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The Art of R Programming: A Tour of Statistical Software Design [Paperback]

by Norman Matloff
4.3 out of 5 stars  See all reviews (68 customer reviews)

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Book Description

October 12, 2011 1593273843 978-1593273842 1

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.

The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to:

  • Create artful graphs to visualize complex data sets and functions
  • Write more efficient code using parallel R and vectorization
  • Interface R with C/C++ and Python for increased speed or functionality
  • Find new packages for text analysis, image manipulation, and thousands more
  • Squash annoying bugs with advanced debugging techniques

Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.


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The Art of R Programming: A Tour of Statistical Software Design + R Cookbook (O'Reilly Cookbooks) + R Graphics Cookbook
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Editorial Reviews

Amazon.com Review

From the Author: Why Use R for Your Statistical Work?

As the Cantonese say, yauh peng, yauh leng, which means “both inexpensive and beautiful.” Why use anything else?
R has a number of virtues:

  • It is a public-domain implementation of the widely regarded S statistical language, and the R/S platform is a de facto standard among professional statisticians.
  • It is comparable, and often superior, in power to commercial products in most of the significant senses -- variety of operations available, programmability, graphics, and so on.
  • It is available for the Windows, Mac, and Linux operating systems.
  • In addition to providing statistical operations, R is a general-purpose programming language, so you can use it to automate analyses and create new functions that extend the existing language features.
  • R includes a library of several thousand user-contributed packages.
  • It incorporates features found in object-oriented and functional programming languages.
  • R is capable of producing beautiful graphics for your presentations, reports or articles.

About the Author

Norman Matloff, Ph.D., is a Professor of Computer Science at the University of California, Davis. He is the creator of several popular software packages, as well as a number of widely-used Web tutorials on computer topics. He has written articles for the New York Times, the Washington Post, Forbes Magazine, the San Francisco Chronicle, and the Los Angeles Times, among others, and is also the author, with Peter Jay Salzman, of The Art of Debugging (No Starch Press).


Product Details

  • Paperback: 400 pages
  • Publisher: No Starch Press; 1 edition (October 12, 2011)
  • Language: English
  • ISBN-10: 1593273843
  • ISBN-13: 978-1593273842
  • Product Dimensions: 2.8 x 3.6 x 0.4 inches
  • Shipping Weight: 1.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (68 customer reviews)
  • Amazon Best Sellers Rank: #6,049 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
132 of 135 people found the following review helpful
5.0 out of 5 stars Excellent guide to the R language November 4, 2011
Format:Paperback|Verified Purchase
There are hundreds of R books, but this is the best one to address the core problem of learning to *program* in R. As reviewer Jason notes, R is used by several audiences with varying needs, but anyone who uses R for long must come to terms with learning to program it. This is the book for that.

What Matloff does is to lay out the essentials of the R language (or S, if you prefer) in depth but in a readable fashion, with well-chosen examples that reinforce learning about the language itself (as opposed to focusing on statistics or data analysis).

I'm a long-time (12 years) R user, which is my platform for analytics every day, and I have programmed in a variety of languages from C to Perl. I have long missed the fact that there is nothing for R comparable to Kernighan & Ritchie ("K&R", The C Programming Language) or similar programming classics; finally there is. Matloff is not quite as beautiful and elegant as K&R (and to be fair, is not in their position as the language creator) but this book has similar goals and comes reasonably close.

I think there are two primary audiences for this book: those who are learning R from a computer science or programming background; and statisticians and others who use the programming language and want a thorough exposition. In my case, for instance, despite having written perhaps 100k lines of R code over the years, there remained areas where I was uneasy (e.g., exactly how do lists relate to data frames). Matloff sets it all straight, in friendly, readable fashion. Even in rudimentary chapters, I learned shortcuts and miscellaneous functions that are quite useful.
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49 of 51 people found the following review helpful
5.0 out of 5 stars Valuable addition to R bookshelf October 30, 2011
Format:Paperback
Jason's juxtaposition of "data analysts" and "serious R programmers" strikes me as a little unfair, but I see what he means. Consider yourself a "serious R programmer" (SRP), and buy this book, if you are interested in the following aspects of R:

Variable scope - Chapter 7
User-defined classes - Ch 9
Debugging - Ch 13
Profiling and performance (mostly, vectorization) - Ch 14
Interfacing with C/C++ and Python - Ch 15
Parallel computation ("pure R" approach using "snow" package, and C++-aided approach using "OpenMP" library) - Ch 16

I have not seen the material of Chapters 15-16 in any other R reference; the other topics have shown up elsewhere - in "R in Nutshell", for example - but get more attention here. The chapters would have been much shorter if written in a "Nutshell" style; however, I do not automatically consider a verbose, user-friendly writing style a negative.

