A book review by Douglas M. Wiig
Aloysius Lim and William Tjhi. R High Performance Programming. Birmingham, UK: Packt Publishing Ltd., 2015. bit.ly/14Rhpp
R High Performance Programming is a well written, informative book most suited for the experienced R programmer. This book offers a handy guide for R users who need speed and efficiency for the tasks that they perform.
The authors begin with an informative chapter discussing some of the inherent constraints on R’s computing performance such as CPU and RAM usage, and how R code is interpreted on the fly rather than compiled. A guide to several methods of profiling R’s code execution time, memory allocation and CPU usage is discussed in the next chapter. Sample code included in the chapter allows the reader to experiment with various benchmarking techniques to measure processing time and memory usage. This chapter provides the reader with some good tools for benchmarking R projects and identifying areas where improvements in processing can be made.
As is always the case with technical books from Packt Publishing, ample code examples are used in the chapter and the complete code used in each chapter is available for download with the book. This is a very handy feature and allows readers to do some live programming with R as the book is read.
The authors discuss a number of simple tweaks that can be easily performed to increase processing speed such as using built in functions and using hash tables. The hash table technique is useful for applications that use frequent lookups and can dramatically reduce processing time when compared to the use of lists. Running example code using this technique shows a large decrease in processing time when using the hash table approach as compared to straight list processing lookups.
In chapter 4 the authors discuss the use of compiled R code and integrating compiled languages into R code. They show several examples of using the R package inline that allows users to embed C, C++, Objective-C, Objective-C++ and Fortran code within R. Once again there are ample code examples to illustrate the use of this technique. For more advanced uses of compiled code the authors discuss how to create entire modules coded in C++ using the Rcpp package. Several completed code examples are included to illustrate the technique.
Another interesting approach to speeding up R is discussed in a chapter that explores several R packages designed to exploit the capability of GPU’s (Graphic Processing Cards) that are a used in many computers. These techniques can facilitate creating very fast and efficient statistical modeling code using R and the GPU.
As indicated above, readers can download the code package included with the book and find a well-organized set of ten folders (one for each chapter) containing 51 files. These files contain the sample code from the book as well as other code segments and benchmark code discussed in the book. The authors indicate that the code has been tested on R 3.1.1, Ubuntu 14.04 Trusty Tahr, Mac OS X 10.9 Mavericks, and Windows 8.1. This allows integration of these code segments into the reader’s own projects with minimal changes.
Other chapters in R High Performance Programming discuss simple tweaks to use less memory, techniques to speed processing of large datasets and using parallel processing and clustering techniques. The last chapter contains a discussion of using R and Hadoop to process Big Data (massive datasets with sizes measured in petabytes -one petabyes is 1,048,576 gigabytes). Processing data of this magnitude presents many challenges and is an area that is currently the subject of much program development.
I found R High Performance Programming to be a useful and informative book for the advanced user of R. A working knowledge of statistics, R and other programming languages such as C++ or Java is necessary to realize the full benefit of the techniques presented in the book. The book also serves as a good learning tool for less knowledgeable R users who are seeking to advance their programming skills.
Readers who are interested in the use of Hadoop and cluster computer processing might find the book Raspberry Pi Super Cluster by Andrew K. Dennis of interest. (Packt Publishing, 2013
PAC-14-1987838-1387169). A review of this book can be found on my web site at http://dmwiig.net.
Douglas M. Wiig, Professor of Political Science
Grand View University
Teaching areas include social science statistics and research methods, comparative politics, international politics.
Long time user and developer of computer and statistical applications
Host of Open Source Technology in Higher Education web site at http://dmwiig.net
Creator and moderator of LinkedIn discussion forum “Open Source Technology in Higher Education”
Regular contributor to several LinkedIn discussion forums
Author of numerous tutorials on using the R statistical programming language and Raspberry Pi computer
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