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R in a Nutshell: A Desktop Quick Reference Paperback – Jan 14 2010

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Product Details

  • Paperback: 636 pages
  • Publisher: O'Reilly Media; 1 edition (Jan. 14 2010)
  • Language: English
  • ISBN-10: 059680170X
  • ISBN-13: 978-0596801700
  • Product Dimensions: 15.2 x 3.1 x 22.9 cm
  • Shipping Weight: 703 g
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #493,284 in Books (See Top 100 in Books)
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Product Description

Book Description

A Desktop Quick Reference

About the Author

Joseph Adler has many years of experience in data mining and data analysis at companies including DoubleClick, American Express, and VeriSign. He graduated from MIT with an Sc.B and M.Eng in Computer Science and Electrical Engineering from MIT. He is the inventor of several patents for computer security and cryptography, and the author of Baseball Hacks. Currently, he is a senior data scientist at LinkedIn.

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Most Helpful Customer Reviews on (beta) HASH(0x9f60f51c) out of 5 stars 33 reviews
78 of 81 people found the following review helpful
HASH(0xa01ca27c) out of 5 stars Gateway into the world of R April 14 2010
By Tony DaBoney - Published on
Format: Paperback Verified Purchase
'R in a Nutshell' is the essential introductory book on R. Do not try to learn R without it.

I made two attempts to learn R before purchasing this book. In both previous attempts, I had to abort and use another tool to solve my problem because it was taking me too long to accomplish very simple things in R.

The reason R is hard to learn is that its documentation is organized for statisticians that already know R, but have forgotten a detail or two. There are a few other books on learning R, but they are setup like a college course - complete the entire book and THEN you can actually accomplish something.

R in a Nutshell allows you to get working immediately. Simply lookup what you need to do. The firsts thing I did was load a file and make a histogram. I found that stuff in the section on "Loading Data" and the section on charts. In no time I was making stacked area charts for cohorts. Now R is an essential tool for me - and I haven't even taken the time to learn it well! With this book, I don't have to. I can learn as I go. So I actually use R.

Do not R without it.
67 of 72 people found the following review helpful
HASH(0x9f5ffa50) out of 5 stars Useful and surprisingly engaging Jan. 14 2010
By Gimpel the Fool - Published on
Format: Paperback
Back in school, I was introduced to using SPSS for use in statistical analysis. While I liked SPSS, it was too expensive for me to procure a copy for my own personal use. A friend suggested that I try R. I was a little nervous about R, because being more enthusiastic about than talented with mathematics, and I was most comfortable with a point and click program. So, before I began, I bought "R in a Nutshell" to learn more. I'm glad that I did.

Adler's book begins with a basic tutorial for R and an introduction to R language. It explains how to use R to draw graphs, statistical analysis and even some bio stuff. All I needed to do was to load in my data, draw a couple charts and compute some t tests and chi-squared statistics.

The book was great, multi-faceted as a teaching tool, and - unexpectedly (and atypically for such works) - entertaining to read. I'm looking forward to using R next time I need to fit a regression model, or do factor analysis. The rare mathematics tutorial that will engage academics, financial traders and baseball stat wonks alike. Nice job.
36 of 39 people found the following review helpful
HASH(0x9f606054) out of 5 stars in purgatory between tutorial and reference June 4 2010
By Joseph Clark - Published on
Format: Paperback
I've just gotten the book, my first resource for learning R, and I find it moderately helpful but in some ways frustrating. O'Reilly's books usually take the form of either a progressive set of lessons in a language (like the famous "Learning Perl") or as an easily navigable reference book (like "Java in a Nutshell"). This book places itself somewhere in the middle. It begins with a fairly limited tutorial that covers basics of the scripting language but doesn't get into what a researcher would really use R for: importing data and running an analysis. This is complemented by a glossary of functions, but it contains little detail (not even the function's required arguments are listed) and they are not in anything like alphabetical order, instead grouped by the several "packages" that contain them. I went looking for the "standard deviation" function and there was no easy way to find it in the glossary, nor was the book's index any help -- it indexes the chapters but not the language reference.

