Repository files navigation

"If I take it up I must understand every detail," said he. "Take time to consider. The smallest point may be the most essential." --- Sherlock HolmesThe Adventure of the Red Circle

For a couple ofdecades, we have been loyal users of theHmisc package in general, and theHmisc::describe function in particular, as a way to explore data before any analyses. As is often the case in the R ecosystem, there are numerous ways to accomplish this task (see summarizing data blog postshere andhere for a dated yet extensive review). Our appreciation forHmisc::describe originated from its concise look (pre-rmarkdown days implementing Sweave/Latex/PDF) and its ability to link with SAS formatted datasets (containing labels, formats, special missing). Indeed, in the clinical research industry, SAS formatted datasets (SAS transport.xpt or native.sas7bdat files) remain widely used while the R language continues to grow inpopularity.Dr. Frank Harrell, who developed the Hmisc package, has been, from our perspective, a luminary as he lays out the possibilities embedded in the R language, particularly in the clinical research environment.

For some time now, we have wanted to reengineer the aforementioneddescribe function to provide a modern and interactive interface to the static (HTML and/or PDF) report. Thedatadigest package was an effort to build an interactive data explorer inspired byHmisc::describe; the package leveragedJavaScript for interactivity, withhtmlwidget andShiny interfaces for use in R. Since the release ofdatadigest, the R community has continued to deliver increasingly powerful frameworks for interactive displays. Therefore, we took the2021 RStudio Table Contest as an opportunity to accomplish the goal of building an interactive interface fordescribe using tools available in R. We have utilized the power ofreactable embedded withplotly interactive figures within aflexdashboard to generate concise summaries of every variable in a dataset with minimal user configuration. In order for other users to readily deploy such a powerful summary table, we wrapped our work into the{describer} package.

For this challenge, we selected aCDISC (Clinical Data Interchange Standards Consortium)ADaM (Analysis Data Model) ADSL (Analysis Data Subject Level) dataset as an illustration. The ADSL dataset structure is one record per subject and contains variables such as subject-level population flags, planned and actual treatment variables, demographic information, randomization factors, subgrouping variables, and important dates originated from thePHUSE CDISC Pilot replication study.