Abstract

There is a lack of data visualization tools, such as UpSet plot, suitable for survey data collected via complex sampling. Ignoring the design specifications when plotting the data could result in ambiguous findings. We discuss a two-step approach that can be used to construct the UpSet plots of weighted frequencies and relative weighted frequencies (percentages). In the first step we compute weighted frequencies corresponding to all possible intersection sets using the SAS® Survey Package software. In the second step, we import the SAS output table in R and use it to construct the UpSet plots in R. We illustrate the application of the two-step method in the study of perceived reasons for e-cigarette use among current e-cigarette users. In the study we analyzed reports of adults (n = 3,614) who responded to the 2014–15 Tobacco Use Supplement to the Current Population Survey. The UpSet plots were constructed to identify the most commonly reported sets of reasons for e-cigarette use. We expect that the discussed method can be adopted for analysis of data from other complex surveys and thus, offer a valuable tool for constructing UpSet plots of intersection sets based on survey data with weights.

摘要

缺乏适合通过复杂抽样收集的调查数据的数据可视化工具，例如 UpSet 图。在绘制数据时忽略设计规范可能会导致不明确的结果。我们讨论了一种两步方法，可用于构建加权频率和相对加权频率（百分比）的 UpSet 图。第一步，我们使用 SAS® Survey Package 软件计算与所有可能的交集相对应的加权频率。第二步，我们在R中导入SAS输出表，并用它在R中构建UpSet图。我们举例说明了两步法在当前电子烟使用感知原因研究中的应用用户。在这项研究中，我们分析了成人的报告（n = 3，614）谁回应了 2014-15 年当前人口调查烟草使用补充。构建 UpSet 图是为了确定最常报告的电子烟使用原因。我们期望所讨论的方法可以用于分析其他复杂调查的数据，从而为基于带权重的调查数据构建交集的 UpSet 图提供有价值的工具。