In this post, I’d like to share one of the more unique plots from esvis - the binned effect size plot. The overall purpose of the binned effect size plot is to evaluate if the differences between two distributions are consistent across the scale. We’ll start with a quick example from the package, and then move to a simulated data example. Quick example The API for esvis is consistent across functions, so we use the same outcome ~ predictor forumula as the first argument, followed by the data for all functions.
Today I wanted to quickly share my first real attempt at making an alluvial diagram. For those not familiar (and I wasn’t previously) an alluvial diagram is a type of flow plot that is essentially equivalent to a sankey diagram. The difference is that while sankey diagrams show flow for different categorical variables, alluvial plots show change over time. To produce the alluvial diagram below, I’ll be using the development version of the excellent ggforce package written by Thomas Lin Pedersen, who’s not only incredibly talented, but also a good follow on twitter.
In this post, I’d like to share some work related to geo-spatial mapping, statewide school ratings, and US Census Bureau data using census tracts. Specifically, I wanted to investigate whether there was a relation between the median housing price in an area, and the statewide achievement ratings for schools in the corresponding area. There is a strong relation between socio-economic status and student achievement, but less is known about how statewide ratings for schools relate to the demographics of the corresponding area.