Explore data presently captured, develop an experimental visualization using tools of your choice. If Jupyter Notebooks against an Augur database/API endpoint collection, use https://github.com/chaoss/augur-community-reports for development.
For GrimoireLab's Kibiter Visualization Exploration,
- On local Kibiter, I created an index pattern, with the index
git-onion_enrichedand Time Filter field name: grimoire_creation_date, also I am visualizing using Kibiter data from Perceval and GrimoireELK
For Jupyter Notebook, I explored the augur-community-reports jupyter notebooks. I investigated all the tables inside the augur_data database, including ones generated by the contributor_breadth_worker using PSequel.
I then created my own Conversion Rate visualization. For all repos inside the Augur DB, I considered all contributors. I designated a "new" time interval and an "assess" time interval. I visualized those who are new in the "new" interval and have become sustained based on a measure of merged pull requests (> 3 by end of "assess" interval). This rate was calculated every month and visualized on a line graph to observe how it evolved.
I also created a test Opensearch connector (Python) for loading data from pandas dataframe columns into Opensearch (reusing the data). This resulted in the visualization seen at the bottom of Microtask 4.
The visualization notebook is available here: https://github.com/mabelbot/chaoss-cr-microtasks/blob/main/viz_example.ipynb