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The Art of R Programming: A Tour of Statistical Software Design Paperback – 15 Oct 2011


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Product details

  • Paperback: 400 pages
  • Publisher: No Starch Press; 1 edition (15 Oct. 2011)
  • Language: English
  • ISBN-10: 1593273843
  • ISBN-13: 978-1593273842
  • Product Dimensions: 17.8 x 3 x 23.5 cm
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (10 customer reviews)
  • Amazon Bestsellers Rank: 23,448 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Product Description

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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25 of 26 people found the following review helpful By renaissance geek on 12 Nov. 2011
Format: Paperback
I could make this review very short. Do you use R as a tool or do you want to use R to make new and better tools. If the former then you may not get full value from this book if the latter buy it. Buy it now. Yes it really is that good.

The Art Of R Programming is an excellent overview of statistical tool building with R and is nicely broken down as follows:

Getting started - the obligatory how to get started with R and how to get help when you're using it. I'm not sure how useful this will be as I'm guessing that the majority of this book's market will be pretty R savvy.

Vectors - an in depth look at the fundamental R data type. Useful but dry.

Matrices and Arrays - matrices are essentially a special type of vector in R. Equally useful but equally dry!

Lists and Data Frames - two separate chapters covering multi-type data formats.

Factors and tables - covering single and multi-dimensional types for categorised data.

As you can see there is a lot of time spent on data types and corresponding functions. This is a little bit dry to read but covers the fundamental building blocks of R. It's worth reading these chapters and getting the basics down pat before trying anything more complex.

Programming structures - covers the programming fundamentals of loops, recursion and control structures.

Mathematics and simulations - this is probably the closest to a "how-to" you'll find in the book. This chapter covers mathematical functions.

Object oriented programming - pretty much as it says. I have to admit that I'm more a procedural than OO programmer so I only skimmed this chapter but it covers what you might expect.

Input/output - reading and writing files.
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11 of 12 people found the following review helpful By J. Tavares on 17 Nov. 2011
Format: Paperback
"The Art of R Programming" by Norman Matloff is an excellent book and probably is the only book you need to learn and master R. Master it as a programmer and not just as an occasional user. I was such a user, using R only for some statistical tests and doing some plots. However, Norman's book completely changed the way I approached R. By teaching it as a programming language rather than a statistical tool, it increased immensely my knowledge on R and making it more useful for my research work. The book covers the fundamentals about R (always with a programmer's view) and guides you to advanced and useful topics such as Parallel R, debugging, interface to other languages, etc. The writing style is very concise and clear. The explanations, advices and examples very good. This book convinced me early on to properly learn R and I am sure it is the best book for that. I highly recommend this book to everyone who needs to use R or wants to add it to their toolbox.
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7 of 8 people found the following review helpful By Lotus-flower on 6 Sept. 2013
Format: Paperback Verified Purchase
I needed some advanced statistical software and R came highly recommended. Having downloaded the software it became rapidly apparent I would need a guide. I have no background in computer program and the Amazon.co.uk reviews indicated this was the book for me. While I am finding the book useful as I slowly get to grips with how R runs, I would disagree about not needing previous computer programming experience. While the author explains some terms used, eg %%, I am left baffled by others and have to resort to online guides (which I was trying to avoid) in order to make sure I understand everything. If the author had included a simple glossary so I could understand why terms such as "==" are required, then I probably would have been all the way through the book by now and a competent R user. These may be simple for those with basic computer programming, but they are baffling to a newbie!

This all said I would recommend the book because it has increased my knowledge. The layout is good and the level of usage builds up gradually. However external sources are also required, hence my four star rating.
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3 of 4 people found the following review helpful By Maxim Kovalenko on 17 Mar. 2013
Format: Paperback Verified Purchase
The book requires no background in programming in general, and does a solid work explaining the basics of R data structures, data manipulations and so forth. Beginning with simple things, the book discusses some of the more advanced subjects as well. I have read a few books on R already, but this one has had the largest impact in terms of understanding the language. My programs became better and faster for sure.
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2 of 3 people found the following review helpful By malachy on 2 Jan. 2015
Format: Paperback
The five star reviews here are highly dubious. That said, this book is useful if you want to master the nuts and bolts of R - data frames, matrices, functions, etc. Where it falls down is when the author begins to to lose himself in "Extended examples" which seem to serve no other useful purpose than that of him trying to show us how clever he is. Example: the section on matrix manupulations - you find yourself spending more time trying to wrap your head around the theoretical underpinnings of Random Markov Chains than actually assimilating the basic R functionality that the author is presumably trying to demonstrate. And this happens fairly regularly throughout the book.

In short, useful as a reference for some of the most important concepts in R but not exactly a pedagogical work of wonder either.
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