  Variable trees are a new method for the exploration of discrete multivariate
data. They display nested subsets and corresponding frequencies and
percentages. Manual calculation of these quantities 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.
