R in a Nutshell: A Desktop Quick Reference Paperback – Jan 14 2010
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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.
Most Helpful Customer Reviews on Amazon.com (beta)
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.
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.
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.
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.
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: cran.r-project.org) 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.
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