Book review: Petchey and Beckerman’s Getting Started with R

I’ve decided to start doing occasional book reviews. There are only a few sources for book reviews in ecology and evolution, and they often miss books I’m thinking of buying. So once in a while I’ll help fill the gap by posting a review.

Full disclosure: I’m starting with a book by two good friends of mine: Owen Petchey and Andy Beckerman’s Getting Started with R: An Introduction for Biologists. I’m starting with this book because it’s short, and because they were kind enough to send me a free copy. That Owen and Andy are friends affects my decision to review the book in the first place, but doesn’t affect my review.

Owen and Andy are ace ecologists, and R evangelists. They’ve used many other stats packages in the past, but now they use R exclusively, and like it so much they decided to write a book about it. This book grows out of their years of experience offering multi-day training courses on how to use R, an open source, freely available, and extremely powerful statistical package. Like their courses, their book is aimed at biologists who know what statistics they want to do, but who have no experience with R and find the prospect of using command line-driven software daunting. If that describes you, you need this book!

It’s six chapters, covering just over 100 pages:

  • Chapter 1 explains why you would want to use R instead of other statistical software: it’s free, it works on any computer with any operating system (Windows, Mac OSX, Linux), it’s powerful, it’s command line driven rather than menu driven (so it forces you to know what you’re doing), and it makes beautiful figures. Chapter I also explains how to download and install R.
  • Chapter 2 gets you ready to work, covering topics like how to store your data so R can find it, the basics of how R works, and how to tell R to do what you want it to do.
  • Chapter 3 explains how to get your data into R, and how to graph and manipulate your data
  • Chapter 4 gets further into graphing, telling you how to make and customize common plots like bar graphs and scatterplots.
  • Chapter 5 walks you through how to do some common, basic statistical tests: chi-square, two sample t-tests, and general linear models (regression, ANOVA, ANCOVA). Coverage includes not just how to execute the test, but how to get and interpret the output you want, how to do assumption checks, etc. This isn’t a statistics textbook: Owen and Andy assume that you understand the stats that you want to do, and just need to be told how to do them in R. Chapter 5 also covers making publication-quality figures to report the results of these tests.
  • Chapter 6 adds some final comments and encouragement.
  • There’s an appendix with some references (mainly R-based statistics textbooks)

Throughout, the book is illustrated with screenshots. The screenshots are annotated with circles, arrows, and little text boxes pointing out and explaining whatever it is that the main text is referring to. And of course all the R commands needed to do everything that the book covers are presented, and their outputs illustrated.

This is an excellent book. It’s totally functional–the focus is on explaining how to get R to do what you want it to do. Further, the focus is on the things that you’re likely to want to do–the book’s not cluttered with extraneous material. There’s an emphasis on good practice about basic things like workflows, so that you’ll always be able to go back later and reconstruct exactly how you processed and analyzed your data. Finally, the book is written in a deliberately conversational style, and pitched at the perfect level for its audience. Working your way through this book (which can be done in a day or two) is pretty much exactly like having Owen or Andy standing beside you, walking you step-by-step through your first experience with R.

I wish I’d had this book long ago. I’ve been using R for years, but I picked it up by teaching myself, reading the help files, using a much longer R-based stats text, and by using an add-on package that lets you perform common statistical tasks with drop-down menus* So until I got this book, I still had really basic gaps in my R knowledge (like “how to make a multi-panel figure”), which this book has now closed.

Want to learn R, or maybe just want to check it out, but scared about the steepness of the learning curve? Buy this book. Problem solved.

Need further convincing? See Graeme Ruxton’s review in TREE.

*Yes, there is an add-on that makes R menu-driven. Using it makes you a sinner. I am a sinner.

3 thoughts on “Book review: Petchey and Beckerman’s Getting Started with R

  1. Sounds like a very good and useful book, an absolute necessity if one wants to use R, given that the help menus for the various functions are frequently awful or cryptic or both.

    R is great, being free and powerful and all, and will certainly be the salvation of mankind by solving all possible analytical problems, but somebody really needs to start letting the unitiated know what a tremendously quirky pain in the ass and time sink it is if you want to write complicated algorithms and are not already an accomplished programmer. OK, I’ll start.

  2. Perfect. Bought it. As someone who’s used other stats packages, I know I’m going to want to do future analyses in R. As a computer programmer, I know that R is not a very nice language to do things in (as opposed to languages that are more properly designed). A straightforward book to match statistical tests (and figure-making!) to the the idiosyncratic syntax of R is just what I need.

  3. Shameless plug here for my new book, just released, titled “How to get rich and famous, and/or solve many, if not all, of the world’s problems, by using R in your spare time”. Unfortunately, it’s already on back-order due to the heavy demand, so be patient please.

    “R in a Nutshell” by Joseph Adler is also very good IMO. Good combination of language description and statistical methods descriptions therein. Somewhat short on promoting becoming rich and famous however.

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