Generate visualizations of GitHub user and repository statistics using GitHub Actions.

Overview

GitHub Stats Visualization

Generate visualizations of GitHub user and repository statistics using GitHub Actions.

This project is currently a work-in-progress; there will always be more interesting stats to display.

Background

When someone views a profile on GitHub, it is often because they are curious about a user's open source projects and contributions. Unfortunately, that user's stars, forks, and pinned repositories do not necessarily reflect the contributions they make to private repositories. The data likewise does not present a complete picture of the user's total contributions beyond the current year.

This project aims to collect a variety of profile and repository statistics using the GitHub API. It then generates images that can be displayed in repository READMEs, or in a user's Profile README.

Since the project runs on GitHub Actions, no server is required to regularly regenerate the images with updated statistics. Likewise, since the user runs the analysis code themselves via GitHub Actions, they can use their GitHub access token to collect statistics on private repositories that an external service would be unable to access.

Disclaimer

If the project is used with an access token that has sufficient permissions to read private repositories, it may leak details about those repositories in error messages. For example, the aiohttp library—used for asynchronous API requests—may include the requested URL in exceptions, which can leak the name of private repositories. If there is an exception caused by aiohttp, this exception will be viewable in the Actions tab of the repository fork, and anyone may be able to see the name of one or more private repositories.

Due to some issues with the GitHub statistics API, there are some situations where it returns inaccurate results. Specifically, the repository view count statistics and total lines of code modified are probably somewhat inaccurate. Unexpectedly, these values will become more accurate over time as GitHub caches statistics for your repositories. Additionally, repositories that were last contributed to more than a year ago may not be included in the statistics due to limitations in the results returned by the API.

For more information on inaccuracies, see issue #2, #3, and #13.

Installation

  1. Create a personal access token (not the default GitHub Actions token) using the instructions here. Personal access token must have permissions: read:user and repo. Copy the access token when it is generated – if you lose it, you will have to regenerate the token.
    • Some users are reporting that it can take a few minutes for the personal access token to work. For more, see #30.
  2. Click here to create a copy of this repository. Note: this is not the same as forking a copy because it copies everything fresh, without the huge commit history.
  3. If this is the README of your fork, click this link to go to the "Secrets" page. Otherwise, go to the "Settings" tab of the newly-created repository and go to the "Secrets" page (bottom left).
  4. Create a new secret with the name ACCESS_TOKEN and paste the copied personal access token as the value.
  5. It is possible to change the type of statistics reported.
    • To ignore certain repos, add them (in owner/name format e.g., jstrieb/github-stats) separated by commas to a new secret—created as before—called EXCLUDED.
    • To ignore certain languages, add them (separated by commas) to a new secret called EXCLUDED_LANGS.
    • To show statistics only for "owned" repositories and not forks with contributions, add an environment variable (under the env header in the main workflow) called EXCLUDE_FORKED_REPOS with a value of true.
  6. Go to the Actions Page and press "Run Workflow" on the right side of the screen to generate images for the first time. The images will be periodically generated every hour, but they can be manually regenerated by manually running the workflow.
  7. Check out the images that have been created in the generated folder.
  8. To add your statistics to your GitHub Profile README, copy and paste the following lines of code into your markdown content. Change the username value to your GitHub username.
    ![](https://github.com/username/github-stats/blob/master/generated/overview.svg)
    ![](https://github.com/username/github-stats/blob/master/generated/languages.svg)
  9. Link back to this repository so that others can generate their own statistics images.
  10. Star this repo if you like it!

Support the Project

There are a few things you can do to support the project:

  • Star the repository (and follow me on GitHub for more)
  • Share and upvote on sites like Twitter, Reddit, and Hacker News
  • Report any bugs, glitches, or errors that you find

These things motivate me to to keep sharing what I build, and they provide validation that my work is appreciated! They also help me improve the project. Thanks in advance!

If you are insistent on spending money to show your support, I encourage you to instead make a generous donation to one of the following organizations. By advocating for Internet freedoms, organizations like these help me to feel comfortable releasing work publicly on the Web.

