Comments about O'Reilly Media R in a Nutshell:

I just got this book, and haven't studied it yet, but just to respond to the two previous reviews, I had no trouble downloading and installing the "nutshell" package (library) that contains the sample data sets. From the link provided on the O'Reilly page, I downloaded the .tgz file, then in Mac OS X version of R used Packages Data -> Package Installer to load the package as a "local binary package." Then used the R command library(nutshell).

Comments about O'Reilly Media R in a Nutshell:

The book seems to be clear but it is annoying when the examples given cannot be used because the "nutshell" package is not available. This happens with many technical books and makes them frustrating to use.

Comments about O'Reilly Media R in a Nutshell:

The book is not bad and I like it as a reference (not something to work through from A-Z). Sometimes, it's too obvious that the author tried to squeeze in too much so some little sections are not helpful: they indicate that a certain functionality is available, but nothing else - in such cases less (different sections) would have been more (in terms of readability). Real downer: the package doesn't install, neither on R 2.10 nor on R 2.12 (for Win 7).

Comments about O'Reilly Media R in a Nutshell:

I am very new to R, and have been reading this book for roughly a week now. The good part about this book is the overall organization. It's pretty easy to find what you're looking for - it's a desktop reference. Unfortunately, learning how to use R is a bit hard from this book. You could go the route of page 1 in learning the basic syntax, then eventually going into graphics and analysis of data - but quite honestly most books I've seen rather go with small projects to give the feel for something then jump into the details. I found I had to go to a tutorial first to get a general feel about what R can do, before diving too far into the book. The largest issue I have with the book is that it contains some typos. I found the most confusing was the constant reference to outputting a pdf (using png..). I kept getting errors trying to open the PDF, and through some googling I found that he actually likely meant the pdf command, where png requires a png extension. I feel like kinda an idiot now for not seeing it earlier, but it's little things like this you'll need to be careful of. Another issue I have with the book is the constant jumping around from various data sets. Some data sets aren't even explained well beforehand on commands done against them. There's a bit of a "leap of faith" about what x is set to currently as operations are done to it. Overall, I think the book is good, especially for reference despite the typos and data descriptions.

Comments about O'Reilly Media R in a Nutshell:

This is a good book for R developers. As a developer myself I feel that the chapters are crisp and well organized.

The book starts of by giving a feel for the language (like a teaser trailer) and then diving into the structure and syntax of R and finally putting it all together to perform statistical analysis.

Comments about O'Reilly Media R in a Nutshell:

The book is just awesome at accomplishing what it set out to do - being used as a great Desktop reference.
Worth every penny/cent.

Comments about O'Reilly Media R in a Nutshell:

This book is a very good introduction to the R programming language.R is a free, general purpose programming language(with a strong support for doing statistics).The language has its idiosyncrasies. For example, the assignment operator is denoted by a reverse arrow (x<-2); this book does a pretty good job of explaining all these in detail. Once you get past these details, you will find that R is a pretty versatile language. Let us take an example (source: [@]). The following code installs the quantmod packageand then displays the stock of Apple along with Bollinger Bands ([@]).install.packages("quantmod") # installs the package quantmodlibrary(quantmod) # loads the packagegetSymbols("AAPL") # load Apple's datachartSeries(AAPL) # Create financial chartsaddBBands() # Add the Bollinger Bands.One of the best parts of R is huge support of libraries for doing commercial grade statistics and the plots/graphs produced by R are of high quality.Though I didn't get a chance to try it out, there is a package called RPy which is a Python interface to the R programming language. More details can be found here: [@]you are mathematically inclined, definitely check this book out. Once you learn R, you can use it anywhere/anytime without ever paying a license fee.

Comments about O'Reilly Media R in a Nutshell:

I've been looking for a good introduction to R for a long time now, and this book really does do remarkable job of introducing you to the basics, while fleshing out the finer details of the language.

However, I feel it is badly let down by frequent typos and omissions. By way of example, on page 318 the reader is instructed to split a data set on a date using "cut":

bwplot(price~cut(saledate,"month"),data=sanfrancisco.home.sales)

This simply will not work, as cut expects a numeric value; the correct method would be to use "cut.Date". It's fairly obvious what has gone wrong, but that's not always the case. Novice users really shouldn't have to chase these, all too frequent, mistakes up.

All in all, a handy textbook that I would recommend, albeit with the caveat you'll occasionally need to search round to find what the author should have written. Errata can't come soon enough in my opinion...

Comments about O'Reilly Media R in a Nutshell:

I use R on a daily basis, and I find this book is a great resource for learning some of the finer points of graphical formatting and data manipulation. Seemingly minor parameters like "mai" and "oma" are clearly explained, which alleviates a major shortcoming of R's online documentation.

My only criticism is that O'Reilly should have found someone with a statistics background to proof-read the section on statistical tests. A typical example is the discussion of the Shapiro test (p.354), which in the text example produced a p-value of 1.307e-12. Such a small p value indicates that the null hypothesis (that the distribution is normal) can be REJECTED, not ACCEPTED. But Adler states, "As you can tell from the p-value, it is quite likely that this data came from a normal distribution" which is backwards. There are several places where these errors creep in. So I would recommend against using this book to learn how to do statistical analysis.

Comments about O'Reilly Media R in a Nutshell:

I've wanted to learn R for a while, but found it difficult and unintuitive when I tried. This book was just what I needed. The early chapters present the basics of R, with plenty of examples, and OS specific information for Windows, Mac, and Linux users. This allowed me to quickly dive into the language.

One very nice feature of the book for those with advanced technical skills is the use of standard terminology for language features of R, and the contrasting with other languages. If you know how a particular feature works in, for example, Java, the feature in R is described with the same terms, and often contrasted with other languages (esp. Java). This allows more advanced readers to rapidly learn features which resemble those in previously known languages, and really speeds up learning the core language features.

Overall, I'd recommend this book to anyone who's trying to learn R, but especially those with little technical background (as there's plenty of detail for beginners) and those with plenty of experience (as the standard terminology and contrast with other languages lets you skip lots of details you'd already know). Really useful book.