Github Star Gazers Analytics (GSA)

Get to know a GitHub repository's star-gazers, by using some simple analytics and visualizations of star-gazers data queried from GitHub.

The image below is a geographical snapshot of FastAI's star gazers at the beginning of November, 2018. It was created by executing the following:

$ python stars_analytics.py query-github --user=<YOUR USER> --password=<YOUR PASSWORD> --repo=https://github.com/fastai/fastai --proxy=https://<YOUR PROXY>:<PORT>

And here's the same data in a different view:

$ python3 stars_analytics.py stars-geo-tbl --cache-file=fastai_star_gazers.csv --format=plot

Choose any github repository, run a query against GitHub to download the star-gazer records, and perform analytics. Currently features:

• Classify star-gazers by country of origin, and draw this using size-bubbles on a world map.
• Create monthly starring activity statistics
• Create day-of-week statistics

Note: You will need a GitHub user and password to query GitHub. There is an API to query without using GiutHub credentials, but it limits the number of queries to a very small number, that is not practical for this application.

Installation

These instructions will help get GSA up and running on your local machine.

1. Clone Star Gazers Analytics
2. Create a Python virtual environment
3. Install dependencies

Notes:

• This repository has only been tested on Ubuntu 16.04 LTS, and with Python 3.5.

Clone Star Gazers Analytics

Clone the Star Gazers Analytics code repository from github:

$ git clone https://github.com/netaz/github_stars_analytics.git

Create a Python virtual environment

I recommend using a Python virtual environment, but that of course, is up to you. Before creating the virtual environment, make sure you are located in directory github_stars_analytics. After creating the environment, you should see a directory called github_stars_analytics/env.

Using virtualenv

If you don't have virtualenv installed, you can find the installation instructions here.

To create the environment, execute:

$ python3 -m virtualenv env

This creates a subdirectory named env where the python virtual environment is stored, and configures the current shell to use it as the default python environment.

Using venv

If you prefer to use venv, then begin by installing it:

$ sudo apt-get install python3-venv

Then create the environment:

$ python3 -m venv env

As with virtualenv, this creates a directory called github_stars_analytics/env.

Activate the environment

The environment activation and deactivation commands for venv and virtualenv are the same.
!NOTE: Make sure to activate the environment, before proceeding with the installation of the dependency packages:

$ source env/bin/activate

Install dependencies

Finally, install GSA's dependency packages using pip3:

$ pip3 install -r requirements.txt

Getting Started

1. Download some metadata We need some metadata about each of the countries in the world: its capital, size of population and all of the major cities. We also download for each country its longitude and latitude so we can draw country-specific data on a map. We will use this data to match star-gazer records to countries.

We only need to download this data once:

$ ./download_metadata.sh

2. Query a specific Github repository and create a file (default name = star_gazers.csv) with the cached information. This process is a bit slow so we cache the results in a file, until we decide to get new data.

python stars_analytics.py query-github --user=<YOUR-GITHUB-USERNAME> --password=<YOUR-GITHUB-PASSWORD> --repo=<YOUR-GITHUB-REPO-URL>

The format of the URL is the same as when you access it via the web. For example: https://github.com/NervanaSystems/distiller

3. Use the cached github star-gazers data file, to create analytics and visualizations. For example, to create the diagram above:

python stars_analytics.py stars-geo-map

To create a chart of the daily stars for the entire period:

python3 stars_analytics.py daily --cache-file=distiller_star_gazers.csv --format=plot