This is a companion discussion topic for the original entry at:
Our biostatistics group has historically utilized SAS for data management and analytics for biomedical research studies, with R only used occasionally for new methods or data visualization. Several years ago and with the encouragement of leadership, we initiated a movement to increase our usage of R significantly. Multiple steps were taken to achieve this goal including: the introduction and training of RStudio, creation of new R functions and packages that mimic locally written SAS macros, and education for current and new staff. Critical to our current success has been a team of R enthusiasts who have become resources for different aspects of R. This presentation will highlight how we as a biomedical research institute were able to transition from SAS to R, lessons learned along the way, and current struggles.
Elizabeth J. Atkinson - Assistant Professor of Biostatistics
A Fellow of the American Statistical Association, and Ordinary Member of the R Foundation. My research is in data visualisation, exploratory data analysis, multivariate methods, data mining and statistical computing. I have developed methods for visualising high-dimensional data using tours, projection pursuit, manual controls for tours, pipelines for interactive graphics, a grammar of graphics for biological data, and visualizing boundaries in high-d classifiers.