- Series: Statistics and Computing
- Hardcover: 500 pages
- Publisher: Springer; 1st ed. 2008. Corr. 2nd printing 2009 edition (August 10, 2009)
- Language: English
- ISBN-10: 0387759352
- ISBN-13: 978-0387759357
- Product Dimensions: 6.1 x 1.1 x 9.2 inches
- Shipping Weight: 2.2 pounds (View shipping rates and policies)
- Average Customer Review: 16 customer reviews
- Amazon Best Sellers Rank: #114,870 in Books (See Top 100 in Books)
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Software for Data Analysis: Programming with R (Statistics and Computing) 1st ed. 2008. Corr. 2nd printing 2009 Edition
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From the reviews:
"R is nowadays the language used in programming for statistics. Most of the thesis and practical programming are implemented in this language. This is a valuable book for every body involved in data analysis, not only statisticians. Being written by the father of S programming language, as R is S based, the development of the presentation as well as the advises are good for fitting the minds of the students within the roots of the art of programming with R." (J. Scheneweiss, Revista Investigación Operacional, Vol. 30 (2), 2009)
“…Written by one of the developers of R’s predecessor, S, the book’s aim…is to take the reader ‘from user to programmer to contributor’ in R. …It is written in plain, clear English. The necessary terminology that is specific to R is defined over the course of the book and is easy to locate should a reader not start from the beginning. The author has attempted to keep chapters somewhat independent so that not starting from the beginning is an option for more advanced R users who are in need of a reference rather than a tutorial. There are two special indexes, separate from the main index, covering ‘R Functions and Documentation’ and ‘R Classes and Types.’…I would expect that this book will find a home on a great many bookshelves. …” (Biometrics 65, 1313, December 2009)
“…This is a book that will appeal to readers of diverse backgrounds. For R users it has a wealth of information on learning to use R effectively; from efficient and reliable programming to writing packages. It is an authoritative reference for programmers and developers. It is the type of book that will be referenced often, as the reader’s experience with R, level of expertise and interest in programming grows.” ( The American Statistician, August 2009, Vol. 63, No. 3)
“This text is about using computer software, in particular R, for obtaining information from the data … . ‘is aimed at those who need to select, modify, and create software to explore data, in other words, to program.’ … The book is aimed at (i) data analysts, namely anyone involved in exploring data, from data arising in scientific research to, say, data collected by the tax office; (ii) researchers in, and teachers of, statistical techniques and theory; (iii) those primarily interested in software and programming.” (Susan R. Wilson, Zentralblatt MATH, Vol. 1180, 2010)
“This book is for software developers and advanced R users who want to become export R users: developing packages and new classes, and working with methods and generic functions. … This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual steps, starting with simple functions. More advanced programming techniques can be added as needed…. Software developers and advanced R users should find that this book is full of wisdom … .” (David J. Olive, Technometrics, Vol. 52 (2), May, 2010)
From the Back Cover
John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S.
Now he turns to R, the enormously successful open-source system based on the S language. R's international support and the thousands of packages and other contributions have made it the standard for statistical computing in research and teaching.
This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated.
The techniques covered include such modern programming enhancements as classes and methods, namespaces, and interfaces to spreadsheets or data bases, as well as computations for data visualization, numerical methods, and the use of text data.
Top customer reviews
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The text assumes that the reader is familiar with packages, generic functions, model fitting formulae, and much of the base functions and libraries. The first instance of an interaction with the R system in this text (Section 2.2, page 13 in my copy) does not quite work if you copy and paste it! The next chapter starts with "constructing a fairly complicated linear model." Again, the code snippet there will not work if you just type it in, and there is no detailed explanation of what the code snippet actually does (but it would be "obvious" to some one experienced with statistical analysis in this language). Still another example is chapter 9 which describes (mostly S4) object classes. I doubt anyone without considerable experience with object oriented programming and the generic function mechanism in R would be able to make sense of this chapter without a lot of effort; consider, for example, that the term "slot" does not even have any entry in the index!
I found the writing style formal, hard to read, and somewhat turgid. There are many seemingly bizzare choices of examples or topics, most notably an introduction to perl programming! I ended up comparing the text with the paper "Evaluating the design of the R language" from the ECOOP 2012 proceedings (easily found on the web). In a few pages that paper seemed to provide a considerable portion of the insight that this book contains, but without the somewhat overwrought philosophizing and Star Trek references. I cannot help but think a better editor would have helped improve this book tremendously. So I have to say that the book was a bit of a let down for me.
I did find parts of this book truly outstanding and enjoyable. In my opinion the final chapter, titled "How R Works", should be required reading for any serious R programmer. The early chapters that dealt with debugging and organizing packages, as opposed to merely detailing language features, were very insightful. The focus is always on why the language works the way it does, and how it was intended to be used. Yes, this book can be considered the "Prime Directive" for R programmers!
In the end this is a book that has definitely found a place on my bookshelf, but it is one I cannot really love. It's hard to read, and meanders too much. But it sprinkles enough truly insightful information through its four hundred odd pages that it is worth reading at least once, and perhaps many more times.