Customer Reviews

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

5 star:		(10)
4 star:		(4)
3 star:		(4)
2 star:		(1)
1 star:		(1)

 

 

'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.

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.

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.

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.

This book is the best on R I've read, but it still lacks in teaching the fundamental basic practices of dataset transformation and manipulation. For instance, I cannot find how to subset a matrix by rowname. All the answers that are known and not in this book are out on the web somewhere. This book is very helpful, especially for focused, specific tasks.

I have dabbled in R for about a year now and picked up most of what I've learned from others and from google searches. I ordered this book and found the information to be immensely helpful in improving my overall understanding of the language and for improving my code development. The examples are very clear and I have gone back to this source over and over again when questions come up.

I also ordered the Data Manipulation in R book and found this to be far superior as far as being easier to understand and more complete.

"R in a Nutshell" is broken into four main parts. The author begins by going over the the basics, including how to set up the R environment. There's also a brief tutorial about the workings of the language. As you progress through the book, it provides a more detailed overview of the language, discussion of how to work with data, and an explanation of how R handles statistical modeling.

This is both a tutorial and a reference, which can sometimes be awkward in programming books. However, in this case it works much better than for most. R is a relatively specialized language that most people won't be using for general purpose programming. While there are some people who use it all the time, more of its users probably keep it in their toolkit as a great way to handle very specific projects. In that situation, having all the information about how the use the language in one book makes a lot of sense.

The one thing that you really need to keep in mind is that this book teaches you to use the programming language to do statistics, but it's not intended to teach the statistics as well. It gives a brief overview of concepts, but if you have no background in the field and you're going to need more than that you'll need a separate reference book on that.

I received an electronic copy of the book from O'Reilly media (the publisher). I haven't yet run into a situation where my programming needs called for R, but this is definitely the reference that I'm planning to go back to when that day comes. If you're looking for a general book on programming, this is far too specialized to be very useful to you. If you need to handle a lot of processing of statistics, it'll be worth your while.

I found this book a Barnes and Nobles and was just planning on skimming through. I also have another book in the nutshell series "Statistics in a Nutshell" which is also quite good for a supposedly concise book. This is pretty thick for a nutshell book with 493 pages of text and about another 110 of appendices, references and indices. I am interested in learning R because it is so useful for statistical methods and graphics at all levels. Chapters 1-4 cover the basics of the language and the computer interface and part II (Chapters 5-11) is pretty detailed about the language and object-oriented programming in general. But what really sold me on the book was part III "Working with data". It covers saving,loading, editing and preparing data followed by statistical graphics, and statistical methods. Statistical method and models are covered in Chapters 16-24 and is actually a tutorial on statistics with applications in R. Chapter 16 is about analyzing data and even includes a section on resampling methods, Chapter 17 covers probability, Chapter 18 is about hypothesis testing and 19 is about experimental design. Chapter 20 covers regression, 21 classification, 22 machine learning including mostly unsepervised learning in the form of cluster analysis. Chapter 23 covers aspects of time series analysis and Chapter 24 is an illustrative example of genetics using a bioconductor. It is a paperback book that costs about $50 and I think it is well worth it as it provides a nice reference for problem solving using R which is free downloadable software with the powerful CRAN library of statistical routines that can be used for any of the application mentioned in the text.

R is a statistical computing environment that is free, open-source, cross-platform, and interactive. And now it has its own O'Reilly Nutshell book, R in a Nutshell, written by Joseph Adler. I am pleased to report that Adler has risen to the challenge of the highly-regarded "Nutshell" franchise. As is traditional for the series, this title mixes introduction, tutorial, and reference material in a style that is well suited to a reader who already has a background in programming, but is a new or occasional user of R.

The book's flow was very effective for addressing the different points of view from which I approached it. As a curious newcomer to R who wanted to get going quickly, I was well-served by Part 1, which provided an R kickstart. As a polyglot programmer who is always interested in seeing how a new language approaches programs and their construction, I enjoyed Part 2, which described the R language. As a busy professional with data sitting on my hard drive that I'd like to understand better, I appreciated Part 3, with its practical emphasis on using R to load, transform, and visualize data. As a non-statistician who still occasionally needs to do some number-crunching, I'm sure I'll be returning to Part 4, with its detailed explanations and illustrations of analysis tools and techniques--almost two-hundred pages worth.

This is an impressive piece of work. In a volume of this size (about 650 pages), navigation is crucial, and I found both the organization of the chapters and index up to the task. I was able to follow the instructions and examples through the first several chapters of the book essentially without a hitch. The amount of detail (especially in the later sections) could have been overwhelming, but Adler keeps the interest high and the mood light by drawing on an engaging variety of data: toxic chemical levels, baseball statistics, the topography of Yosemite Valley, demographic data, and even turkey prices.

I won't claim perfection for this book. There were a couple of explanations that could have been clearer, and one or two odd turns of phrase or rough edits. Out of all the code examples that I tried, I found exactly one that didn't seem to work without a minor correction. For a work of this size, that's actually pretty amazing!

As a long-time O'Reilly reader, I see Joseph Adler's R in a Nutshell as a welcome addition to the menagerie.

The problem may be that I am getting old, but as an experienced matlab, maple, fortran, C++, pascal, and java programmer who needs to learn R in a very short time, this book has been of limited value. I've used the " in a Nutshell" books before, but this one just does not work for me. I've found a couple university course websites and the books and supplementary web materials by Crawley, Dalgaard, and Bolker to be more useful.

For this review-- my point of view is that of a person who wants to learn R as a programming language-- not someone who wants to just learn how to use R for statistical analysis. I am concurrently using multiple programming languages for different projects and need references that offer good examples and have excellent indices.