Introductory Statistics with R (Statistics and Computing) | Peter Dalgaard

Introductory Statistics with R (Statistics and Computing) | Peter Dalgaard | A good book where there are few

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Introductory Stati...

	Introductory Statistics with R (Statistics and Computing) Peter Dalgaard Springer, 2008 - 364 pages average customer review:based on 19 reviews view larger image
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highly recommended

Very readable introduction

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.

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A good book where there are few

Introductory Statistics with R is an important book for a rapidly developing field. R is an extremely powerful statistical computing environment which suffers from the same problem as almost every other free software project -- a lack of quality documentation. Dalgaard fills a major gap with this book, that is, a guide to using R for many standard statistical problems.

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.

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basic R commands but not R code examples

If you are new to R and would like to know how to use R commands, get this book. However, if you already know R and would like to implement a huge code in R, this book might not be appropriate for you. Cheers!

Good overall, but missing key elements.

The book provides good examples of how to run various types of functions and models in R. It could, however, better address the issue of how to import data from large pre-existing datasets into a format R can read. If an external program, such as Sed or Awk, is needed to prepare common file structures (rectangular or hierarchical) beforehand, it would be nice to know this up front. The book lives up to its claim that it provides a subset of information that R beginners may find useful and that it is not a definitive guide. The reader should beware, however, that while it gives a fairly thorough description of built-in R functionality, it may not be sufficient.

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reviews: 1, 2, 3, page 4

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