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The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff
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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.
- Sales Rank: #21322 in Books
- Brand: Brand: No Starch Press
- Published on: 2011-10-15
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.20" w x 7.00" l, 1.65 pounds
- Binding: Paperback
- 400 pages
- Used Book in Good Condition
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).
Most helpful customer reviews
214 of 218 people found the following review helpful.
Excellent guide to the R language
By Sitting in Seattle
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. The examples throughout are more "CS-like" than statistical, which is highly advantageous for this topic.
In addition to the tutorial content, it is well-suited as a quick reference. It doesn't aim to be comprehensive from a function point of view (which is almost impossible, and what R Help is for), but it is comprehensive from a programming conceptual point of view.
In short, if you program R, and unless you're a member of R-Core, then I believe you'll enjoy this, will learn something, and will refer back to it repeatedly.
69 of 72 people found the following review helpful.
Valuable addition to R bookshelf
By Dimitri Shvorob
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.
51 of 55 people found the following review helpful.
A Programmers Introduction to R
By Code Monkey
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
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