,11,day,0.168,0.06744284277638066
+vsi1995pnx39,1994-11-29,1994,11,day,0.034,0.014520538757923713
+vsi1995pnx40,1994-11-29,1994,11,day,3.351,0.6385890832927169
+vsi1995pnx41,1994-11-29,1994,11,night,17.67,1.2711443179490782
+vsi1995pnx42,1994-11-29,1994,11,night,3.515,0.6546577546495247
+vsi1995pnx43,1994-11-28,1994,11,day,0.091,0.037824750588341866
+vsi1995pnx44,1994-11-30,1994,11,day,3.836,0.6844862921887342
+vsi1995pnx45,1994-11-30,1994,11,day,0.667,0.22193559982800534
+vsi1995pnx46,1994-11-30,1994,11,day,22.353,1.368342679324224
+vsi1995pnx47,1994-11-30,1994,11,day,0.095,0.03941411917613713
+vsi1995pnx48,1994-11-30,1994,11,day,1.167,0.33585891131981793
+vsi1995pnx49,1994-11-30,1994,11,night,0.185,0.07371835034612269
+vsi1995pnx50,1994-11-30,1994,11,night,0.379,0.13956426617584974
+vsi1995pnx51,1994-11-29,1994,11,day,151.313,2.1827369721956655
+vsi1995pnx52,1994-12-01,1994,12,day,0.487,0.17231096852195424
+vsi1995pnx53,1994-12-01,1994,12,day,1.158,0.3340514403468918
+vsi1995pnx54,1994-12-01,1994,12,day,0.074,0.031004281363536824
+vsi1995pnx55,1994-12-01,1994,12,day,0.015,0.006466042249231681
+vsi1995pnx56,1994-12-01,1994,12,day,4.972,0.7761197990529878
+vsi1995pnx57,1994-12-01,1994,12,day,3261.473,3.5135469261702355
+vsi1995pnx58,1994-12-01,1994,12,night,0.051,0.02160271602824219
+vsi1995pnx59,1994-12-01,1994,12,night,0.479,0.16996817399689249
+vsi1995pnx60,1994-12-02,1994,12,day,0.288,0.1099158630237933
+vsi1995pnx62,1994-12-02,1994,12,day,9.637,1.0268191592412268
+vsi1995pnx63,1994-12-02,1994,12,day,212.793,2.3299934814567558
+vsi1995pnx64,1994-12-02,1994,12,day,3.345,0.6379897807846854
+vsi1995pnx65,1994-12-02,1994,12,night,0.46,0.1643528557844371
+vsi1995pnx66,1994-12-02,1994,12,night,0.044,0.01870049866624337
+vsi1995pnx67,1994-12-02,1994,12,day,0.16,0.06445798922691845
+vsi1995pnx68,1994-12-03,1994,12,day,0.906,0.28012289630230763
+vsi1995pnx69,1994-12-03,1994,12,day,0.06,0.02530586526477026
+vsi1995pnx7,1994-11-27,1994,11,day,0.126,0.05153839051532738
+vsi1995pnx70,1994-12-03,1994,12,day,0.357,0.13257984765973704
+vsi1995pnx71,1994-12-03,1994,12,day,6.059,0.8487431818956838
+vsi1995pnx72,1994-12-03,1994,12,day,1.022,0.3057811512549823
+vsi1995pnx73,1994-12-03,1994,12,day,0.062,0.026124516745450282
+vsi1995pnx75,1994-12-03,1994,12,night,0.015,0.006466042249231681
+vsi1995pnx76,1994-12-04,1994,12,day,1.215,0.3453737305590883
+vsi1995pnx77,1994-12-04,1994,12,day,0.485,0.17172645365323114
+vsi1995pnx78,1994-12-04,1994,12,day,0.182,0.07261747654523654
+vsi1995pnx79,1994-12-04,1994,12,day,38.417,1.5956835673552656
+vsi1995pnx8,1994-11-27,1994,11,night,0.26,0.10037054511756291
+vsi1995pnx80,1994-12-04,1994,12,day,37.005,1.5798407368684422
+vsi1995pnx81,1994-12-04,1994,12,night,0.091,0.037824750588341866
+vsi1995pnx82,1994-12-04,1994,12,night,0.558,0.19256745333654568
+vsi1995pnx83,1994-12-03,1994,12,day,0.827,0.2617385473525378
+vsi1995pnx84,1994-12-05,1994,12,day,2.973,0.5991185650553628
+vsi1995pnx85,1994-12-05,1994,12,day,89.984,1.9589650261380407
+vsi1995pnx86,1994-12-05,1994,12,day,18.905,1.2989621819201167
+vsi1995pnx87,1994-12-05,1994,12,night,69.664,1.8491982174288495
+vsi1995pnx88,1994-12-05,1994,12,night,0.168,0.06744284277638066
+vsi1995pnx89,1994-12-05,1994,12,day,0.912,0.2814878879400812
+vsi1995pnx90,1994-12-04,1994,12,day,0.119,0.04883008652835004
+vsi1995pnx91,1994-12-04,1994,12,day,0.212,0.0835026198302674
+vsi1997pnx1,1996-12-23,1996,12,day,0.198,0.07845681805329255
+vsi1997pnx164a,1996-12-22,1996,12,day,0.603,0.2049335223541448
+vsi1997pnx164b,1996-12-23,1996,12,day,0,0
+vsi1997pnx168,1996-12-23,1996,12,night,4.906,0.7712934426290596
+vsi1997pnx17,1996-12-22,1996,12,night,406.068,2.609666963421769
+vsi1997pnx18,1996-12-22,1996,12,night,11.544,1.0984360453403632
+vsi1997pnx19,1996-12-21,1996,12,day,0.035,0.014940349792936524
+vsi1997pnx2,1996-12-23,1996,12,day,0.924,0.28420506770179416
+vsi1997pnx20,1996-12-21,1996,12,day,1.09,0.320146286111054
+vsi1997pnx21,1996-12-21,1996,12,day,4.218,0.717504074764202
+vsi1997pnx22,1996-12-21,1996,12,day,7.275,0.9177680024477564
+vsi1997pnx23,1996-12-21,1996,12,day,0,0
+vsi1997pnx24,1996-12-21,1996,12,night,0.026,0.011147360775797477
+vsi1997pnx25,1996-12-21,1996,12,night,7.103,0.9086458389071134
+vsi1997pnx26,1996-12-20,1996,12,day,11.848,1.1088355279346058
+vsi1997pnx27,1996-12-20,1996,12,day,12.129,1.1182316483270274
+vsi1997pnx28,1996-12-20,1996,12,day,11.941,1.1119678372060728
+vsi1997pnx29,1996-12-20,1996,12,day,0.187,0.07445071895459124
+vsi1997pnx3,1996-12-22,1996,12,day,0.159,0.064083435963596
+vsi1997pnx30,1996-12-20,1996,12,day,495.105,2.6955736041002187
+vsi1997pnx31,1996-12-20,1996,12,night,26.3,1.436162647040756
+vsi1997pnx32,1996-12-20,1996,12,night,0.354,0.13161866434912553
+vsi1997pnx33,1996-12-19,1996,12,day,0.271,0.10414555055400815
+vsi1997pnx34,1996-12-19,1996,12,day,0.308,0.11660774398824847
+vsi1997pnx35,1996-12-19,1996,12,day,5.756,0.8296896408989711
+vsi1997pnx36,1996-12-19,1996,12,day,0.181,0.07224989761351482
+vsi1997pnx37,1996-12-19,1996,12,night,0.088,0.03662889536216113
+vsi1997pnx38,1996-12-19,1996,12,night,2.285,0.5165353738957996
+vsi1997pnx39,1996-12-18,1996,12,day,10.121,1.046143840749226
+vsi1997pnx4,1996-12-23,1996,12,day,392.094,2.594496415006103
+vsi1997pnx40,1996-12-18,1996,12,day,22.28,1.366982975977851
+vsi1997pnx41,1996-12-18,1996,12,day,59.287,1.7802236730532413
+vsi1997pnx42,1996-12-18,1996,12,day,6.055,0.8484970180903666
+vsi1997pnx43,1996-12-18,1996,12,day,12.625,1.13433651094868
