**Title:**A Handbook of Statistical Analyses Using R

**Author(s):**Brian S. Torvitt; Torsten Hothorn

**Publisher/Date:**Chapman & Hall/2006

**Statistics level:**Intermediate to advanced

**Programming level:**Intermediate

**Overall recommendation:**Highly recommended

*A Handbook of Statistical Analyses Using R* addresses a list of several common statistical analyses in great detail. Over a course of 15 chapters, the handbook takes the reader from an introduction to R through a discussion of statistical inference, to linear and logistic regression, tree analysis, survival analysis, longitudinal analysis, meta-analysis, factoring, scaling, and clustering. The handbook has a peer-reviewed journal style that will be familiar to academic researchers and each chapter stands on its own. This approach makes the text exceptionally useful in the academic setting as a professor can distribute and assign the first chapter of the book to her Research Methods 101 course; the final chapters on scaling and dimensionality to her Psychometrics Methods course; the last chapter on clustering to her Marketing Research course; and require the entire book for her graduate methods course. For custom research shops making the transition to R or who frequently hire new entry level R users, this book will work well as a reference and training manual.

The handbook does show typical first edition flaws. There are sporadic mistakes in grammar such as misspellings and incorrect words. The overall organization of the book is strong, but the chapter level organization is less effective. Each chapter begins with a discussion of all of the datasets used in that chapter and is followed by examples and applications based on those datasets. In chapters where there are several examples, the discussion of the data is too detached from its corresponding example. When the reader reaches the example based on the first dataset they have likely forgotten the relevant details about that data’s structure. Grouping the data discussions with the examples they accompanied would have made the example based approach more effective.

The introductory section on R is one of the best introductory sections I have read. It strikes an almost perfect balance between the programming and statistical features of R. I frequently recommend this initial chapter to colleagues who have research experience but are new to R. There are numerous graphs included in the examples in the text and although there is virtually no general discussion of producing graphs in R, each graph presented in this text includes the code required to reproduce it. This omission is a welcome one, as it allows the authors to focus more on statistical details. Readers looking for a more general discussion of how to produce graphs in R should consider Data Analysis and Graphing Using R.

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