The early chapters introduce R in a way similar to other books - except for (a) eschewing discussion of the language's statistical repertoire, which makes sense given "programming" focus, and (b) showing a greater interest in the "matrix" class - and although they do it quite nicely (this said, let me ask the author to reconsider his "extended examples"), I would not recommend "Art of R Programming" to non-SRPs, and point them to Robert Kabacoff's "R in Action" or (the E-Z version) Paul Teetor's "R Cookbook" instead.

Overall, while the book did not quite click for me - I am a "data analyst" and at present do not have much "need for speed" (cf. C/C++); on the other hand, I would like a firmer grasp on R's OOP, but here, "Art of R Programming" only whets one's appetite - I cannot deny its quality and unique value for budding SRPs. If there was any wavering between four and five stars on my part, the appreciation of how pretty and inexpensive the book is tipped the scales.
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40 of 45 people found the following review helpful
3.0 out of 5 stars OK but somewhat disorganized July 6, 2012
Format:Paperback|Verified Purchase
This books main strength is also its greatest weakness, it tries to be too much of everything to everyone. The author obviously is a great R programmer (as he will demonstrate way too much) having a masters degree in CS and teaching R at college. However often he is too clever by half, adding non-relevant examples of overly complex and somewhat confuted code. I think he is doing this more out of love for the language then to show off but the effect is the same, much of the book comes off as disorganized and too complex for the beginner/intermediate R user to be helpful given the topic discussed. I will say that anybody who buys this book will find something to about it to like, so it is a useful addition to any R library.

Iterating the main theme, the book is very desultory. Especially when comparing it to a great book like "R Tutorial and Exercise Solution " by Chi Yau, which is organized properly. In the first few chapters of The Art of R Programming the author will lay out and explain some basic concepts and code examples then in the next page he is showing how to manipulate various data frames with 12-20 lines of complex code. I'm not sure what audience is reading introductory chapters and would find this abstruse and erudite code useful at all given the basic chapter concepts. Also the chapter layout itself seems odd as salient and trivial topics get uneven treatment relative to their important in the real world. As a Engineer and a holder of a CS degree myself, it isn't as if the code is too complex per se, its just too complex and superfluous given the topic discussed.

The author would have been much better served saving the fancy coding to advanced topics in which it would have been more relevant later in the book.
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Most Recent Customer Reviews
4.0 out of 5 stars Learning to Program
I need this book to help me with programming. Working system programming and I hope this should help. Mostly C.
Published 2 days ago by jason s arnold
1.0 out of 5 stars Not a book for learning R from scratch
I bought this book because it was touted as a great way to learn R. However, many of the examples rely on functions that won't be explained until 3 or 4 chapters later, and there... Read more
Published 12 days ago by Larry G. Kirby
3.0 out of 5 stars Okay,but ...
I wish the book was more involved in programming. Recursive designs were catered in couple of pages
at most. Read more
Published 24 days ago by Sam Sengupta
2.0 out of 5 stars So-so
Perhaps this will be more useful as I begin to code more. However, at this point I don't understand what specifically he is trying to DO with the code he provides. Read more
Published 27 days ago by Anne S
5.0 out of 5 stars the best book on R I had so far
very in-depth and informative! - this really shines among R's books
the text is very easy to grasp, concepts explained quite well
Published 1 month ago by Xin Pei
5.0 out of 5 stars A wonderfu book
An excellent book for learning in R. I started using R as a novice, but after reading this book I have come to appreciate that R is a great programming language!
Published 1 month ago by Eliab Luvanda
5.0 out of 5 stars Great book
Practical program snippets in R. Good coverage of basic R language functionality.
Very good coverage on Matrix, Vectors and array
Published 2 months ago by Gargamelico Voador
4.0 out of 5 stars A lot of cool stuff in here
Written from a programmers perspective rather than an R user perspective, I learned a lot from this book. Read more
Published 2 months ago by Dirk Dittmer
5.0 out of 5 stars Just the kind of book I wanted
I'm a moderately experienced R user. I do a fair amount of data analysis and modeling and R is almost exclusively the tool I use. Read more
Published 2 months ago by David Huber
3.0 out of 5 stars A decent book but left something to be desired
This was my first R book. I worked through a good bit of it but still felt that there was a lot missing in my understanding of R. Read more
Published 2 months ago by P. Abernathy
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