Environmental Performance Index (EPI) Analysis

This project provides an interactive analysis of the Environmental Performance Index (EPI) using decision tree modeling. It helps explore how different environmental indicators relate to a country's overall EPI score and classifies countries into performance categories (Low/Medium/High).

Features

• Interactive Decision Tree: Visual representation of how environmental indicators predict EPI classes
• Searchable Country Data: Complete dataset with filtering and sorting capabilities
• Performance Metrics: Model accuracy and prediction results
• Responsive Design: Works well on different screen sizes

Requirements

• R (>= 4.0.0)
• R packages: tidyverse, rpart, rpart.plot, DT
• R Markdown (for report generation)

Installation

1. Clone this repository
2. Install required R packages:

   install.packages(c("tidyverse", "rpart", "rpart.plot", "DT", "rmarkdown"))

Usage

1. Run the analysis:

   Rscript -e "rmarkdown::render('analysis_report.Rmd')"

2. Open analysis_report.html in your web browser to view the interactive report

Project Structure

• analysis_report.Rmd: Main analysis file with decision tree model and visualizations
• Data/epi2024_data.csv: Dataset containing country-level environmental indicators and EPI scores
• biodiversity_analysis_clean.R: Supporting R script with data processing functions
• LICENSE: Apache 2.0 license file

How to Use the Interactive Report

1. Explore the Decision Tree: Understand how environmental indicators predict EPI classes
2. Search and Filter: Use the search box and column filters to find specific countries
3. Sort Data: Click on column headers to sort the country data
4. Compare Predictions: Examine actual vs. predicted EPI classes

Data Sources

• EPI 2024 Dataset
• Environmental indicators include:
  • BDH: Biodiversity & Habitat
  • ECS: Ecosystem Services
  • FSH: Fish Stocks
  • APO: Air Pollution
  • AGR: Agriculture

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an issue for any suggestions or bug reports.