🤖 AI Summary
Traditional “Table 1” formats inadequately convey details of numerical variables when presenting baseline characteristics across groups, hindering intuitive comparison. This work proposes “snapshot plots,” which transform summary tables into parallel univariate visualizations with consistent color encoding—a specialized form of hammock plots—to substantially enhance intergroup comparability and information density. Built upon the principles of parallel coordinates, the approach uniformly handles mixed variable types and is implemented in Python, accompanied by an interactive web application. Validation on two real-world “Table 1” examples demonstrates that snapshot plots significantly improve readability and facilitate cross-group comparisons.
📝 Abstract
For empirical studies, social and health scientists give background characteristics of their sample and summarize them in the famous "Table 1". When treatment/ control groups are present, this table gives summary statistics by group to see whether the background characteristics differ by group. We propose snapshot plots --- parallel univariate plots with consistent highlighting --- to visualize such tables. Compared to "Table 1", such plots are designed to facilitate comparisons of background characteristics --- in particular among groups --- and give more detail on numerical variables. We provide a web app as well as a python implementation of snapshot plots. Snapshot plots arise as edge cases of hammock plots (parallel coordinate plots for mixed categorical/ numerical data). We demonstrate the usefulness of snapshot plots for two "Table 1"s.