Using the Python plotting library Altair to explore bar passage rates<br>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.

<br>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.

Hierarchical Bayesian model of state bar passage rates<br>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.

R shiny app that interactively displays the relationship between law school admissions factors and bar passage<br>Programing Language: Platform: Shiny A shiny app that interactively highlights the positive relationship between a law school&rsquo;s median undergraduate GPA, median LSAT score, and bar passage. View App GitHub repo The bulk of the application resides in an app.R file, the contents of which are below: library(tidyverse) library(plotly) library(shiny) library(DT) # function that creates scatterplot --------------- plot_rates &lt;- function(df, select_year, eval, metric, select_school) { # create axis labels ------ # create named list of axis components and their labels as strings # then we will access string to create axis label axis_labels &lt;- list('lsat50' = 'Median LSAT', 'uggpa50' = 'Median Undergraduate GPA', 'scaled_admissions' = 'LSAT / Undergrad GPA Combo (Standardized to mean of 0 and st.