+vsi1997pnx44,1996-12-18,1996,12,night,0,0
+vsi1997pnx45,1996-12-18,1996,12,night,2.03,0.48144262850230496
+vsi1997pnx46,1996-12-17,1996,12,day,7.45,0.9268567089496923
+vsi1997pnx47,1996-12-17,1996,12,day,499.037,2.69900214093864
+vsi1997pnx48,1996-12-17,1996,12,day,577.639,2.7624077015449267
+vsi1997pnx49,1996-12-17,1996,12,day,0.067,0.028164419424469872
+vsi1997pnx5,1996-12-23,1996,12,day,3253.385,3.5124689295119964
+vsi1997pnx50,1996-12-17,1996,12,day,0.087,0.03622954408629452
+vsi1997pnx51,1996-12-17,1996,12,night,0.509,0.1786892397755898
+vsi1997pnx52,1996-12-16,1996,12,day,4.521,0.7420177471401382
+vsi1997pnx53,1996-12-16,1996,12,day,1.741,0.4379090355394984
+vsi1997pnx54,1996-12-16,1996,12,day,1.061,0.31407799177921286
+vsi1997pnx55,1996-12-16,1996,12,day,97.824,1.9948624284116263
+vsi1997pnx56,1996-12-16,1996,12,day,31.363,1.5100487732324537
+vsi1997pnx57,1996-12-16,1996,12,night,73.869,1.8743020324417528
+vsi1997pnx58,1996-12-15,1996,12,day,38.696,1.5987467469281338
+vsi1997pnx59,1996-12-15,1996,12,day,12.366,1.1260014567876744
+vsi1997pnx6,1996-12-24,1996,12,day,10.107,1.0455967718675787
+vsi1997pnx60,1996-12-15,1996,12,day,40.454,1.6175664430675372
+vsi1997pnx61,1996-12-15,1996,12,day,18.795,1.2965555060882235
+vsi1997pnx62,1996-12-15,1996,12,day,40.938,1.6226077153597795
+vsi1997pnx63,1996-12-15,1996,12,night,7.015,0.9039035266901636
+vsi1997pnx64,1996-12-15,1996,12,night,3.406,0.6440444928147488
+vsi1997pnx65,1996-12-14,1996,12,day,21.964,1.3610475381955816
+vsi1997pnx66,1996-12-14,1996,12,day,713.538,2.8540253300499767
+vsi1997pnx67,1996-12-14,1996,12,day,1.823,0.45071087814691935
+vsi1997pnx68,1996-12-14,1996,12,night,63.894,1.8122045444529067
+vsi1997pnx69,1996-12-13,1996,12,day,10.198,1.049140463158965
+vsi1997pnx7,1996-12-23,1996,12,day,0.574,0.1970047280230457
+vsi1997pnx70,1996-12-13,1996,12,day,1033.23,3.014617131236758
+vsi1997pnx71,1996-12-13,1996,12,day,25.263,1.4193443336738432
+vsi1997pnx72,1996-12-13,1996,12,day,30.966,1.5046882951202278
+vsi1997pnx73,1996-12-13,1996,12,day,92.177,1.9693087234504743
+vsi1997pnx74,1996-12-13,1996,12,night,15.634,1.2209966971473682
+vsi1997pnx75,1996-12-13,1996,12,day,5.173,0.7904962769671096
+vsi1997pnx76,1996-12-12,1996,12,day,15.761,1.2242999260798269
+vsi1997pnx77,1996-12-12,1996,12,day,40.55,1.6185710281201298
+vsi1997pnx78,1996-12-12,1996,12,day,368.871,2.568050281481103
+vsi1997pnx79,1996-12-12,1996,12,day,66.277,1.827866617265269
+vsi1997pnx8,1996-12-22,1996,12,day,0.562,0.19368102954128152
+vsi1997pnx80,1996-12-12,1996,12,night,103.494,2.019091354162853
+vsi1997pnx81,1996-12-12,1996,12,night,492.633,2.6934041851395394
+vsi1997pnx82,1996-12-11,1996,12,day,371.593,2.5712346914353437
+vsi1997pnx83,1996-12-11,1996,12,day,14.322,1.1853154580036565
+vsi1997pnx84,1996-12-11,1996,12,day,331.332,2.521572161183355
+vsi1997pnx85,1996-12-11,1996,12,day,663.733,2.8226472394212823
+vsi1997pnx86,1996-12-11,1996,12,night,298.037,2.4757249271260453
+vsi1997pnx87,1996-12-10,1996,12,day,12.542,1.1316828094614055
+vsi1997pnx88,1996-12-10,1996,12,day,5.896,0.8385972528166564
+vsi1997pnx89,1996-12-10,1996,12,day,25.818,1.4284263864405882
+vsi1997pnx90,1996-12-10,1996,12,day,48.356,1.6933399555567998
+vsi1997pnx91,1996-12-10,1996,12,day,49.29,1.701481635620927
+vsi1997pnx92,1996-12-11,1996,12,day,2.273,0.5149460053080044
+vsi1997pnx93,1996-12-11,1996,12,day,10.492,1.0603956173199098
+vsi1997pnx94,1996-12-14,1996,12,night,0,0
+vsi1997pnx95,1996-12-14,1996,12,day,829.56,2.919371011534774
+vsi1997pnx96,1996-12-16,1996,12,day,0.549,0.19005141775920598
+vsi1997pnx97,1996-12-17,1996,12,night,24.928,1.4137690180241536
+vsi2000cca102,2000-01-19,2000,1,night,0,0
+vsi2000cca1024,2000-01-31,2000,1,day,0,0
+vsi2000cca1025,2000-02-02,2000,2,night,8.948,0.9977357765978984
+vsi2000cca1026,2000-02-02,2000,2,day,963.464,2.984286021618953
+vsi2000cca1028,2000-02-03,2000,2,day,0,0
+vsi2000cca103,2000-01-19,2000,1,day,0,0
+vsi2000cca104,2000-01-20,2000,1,night,0,0
+vsi2000cca106,2000-01-21,2000,1,night,0,0
+vsi2000cca107,2000-01-21,2000,1,day,0.49,0.17318626841227402
+vsi2000cca1132,2000-01-31,2000,1,night,0,0
+vsi2000cca1220,2000-01-22,2000,1,night,0.038,0.01619735351243906
+vsi2000cca1221,2000-01-22,2000,1,day,63.287,1.8081231595638672
+vsi2000cca1329,2000-02-04,2000,2,night,0,0
+vsi2000cca1331,2000-02-05,2000,2,night,208.476,2.3211342723398483
+vsi2000cca1332,2000-02-05,2000,2,day,13.764,1.1692040365236196
+vsi2000cca1433,2000-02-02,2000,2,night,15.617,1.2205526198663461
+vsi2000cca1434,2000-02-02,2000,2,day,22.384,1.3689188023493801
+vsi2000cca1522,2000-01-24,2000,1,day,54.335,1.7429999142824846
+vsi2000cca1523,2000-01-25,2000,1,night,0.305,0.11561051167429974
+vsi2000cca1633,2000-02-06,2000,2,day,402.112,2.605425726598133
+vsi2000cca1634,2000-02-07,2000,2,night,17.255,1.2613818372857462
+vsi2000cca1640,2000-02-11,2000,2,night,55.26,1.750199727829182
+vsi2000cca1735,2000-02-04,2000,2,night,23.795,1.3943641126271287
+vsi2000cca1736,2000-02-04,2000,2,day,0,0
+vsi2000cca1825,2000-01-26,2000,1,day,0.068,0.028571252692537637
+vsi2000cca1826,2000-01-27,2000,1,night,29.791,1.488423793723904
+vsi2000cca1827,2000-01-27,2000,1,day,181.417,2.261065309113884
+vsi2000cca1935,2000-02-08,2000,2,night,10.621,1.0652435011864638
+vsi2000cca1937,2000-02-09,2000,2,night,5.11,0.7860412102425542
+vsi2000cca1938,2000-02-09,2000,2,day,0,0
+vsi2000cca2009,2000-01-21,2000,1,night,0,0
+vsi2000cca2010,2000-01-22,2000,1,day,0,0
