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Software for Data Analysis: Programming with R (Statistics and Computing)

3.7 out of 5 stars 13 customer reviews
ISBN-13: 978-1441926128
ISBN-10: 1441926127
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Editorial Reviews

Review

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.

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Product Details

  • Series: Statistics and Computing
  • Paperback: 500 pages
  • Publisher: Springer (November 23, 2010)
  • Language: English
  • ISBN-10: 1441926127
  • ISBN-13: 978-1441926128
  • Product Dimensions: 9 x 6 x 1.3 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (13 customer reviews)
  • Amazon Best Sellers Rank: #1,219,292 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover
This is not an introductory text, and should not be the first R book in your collection. However, if you are a "pretty good" R programmer and want to take the next step in becoming an "expert" R programmer, this is your Bible.

For me, this book fills the hole of understanding how R thinks. To get a complete and accurate view of why R works the way it does, the author supplements the technical discussion with the philosophy of R, as well as pieces of the history of statistical computing and computing in general.

Others might consider this integration of technical detail with philosophical and historical background (complete with Star Trek references) to be "wordy", but this is precisely why I bought the book. If one is interested only in the purely technical aspects, the thorough documentation on the R website is free. I consider the insights - provided by the mind that laid the foundation for R in S - to be well worth the price of the book.

That said, this book is an invaluable guide (both technical and philosophical) on the road to becoming an R expert. I'm looking forward to putting some dog ears on my copy.
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Format: Hardcover
For the past year or so I have been puttering with R, but never really "got it". This book is just what I've been looking for, to understand what R is "thinking". It isn't a cookbook with loads of examples, but a thorough guide to understanding how R works and how to be productive in it. After only an hour, I understand data.frames, and the environment structure better than several nights of struggling with the online documentation. This isn't really a book about how to analyze data, it's about becoming comfortable and expert in R to make it easy to analyze data. Once you understand the tool, the data analysis becomes much much easier.

I agree with the reviewers who say it's chatty, but that makes it very readable. You don't have to work every example to understand the points the book is making. Likewise, it _is_ cross referenced to death, but it's easy enough to read over the links, and when you're trying to make sense of something, the cross references do take you to the right information to round out a picture.
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Format: Hardcover Verified Purchase
Reading the thoughts of a key designer or inventor of a programming language is always a treat. John Chambers was there when S was born, and perhaps no one is better qualified to write about the rationale behind the design of S-Plus and R than him. Unlike many books written by the creators of a programming language this one is not an introductory text. As the preface makes clear, it is written for relatively experienced R/S-Plus programmers who want to understand the design choices behind the language.

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).
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Format: Hardcover Verified Purchase
Well, there is not a better way to understand any kind of processes than knowing the way it works. That is exactly the point of this book, and it is done in a didactic, uncomplicated way. You can find your own pathways to interact, program and get more and more from R. It will help with functions understanding and customizations, starting from the basic S language to R's specifics characteristics and goals. This book turns R easier than I have expected.
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Format: Hardcover Verified Purchase
I agree with most reviews here. On one hand, it provides a lot of information about R that I cannot find anywhere else. On the other hand, the presentation style is awkward. I think the author could have done a better job organizing the information and explaining how R works. Each chapter seems to stand alone and does not follow any particular orders. The examples used are not particularly illuminating. For example, there are some examples that the author used to split the work between R and Perl. I don't quite understand why it was done that way and why it was discussed in the chapter of text processing. The examples work and if you need to call Perl from R to do some work, it maybe worthwhile to read that section. I give it a 3 star because it does provide some very useful information.
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