A lightweight R-based tool for analyzing GitHub repository language statistics with automated visualization.

This project was originally part of my major research from my school work, and I decided to open source it for the community. This is also my first R project on GitHub!

You can build your own language statistics visualization by forking this repository. Welcome to star, contribute and any suggestions are welcome.

They are the examples output for my GitHub profile. For more example and formats, please check the data branch.

• Fetches repository data via GitHub API
• Calculates weighted language distribution based on:
  • Repository update recency
  • Stars and forks count
  • Repository count per language
• Generates multiple visualizations, both in SVG and PNG formats:
  • Pie Charts
  • Treemaps
• Automated monthly updates via GitHub Actions

# Set environment variables
export GITHUB_USERNAME="your_username"

# Run analysis
Rscript R/main.R

The workflow runs automatically on the 1st of each month, or can be triggered manually via workflow dispatch. Results are committed to the data branch with the following structure:

data/{username}/
├── raw_YYYYMMDD.json       # Raw API response
├── plotted_YYYYMMDD.csv    # Statistical summary
├── piechart_YYYYMMDD.svg   # Date-stamped pie chart
├── piechart_YYYYMMDD.png
├── treemap_YYYYMMDD.svg    # Date-stamped treemap
├── treemap_YYYYMMDD.png
├── latest.svg              # Latest pie chart
├── latest.png
├── treemap_latest.svg      # Latest treemap
└── treemap_latest.png

You can link to these images in your GitHub README or personal website to showcase your language statistics. The latest.png and treemap_latest.png (or svg) files always point to the most recent visualizations.

• R >= 4 (tested on 4.5.1)
• R packages: httr, jsonlite, dplyr, magrittr, showtext, ggplot2, treemapify, svglite

You can also manually start the GitHub Actions process from the "Actions" tab in your repository, and then execute the analysis by clicking the "Run workflow" button.

Special thanks to Fira Code for the beautiful font used in the visualizations.

This project is licensed under the MIT License, see LICENSE.md for details.