Exploring data subsets with vtree

Variable trees, a new method for the exploration of discrete multivariate data, allow
exploration of nested subsets and calculation of corresponding percentages. These calculations can be laborious, especially when there are many multi-level factors and missing
data. Here we introduce variable trees and their implementation in the vtree R package,
draw comparisons with existing methods (contingency tables, mosaic plots, Venn/Euler
diagrams, and UpSet), and illustrate their utility using two case studies. Variable trees
can be used to (1) reveal patterns in nested subsets, (2) explore missing data, and (3)
generate study-flow diagrams (e.g. CONSORT diagrams) directly from data frames, to
support reproducible research and open science.