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R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly)) 1st Edition

3.9 out of 5 stars 33 customer reviews
ISBN-13: 978-0596801700
ISBN-10: 059680170X
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Editorial Reviews

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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Product Details

  • Series: In a Nutshell (O'Reilly)
  • Paperback: 636 pages
  • Publisher: O'Reilly Media; 1 edition (January 14, 2010)
  • Language: English
  • ISBN-10: 059680170X
  • ISBN-13: 978-0596801700
  • Product Dimensions: 6 x 1.2 x 9 inches
  • Shipping Weight: 1.6 pounds
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (33 customer reviews)
  • Amazon Best Sellers Rank: #421,625 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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.
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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.
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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.
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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.
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