+vsi2000cca2011,2000-01-22,2000,1,night,0,0
+vsi2000cca2012,2000-01-23,2000,1,day,0,0
+vsi2000cca2013,2000-01-24,2000,1,night,3.884,0.6887756552728449
+vsi2000cca2014,2000-01-24,2000,1,day,0,0
+vsi2000cca2015,2000-01-25,2000,1,night,0,0
+vsi2000cca2016,2000-01-25,2000,1,day,0,0
+vsi2000cca2017,2000-01-25,2000,1,night,0,0
+vsi2000cca2018,2000-01-26,2000,1,day,0.576,0.19755621315353653
+vsi2000cca2019,2000-01-27,2000,1,night,0,0
+vsi2000cca2020,2000-01-27,2000,1,day,0,0
+vsi2000cca2021,2000-01-27,2000,1,night,0,0
+vsi2000cca2022,2000-01-28,2000,1,day,0,0
+vsi2000cca2023,2000-01-28,2000,1,night,0,0
+vsi2000cca2024,2000-01-29,2000,1,day,0,0
+vsi2000cca2025,2000-01-29,2000,1,night,0,0
+vsi2000cca2026,2000-01-30,2000,1,day,0,0
+vsi2000cca2027,2000-01-31,2000,1,night,0,0
+vsi2000cca2028,2000-01-31,2000,1,day,0,0
+vsi2000cca2029,2000-02-01,2000,2,night,0,0
+vsi2000cca205,2000-01-16,2000,1,night,0.173,0.06929801211552926
+vsi2000cca208,2000-01-17,2000,1,night,0.013,0.005609445360280386
+vsi2000cca210,2000-01-18,2000,1,night,0.127,0.05192391604610654
+vsi2000cca211,2000-01-18,2000,1,day,0.393,0.1439511164239635
+vsi2000cca212,2000-01-19,2000,1,night,0.132,0.05384642685225267
+vsi2000cca213,2000-01-19,2000,1,day,3.764,0.6779717528107398
+vsi2000cca3003,2000-01-14,2000,1,day,0,0
+vsi2000cca3004,2000-01-15,2000,1,night,0.341,0.1274287778515989
+vsi2000cca301,2000-01-11,2000,1,night,0.889,0.2762319579218336
+vsi2000cca302,2000-01-11,2000,1,day,0.196,0.07773117965239201
+vsi2000cca303,2000-01-12,2000,1,night,0.08,0.03342375548694973
+vsi2000cca304,2000-01-12,2000,1,day,0.428,0.15472820744015556
+vsi2000cca305,2000-01-13,2000,1,night,0.552,0.19089171692216964
+vsi2000cca306,2000-01-13,2000,1,day,0.693,0.22865695810893527
+vsi2000cca307,2000-01-14,2000,1,night,12.372,1.126196367920531
+vsi2000cca4028,2000-01-29,2000,1,night,23.563,1.390281408229663
+vsi2000cca4029,2000-01-29,2000,1,day,9.328,1.0140162292583639
+vsi2000cca4030,2000-01-30,2000,1,night,5.834,0.834674974462744
+vsi2000cca408,2000-01-22,2000,1,day,11.754,1.1056464126512362
+vsi2000cca409,2000-01-23,2000,1,night,1.573,0.41043978621034655
+vsi2000cca412,2000-01-24,2000,1,day,0,0
+vsi2000cca413,2000-01-25,2000,1,night,0,0
+vsi2000cca414,2000-01-25,2000,1,day,0,0
+vsi2000cca415,2000-01-26,2000,1,night,0,0
+vsi2000cca5029,2000-01-29,2000,1,night,23.098,1.3819810000434645
+vsi2000cca5030,2000-01-29,2000,1,day,49.255,1.7011792773240335
+vsi2000cca5031,2000-01-30,2000,1,night,74.444,1.8776247063978122
+vsi2000cca5032,2000-01-30,2000,1,day,3.492,0.6524397475894201
+vsi2000cca5033,2000-01-31,2000,1,night,31.829,1.5162576539417576
+vsi2000cca5034,2000-01-31,2000,1,day,2.006,0.4779889762508893
+vsi2000cca5035,2000-02-01,2000,2,night,95.124,1.9828318347679694
+vsi2000cca5036,2000-02-01,2000,2,day,3.092,0.6119356250401226
+vsi2000cca5037,2000-02-02,2000,2,night,50.269,1.7098548466247698
+vsi2000cca514,2000-01-20,2000,1,day,2.832,0.5834255004065068
+vsi2000cca515,2000-01-21,2000,1,night,24.006,1.3980442268420141
+vsi2000cca517,2000-01-22,2000,1,night,0.571,0.1961761850399733
+vsi2000cca519,2000-01-23,2000,1,night,1.062,0.3142886609474978
+vsi2000cca520,2000-01-23,2000,1,day,0,0
+vsi2000cca521,2000-01-24,2000,1,night,0.224,0.08778141780954238
+vsi2000cca6001,2000-01-17,2000,1,day,0,0
+vsi2000cca6002,2000-01-17,2000,1,night,0,0
+vsi2000cca6003,2000-01-18,2000,1,day,57.343,1.7659887568040198
+vsi2000cca6004,2000-01-18,2000,1,night,0,0
+vsi2000cca6005,2000-01-19,2000,1,day,0,0
+vsi2000cca6006,2000-01-20,2000,1,night,1.699,0.4312028845565166
+vsi2000cca6007,2000-01-20,2000,1,day,0,0
+vsi2000cca6008,2000-01-20,2000,1,day,0,0
+vsi2000cca608,2000-01-15,2000,1,night,3.201,0.6233526815379921
+vsi2000cca609,2000-01-15,2000,1,day,10.229,1.0503410818340844
+vsi2000cca610,2000-01-16,2000,1,night,1.532,0.4034637013453175
+vsi2000cca611,2000-01-16,2000,1,day,0.02,0.008600171761917567
+vsi2000cca612,2000-01-17,2000,1,night,0.434,0.1565491513317813
+vsi2000cca613,2000-01-17,2000,1,day,0,0
+vsi2000cca718,2000-01-28,2000,1,night,5.454,0.8098289610678862
+vsi2000cca720,2000-01-29,2000,1,night,12.708,1.1369740957578294
+vsi2000cca722,2000-01-30,2000,1,night,175.597,2.2469833215829302
+vsi2000cca822,2000-01-25,2000,1,day,1.2,0.3424226808222063
+vsi2000cca823,2000-01-26,2000,1,night,3.293,0.6327608884794388
+vsi2000cca824,2000-01-26,2000,1,day,0.268,0.10311925354571388
+vsi2000cca825,2000-01-27,2000,1,night,9.189,1.0081315622406624
+vsi2000cca827,2000-01-28,2000,1,day,0.142,0.05766610390982921
+vsi2000cca914,2000-01-19,2000,1,night,14.018,1.1766120999918668
+vsi2000cca915,2000-01-19,2000,1,day,16.713,1.2482921226303156
+vsi2000cca917,2000-01-20,2000,1,day,68.068,1.839276880335964
+vsi2000cca918,2000-01-21,2000,1,night,31.261,1.5086778251654933
+vsi2000cca919,2000-01-21,2000,1,day,0,0
+vsi2001ste193,2001-02-02,2001,2,day,0.296,0.11260500153457453
+vsi2001ste201,2001-02-01,2001,2,day,15.32,1.2127201544178423
+vsi2001ste202,2001-02-01,2001,2,day,0.168,0.06744284277638066
+vsi2001ste203,2001-02-01,2001,2,day,16.03,1.2312146479626012
+vsi2001ste26,2001-02-02,2001,2,day,4.612,0.7491176623563223
+vsi2001ste27,2001-02-02,2001,2,day,3.864,0.6869935662646786
+vsi2001ste28,2001-02-02,2001,2,day,4.177,0.714078164981856
+vsi2001ste29,2001-02-02,2001,2,night,0.653,0.2182728535714475
+vsi2001ste30,2001-02-02,2001,2,night,7.183,0.9129125511760969
+vsi2001ste302,2001-02-01,2001,2,night,31.138,1.507018846416433
+vsi2001ste303,2001-02-01,2001,2,day,11.184,1.0857898903279861
