Book reviews

If there is one book that every beginning R user coming from a programming background should have, it is Spector’s Data Manipulation with R. New R users with analytic backgrounds and experience with software packages such as SAS and SPSS will do well to start with Muenchen’s R for SPSS and SAS users, especially given that a free abbreviated version is available, but those users should also make Data Manipulation with R a quick second addition to their library.

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.

Data Analysis and Graphics Using R (DAAG) covers an exceptionally large range of topics. Because of the book's breadth, new and experienced R users alike will find the text helpful as a learning tool and resource, but it will be of most service to those who already have a basic understanding of statistics and the R system.

Although the text includes both an Introduction to R section (chapter one) and a discussion of the basics of quantitative data analysis (chapters two through four), these chapters will be most useful as overviews (or reviews for more experienced readers), as they lack the detail required to take a reader from no knowledge of these subjects to a functional understanding. For example, chapter one discusses importing data in .txt and .csv format, but the foreign package is not discussed until chapter fourteen - the final chapter of the book. In practice, .txt data structures are not common enough to justify relegating a discussion of the foreign package to the supplemental materials and a researcher stuck with a .sav or .dbf file would not leave chapter one with enough knowledge to import their data into R.