R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.

The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.

In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix.

Customer Rating:
Summary: Intro. stats with R
Comment: This book seems like an excellent reference if you read though it in order and follow along using the example dataset provided online. However, I find that the transition to using my own data is far from clear. The book does not prepare you very well for using your own data, and barely discusses any type of matrices. This book if for univariate analysis, and univariate data.
As a reference it is definitely not as well suited. Looking up a topic in the index and jumping to that page often drops you in the middle of an example and you have to go back to the beginning of the at least the chapter to understand what's going on.
Basically after using this book I have found that though I can parrot the examples on the page, I do not understand the reasoning behind the code. Therefore when I go on to try and use my own data, I do not have the understanding and vocabulary necessary to adjust the commands to the needs of my dataset. For example there is a section on graphics (chapter 3) but I was not able to use it to help me figure out how to label a simple chart, unless it was the same chart that was used in the example.

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Customer Rating:
Summary: Good book on how to use R for basic statistical analysis
Comment: If you are new to statistics or have a limited knowledge of basic programming skills this book is not for you. If you understand basic statistics and know something about programming then this is an excellent introduction to how to use R to perform basic statistical analysis. It is not an R manual, as was stated in the preface. Nor is it an introductory statistics book by itself. It describes the analysis technique in high level, walks through the analysis step by step, and shows you how to use R to do the analysis. The chapter on linear models, specifically where he designs the matrices and dummy variables was a bit confusing. That was the only issue I had with the book.

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Customer Rating:
Summary: Fast shipping, good quality. Thanks!
Comment: The shipping is fast and the book arrived in good quality. I appreciate it very much. Thanks!

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Customer Rating:
Summary: Excellent resource
Comment: I bought this book a little over a year ago when a friend and colleague insisted I learn the R system for our collaborative work. I am not a professional statistician, but an engineer and researcher who needs and uses statistics in the course of my professional work.

I found this book approachable and informative from the non-professional perspective. (That is, from the viewpoint of a non-statistician.) I found enough examples to guide me through the process of bringing my datasets into the R environment, and then enough guidance to get me through the initial analyses necessary to make meaningful use of the statistical computations contained within the R system.

There are many other texts that treat the kinds of advanced statistics capability in the R system. Those are also necessary references for the non-statistician. There are also other texts on using the graphics subsystem present in R (which is substantial). Those references are also useful for preparation of reports and other written material.

But, this text is most useful as a primer for the system and is a first source on my shelf when I need to know the "how-to" of the basics. Then, if my needs are more substantial than those addressed by Dalgaard, I'll turn to other references.

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Customer Rating:
Summary: Great for learning the language
Comment: If you have learned the stat concepts behind these procedures already, this is book is great. I think this book is very helpful for people who have had a few stat courses where the professor used a competing stat software package.

I really like how the author repeats some of the important syntax explanations throughout the book (for example, why attaching a data frame makes the syntax more concise). This approach makes it better than trying to learn R from the isolated html help files.

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