Back in 2020, Atorus had the initial release of the R package Tplyr, which was built to simplify the creation of clinical summary tables. Now in 2022, new updates and enhancements have been added to Tplyr to give the user more, particularly in the area of Shiny. In this presentation we will discuss how Tplyr collects metadata that provides traceability to every result it derives. Furthermore, these metadata features are externalized, allowing users to extend Tplyr’s metadata or even build their own, which enables all these of these features to extend beyond Tplyr itself. Ultimately when paired with Shiny, users can utilize Tplyr to build click-through tables where a reviewer can click on a result and immediately view the subset of data that was used to derive it.
Mike Stackhouse
Atorus Research
Drill Down Summary Tables in Shiny with Tplyr
Back in 2020, Atorus had the initial release of the R package Tplyr, which was built to simplify the creation of clinical summary tables. Now in 2022, new updates and enhancements have been added to Tplyr to give the user more, particularly in the area of Shiny. In this presentation we will discuss how Tplyr collects metadata that provides traceability to every result it derives. Furthermore, these metadata features are externalized, allowing users to extend Tplyr’s metadata or even build their own, which enables all these of these features to extend beyond Tplyr itself. Ultimately when paired with Shiny, users can utilize Tplyr to build click-through tables where a reviewer can click on a result and immediately view the subset of data that was used to derive it.