论文标题
使用VTREE探索数据子集
Exploring data subsets with vtree
论文作者
论文摘要
可变树是一种探索离散多元数据的新方法。他们显示嵌套子集以及相应的频率和百分比。这些数量的手动计算可能会很费力，尤其是当有许多多层次因素和缺少数据时。在这里，我们在VTREE R软件包中介绍了可变树及其实现，与现有方法（应变表，马赛克图，Venn/Euler图和IST）进行了比较，并使用两个案例研究来说明其效用。可变树可用于（1）在嵌套子集中揭示模式，（2）探索丢失的数据，（3）直接从数据框架中生成研究流程图（例如，配偶图），以支持可重复的研究和开放科学。
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.
