- Paperback: 400 pages
- Publisher: No Starch Press; 1 edition (October 11, 2011)
- Language: English
- ISBN-10: 1593273843
- ISBN-13: 978-1593273842
- Product Dimensions: 7.1 x 1 x 9.2 inches
- Shipping Weight: 2.4 pounds (View shipping rates and policies)
- Average Customer Review: 127 customer reviews
- Amazon Best Sellers Rank: #69,689 in Books (See Top 100 in Books)
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The Art of R Programming: A Tour of Statistical Software Design 1st Edition
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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 is a professor of computer science (and was formerly a professor of statistics) at the University of California, Davis. His research interests include parallel processing and statistical regression, and he is the author of a number of widely-used Web tutorials on software development. He has written articles for the New York Times, the Washington Post, Forbes Magazine, and the Los Angeles Times, and is the co-author of The Art of Debugging (No Starch Press).
Top customer reviews
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.
One should keep in mind that although R has excellent graphics capabilities via the lattice and ggplot2 libraries, only the base plotting routines are introduced here. By no means do I consider this a shortcoming for this book because there are whole books dedicated to R graphics, and this is a programming-oriented book.
What this book does cover beyond the usual things you'd expect in an R book (e.g., data frames, arrays, etc.) are things like object-oriented programming, building up simulations, debugging tools and techniques, performance enhancements, interfacing to other languages, and parallel processing. The kind of things I want to master in order to exploit the real power of R.
Most recent customer reviews
I have no "R" background and have been learning R from watching youtube videos as well as stack overflow.Read more