A relatively routine process for me is to combine multiple files into a single data frame by row. For example, the data might be stored in separate CSV files by grade and content area, but I want to load them all and treat it as a single data frame with a grade and content indicator. A good default for this sort of process, is to keep all the variables that are present in any data file, and pad with missingness for the files that don’t have that specific variable.
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.