61 of 62 people found the following review helpful:
5.0 out of 5 stars
Gateway into the world of R, April 14, 2010
This review is from: R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly)) (Paperback)
'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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63 of 67 people found the following review helpful:
5.0 out of 5 stars
Useful and surprisingly engaging, January 14, 2010
This review is from: R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly)) (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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28 of 30 people found the following review helpful:
5.0 out of 5 stars
Excellent resource for a language with a steep learning curve!, April 14, 2010
This review is from: R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly)) (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.
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