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.

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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.

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