The R language is closely tied to statistics. It’s ancestor was named S, because it was a language for Statistics. The open source descendant could have been named ‘T’, but its creators chose to call it’R.’

Most people learn R as they learn statistics: Here’s a statistical concept, and here’s how you can compute it in R. Statisticians aren’t that interested in the R language itself but see it as connective tissue between commands that are their primary interest.

This works for statisticians, but it makes the language hard for non-statisticians to approach. Years ago I managed a group of programmers who supported statisticians. At the time, there were no books for learning R without concurrently learning statistics. This created quite a barrier to entry for programmers whose immediate concern was not the statistical content of an R program.

Now there are more books on R, and some are more approachable to non-statisticians. The most accessible one I’ve seen so far is Learning Base R by Lawrence Leemis. It gets into statistical applications of R—that is ultimately why anyone is interested in R—but it doesn’t *start* there. The first 40% or so of the book is devoted to basic language features, things you’re supposed to pick up by osmosis from a book focused more on statistics than on R *per se*. This is the book I wish I could have handed my programmers who had to pick up R.

The creators called it R because their names were (R)obert Gentleman and (R)oss Ihaka. (From memory I thought they originally called it R&R but I could be wrong about that.)

http://www.mathsreach.org/wiki/images/9/97/07ASingleLetterThatGrew.pdf