Statistical Analysis with R, a Review

February 12, 2011
By

(This article was first published on Nor Talk Too Wise » R, and kindly contributed to R-bloggers)

[To all of the R-bloggers out there who recognize this, I apologize.  To those that don’t, This is at least the 5th review of this book to go on the feed.  The author is linking to the others here.]

Long Version: I have a Bachelor’s degree in Computer Science.  I’m pretty handy when it comes to programming.  So when I look for a book about a language, I’m looking for a book that will dispense with a whirlwind review of code logic within the first Chapter, if at all.  That said, R isn’t just a programming language, now is it?  This book comes across as a Programming textbook, in large part because R comes across as a Programming environment.  However, there’s a very fine line the book walks between Programming and Statistical Analysis.  The book makes no effort to explain control structures like loops, conditional statements, and logic.  In this, it is an entirely honest title–the book teaches how to use R for the usual suspects of statistics.  But I’m getting ahead of myself.

Quick (the author) starts the book by framing you as the successor to a 3rd century CE Chinese General, preparing for war against another kingdom.  The exercises all follow this cheesy lively narrative.  It’s a creative presentation of sample data, but it does occasionally distract from the general utility of the tools he is presenting.

After a couple of plodding chapters on installing R (all screenshots from an apple, annoyingly), Quick delves into the statistics.  He moves quickly from basic arithmetic to correlation analysis, regression, and ANOVA.  There’s no explanation of hypothesis testing or probability theory.  There’s no discussion of the underlying mechanics of the output.  This is exactly as advertised.  Note the tagline: less theory, more results.  For this reason, I wouldn’t recommend this book for statistics beginners.  What’s the point of getting Regression output if you have no idea what your p-value, or R-squared mean?  However, for anyone who already understands these statistics in principle, it is fairly straightforward to pick up these R commands.

As I previously mentioned, there’s also no discussion of looping or conditionality (though he uses an if statement in one of the exercises).  I find it puzzling on this note that the author includes a brief tutorial on function writing, but this is a small comment–it seems a strange place to halt the discussion of the language.

Quick then shifts to a fantastic tutorial on how to use the built-in graphing tools.  Slow, but fairly comprehensive for R’s default graphing functionality.  If you’re looking for a way to graph something without jumping into ggplot2, this book can show you a reasonable alternative.  Although, if you are looking to make even higher quality graphs, Quick sends you out on a brief tutorial on installing new packages, along with web resources for finding what information you could need to get functionality from them.  The brave can take it from there.  The less adventuresome can look forward to Packt Publishing’s other book on R, R Graphs Cookbook.  I’m working through it now and will post a similar review in due time.

I recommend this book if you fit the profile of someone who could benefit from it.  Read the tl;dr version for clarification on this point.

tl;dr Version: Fantastic for fans of the … for Dummies approach to learning.  Deliberate pacing, yet fairly comprehensive.  Good for those with a rudimentary understanding of statistics.  Not good for programmers.

Disclaimer: I, like the other reviewers, received a free copy of the e-book from Packt in order to write this post.  That said, I warned them if it was a bad book, I would write a critical review.  Let the fact that I ultimately endorse it stand for itself.

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