AI 中文总结
该研究针对实证研究中用于展示样本背景特征的“表1”，提出了快照图这一可视化方法，可促进组间背景特征比较并提供更多数值变量细节，还提供了网页应用与Python实现，经两个“表1”验证了其实用性。
AI 中文摘要
在实证研究中，社会与健康科学家会提供样本的背景特征，并将其汇总为著名的“表1”。当存在处理组/对照组时，该表会按组给出汇总统计量，以判断背景特征是否随组而异。我们提出快照图——带有一致高亮的平行单变量图——来可视化此类表格。与“表1”相比，这类图旨在促进背景特征（尤其是各组间）的比较，并为数值变量提供更多细节。我们提供了快照图的网页应用及Python实现。快照图是吊床图（用于混合分类/数值数据的平行坐标图）的边缘情况。我们通过两个“表1”演示了快照图的实用性。
英文摘要
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.
