- 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: 128 customer reviews
- Amazon Best Sellers Rank: #34,154 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.
all the way to more advanced topics like performance enhancing and debugging. All through the
book, the author gives practical examples closely related with real-life problems (think at the
k-means clustering problem in chapter 16). The book focuses on the main core features of R,
neglecting specific libraries (ggplot for examples). I tend to agree with this approach which keeps
the focus of the book more consistent, making it a very good and practical reference resource whenever
one is need of a quick technical advice. It is a great, clear way to get someone's way into R, a tool
that is becoming more and more central in many data analysis fields thanks to its versatility.
I came to "The Art of R Programming" with much less experience and used this book as a primary resource for learning the language.
Helpfully, the book starts at the beginning of R--the vector--and works through the other data types in Chapters 2-6. Alone, these chapters serve as an excellent introduction to R; but, the book continues to cover such topics as object oriented programming, control structures, debugging, and parallel processing (as well as many others).
The best parts of the book, in my opinion, are the extended examples. In these, Matloff shows multiple ways of coding a specific problem. Usually, the examples build on each other--first showing an elementary approach and then an improved approach (or two). These examples are excellent at showing why you might want to employ one coding technique or a different one.
Highly recommended for anyone who works in R.
Overall, this is a very solid programming book.
What is noteworthy, however, is that this book is rather light on regression and statistical analysis. You can say this book focuses on the R programming (as the title states), but if you want to how to best utilize R to do some regression analysis, I think there are better books out there for more detailed explanation.
1)Excellent guide to writing R scripts.
2)The author is a guru not only in R but also in stats and CS.
1)Very light on how to use R to perform regression analysis.
2)Could have used some case studies on how to do real-world statistical analysis using 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