🎯 About this series

What you'll discover in this series is the knowledge we got by working on the data our activity did generate on GitHub.com, only by using its (always richer) set of APIs.

❔ Intro

GitHub.com is a complete platform on which people :

• 🧑‍🤝‍🧑 Build & socialize around code
• 🎁 Contribute
• 📅 Manage projects
• 📦 Deliver software
• 🚀 Trigger deployments

Each of these activities generate structured data that describe what you did, when, with who... and (hopefully 😅) why.

Some reporting is already delivered through charts by GitHub:

... but sometimes you can be challenged by many questions, including (but not only) one of the most crucial one :

"How much time (and where) do you spend on BUILD vs. RUN"

or this one:

"You talk about BOTs and third party services... but how can you tell me more about the ROI ... and how it helps achieve more things ?")

👉 In this episode, we'll only focus on reporting which repositories have been the most active during the past year... through a format anyone can immediately understand.

🍿 Demo

The chart race of the most active repositories, based on issues activity :

🧰 Stack

Here is the flow we used to produce the content :

1️⃣ Dump GitHub issues to csv with Python invoke tasks
2️⃣ Use Jupyter Notebook
3️⃣ Generate Race Chart with Bar Chart Race

💰 Benefits

By a single movie we could show how our most trendy activity evolved over the months since our migration from onPrem GitLab to GitHub.com... without having ever asked anyone to fill a single timesheet. 😅