Given the relative dearth of books available, this may or may not be the best introduction to the language available, but it leaves me wanting two better books: one for learning more about R, and one for a better reference.
30 of 33 people found the following review helpful
HASH(0x9f6275f4) out of 5 stars Excellent resource for a language with a steep learning curve! April 14 2010
By Jay Thomas - Published on
Format: Paperback
While R, the free statistical computing and graphics software environment and language, is quickly becoming ubiquitous in both academia and the corporate world, many new (especially non-academic) users find its learning curve prohibitively steep. To make matters worse, most documentation is written by and for academic statisticians already relatively familiar with the software, and R's syntax is quite different from most conventional programming languages.

Thanks to Joseph Adler's book, there's finally a comprehensive and definitive resource for the rest of us. The book is divided into five sections: Basics gives you all you need to get up and running; The R Language delves into the details of the language itself; Working with Data addresses such topics as loading, transforming, summarizing, and plotting data; Statistics with R covers statistical tests and modeling; and an Appendix describes the many functions and data sets included with the R base distribution.

R in a Nutshell touches on all of the major R use cases and subject areas, including lattice graphics, regressions, tests of statistical significance, classification, machine learning, time series analysis, and bioinformatic applications.

The book's prose is exceptionally clear, readable, and to-the-point. Each function or feature is presented with a full list of arguments and options, and generously illustrated with numerous examples of code, plots, and graphics. As one expects from the best O'Reilly books, there's hardly a page without code snippets and illustrations.

Personally, one of the sections I've found most useful in my daily use of R is the section on data transformation. R's data structures and how to coerce them into forms appropriate for certain types of analysis have been among my top R-related stumbling blocks. R in a Nutshell has taught me techniques I would never have known existed, and has saved me from writing countless lines of code in attempts to reproduce native but non-obvious functionality.

If you need to use R often, this is a book that will quickly become thoroughly bookmarked, and a permanent fixture on your desk.
14 of 17 people found the following review helpful
HASH(0x9fd9bc00) out of 5 stars The more I use it, the less I like it. May 8 2011
By Greg James ( - Published on
Format: Paperback Verified Purchase
This book tries to be all things R to all people - and it comes up short.

First, let me just say that I bought 2 copies of this book practically as soon as it was in print based solely on my past experience with O'Reilly books. One for home and one for the office. I was expecting a "go-to" book that I could pick up whenever I need a quick, but thorough, reference to some aspect of R programming. I thought that's what the O'Reilly Nutshell series is all about, but I should have waited as this is NOT what I was expecting.

The book covers a lot of ground without much depth. From the Table of Contents it looks like it's all here, but when I actually get into the material I find it lacking. R and all its packages is huge: there has to be less material in a printed reference than what is available online through CRAN (The Comprehensive R Archive Network: or many other sources. But this book reads more like the introductions of major topics rather than a vetted reference. There are just too many important details completely missing. For example, nowhere in the entire book is even a short description of the R workspace or how your project files are organized and stored by the R system.

In my opinion Part IV, "Statistics with R," should have been left out entirely. The space could have been better devoted to the details missing in the first 3 sections covering the language and system pragmatics. Instead, what we have are very basic intro's to common statistics and machine learning models. Maybe a better alternative would have been a single chapter that provides an overview of the myriad packages and algorithms available to the R programmer. Certainly a reference to CRAN's "Task View" page would be in order?

As I said, R is huge. A true "Nutshell" book would be invaluable to me and I'm sure many other R programmers. That's what O'Reilly books are renowned for. Unfortunately, this is not the one for R. This book provides a good overview of R, but you will probably outgrow it in a couple of months. I truly hope they come out with a 2nd edition that achieves that goal. Until then I continue to search through the R manuals.