R is a free software environment and scripting language for analyzing and visualizing data. R implements a dialect of the statistical language S, which was developed by AT&T. This computational framework enables statistical computing, interactive data analysis, data mining, and testing relationships between large amounts of data. An active community of users provides regular updates to the language and contributes a growing number of packages, collections of R functions and datasets that can be dynamically loaded and unloaded at runtime.

This encyclopedic second edition contains 29 chapters and more than 1,000 pages. It improves on the first edition with new chapters on meta-analysis and Bayesian statistics.

The chapters form two groups. The first includes chapters 1 through 7, covering R language essentials such as data handling, graphics, and mathematical functions. The second group includes chapters 8 through 29, addressing a wide range of statistical techniques such as classical tests, regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), generalized linear modeling, Bayesian analysis, spatial statistics, multivariate methods, tree models, mixed-effects models, and time series analysis.

In the first chapter, “Getting Started,” the author provides a navigation map and suggested reading plans for various categories of users.

One of the book’s weaknesses is the lack of a thread that binds the various chapters. The first seven chapters allocate considerable space for material that is covered in plenty of other texts, as well as in open-source R user manuals. The content on statistical computing topics seems to lack motivation and seems shallow in places, or it offers recipes without explanation or evidence. For example, chapter 9 on statistic modeling is crammed with information, but does not present any case studies or empirical evidence on the prescribed model-fitting recipes.

The seeming lack of uniformity in the detail and depth of the material may be due to the author’s effort to address multiple categories of users with different levels of knowledge and backgrounds on R and statistics, and to have the book meet their goals and be used both for reading as a text and for dipping into as a reference manual.

The book is a valuable addition to the literature on the R environment and is packed with expert knowledge on using R for statistical computing tasks. The book is aimed at beginners and intermediate users seeking to learn skills for using R for statistical computing and data analysis applicable to disciplines ranging from science to economics and from medicine to engineering.