[This article was first published on Why? » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
My sabbatical is rapidly coming to an end, and I have to start thinking more and more about teaching. Glancing over my module description for the introductory computational statistics course I teach, I noticed that it’s a bit light on recommend/background reading. In fact it has only two books:
- A first course in statistical programming with R (Braun & Murdoch)
- Pros: I quite like this book (hence the reason I put it on my list). It has a nice collection of exercises, it “looks nice” and doesn’t assume knowledge of programming. It also doesn’t assume (or try to teach) any statistics.
- Cons: When describing for loops and functions the examples aren’t very statistical. For example, it uses Fibonacci sequences in the while loop section and the sieve of Eratosthenes for if statements.
- An introduction to R (Venables & Smith)
- Pros: Simple, short and to the point. Free copies available. Money from the book goes to the R project.
- Cons: More a R reference guide than a textbook.
What other good R books could I recommend? In particular, I’m looking for books that:
- Assume no prior knowledge of programming.
- Assume very little knowledge of statistics. For example, no regression.
- Doesn’t try to teach statistics. So no “R with ….” type books.
- Are cheap!
Suggestions welcome (needed!)
To leave a comment for the author, please follow the link and comment on their blog: Why? » R.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.