Related Projects

Owner
JoelImgu
JoelImgu
Lightspin AWS IAM Vulnerability Scanner

Red-Shadow Lightspin AWS IAM Vulnerability Scanner Description Scan your AWS IAM Configuration for shadow admins in AWS IAM based on misconfigured den

Lightspin 90 Dec 14, 2022
A GUI for Pandas DataFrames

About Demo Installation Usage Features More Info About PandasGUI is a GUI for viewing, plotting and analyzing Pandas DataFrames. Demo Installation Ins

Adam Rose 2.8k Dec 24, 2022
Color maps for POV-Ray v3.7 from the Plasma, Inferno, Magma and Viridis color maps in Python's Matplotlib

POV-Ray-color-maps Color maps for POV-Ray v3.7 from the Plasma, Inferno, Magma and Viridis color maps in Python's Matplotlib. The include file Color_M

Tor Olav Kristensen 1 Apr 05, 2022
The Metabolomics Integrator (MINT) is a post-processing tool for liquid chromatography-mass spectrometry (LCMS) based metabolomics.

MINT (Metabolomics Integrator) The Metabolomics Integrator (MINT) is a post-processing tool for liquid chromatography-mass spectrometry (LCMS) based m

Sören Wacker 0 May 04, 2022
High performance, editable, stylable datagrids in jupyter and jupyterlab

An ipywidgets wrapper of regular-table for Jupyter. Examples Two Billion Rows Notebook Click Events Notebook Edit Events Notebook Styling Notebook Pan

J.P. Morgan Chase 75 Dec 15, 2022
FairLens is an open source Python library for automatically discovering bias and measuring fairness in data

FairLens FairLens is an open source Python library for automatically discovering bias and measuring fairness in data. The package can be used to quick

Synthesized 69 Dec 15, 2022
Simple plotting for Python. Python wrapper for D3xter - render charts in the browser with simple Python syntax.

PyDexter Simple plotting for Python. Python wrapper for D3xter - render charts in the browser with simple Python syntax. Setup $ pip install PyDexter

D3xter 31 Mar 06, 2021
A small tool to test and visualize protein embeddings and amino acid proportions.

polyprotein_stats A small tool to test and visualize protein embeddings and amino acid proportions. Currently deployed on streamlit.io. Given a set of

2 Jan 07, 2023
PyFlow is a general purpose visual scripting framework for python

PyFlow is a general purpose visual scripting framework for python. State Base structure of program implemented, such things as packages disco

1.8k Jan 07, 2023
DALLE-tools provided useful dataset utilities to improve you workflow with WebDatasets.

DALLE tools DALLE-tools is a github repository with useful tools to categorize, annotate or check the sanity of your datasets. Installation Just clone

11 Dec 25, 2022
A set of useful perceptually uniform colormaps for plotting scientific data

Colorcet: Collection of perceptually uniform colormaps Build Status Coverage Latest dev release Latest release Docs What is it? Colorcet is a collecti

HoloViz 590 Dec 31, 2022
A minimalistic wrapper around PyOpenGL to save development time

glpy glpy is pyOpenGl wrapper which lets you work with pyOpenGl easily.It is not meant to be a replacement for pyOpenGl but runs on top of pyOpenGl to

Abhinav 9 Apr 02, 2022
Create artistic visualisations with your exercise data (Python version)

strava_py Create artistic visualisations with your exercise data (Python version). This is a port of the R strava package to Python. Examples Facets A

Marcus Volz 53 Dec 28, 2022
An open-source plotting library for statistical data.

Lets-Plot Lets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language. The design of Le

JetBrains 820 Jan 06, 2023
Show Data: Show your dataset in web browser!

Show Data is to generate html tables for large scale image dataset, especially for the dataset in remote server. It provides some useful commond line tools and fully customizeble API reference to gen

Dechao Meng 83 Nov 26, 2022
mysql relation charts

sqlcharts 自动生成数据库关联关系图 复制settings.py.example 重命名为settings.py 将数据库配置信息填入settings.DATABASE,目前支持mysql和postgresql 执行 python build.py -b,-b是读取数据库表结构,如果只更新匹

6 Aug 22, 2022
Material for dataviz course at university of Bordeaux

Material for dataviz course at university of Bordeaux

Nicolas P. Rougier 50 Jul 17, 2022
Data visualization electromagnetic spectrum

Datenvisualisierung-Elektromagnetischen-Spektrum Anhand des Moduls matplotlib sollen die Daten des elektromagnetischen Spektrums dargestellt werden. D

Pulsar 1 Sep 01, 2022
This is a place where I'm playing around with pandas to analyze data in a csv/excel file.

pandas-csv-excel-analysis This is a place where I'm playing around with pandas to analyze data in a csv/excel file. 0-start A very simple cheat sheet

Chuqin 3 Oct 05, 2022
Simple Python interface for Graphviz

Simple Python interface for Graphviz

Sebastian Bank 1.3k Dec 26, 2022