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I loved reading this book. To be honest, this is the not the first R book I have read. I knew a bit about R when I read this book. I liked the way the author explains each concept. Each chapter starts with an example and the author explains what all you can do with the given data and then shows how the same can be done in R.
I have read about R graphics from many sources. But I was not very confident of myself. But after reading chapter 3 of the book, I have started building really nice graphs.
I wish I read this book before.
I have a small complain however. The book (the back cover page) says I am eligible to receive a free soft copy of the book. But I do not find the link anywhere in the website stated in the book. Hope the publisher takes care of it.

Quite a few texts on R have entered the marketplace over the last few years, so my decision to go with this text about a year ago was based on my survey of a number of reviews others have written for this and other books in this space, along with my personal experience with other Manning publications such as "Spring in Action", "Java Persistence with Hibernate", and "Hadoop in Action". In general, my experience working through this book has been pleasant, so I personally do not understand some of the complaints that other reviewers have made about the content that Kabacoff provides here.

That said, however, be forewarned that as with other texts, you should not expect to find all of your answers about R in this book. In my opinion, Manning publications are typically written in a format that fits well with the agile learning method with which I have grown accustomed during my consulting career. The author introduces topics along the way, sometimes more piecemeal that I would like, but his style forced me to explore other resources for more detail, bringing familiarity to other available resources. The number of plugin statistical packages for R has grown exponentially over the years (there are now over 2500), for example, so no book, not even "R in a Nutshell: A Desktop Quick Reference (Second Edition)", which I purchased recently, should be expected to be a one-stop shop.

This text quickly brought me up to speed with R language basics working with data sets, and introduced me to specifics with regard to R statistical methods and visualizations. Using R 2.15.0 for Windows, starting with the small data sets the author provides with which to run his examples, as well as sample data sets that the R language itself provides, I soon found myself working with larger data sets that the City of Chicago makes publicly available via its website, followed by using R at work. Your comfort level will be greater or lesser depending on your experience working with data.

As someone new to R, but not new to working with data, I especially appreciated the first five chapters that encompass the first of four parts in the book ("Introduction to R", "Creating a Dataset", "Getting Started with Graphs", "Basic Data Management", and "Advanced Data Management"). Like it or not, but as with any language, most data work revolves around first getting it into the correct format. After these first five chapters, the author walks the reader through basic graphs and statistics, followed by intermediate methods such as regression and analysis of variance (ANOVA), and advanced methods such as generalized linear models and more advanced graphics than was covered earlier in the book.

I have three R books: A Beginner's Guide to R (OK for the most basic stuff like importing data), R Cookbook (a good how-to guide for specific actions) and this excellent book. This one is my favorite in large part because it has an entire chapter on resampling/randomization tests (which is often missing from texts but should be a required part of every scientist's toolkit). This is the best overall beginner's book for someone who wants to learn by example. The writing is extremely clear, enjoyable to read (yes, enjoyable!), and he seems to explain just the right amount without saying too much or too little. A+

A very well written book. Very good for showing a newbie most of the capabilities of R.
Glad I didn't buy it for reading on a Kindle as it gets too awkward for bookmaking important pages that you need to revisit.

This is still the default R manual. Kabacoff gets R.

The author provides examples and explanations that are coherent and useful.

The examples are excellent and the code is well presented. This is a must have book if you are new to R. So good, I bought the equivalent style text for 3 other functional languages. But this author is particularly clear and clever. Well done, Kabacoff! If only all computer language books were written as well as yours.

Excellent starter book.

The book is a good text to use to learn statistical methods in R and the companion website is an excellent reference.