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Beginning R: an introduction to statistical programming (2nd ed.)
Wiley J., Pace L., Apress, New York, NY, 2015. 356 pp. Type: Book (978-1-484203-74-3)
Date Reviewed: Mar 24 2016

R is a language and environment for statistical computing, data manipulation, calculation, and graphical display. The R language has been widely used for data mining and predictive analytics in many fields including computational finance, computational biology, predictive marketing, political and social sciences research, and analytics. Among the reasons for R’s popularity are that it is free, it has superior graphics, and its extensibility by contributors worldwide enables getting better results faster.

This is an introductory book on statistical computing with R. The book is a major revision of the first edition, with a new coauthor; all chapters have been edited, expanded, and given new titles and added references.

There are 19 chapters, which could be partitioned into four parts. The first part, chapters 1 through 5, is written for programmers/analysts new to R, and attempts to introduce the basics of the R language. The second part, chapters 6 through 10, focuses on basic statistical computing using R. The third part, chapters 11 through 16, introduces more advanced topics in statistical computing with R. The fourth part, chapters 17 through 19, includes selected topics on data visualization, high-performance computing, and text mining.

Each chapter in the book has examples that are well crafted and appear to be very helpful in illustrating the covered topics. The examples are also made available in the format of R scripts that could be run interactively in any integrated development environment (IDE) for R such as RStudio. This makes the book quite useful for anyone that needs a quick jumpstart in statistical computing with R.

The book appears to have some shortcomings. One shortcoming is missing datasets, in the book’s code distribution bundle, making it impossible to run related examples. Instances of missing datasets include the General Social Survey (GSS) data used throughout the book and data files for running the examples in the text mining chapter. Another shortcoming is that the coverage of some topics appears to be weak or somewhat shallow. For instance, chapter 14 on multiple regression does not offer any method that can be applied to other examples, and the discussion seems to be largely ad hoc. There are many good books on R and regression models, such as the reference below [1] among others, and none are cited in this book. The last two chapters address two important topics--big data and text mining--but unfortunately the treatment is cursory and scattered at best.

The chapters can be arranged in different ways depending on the reader’s interest and background. For example, for nonstatisticians, the book can be used as a primer to R, reading only the first five chapters plus chapter 9. Readers interested in special topics with backgrounds in R and/or statistics can dive in directly to relevant portions of the book and skip the rest. A reader interested in R applications to big data or text mining may focus only on the last two chapters.

The book can be used as basic text for a course on R for statistical computing or data analytics. It can also serve as a general reference for software developers and data scientists.

More reviews about this item: Amazon, BCS

Reviewer:  Yousri El Fattah Review #: CR144258 (1606-0380)
1) Lafaye de Micheaux, P.; Drouilhet, R.; Liquet, B. The R software: fundamentals of programming and statistical analysis. Springer, New York, NY, 2013.
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