The third post in my series on standards in Predictive Analytics is on R, a hot topic in analytic circles these days. R is fundamentally an interpreted language for statistical computing and for the graphical display of results associated with these statistics. Highly extensible, it is available as free and open source software. The core environment provides standard programming capabilities as well as specialized capabilities for data ingestion, data handling, mathematical analysis and visualization. The core contains support for linear and generalized linear models, nonlinear regression, time series, clustering, smoothing and more. The language has been in development and use since 1997 with the 1.0 release coming in 2000. The core is now at release 3.0. New capabilities can be added by creating packages typically written in the R language itself. Over 5,000 packages have been added through the open source community.