AURE: Advanced Environmental Performance Index (EPI) Analysis

A comprehensive R-based tool for analyzing Environmental Performance Index (EPI) data, generating detailed reports, and providing advanced environmental analytics insights.

Features

• Comprehensive EPI Analysis:

  • Country performance evaluation
  • Trend analysis over time
  • Regional comparisons
  • Income group analysis

• Interactive Visualizations:

  • World maps of EPI scores
  • Correlation matrices
  • Cluster analysis
  • Time series visualizations

• Automated Reporting:

  • HTML and PDF report generation
  • Country-specific analysis
  • Regional comparisons
  • Environmental indicator deep dives

Requirements

• R (>= 4.0.0)
• RStudio (recommended)
• Required R packages:
  • tidyverse
  • rmarkdown
  • knitr
  • kableExtra
  • ggplot2
  • plotly
  • sf
  • rnaturalearth
  • countrycode
  • viridis
  • ggcorrplot
  • factoextra
  • cluster

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/AURE.git
   cd AURE

2. Install the required R packages:

   Rscript install_deps.R

Usage

Basic Usage

# Load the required libraries
library(tidyverse)
library(rmarkdown)

# Generate a basic EPI report
source("analyze_epi.R")
analyze_epi("path/to/epi_data.csv", "epi_report.html")

Command Line Interface

# Generate a detailed EPI analysis report
Rscript detailed_epi_analysis.R epi2024_data.csv epi_report.html

Project Structure

AURE/
.
├── analyze_epi.R              # Basic EPI analysis script
├── detailed_epi_analysis.R    # Comprehensive EPI analysis script
├── detailed_epi_analysis_report.html  # Sample output
├── epi_analysis_report.pdf    # Basic analysis report
├── install_deps.R             # Dependency installer
├── LICENSE
├── R/
│   ├── advanced_analytics.R   # Advanced analysis functions
│   ├── other_helpers.R        # Helper functions
│   └── utils.R                # Utility functions
└── templates/                 # Report templates
    └── report_template.Rmd    # R Markdown template

Data Format

The input data should be in CSV format with the following columns:

• country: Country name
• iso: ISO country code
• EPI: Environmental Performance Index score (0-100)
• BDH: Biodiversity & Habitat score
• ECS: Ecosystem Services score
• FSH: Fisheries score
• APO: Air Pollution score
• AGR: Agriculture score
• WRS: Water Resources score
• AIR: Air Quality score
• H2O: Drinking Water score
• HMT: Heavy Metals score
• WMG: Waste Management score
• CCH: Climate Change score
• Income Group: Country income classification

Examples

Basic Analysis

# Load and preprocess EPI data
epi_data <- read_csv("epi2024_data.csv")
summary(epi_data)

Advanced Analytics

Country Clustering

# Cluster countries based on EPI indicators
source("R/advanced_analytics.R")
clusters <- cluster_countries(epi_data, n_clusters = 4)

Correlation Analysis

# Calculate correlations between EPI indicators
cor_matrix <- cor(select(epi_data, where(is.numeric)), use = "complete.obs")
ggcorrplot(cor_matrix, type = "lower", lab = TRUE)

Generate Interactive Map

# Create an interactive world map of EPI scores
library(sf)
library(rnaturalearth)

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  left_join(epi_data, by = c("iso_a3" = "iso3c"))

ggplot(world) +
  geom_sf(aes(fill = EPI), color = "white", size = 0.1) +
  scale_fill_viridis_c("EPI Score") +
  theme_minimal()

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/my-feature)
5. Create a new Pull Request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

• Yale Center for Environmental Law & Policy and CIESIN for the EPI data
• Tidyverse team for amazing R packages
• RStudio for the fantastic development environment

Contact

For questions or feedback, please open an issue in the repository.