+vsi2001ste304,2001-01-31,2001,1,day,189.221,2.279258460449328
+vsi2001ste31,2001-02-02,2001,2,night,37.101,1.5809363743313305
+vsi2001ste32,2001-02-01,2001,2,day,33.085,1.532563298334475
+vsi2001ste33,2001-02-01,2001,2,day,38.105,1.59223229022401
+vsi2001ste34,2001-02-01,2001,2,day,4.86,0.7678976160180907
+vsi2001ste4,2001-02-04,2001,2,day,17.615,1.2698630405544111
+vsi2001ste402,2001-01-29,2001,1,day,1354.139,3.1319838441675354
+vsi2001ste403,2001-01-29,2001,1,day,18.184,1.2829391657547735
+vsi2001ste404,2001-01-29,2001,1,day,648.264,2.812421323040953
+vsi2001ste44,2001-01-30,2001,1,day,4.113,0.708675792726537
+vsi2001ste45,2001-01-30,2001,1,day,14.896,1.2012878541017589
+vsi2001ste46,2001-01-31,2001,1,night,67.771,1.8374053394696124
+vsi2001ste47,2001-01-31,2001,1,night,372.72,2.5725463401756126
+vsi2001ste49,2001-01-31,2001,1,day,0.281,0.10754912974468635
+vsi2001ste5,2001-02-04,2001,2,day,1.447,0.3886339693517892
+vsi2001ste50,2001-01-31,2001,1,day,0.127,0.05192391604610654
+vsi2001ste501,2001-01-29,2001,1,night,2.416,0.5335178620169674
+vsi2001ste502,2001-01-29,2001,1,night,14.438,1.1885910365939902
+vsi2001ste503,2001-01-28,2001,1,day,15.814,1.2256710430811442
+vsi2001ste504,2001-01-28,2001,1,day,304.51,2.4850254302045673
+vsi2001ste51,2001-01-31,2001,1,day,0.463,0.16524432612531084
+vsi2001ste590,2001-01-27,2001,1,day,13.005,1.146283113159587
+vsi2001ste591,2001-01-27,2001,1,day,2.724,0.571009672309305
+vsi2001ste6,2001-02-04,2001,2,night,26.191,1.4344251798749517
+vsi2001ste60,2001-01-30,2001,1,day,22.288,1.3671321924307545
+vsi2001ste61,2001-01-30,2001,1,day,3.751,0.6767850304192055
+vsi2001ste62,2001-01-30,2001,1,day,66.642,1.8302164400865957
+vsi2001ste63,2001-01-30,2001,1,day,5.853,0.8358807318173946
+vsi2001ste64,2001-01-30,2001,1,night,210.007,2.324296862932512
+vsi2001ste65,2001-01-30,2001,1,night,24.175,1.4009694792256555
+vsi2001ste66,2001-01-29,2001,1,day,0.093,0.03862016194970278
+vsi2001ste67,2001-01-29,2001,1,day,2.155,0.49899936358015307
+vsi2001ste7,2001-02-04,2001,2,night,1731.133,3.238581235804002
+vsi2001ste76,2001-01-27,2001,1,day,1.235,0.3492775274679554
+vsi2001ste77,2001-01-27,2001,1,day,23.366,1.3867842398736805
+vsi2001ste78,2001-01-28,2001,1,night,0.582,0.1992064791616577
+vsi2001ste79,2001-01-28,2001,1,night,1.169,0.3362595520141933
+vsi2001ste80,2001-01-28,2001,1,night,0.454,0.162564406523019
+vsi2001ste81,2001-01-28,2001,1,day,21.968,1.3611231794884495
+vsi2001ste82,2001-01-28,2001,1,day,3.188,0.6220066730068048
+vsi2001ste83,2001-01-28,2001,1,day,0.483,0.17114115102838204
+vsi2001ste93,2001-01-28,2001,1,day,15.162,1.2084951024298374
+vsi2001ste95,2001-01-29,2001,1,day,20.091,1.324097171687614
+vsi2001ste97,2001-01-31,2001,1,day,44.194,1.6550807812747481
+vsi2004lak1021,2004-04-10,2004,4,night,0.024,0.010299956639811961
+vsi2004lak1022,2004-04-10,2004,4,day,0,0
+vsi2004lak1023,2004-04-10,2004,4,day,1.153,0.3330440298234872
+vsi2004lak1024,2004-04-10,2004,4,night,35.597,1.5634454860270475
+vsi2004lak1025,2004-04-09,2004,4,night,37.715,1.5878792635995502
+vsi2004lak1026,2004-04-09,2004,4,day,0,0
+vsi2004lak1027,2004-04-09,2004,4,day,0,0
+vsi2004lak1028,2004-04-09,2004,4,night,12.331,1.1248627284230568
+vsi2004lak1029,2004-04-09,2004,4,night,2.621,0.5588285248170117
+vsi2004lak1030,2004-04-09,2004,4,night,3.834,0.6843066460716315
+vsi2004lak1031,2004-04-08,2004,4,night,0.361,0.1338581252033347
+vsi2004lak1032,2004-04-08,2004,4,day,0,0
+vsi2004lak1033,2004-04-08,2004,4,day,0.293,0.11159852488039401
+vsi2004lak1034,2004-04-08,2004,4,night,0.683,0.22608411597582387
+vsi2004lak1035,2004-04-08,2004,4,night,1.158,0.3340514403468918
+vsi2004lak1036,2004-04-08,2004,4,night,11.457,1.0954134644484463
+vsi2004lak1037,2004-04-07,2004,4,night,115.023,2.0645440908040595
+vsi2004lak1038,2004-04-07,2004,4,day,0,0
+vsi2004lak1039,2004-04-07,2004,4,day,0,0
+vsi2004lak1040,2004-04-07,2004,4,night,0.023,0.009875633712160119
+vsi2004lak1041,2004-04-11,2004,4,night,8.231,0.9652487509671208
+vsi2004lak1042,2004-04-11,2004,4,day,0.047,0.019946681678842303
+vsi2004lak1043,2004-04-11,2004,4,day,0,0
+vsi2004lak1044,2004-04-12,2004,4,night,8.781,0.9903832589062336
+vsi2004lak1045,2004-04-12,2004,4,night,2.002,0.47741068790725155
+vsi2004lak1046,2004-04-12,2004,4,day,0.601,0.2043913319192997
+vsi2004lak1047,2004-04-12,2004,4,day,0,0
+vsi2004lak1048,2004-04-12,2004,4,night,3.219,0.6252095253818809
+vsi2004lak1049,2004-04-13,2004,4,night,4.206,0.7165041637732169
+vsi2004lak1050,2004-04-13,2004,4,night,7.919,0.9503161639246003
+vsi2004lak1051,2004-04-13,2004,4,day,0,0
+vsi2004lak1052,2004-04-13,2004,4,day,0,0
+vsi2004lak1053,2004-04-13,2004,4,night,4.05,0.7032913781186614
+vsi2004lak1054,2004-04-14,2004,4,night,4.556,0.744762237065578
+vsi2004lak1055,2004-04-14,2004,4,night,4.821,0.7649975992848805
+vsi2004lak1056,2004-04-14,2004,4,night,0.252,0.09760432887441087
+vsi2004lak1057,2004-04-14,2004,4,day,0,0
+vsi2004lak1058,2004-04-14,2004,4,day,0,0
+vsi2004lak1059,2004-04-14,2004,4,night,43.78,1.6510840892430114
+vsi2004lak1060,2004-04-14,2004,4,night,38.925,1.6012449248105205
+vsi2004lak1062,2004-04-18,2004,4,night,0.092,0.038222638368718455
+vsi2004lak1063,2004-04-18,2004,4,day,0,0
+vsi2004lak1064,2004-04-18,2004,4,night,0,0
+vsi2004lak1065,2004-04-18,2004,4,night,5.323,0.8009231818132183
+vsi2004lak1066,2004-04-17,2004,4,night,14.446,1.1888160305023527
+vsi2004lak1067,2004-04-17,2004,4,day,0,0
+vsi2004lak1068,2004-04-17,2004,4,night,0.251,0.0972573096934199
