Thought-doing

This post is a rundown of a workflow that I use for preparing group-comparison graphs that are ready to include in nearly any kind of report. This post is sectioned out piece-by-piece to better demonstrate the impacts of each function and argument for ggplot, and also incorporates statistical hypothesis tests.

Building off of raincloud and fadecloud plots, I introduce two new styles for plotting data transparently: (1) the faded dotplot and (2) the shadeplot. Both leverage the r package ggdist to present stacked raw data, overall distribution, and summary stats while minimizing visual clutter.

The raincloud plot is probably the ‘gold standard’ in transparent data vizualization, but it requires charting a lot of objects. To improve the legibility of the raincloud, we’ll explore a modification that colors in the density distribution according to meaningful cut-off points. The result is a ‘fadecloud’ plot, which can be presented without the boxplot. Done primarily with ggplot and ggdist.

oTree is an open-source, Python-based framework that lets you build Multiplayer strategy games, behavioral experiments, and surveys/quizzes. What I especially like is that entire projects can be saved in their raw code form, being easily shared, reproduced, and modified.