The data wrangling and manipulation capabilities in R make it perfectly suited for transforming raw clinical database data into structured, submission-ready CDISC datasets. By extensively using the dplyr, tidyr, and other packages in the tidyverse we create datasets that are ready for: 1. pharmacokinetic analysis (done in other software), 2. generation of tables, listings, and figures (TLFs) and 3. submission to the FDA. I'll also go over report ready TLF generation in R and how we've had great success in being able to produce beautiful and easy to read TLFs for our analysts and clients by using the ggplot2 and the officer packages.
Jessica Higgins
Nuventra Pharma Sciences
From CDISC to TLFs, using R to support Pharmacokinetic Analyses
The data wrangling and manipulation capabilities in R make it perfectly suited for transforming raw clinical database data into structured, submission-ready CDISC datasets. By extensively using the dplyr, tidyr, and other packages in the tidyverse we create datasets that are ready for 1. pharmacokinetic analysis (done in other software), 2. generation of tables, listings, and figures (TLFs) and 3. submission to the FDA. I'll also go over report ready TLF generation in R and how we've had great success in being able to produce beautiful and easy to read TLFs for our analysts and clients by using the ggplot2 and the officer packages.