+vsi2004lak1069,2004-04-17,2004,4,night,4.418,0.7338390006971495
+vsi2004lak1070,2004-04-17,2004,4,night,6.487,0.874307833128039
+vsi2004lak1071,2004-04-18,2004,4,night,7.008,0.9035240644712622
+vsi2004lak1072,2004-04-16,2004,4,day,0,0
+vsi2004lak1073,2004-04-16,2004,4,day,0,0
+vsi2004lak1074,2004-04-16,2004,4,day,0,0
+vsi2004lak1075,2004-04-16,2004,4,night,1.197,0.34183005692051033
+vsi2004lak1076,2004-04-16,2004,4,night,1.78,0.44404479591807633
+vsi2004lak1077,2004-04-15,2004,4,night,7.257,0.916822284595912
+vsi2004lak1078,2004-04-15,2004,4,night,5.419,0.8074673756842778
+vsi2004lak1079,2004-04-15,2004,4,day,0,0
+vsi2004lak1080,2004-04-15,2004,4,day,0.518,0.18127177155946156
+vsi2004lak1084,2004-04-19,2004,4,night,0.676,0.22427401429425775
+vsi2004lak1085,2004-04-19,2004,4,day,0,0
+vsi2004lak1086,2004-04-20,2004,4,night,1.966,0.47217114669236326
+vsi2004lak1087,2004-04-20,2004,4,night,1.291,0.3600250891893975
+vsi2004lak1088,2004-04-20,2004,4,night,0,0
+vsi2004lak1089,2004-04-20,2004,4,day,0,0
+vsi2004lak1090,2004-04-20,2004,4,night,53.763,1.7384872314224726
+vsi2004lak1091,2004-04-21,2004,4,night,5.027,0.7801011914679117
+vsi2004lak1092,2004-04-21,2004,4,day,0.183,0.07298474462793038
+vsi2004lak1093,2004-04-21,2004,4,day,0,0
+vsi2004lak1094,2004-04-21,2004,4,day,0,0
+vsi2004lak1095,2004-04-21,2004,4,night,10.002,1.0414716406137472
+vsi2004lak1096,2004-04-22,2004,4,night,23.365,1.3867664157173134
+vsi2004lak1097,2004-04-22,2004,4,night,2.61,0.557507201905658
+vsi2004lak1098,2004-04-22,2004,4,day,0,0
+vsi2004lak1099,2004-04-22,2004,4,day,0,0
+vsi2004lak1100,2004-04-22,2004,4,night,6.801,0.8921502779013641
+vsi2004lak1101,2004-04-23,2004,4,night,54.39,1.7434313651466837
+vsi2004lak1102,2004-04-23,2004,4,night,2.95,0.5965970956264602
+vsi2004lak1103,2004-04-23,2004,4,day,0,0
+vsi2004lak1104,2004-04-23,2004,4,day,138.95,2.145972902802181
+vsi2004lak1105,2004-04-23,2004,4,night,19.239,1.3061890504004259
+vsi2004lak1106,2004-04-24,2004,4,night,18.849,1.2977386317328024
+vsi2004lak1107,2004-04-24,2004,4,night,19.603,1.3139304625334625
+vsi2004lak1108,2004-04-24,2004,4,day,175.123,2.2458160744274456
+vsi2004lak1109,2004-04-24,2004,4,day,0.112,0.0461047872460387
+vsi2004lak1110,2004-04-25,2004,4,night,3.073,0.6099144100859978
+vsi2004lak1111,2004-04-25,2004,4,day,0.632,0.21272015441784237
+vsi2004lak1112,2004-04-25,2004,4,day,0,0
+vsi2004lak1113,2004-04-25,2004,4,day,0,0
+vsi2004lak1114,2004-04-25,2004,4,night,68.804,1.8438803098459926
+vsi2004lak1115,2004-04-26,2004,4,night,52.062,1.7247836152768095
+vsi2004lak1116,2004-04-26,2004,4,night,99.287,2.0012446399578305
+vsi2004lak1117,2004-04-26,2004,4,day,9692.214,3.9864678008930063
diff --git a/modules/module_2/assignments/a3/m2a3.qmd b/modules/module_2/assignments/a3/m2a3.qmd
new file mode 100644
index 0000000..934af47
--- /dev/null
+++ b/modules/module_2/assignments/a3/m2a3.qmd
@@ -0,0 +1,224 @@
+---
+title: "Module 2, Assignment 3: Final Project Kickoff"
+author: "your name here"
+date: today
+format: pdf
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Description
+
+### Purpose
+
+The goal of this assignment is to get started working on the final project for the course.
+
+### Task
+
+Choose a dataset to work with for the final, write a research question, summarize and plot the data.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit
+-   Written answers address the questions in sufficient detail
+
+# Assignment Questions
+
+All questions are worth 1 point unless otherwise specified.
+
+## Explore Datasets
+
+Click on the links for each dataset and read a little bit about each one.
+
+-   [KrillBase](https://www.bas.ac.uk/project/krillbase/#about)
+-   [Fur Seal Pups](https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=bc6d802a-fce5-495d-b5e1-bb26f824847c) at Maiviken
+-   [Gentoo Penguins](https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=1fd5bc8a-96b5-4f53-aa15-33e5330e7705) on Bird Island
+-   [Chinstrap Penguins](https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=d784af69-c92b-444c-a12e-ee0361802cb1) on Signy Island
+
+Now that you have an idea of what each data set is about, let's explore the actual data.
+
+First, we need to load the `tidyverse` package.
+
+```{r}
+# load the tidyverse
+
+```
+
+Read all four datasets (.csv files) into RStudio using the `read_csv()` function (*not* `read.csv`). Remember to save each dataset as an object with a descriptive name.
+
+```{r}
+
+```
+
+1.  Use a function (e.g., `head()` or `str()`) to look at the data in each data frame.
+
+```{r}
+
+```
+
+## Choose Your Dataset
+
+2.  Now that you've explored the datasets, decide which one you would like to focus on for the final project. Below, tell us which one and why you find it interesting.
+
+*Answer*:
+
+### Modifications
+
+Find your dataset below.
+
+I've made a few modifications to each of the dataset to make them a bit more manageable to work with. Based on how some of these variables will be used in future assignments, you should treat certain variables in the way I've listed below (even if they could be considered either *numeric* or *categorical)*.
+
+#### Krill
+
+-   You will want to use the `Krill_per_1m2_log` column as your dependent variable. This is a log-transformed version of the `Krill_per_1m2` column. You don't need to worry about *why* we transformed the data, but this column should be a bit easier to work with.
+-   The `Date`, `Year`, and `Month` columns should all be treated as *numeric* variables.
