I find it useful to have actual paper books at hand when I learn something new, even if it is so computer-related as a new programming language. Even further, I think it is a good approach to read for a while without actually trying examples (although that is extremely important, too) to get an overview.

And that’s exactly what I did this week for a day with Programming With Data – A Guide to the S Language by John M. Chambers. A great book and much of it is, naturally, applicable to R.

I look forward to do the same with Software for Data Analysis: Programming with R (Statistics and Computing) by the same author, soon. I won’t even try to grasp the many available online resources (manuals, the R Journal, not to be underestimated “other documents“, …), but I can point out some resources if anyone is interested. And I intend to add another, quite specific, online document:

I’ve had a great experience when I hat to program with C# during an internship with a document called “C# for Java Developers”. The idea is simple: Java and C# are often similar, but there are a few, distinct points where they differ – the document focuses on that, in they way of a FAQ document. I know Java, I know the basics of programming, so I don’t need long introductions about what loops are and design patterns, but simply the differences between my “main language” and the language I want to learn.

Sadly, I did not find something similar for Java and R. So, join me, if you like, in creating such a list of questions: http://ifgipedia.uni-muenster.de:9000/RforJavaDevelopers.

And because it somehow fits: Be a good programmer, be a pragmatic programmer.

Software for Data Analysis: Programming with R (Statistics and Computing)