R is a freely available statistical software system that is part of Project GNU. This thin book introduces statistical programming using R.

The introductory chapter describes, among other things, statistical programming and how to get and install R. The next three chapters run through R basics: the command-line interface, data structures, and operations available (chapter 2); graphing data (chapter 3); and writing functions (chapter 4). The remaining three chapters consider examples drawn from simulation, linear algebra, and numerical optimization (chapters 5 through 7, respectively). A three-page appendix reviews random variables and distributions. Most sections and most chapters end with straightforward exercises of varying (unmarked) difficulty; answers to selected exercises are available on the book’s Web site. There is no bibliography, although there is the occasional footnoted reference.

The reader is expected to be familiar with calculus, although it is needed only to understand some of the more advanced examples. Some familiarity with statistical concepts is required. Given the size of the R system, as well as the book’s intent, coverage is necessarily neither broad nor deep, but it is enough to get started with R, and may be all that is needed for simpler data analysis and presentation tasks. The book is clear about its limitations, and has occasional pointers to more details, but a more thorough catalog of R features and packages would have increased the book’s value as a reference, and provided a stronger selling point for learning R.

Not quite a cookbook, and certainly not a reference, this book is best used as an auxiliary text for a course requiring, but not centered around, data analysis and presentation. The book’s simple and straightforward presentation also make it useful for self-study, but the motivation for doing so is less clear, particularly if the reader wants to gain a deeper knowledge of either R or applying R to specific kinds of analysis.