R has become a prominent data science tool, empowered by a fast-growing modern R eco-system. At Novartis, Shiny and markdown have gained a lot of popularity in analyzing, visualizing and reporting of clinical trial data. Traditional report analysis plan (RAP) process was designed to create static table, figure and listings. In this talk, I will use ShinyRAP (a shiny app) to illustrate a novel framework/workflow of planning and executing of both pre-specified and ad-hoc analyses, as well as building dynamic/interactive reports through R/Shiny/Markdown. The app features efficient and organized programming through meta-data and shiny modules, and dynamic display of results via multi-select, grouping and searching, etc. Although motivated from a clinical trial data context, this framework can also be used for other types of data.
Xiao Ni
Novartis
ShinyRAP - a framework for analysis and building interactive/dynamic reports using Shiny/Markdown
R has become a prominent data science tool, empowered by a fast-growing modern R eco-system. At Novartis, Shiny and markdown have gained a lot of popularity in analyzing, visualizing and reporting of clinical trial data. Traditional report analysis plan (RAP) process was designed to create static table, figure and listings. In this talk, I will use ShinyRAP (a shiny app) to illustrate a novel framework/workflow of planning and executing of both pre-specified and ad-hoc analyses, as well as building dynamic/interactive reports through R/Shiny/Markdown. The app features efficient and organized programming through meta-data and shiny modules, and dynamic display of results via multi-select, grouping and searching, etc. Although motivated from a clinical trial data context, this framework can also be used for other types of data.