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The Art of R Programming: A Tour of Statistical Software Design 1st Edition

4.3 out of 5 stars 106 customer reviews
ISBN-13: 858-2592222227
ISBN-10: 1593273843
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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).

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Product Details

  • Paperback: 400 pages
  • Publisher: No Starch Press; 1 edition (October 15, 2011)
  • Language: English
  • ISBN-10: 1593273843
  • ISBN-13: 978-1593273842
  • Product Dimensions: 7 x 1.2 x 9.2 inches
  • Shipping Weight: 1.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (106 customer reviews)
  • Amazon Best Sellers Rank: #17,185 in Books (See Top 100 in Books)

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Customer Reviews

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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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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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The uniformly good reviews for "The Art Of R Programming" led me to read it, and I'm glad I did. I've used R casually for years as a sort of "secret weapon" to quickly analyze a few millions data points, graph it, and draw useful conclusions, all before some one could load the data into a SQL database. I've long believed that R is a clean, well designed language for data analysis that was missing a good introductory text for programmers. R's type system, lexical structure, run time mechanics, and functional nature make it one of the best designed languages around, but this also seems to be one of the best kept secrets in the software community. Until I read "The Art of R Programming" I'd never come across material on R that introduced R as a programming language. Most of what I saw presented it as a statistical toolbox that you could, almost accidentally, program.

However, be warned that the book is not rigorous, either as an introduction or a reference. It is concise, easy to read, and much is driven by case studies to show you how to do things. But it often left me uneasy as a software engineer. For example, it states that R uses "lazy evaluation" when a more accurate statement would be that it is simply evaluates function arguments lazily. The description of the run time object environment is clunky: evaluation contexts, closures, and recursion are treated separately. It does not entirely explain how symbol look up works for functions (you won't learn why "sum <- 1; sum(1,2,3)" will still evaluate to 6). The discussion on object copy-on-change was so vague that I failed to understand how I could use that information.

Okay, so it's not perfect, and it's definitely no K&R. But it's still way better than any other introduction I've seen before.
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