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Book review: 25 Recipes for Getting Started with R

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Recently I was asked by O’Reilly publishing to give a book review for Paul Teetor new introductory book to R.  After giving the book some attention and appreciating it’s delivery of the material, I was happy to write and post this review.  Also, I’m very happy to see how a major publishing house like O’Reilly is producing more and more R books, great news indeed.

And now for the book review:

Executive summary: a book that offers a well designed gentle introduction for people with some background in statistics wishing to learn how to get common (basic) tasks done with R.

Information

By: Paul Teetor
Publisher:O’Reilly
MediaReleased: January 2011
Pages: 58 (est.)

Format

The book “25 Recipes for Getting Started with R” offers an interesting take on how to bring R to the general (statistically oriented) public.

Instead of teaching R (or topics in statistics) in a systematic way, the author chose to assemble a likely set of cheat-sheet-like how-to tasks (“R recipes”) that a new user of R is assumed to encounter in their first steps of using R.  Tasks like: Installing R, finding help, reading data, selecting data, basic summary statistics, plotting some graphs, loading packages, and performing/diagnosing OLS regression.

These recipes were taken from the “R Cookbook” (O’Reilly) which contains over 200 such recipes.

Each of the 25 “R recipe” is comprised of four sections:

The book is modest in it’s presumptions of scope (which I appreciate) and tries only to offer a bird’s eye view for statistically oriented, first time (short on time) users, wanting to feel they can get to do “something” using R.

Audience

I can imagine a first year student (or an IT professional with some stats background), benefiting from such a book if they have learned their stats with another package (like stata, SAS , SPSS and so on).

The books scope is both an advantage and a disadvantage, depending on the target audience.  I would find it surprising if experience R users will have much (or any) to gain from it, and it can not serve as a reference.  Although this might be a different case with the extended “R cookbook” (which I hope to get my hands on at this point or another, since I enjoyed the authors writing).

Lastly, I should mention that someone who is already well versed in SAS or SPSS would probably prefer Robert Muenchens superb book ”R for SAS and SPSS Users” in order to make the transition to R smoother.

Content outline (with some notes)

I added some notes to the chapter names.  I’d like to state again that my general impression of the book is good.  The points I make are mostly subtle and only placed to guide you in case you give the book as a gift to a friend, in case you might wish to emphasize some things to your friend that were not mentioned in this book.

The books content includes:

* * *

If you got to have a look at the book, I’d be very curious to read your thoughts about it in the comments.

To leave a comment for the author, please follow the link and comment on their blog: R-statistics blog.

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