I stumbled the Python visualization library Altair about 6 months ago. I was instantly hooked, as it blows away the reinging king of Python visualizations matplotlib. But, I hate matplotlib. Between matplotlib and a half-missing box crayons, I’ll take the crayons. That’s why Altair’s clean graphics and logical syntax easily capterd me. But, I’ve really only dabbled in Altair, and have not explored its interactivity at all. I’ll dig a little deeper into Altair’s interactivity in this post, and see if I can uncover some bar passage insights along the way.

Continue Reading

I recently started learning D3 in an attempt to add another arrow to my data visualization quiver. D3 is a data visualization library built on top of JavaScript, and is generally considered the most detail-oriented, top of the line, data visualization tool in the business. You control everything in your graphics. I’m currently 100 pages into the wonderful book Interactive Data Visualization for the Web by Scott Murray and I figured this is a great time to take the training wheels off and build my first D3 plot outside of a canned tutorial.

Continue Reading

Technical notes: I conducted this analysis in R with the rstanarm package for Bayesian modeling. The dataset represents law school ABA 509 disclosures, aggregated by AccessLex. What are hierarchical models? We start with a simple question: Which states have the lowest and highest bar passage rates? On the surface, this is simple arithmetic. Just divide the number of bar passers by the number of takers.

Continue Reading