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Sams teach yourself R in 24 hours
Nicholls A., Pugh R., Gott A., Sams, Carmel, IN, 2016. 624 pp. Type: Book (978-0-672338-48-9)
Date Reviewed: May 25 2017

Sams teach yourself R in 24 hours is an operating system (OS)-agnostic book. R and the recommended graphical user interface, RStudio, can be run on Windows, Macintosh, and Linux computers.

R is a statistical programming language that might be considered as a descendant of the Unix-based S language. S is still available commercially as S-PLUS.

The book is divided into hours, and there are 24 of them. The first 18 hours are the most important and they cover an introduction to R and end with code efficiency and managing memory usage.

Although the authors suggest this book was written for professional statisticians, data scientists, and analysts, it could be used by graduate students working in a classroom environment or by an individual working alone.

Realistically, each of the hours in this book would take about an evening of study in front of a computer where you could work out some of the code samples that are presented and get an understanding of what is being emphasized in the chapter (hour).

Each hour ends with a summary, a questions and answers (Q&A) section, a workshop section (consisting of a short quiz and the answers to the quiz), and a suggested set of activities related to the discussion.

The version of R used by the authors was 3.2.0, dated 2015-04-16.

I tested the sample code in the book by running it within an R non-graphical user interface (GUI) environment and RStudio (under Fedora 25 using R version 3.3.3). Since this book could be used by graduate students, I also tested the sample code in the book on a Raspberry Pi 3 Model B running Jessie with R version 3.1.1. Within this environment, I was able to run rkward as a GUI.

The authors recommend using RStudio for the sample code and exercises in this book. If RStudio is already available on a server for students who follow this book, they will encounter few problems starting and running R in this environment. If working alone, you can download and install RStudio for Windows, Mac OS X, or Linux. Binaries for RStudio are available for download (https://www.rstudio.com/). You can also build and install RStudio from source.

You can access the exercises, sample code, and datasets used in the book online (http://www.mango-solutions.com/wp/teach-yourself-r-in-24-hours-book/).

The book is well edited and there were few typographical errors. My only criticism of the book is the reliance on RStudio. RStudio is used by the authors because it makes the narrative in the book much easier to understand by the reader. RStudio also converts some tasks done within the GUI environment into command-line equivalents for R. This is very useful because a reader may at a later date encounter an R environment without a GUI. It might be helpful to add an appendix entry that maps out RStudio commands to their equivalent within the R non-GUI environment.

Overall, this is an excellent book that is well written for its target audience.

More reviews about this item: Amazon

Reviewer:  W. E. Mihalo Review #: CR145299 (1708-0507)
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