+-   While the `Station` column can *technically* be considered a categorical variable, please use the `Day_Night` column as your categorical variable for this assignment and for your research question.
+
+#### Fur Seals
+
+-   If you choose to use the `Date` or `Year` column, treat them as *numeric* variables.
+-   On the other hand, if you use the `Month` column, treat it as a *categorical* variable.
+
+#### Gentoo Penguins
+
+-   If you are using the `Year` column, treat it as a *numeric* variable.
+-   The dataset has a column for the previous year's December temperature. Because counts of chicks took place in January or February (and nests were counted in October/November), the number of chicks is most likely to be related to the temperatures directly after chicks hatched (December of the previous year).
+
+#### Chinstrap Penguins
+
+-   I do not recommend using the `MONTH` or `DAY` column.
+-   If you use the `YEAR` column, treat it as a *numeric* variable.
+
+------------------------------------------------------------------------
+
+## Writing a Research Question
+
+3.  For each of the columns in your dataset, identify whether they contain categorical or numeric data.
+
+    *NOTE! Some data, such as dates (month, year), can act as both types of data. See the section above for clarification about columns in your chosen dataset.*
+
+    Type out the name of the column and then the descriptor. For example:
+
+    -   island_name: categorical
+    -   island_size_km: numeric
+
+*Answer*:
+
+In order to write a successful research question, we need to identify our *dependent* variable and the *independent* variable that we think affects the dependent variable.
+
+4.  First, determine which variable is the *dependent* variable in your dataset. Remember, it must be *numeric*.
+
+*Answer*:
+
+5.  Next, choose 1 *independent* variable from your dataset (each dataset has more than one from which to choose). This should be a variable that you believe might affect the values of the dependent variable. It can be either categorical OR numeric.
+
+*Answer*:
+
+6.  Now, let's put those 2 variables into a research question.
+
+*Answer*:
+
+## Summarize the Data
+
+Let's get to know our data a little better.
+
+7.  Focus on your *dependent* variable. Calculate the minimum value (`min()`), maximum (`max()`) value, and average (`mean()`) value using the `tidyverse` functions (you might need `na.rm = TRUE`!).
+
+-   If your research question has a *categorical* independent variable, use the `group_by` function to find the above values of your dependent variable for each category of the independent variable
+-   If your research question has a *numeric* independent variable, calculate the minimum, maximum, and mean values for the independent variable in addition to the dependent variable
+
+```{r}
+
+
+```
+
+## Plot the Data
+
+------------------------------------------------------------------------
+
+### Important Instructions and Info!
+
+(a) You can use *any* variable from the dataset, not only the ones in your research question. In fact, you will *need* to use different variables to successfully create all of the plots.
+
+(b) For *all* of the plots below, be sure to add:
+
+    -   improved labels for the x-axis, y-axis, and the legend (if present)
+    -   one of the following themes to your code: `theme_bw`, `theme_classic`, or `theme_light`.
+
+(c) For each question, ***you get to choose the variables you use for each plot!*** Pay attention to which variables are numeric vs. categorical and where they should go in the code to produce the correct type of plot.
+
+(d) Because I'm having you choose your own variables, there is no Answer Key for this assignment.
+
+(e) Each plot is worth 2 points. Your interpretation is worth 1 point. Some of your plots may be hard to interpret. That's ok! In that case, explain why the plot is challenging to interpret.
+
+------------------------------------------------------------------------
+
+#### STOP! Did you read the "Important Instructions and Info" section above?
+
+8.  Produce a histogram plot (just one, not multiple on the same plot).
+
+    Write 1-2 sentences describing what information you can gather from plot. (2 points for plot, 1 for description)
+
+```{r}
+#| warning: false
+
+```
+
+*Answer:*
+
+9.  Create a multiple histogram plot. Make sure we can see each histogram plot (make sure one isn't blocking another by modifying the transparency and position).
+
+    Write 1-2 sentences describing what information you can gather from plot. (2 points for plot, 1 for description)
+
+```{r}
+#| warning: false
+
+```
+
+*Answer:*
+
+10. Create a box-and-whisker plot. Changing the color of the boxes is optional. Make sure to add points to the plot.
+
+    Write 1-2 sentences describing what information you can gather from plot. (2 points for plot, 1 for description)
+
+```{r}
+#| warning: false
+
+```
+
+*Answer:*
+
+11. Create a scatter plot.
+
+    Write 1-2 sentences describing what information you can gather from plot. (2 points for plot, 1 for description)
+
+```{r}
+#| warning: false
+
+```
+
+*Answer:*
+
+------------------------------------------------------------------------
+
+12. Do any of the plots you made above match up with your research question?
+
+-   If so, identify which plot and explain how you know it matches up correctly (based on numeric vs. categorical variables).
+-   If not, explain which plot type *would* match your research question and how you know (based on numeric vs. categorical variables).
+
+*Answer*:
+
+# Turning in Your Assignment
+
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_3/assignments/a1/m3a1.qmd b/modules/module_3/assignments/a1/m3a1.qmd
new file mode 100644
index 0000000..d08349f
--- /dev/null
+++ b/modules/module_3/assignments/a1/m3a1.qmd
@@ -0,0 +1,134 @@
+---
+title: "Module 3, Assignment 1: Comparing Two Means"
+author: "your name here"
+date: today
+format: pdf
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Details
+
+### Purpose
+
+The goal of this assignment is to assess your ability to compare means numerically, visually, and statistically
+
+### Task
+
+Write R code which produces the correct answers and correctly interpret the results of visualizations and statistical tests.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit
+-   Written answers address the questions in sufficient detail
+
+# Assignment Questions
+
+In this assignment, we're going to explore another data set on wind turbines that generate a significant portion of the energy for us down here in Antarctica.
+
+### Set-Up
+
+Let's load the `tidyverse` and read in the data set. Call the data `turbines`.
+
+```{r}
+#| message: false
+
+```
+
+1.  Explore the data set, either through the environment or through code. Answer the following questions (2 point):
+
+    a.  How many turbine companies are represented in the data?
+    b.  What do the data in each row of data represent?
+
+```{r}
+# optional; only if you want space for coding
+```
+
+### Numeric
+
+2.  Generate a summary of the data set that calculates the average required [wind speed]{.underline} and mean [power output]{.underline} for each wind turbine company. Save this as a data frame called `turbine_summary`. (2 point)
+
+```{r}
+
+```
+
+### Visual
+
+3.  Create a multiple histogram plot for the [power output]{.underline} variable. (3 points)
+
+    -   be sure to have a histogram for each turbine producer; the color and/or the fill should be determined by the maker of the turbine. They should also be transparent and not stacked vertically.
+    -   set the number of bins to 10
+    -   add in vertical lines for the mean values in the same color as the turbine makers (remember to reference the correct data frame!)
+    -   make sure the x-axis, y-axis, and legend labels are capitalized and easier to understand
+    -   use the `theme_classic()` function
+
+```{r}
+
+```
+
+4.  Generate a box-and-whisker plot that compares the required [wind speed]{.underline} between different turbine makers (3 points).
+
+    The plot should:
+
+    -   have capitalized and more descriptive axis labels (hint: wind speed is measured in kilometers per hour---km/hr)
+    -   show raw data points in addition to the boxes. The points should be jittered.
+    -   use the `theme_classic()` function
+
+```{r}
+
+```
+
+### Statistic
+
+5.  Write a null hypothesis and an alternative hypothesis for each question we are asking. (2 points)
+
+Is there a significant difference in the [power output]{.underline} from turbines from the different companies?
+
+-   **Null Hypothesis** (H~0~):\
+-   **Alternative Hypothesis** (H~A~):
+
+Is there a significant difference in the required [wind speeds]{.underline} for turbines from different companies?
