- Paperback: 400 pages
- Publisher: No Starch Press; 1 edition (October 11, 2011)
- Language: English
- ISBN-10: 1593273843
- ISBN-13: 978-1593273842
- Product Dimensions: 7 x 0.9 x 9.2 inches
- Shipping Weight: 2.4 pounds (View shipping rates and policies)
- Average Customer Review: 131 customer reviews
- Amazon Best Sellers Rank: #55,425 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
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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.
Being new to R and having worked through the first five chapters I was struggling with the data files that are referenced in the book. Normally, when learning a new programming language working the examples works fine for me, but for this book it proved a nightmare: 1) does not explain where the data files can be found. 2) After searching the internet, I found a link to "the data files" on the publishers web site, only to be disappointed even more: many files are missing or have different names from the ones used in the book. Some are corrupt and/or contain different values from those shown in the book.
It really made me wonder where all the five star ratings for this book were based on. I cannot belief that these reviewers used the book intensively.
This problem is not new although only few reviewers mention it: if you google "missing data files art of r programming" you will find many other people that encountered the same problem.
A second problem is that the code fragments often have errors that are really hard to solve for beginners. One example being the mount rushmore code on page 65 and another one the code for the words frequency problem on page 98. On the web I found some solutions/corrections by other readers.
Then why did this book earn so many five-star ratings? It probably has to do with the fact that it could be a very good introduction to R, if only the author (and editorial staff at No Starch Press) had payed more attention to detail and had spent some extra work in providing correct data files.
Things that could have helped the book is a start with a more conceptual analysis of the data-structures in R, after all, there is plenty of free information in the Internet with "informal descriptions" of R lists, arrays, vectors, and subsequent paraphernalia of ways of aggregating data. I should be fair and add that the book simply can not describe something that apparently does not exist, i.e., R as a coherent data-handling framework.
In the good side, the book enforced my belief that R is a backwater in terms of programming environment/language, even if it does have a lot of statistical functions that successive generations of statisticians inmates have left there.