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Product Description
Product Description
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 R packages for text analysis, image manipulation, and 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.
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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Customer Reviews
Most Helpful Customer Reviews
6 of 6 people found the following review helpful
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. This is actually a really useful chapter - if your chomping through large data files as I tend to do then having a good grip of how to get them into and out of R is going to be high on your list of requirements. The chapter covers both reading and writing files and reading and writing across the internet using sockets.
String manipulation - you might not expect that a statistical programming language would have much use for string manipulation but string functions are very useful for parsing textual information out of pre-existing data files. Most usefully it has a full suite of regular expression functions.
Graphics - or possibly more accurately graphs. When working with large and complex data sets a good visualisation is worth several million data points and this is where you will find out how to use R's graphing functions.
I'm going to lump the final topics together as they are pretty much all hardcore programming chapters covering debugging, performance enhancement, interfacing R to other languages and parallel programming. Of these chapters the one covering interfacing to other languages (other languages being C/C++ and Python) is probably going to see the most use. The others are much more niche but if you need them you're going to really need them.
The author's writing style is clear, concise and excellent and covers all the programming bases. If there is an omission at all it's that there isn't a functional cheat sheet or appendix which I like to see in any programming book. That aside if you work with R for anything other than just using a suite of existing tools this is an essential for your bookshelf.
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. This is actually a really useful chapter - if your chomping through large data files as I tend to do then having a good grip of how to get them into and out of R is going to be high on your list of requirements. The chapter covers both reading and writing files and reading and writing across the internet using sockets.
String manipulation - you might not expect that a statistical programming language would have much use for string manipulation but string functions are very useful for parsing textual information out of pre-existing data files. Most usefully it has a full suite of regular expression functions.
Graphics - or possibly more accurately graphs. When working with large and complex data sets a good visualisation is worth several million data points and this is where you will find out how to use R's graphing functions.
I'm going to lump the final topics together as they are pretty much all hardcore programming chapters covering debugging, performance enhancement, interfacing R to other languages and parallel programming. Of these chapters the one covering interfacing to other languages (other languages being C/C++ and Python) is probably going to see the most use. The others are much more niche but if you need them you're going to really need them.
The author's writing style is clear, concise and excellent and covers all the programming bases. If there is an omission at all it's that there isn't a functional cheat sheet or appendix which I like to see in any programming book. That aside if you work with R for anything other than just using a suite of existing tools this is an essential for your bookshelf.
4 of 4 people found the following review helpful
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.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
21 reviews
48 of 49 people found the following review helpful
Excellent guide to the R language
4 Nov 2011
Format:Paperback|Amazon Verified Purchase
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.
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.
21 of 22 people found the following review helpful
Valuable addition to R bookshelf
30 Oct 2011
Format:Paperback
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.
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.
20 of 21 people found the following review helpful
Good from cover to cover
1 Nov 2011
Format:Paperback
I'm always very wary of books about programming that have titles in the form "The Art of ... Programming", but this book is good despite the title. Matloff is clear and thoughtful writer who takes the reader through their first steps with R (which has a syntax that requires learning as it is nothing like other languages that a regular programmer would have encountered).
I did find, however, the comparisons with C programming annoying in the first part of the book. The author continuously goes on about "if you're a C programmer" and then some comparison to C. I didn't find this helpful (and I am a C programmer) and I think it could have been safely left out. A good example of this is on page 12 where is says "Matrices are indexed using double subscripting, much as in C/C++, although subscripts start from 1 instead of 0." So pretty much not like C/C++. That's a good example of how the C interludes don't help the new reader.
Just occasionally the author gets ahead of himself. Early on in the book he introduces matrices and on page 28 does a matrix addition in the form m + 10:13. He hasn't explained how that addition is going to work.
However, these complaints are pretty minor. The book does a good job of taking you from knowing nothing about R to working with complex programs and data. The chapter on S3 and S4 classes is particularly welcome, but I think it could have been more in depth and earlier in the book. They are an important topic.
Overall this is a very good book to learn R from and has enough depth that the experienced R user will find useful things in the later chapters.
I did find, however, the comparisons with C programming annoying in the first part of the book. The author continuously goes on about "if you're a C programmer" and then some comparison to C. I didn't find this helpful (and I am a C programmer) and I think it could have been safely left out. A good example of this is on page 12 where is says "Matrices are indexed using double subscripting, much as in C/C++, although subscripts start from 1 instead of 0." So pretty much not like C/C++. That's a good example of how the C interludes don't help the new reader.
Just occasionally the author gets ahead of himself. Early on in the book he introduces matrices and on page 28 does a matrix addition in the form m + 10:13. He hasn't explained how that addition is going to work.
However, these complaints are pretty minor. The book does a good job of taking you from knowing nothing about R to working with complex programs and data. The chapter on S3 and S4 classes is particularly welcome, but I think it could have been more in depth and earlier in the book. They are an important topic.
Overall this is a very good book to learn R from and has enough depth that the experienced R user will find useful things in the later chapters.
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