The R statistical programming language can handle any statistical challenge you throw at it. It knows about information arranged in tables, and can calculate means, variances, correlations, and many other summary statistics. With supporting libraries it can produce graphic plots in 2D and 3D. Perhaps its greatest strength is its Comprehensive R Archive Network (CRAN), a repository of thousands of packages that allow users to move comfortably in and between statistical domains.
In this article we consider three scenarios that show the adaptability of R.