+
+-   **Null Hypothesis** (H~0~):\
+-   **Alternative Hypothesis** (H~A~):
+
+6.  Based on the mean values in the `turbine_summary` data frame and the plots you've created above, [predict the outcome]{.underline} of each t-test that we will run: (1) comparing wind speeds between turbine companies and (2) comparing power outputs between companies. (This question graded for completion, not accuracy; 2 points)
+
+    Explain your reasoning (1-2 sentences for each t-test is fine).
+
+    *Answer:*
+
+7.  Perform a t-test to determine if there is a significant difference in the [power output]{.underline} between turbine makers. (1 point)
+
+```{r}
+
+```
+
+8.  In 2-3 sentences, interpret the output from Question 7. Focus on what the p-value is in reference to the cutoff of 0.05, what that means, and whether that means we reject or fail to reject the null hypothesis. (2 points)
+
+    *Answer:*
+
+9.  Perform another t-test, this time to determine whether the required [wind speed]{.underline} differs significantly between manufacturers. (1 point)
+
+```{r}
+
+```
+
+10. In 2-3 sentences, interpret the output from Question 9; focus on the same ideas as in Question 8. (2 points)
+
+    *Answer:*
+
+# Turning in Your Assignment
+
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_3/assignments/a1/wind_turbines.csv b/modules/module_3/assignments/a1/wind_turbines.csv
new file mode 100644
index 0000000..7a11313
--- /dev/null
+++ b/modules/module_3/assignments/a1/wind_turbines.csv
@@ -0,0 +1,68 @@
+turbine_id,manufacturer,wind_speed,power_output
+1,Windmill Inc,14.81747430500711,65.4083097219102
+2,Windmill Inc,9.07655056972168,3.835687265885688
+3,Windmill Inc,6.5106937879683215,32.04613384367114
+4,Windmill Inc,13.9147143662196,37.74656329973049
+5,Windmill Inc,17.087264003405085,49.927018467915225
+6,Windmill Inc,15.031395105422918,46.58415894124093
+7,Windmill Inc,8.971803082008826,48.45431724191048
+8,Windmill Inc,7.792977146891361,2.9730384303115067
+9,Windmill Inc,3.4901058013210235,1.9442849141272192
+10,Windmill Inc,9.88815623914844,65.32332098722654
+11,Windmill Inc,9.55566494381578,21.955901643711695
+12,Windmill Inc,9.34570007721524,64.01020284733737
+13,Windmill Inc,0.6769110447983877,14.899530477269282
+14,Windmill Inc,9.568617546617746,36.73945592147744
+15,Windmill Inc,2.2664364014395364,24.64174573744712
+16,Windmill Inc,4.293388303541528,0.14622895770859437
+17,Windmill Inc,10.063736431930396,23.49452811653631
+18,Windmill Inc,11.264387881337008,80.46360378711456
+19,Windmill Inc,3.4634696437812797,44.71354939573674
+20,Windmill Inc,16.344713822984534,72.31316676279643
+21,Windmill Inc,7.33497111063025,36.42532140449675
+22,Windmill Inc,10.071259610057396,25.923328507632867
+23,Windmill Inc,9.859797016319192,43.15507179797509
+24,Windmill Inc,5.891980596087259,30.167817035612597
+25,Windmill Inc,14.371303559287544,44.471734138203665
+26,Windmill Inc,7.555152747155024,33.77778266968144
+27,Windmill Inc,7.377788312980606,48.13072644546168
+28,Windmill Inc,5.041420853919237,19.97150778623041
+29,Windmill Inc,12.793396243782745,34.65520366505799
+30,Windmill Inc,9.80890501416891,23.80721493963789
+31,Windmill Inc,9.57631649096204,45.2577745252406
+32,Windmill Inc,16.981753245612698,78.01484727750237
+33,Windmill Inc,15.89948617652854,45.73620679187866
+34,Windmill Inc,8.512151058623427,40.30754696837628
+35,Windmill Inc,15.325182131483729,40.8558686970162
+36,Windmill Inc,1.6621016780010829,18.007024675698332
+37,Windmill Inc,3.1235647643251188,23.469772531824283
+38,Windmill Inc,10.217576247443688,54.63057298051538
+39,Windmill Inc,9.283823051026554,12.3988428002898
+40,Windmill Inc,8.658130814500087,33.25343613805042
+41,Windmill Inc,11.806866640889606,30.574920088667252
+42,Windmill Inc,3.6727543337893738,23.88614679060617
+43,Windmill Inc,15.142102755487132,88.13716273637307
+44,Windmill Inc,20.61459814627487,73.67977578012639
+45,Windmill Inc,6.790689841472066,2.1253052889281427
+46,Windmill Inc,10.388304110640572,13.057030350069624
+47,Windmill Inc,12.706902232135258,42.36806740923551
+48,Turbo Turbines,13.44429892396645,8.655438453898249
+49,Turbo Turbines,6.626352962262523,2.799682790561296
+50,Turbo Turbines,14.98540571824407,12.837892598761172
+51,Turbo Turbines,1.7767233813392664,2.3141193471542856
+52,Turbo Turbines,13.74186272127992,21.53179551605007
+53,Turbo Turbines,13.397573123835716,17.72034791696825
+54,Turbo Turbines,10.11642115933438,23.56009366027324
+55,Turbo Turbines,10.87339026096168,16.15599536157921
+56,Turbo Turbines,20.40834270002923,30.38382260864207
+57,Turbo Turbines,6.44994541576237,6.41278671111802
+58,Turbo Turbines,9.9892865170067,9.249487765963542
+59,Turbo Turbines,10.04694455495925,29.306298637968336
+60,Turbo Turbines,6.759082785601903,21.7835470991122
+61,Turbo Turbines,6.787268177366852,2.624183777838983
+62,Turbo Turbines,5.975386695123835,21.67223184309381
+63,Turbo Turbines,16.457159462031953,20.12890738662136
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diff --git a/modules/module_3/assignments/a2/fish.csv b/modules/module_3/assignments/a2/fish.csv
new file mode 100644
index 0000000..84aedb2
--- /dev/null
+++ b/modules/module_3/assignments/a2/fish.csv
@@ -0,0 +1,1921 @@
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+2021-04-01,1,PM,Iceberg,24.145537497472343
+2021-04-01,2,PM,Iceberg,41.28340211084742
+2021-04-01,3,PM,Iceberg,20.40623985546546
+2021-04-01,4,PM,Iceberg,36.854043084273485
+2021-04-01,5,PM,Iceberg,22.781111897519725
+2021-04-01,1,AM,Hope,32.88671288431921
+2021-04-01,2,AM,Hope,24.534936589376386
+2021-04-01,3,AM,Hope,35.194911186242045
+2021-04-01,4,AM,Hope,11.643052482300234
+2021-04-01,5,AM,Hope,26.70832749633973
+2021-04-01,1,PM,Hope,21.071414697078367
+2021-04-01,2,PM,Hope,21.438538219226306
+2021-04-01,3,PM,Hope,13.362191260672144
+2021-04-01,4,PM,Hope,40.06750577663274
+2021-04-01,5,PM,Hope,37.37980049395292
diff --git a/modules/module_3/assignments/a2/m3a2.qmd b/modules/module_3/assignments/a2/m3a2.qmd
new file mode 100644
index 0000000..fb88d75
--- /dev/null
+++ b/modules/module_3/assignments/a2/m3a2.qmd
@@ -0,0 +1,203 @@
+---
+title: "Module 3, Assignment 2: Comparing Multiple Means"
+author: "your name here"
+date: today
+format: pdf
+---
+
+```{r}
+#| include: false
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Assignment Details
+
+### Purpose
+
+The goal of this assignment is to assess your ability to compare *multiple* means numerically, visually, and statistically and interpret the results.
+
+### Task
+
+Write R code which produces the correct answers and correctly interpret the results of visualizations and statistical tests.
