Introductory Statistics with R (Statistics and Computing)
July 8, 2010 by BioinformaticsDirectory.com
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.
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(out of 25 reviews)
List Price: $ 64.95
Price: $ 42.79
Review by Roger Peng for Introductory Statistics with R (Statistics and Computing)
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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.
Review by Isaac S. Kohane for Introductory Statistics with R (Statistics and Computing)
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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.
Review by Alan Mead for Introductory Statistics with R (Statistics and Computing)
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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.
Review by Adam Baker for Introductory Statistics with R (Statistics and Computing)
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As an introduction to R, this book is very good. It’s much clearer than the R documentation that comes with the code, and satisfied most of my needs. The statistical text was not very helpful, however. Discussion is very brief, and several points that would seem important are dismissed as beyond the scope of the work. I wasn’t able to get a handle on the statistical tests I wasn’t familiar with to begin with. The ideal audience for the book is people who know the stats already, and would like to learn R.
Review by Brian M. Napoletano for Introductory Statistics with R (Statistics and Computing)
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I purchased this book after wading through the R help files for what seemed like hours. I was looking for a simple and straightforward guide that I could refer to for the basic operations of R. I am currently teaching myself C++ and learning how to interact with the Unix environment, but have very little experience with statistical programming. Therefore, I was looking for an accessible reference to help me become more comfortable with the R environment. *Introductory Statistics with R* provides concise answers to the “new user” questions that inevitably arise when programming in a new environment. In addition to its role as a programming resource, Dalgaard provides very useful information about the statistical methods he describes. I find this feature very useful as well, as I can rarely recall all the details of various statistical procedures from memory. My only caution is that this is an *introductory* guide to R. You will not find instructions for most (if any) of the additional libraries available to R. That said, I highly recommend this book to anyone who is interested in learning how to use R for statistical analyses.