Introductory Statistics with R (Statistics and Computing)

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.

For some time now, users have had to make do with S-PLUS books which contained some overlap with R. Now R users have a book they can call their own. After briefly discussing the R system and the language basics, Dalgaard goes through what might be covered in an advanced undergraduate data analysis course. Throughout the book, code examples and output are carefully interspersed so that the reader doesn't go too long without having a concrete example.

Dalgaard leaves out some advanced topics such as time series, spatial statistics, etc. (some of which are nicely covered in Modern Applied Statistics with S by Venables and Ripley) but that is probably for the best. The book is not bloated, nicely priced and I would recommend it to any advanced undergrad or first year grad student wanting to learn how to do statistical analysis in R.


Twofold win: great introduction to useful statistics and R programming   August 29, 2005
Isaac S. Kohane (Newton, MA USA)
40 out of 41 found this review helpful

Despite the web, there are learning curves sufficiently steep that a well-organized book is the most effective introduction. However, too many of these introductions, particularly in programming and/or statistics are written with low content and high redundancy or with impenetrably high-density content. So, it is a rare sign of pedagogical mastery combined with the genuine confidence of the experienced practioner when an introductory book manages to achieve a balance that is just right.

As I become more familiar with R, I still carry around this book in my briefcase for the occasional reread during which I uncover a nugget I had missed. When I have told this to my colleagues in computer science or bioinformatics, they immediately reveal that they share my enthusiasm for Dalgaard's work.

Let's be clear: this is a book that walks you through introductory and highly useful statistics while introducing you to the most effective ways to use R to perform these biostatistical analyses. It is not a programming book, nor is that its intent.


Very readable introduction   April 24, 2003
Alan Mead (Lockport, IL United States)
49 out of 54 found this review helpful

This book provides a very readable introduction to basic statistical analysis using R (with occational references to S-Plus). The table of contents displays the topics and I thought they were generally well covered in enough detail to compute the statistics (but this is not a statistics text). Especially helpful are the additional analysis steps, such as graphing results, and the peripheral R issues. Small things I would change: expanded coverage of manipulating data (e.g., SPSS's RECODE, TEMPORARY, MERGE FILE,...), more explicit instructions on installing the example data (it's at the end of the installation Appendix), discussion of interactions in ANOVA and regression, discussion of ANCOVA, and finally I would have liked a quick overview of the available packages and the stats they provide. But these are small issues; it's a great book.


Good starter for R   March 5, 2005
lelliott (San Antonio, TX USA)
13 out of 13 found this review helpful

I found this book very readable and a great reference for getting started with R. I was quickly able to run various tests from chi-square to logistic regression using this guide. I would agree that this would not be good for someone familiar with R, which is why it's called "Introductory". It also serves as a handy reference, providing easy look-up for how to accomplish various common (and some not so common) statistical tests.


Read this after Using R for Introductory Statistics   December 18, 2008
Love J (NY USA)
5 out of 5 found this review helpful

This is a great intro-intermediate R book for intro stats.
I recommend you to read "Using R for Introductory Statistics" by John Verzani first, if you know general things about stats.
Using R is written in more step-by-step way and there are a lot of repetitions that helped you learn R language by merely reading through the book.
After you finish Using R, proceed to Dalgaard's book.
As one of the reviewer said, Dalgaard's book can be a concise reference book since it covers more stuff than Using R does.
It is a nice, compact book on many techniques, but it sometimes lacks suffice explanations.
This is why Using R should come first and Dalgaard's book comes next.
If you finish these books, you are ready to explore other R and S-Plus books as you need.