+
+### Criteria for Success
+
+-   Code is within the provided code chunks
+-   Code is commented with brief descriptions of what the code does
+-   Code chunks run without errors
+-   Code produces the correct result
+    -   Code that produces the correct answer will receive full credit
+    -   Code attempts with logical direction will receive partial credit
+-   Written answers address the questions in sufficient detail
+
+# Assignment Questions
+
+In this assignment, we're going to explore data from four bays down in Antarctica: Emperor Bay, Hope Bay, Iceberg Bay, and Sulzberger Bay.
+
+We've assessed our fleet and feel like we have enough fishing boats to fish from *two of the four* nearby bays. How do we decide which two bays to fish?
+
+First, we need to assess how many fish are in each bay. Then we will assess how many leopard seals hang around each bay. Based on these two aspects, we will make a determination for which two bays we can most safely fish and still get enough fish for the station.
+
+## Set-Up
+
+1.  As always, our first order of business is to load the `tidyverse` and our data sets. For this assignment, we have two different data sets: one for our survey of fish amounts and one for seal observations. Read both of them into the workspace and name them `fish` and `seals`, respectively.
+
+```{r}
+
+```
+
+## Fish
+
+The first question we've been asked to tackle is if the four bays have different amounts of fish. Let's find out.
+
+Explore the data set by using your favorite function to look at the data frame (e.g., `head()`, `str()`).
+
+```{r}
+
+```
+
+Early last year, we had our fishers head out into each of the four bays every day for a month to sample fish. They sampled using 5 nets in both the morning and the afternoon. They recorded how many kilograms of fish they caught in each net. The two variables that we will be using to help us make our evaluation are `bay` and `fish_kg`.
+
+### Conceptual
+
+2.  Write out our two hypotheses for these data.
+
+    **Null Hypothesis** $H_{0}$:\
+    **Alternative Hypothesis** $H_{A}$:
+
+### Numeric
+
+3.  Let's first find out what the mean (average) and standard deviation (spread) of `fish_kg` is in each bay. Save this data in a new data frame called `fish_summary`.
+
+```{r}
+
+```
+
+Take a look at the values. Do you think the bays have different amounts of fish based on these values? Let's keep exploring.
+
+### Visual
+
+4.  Plot the fish data using a box-and-whisker plot. It doesn't need to be fancy (no need to fix axis labels and such) but be sure it has the following: Make sure your plot has the following:
+
+    -   data points overlaying the boxes
+    -   the points should be partially transparent and jittered
+
+```{r}
+
+```
+
+### Statistics
+
+5.  In our data set, which variable is the independent variable? Is it categorical or numeric? Which is the dependent variable, and is it categorical or numeric?
+
+    **Independent:**
+
+    **Dependent:**
+
+6.  Run an ANOVA to determine if there is an overall difference in means in the four bays. Save the model as an object called `fish_model`, then display the results of the model using the `summary()` function. Remember, the syntax for the model is: `dependent ~ independent`
+
+```{r}
+
+```
+
+7.  Interpret the results of the model and answer the following questions (2 points):
+
+    -   What is the value of the test statistic?
+
+    -   What is our p-value?
+
+    -   Is our p-value higher or lower than our cut-off value of 0.05?
+
+    -   Write 2-3 sentences explaining that this result means. How do we interpret this value? What does it mean for our hypotheses?
+
+        *Answers:*
+
+8.  Run some code to compute pairwise comparisons.
+
+```{r}
+
+```
+
+9.  Write 2-3 sentences interpreting the pairwise comparisons. Which pairs of bays are significantly different from each other, if any?
+
+    *Answer:*
+
+## Seals
+
+Let's do the same for our seal data. The `seals` data frame has data on how many seals were observed each day in each survey area of the bay. The columns we will be focusing on are `bay` and `num_seals`.
+
+### Conceptual
+
+10. Write out our two hypotheses for the seal data.
+
+    **Null Hypothesis** $H_{0}$:\
+    **Alternative Hypothesis** $H_{A}$:
+
+### Numeric
+
+11. Calculate the mean and standard deviation for the number of seals in each bay. Save this to a new data frame called `seal_summary`.
+
+```{r}
+
+```
+
+Take a look at the values. Do you think the bays have different numbers of seals based on these values? Let's keep exploring.
+
+### Visual
+
+12. Plot the seal data using a box-and-whisker plot. It doesn't need to be fancy (no need to fix axis labels and such) but be sure it has the following: Make sure your plot has the following:
+    -   data points overlaying the boxes
+    -   the points should be partially transparent and jittered
+
+```{r}
+
+```
+
+### Statistics
+
+13. Run an ANOVA to determine if there is an overall difference in means in the four bays.
+
+    Save the model as an object called `seal_model`, then display the results of the model using the `summary()` function.
+
+    Remember, the syntax for the model is: `dependent ~ independent`
+
+```{r}
+
+```
+
+14. Interpret the results of the model and answer the following questions (2 points):
+
+    -   What is the value of the test statistic?
+    -   What is our p-value?
+    -   Is our p-value higher or lower than our cut-off value of 0.05?
+    -   Write 2-3 sentences explaining that this result means. How do we interpret this value? What does it mean for our hypotheses?
+
+    *Answers:*
+
+15. Run a line of code to compute pairwise comparisons.
+
+```{r}
+
+```
+
+16. Write 2-3 sentences interpreting the pairwise comparisons. Which pairs of bays are significantly different from each other, if any?
+
+    *Answer:*
+
+## Decision-Making
+
+Think through our goals in doing this analysis.
+
+We want to choose two out of four bays to fish in. We want bays that have enough fish to feed our station but the least number of leopard seals to keep our fishing crews as safe as possible.
+
+17. Based on *all* our results, which two bays do you think we should fish? Explain your rationale (2 points)
+
+    *Answer:*
+
+# Turning in Your Assignment
+
+1.  Make sure your name is filled in at the top of the document.
+2.  Click the **Render** button at the top of this document. This will produce a PDF that opens in a new tab and also saves to the Files panel on the bottom-right of your screen.
+3.  To download the PDF from Posit Cloud, click the empty box to the left of the PDF file in the Files panel.
+4.  Click the blue gear at the top of the Files panel and choose Export.
+5.  Put your last name at the front of the file name when prompted, then click Download. The PDF is now in your Downloads folder.
\ No newline at end of file
diff --git a/modules/module_3/assignments/a2/seals.csv b/modules/module_3/assignments/a2/seals.csv
new file mode 100644
index 0000000..1bf24f4
--- /dev/null
+++ b/modules/module_3/assignments/a2/seals.csv
@@ -0,0 +1,801 @@
+date,survey_area,bay,num_seals
+2021-03-01,1,Emperor,9
+2021-03-01,2,Emperor,9
+2021-03-01,3,Emperor,9
+2021-03-01,4,Emperor,11
+2021-03-01,5,Emperor,9
+2021-03-01,1,Emperor,11
+2021-03-01,2,Emperor,11
+2021-03-01,3,Emperor,11
+2021-03-01,4,Emperor,11
+2021-03-01,5,Emperor,10
+2021-03-01,1,Sulzberger,2
+2021-03-01,2,Sulzberger,3
+2021-03-01,3,Sulzberger,1
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+2021-03-01,5,Sulzberger,3
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+2021-03-01,3,Sulzberger,4
+2021-03-01,4,Sulzberger,2
+2021-03-01,5,Sulzberger,0
+2021-03-01,1,Iceberg,5
+2021-03-01,2,Iceberg,8
+2021-03-01,3,Iceberg,9
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+2021-03-01,5,Iceberg,6
+2021-03-02,1,Iceberg,5
+2021-03-02,2,Iceberg,4
+2021-03-02,3,Iceberg,8
+2021-03-02,4,Iceberg,6
+2021-03-02,5,Iceberg,5
+2021-03-02,1,Hope,2
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+2021-03-02,3,Hope,1
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+2021-03-02,1,Emperor,9
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+2021-03-03,4,Sulzberger,4
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