There are a few minor errors:<p>* the explanation of 8.6 is wrong - it's cbind that's coercing all columns to a common type, not data.frame<p>* 8.7 is a bad example of good R code - use column names not indices!<p>* in 9 (and elsewhere) it's not necessary to continually strips names off vectors or use return in functions<p>* 11.2.1 to count TRUEs in logical vector, sum it.<p>...
I write PHP and some perl, and recently learned R. This guide would have been useful to me at the beginning. What it does well is that it jumps right into concepts that programmers will naturally wonder about and already conceptually grok. It wastes no time on explaining basic programming concepts, nor does it dig deep into esoteric R-isms. It helps you translate your PHP/Perl/Java/whatever mindset into the R language. An extremely useful introductory document.
My mother has a PhD in Cognitive Linguistics and is working at UCSC. She is using R, she hasn't programmed before, so I wonder how well she is doing with R. Well, I will meet her again around Christmas, maybe I could help her out a bit with R, I'm sure there's saomething she has missed, since she's a non-programmer.
Google's R Style Guide may be helpful:<p><a href="http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html" rel="nofollow">http://google-styleguide.googlecode.com/svn/trunk/google-r-s...</a>
most of my issues with r can be traced back to the nonintuitive (for me) data structures. i found <a href="http://www.amazon.com/Data-Manipulation-R-Use/dp/0387747303" rel="nofollow">http://www.amazon.com/Data-Manipulation-R-Use/dp/0387747303</a> to be the most concise guide for how to manipulate common data structures.
"...who want to do statistics", right? Are there any good use cases for R for other stuff? (Or some interesting ways you used statistics as a "working programmer" recently?)
This is a good resource to start. Then it might be better resources (there are some other tutorials around) but as a beginner in R I found it very good.