Statistical programming of summary tables is a well-established task within the clinical world. In the last few years, the pharmaceutical industry has seen several new packages emerge to support these activities, including the Atorus package {Tplyr}. Much of the focus has been on creating static tables, be it in HTML, or more classically RTF. With the visualization power available within R and JavaScript, the natural next step is to enhance interactive capabilities. This presentation will demonstrate how {Tplyr} take this next step. Using metadata captured by Tplyr, we will show how a user can hit rewind and drill into the source data that lead to a summary data point.
Maya Gans | Mike Stackhouse
Atorus Research | Atorus Research
Summary Table Rewind: From Source to Summary and Back Again
Statistical programming of summary tables is a well-established task within the clinical world. In the last few years, the pharmaceutical industry has seen several new packages emerge to support these activities, including the Atorus package  Tplyr . Much of the focus has been on creating static tables, be it in HTML, or more classically RTF. With the visualization power available within R and JavaScript, the natural next step is to enhance interactive capabilities. This presentation will demonstrate how  Tplyr  take this next step. Using metadata captured by Tplyr, we will show how a user can hit rewind and drill into the source data that lead to a summary data point.