I use R for three reasons: (1) It's Free Software; (2) It's a programming language; (3) Other statisticians use it so it's easier for me to collaborate.
There are the usual supporting arguments for (1). (2), I've only used SAS a little bit, and it was extremely unpleasant to use it for non-built-in stuff, which makes research harder for no good reason. For (3), I have nothing against Python but most other statisticians don't use it. If I want to share my work in R, it's easy (statisticians know how to install R packages). If I want to share my work in Python, I first have to teach [most] other statisticians how to use Python. There's nothing wrong with that, but why raise the start-up cost for them?
tl;dr I conjecture that most statisticians don't want what the author is suggesting. Also, there are plenty of companies that are trying to do what the author is asking for, but most of them seem to miss the desired sweet spot, or charge lots of money, or both. I haven't taken a survey of the available software in quite some time.
First of all, you need to decide if you want a language reference, or an application guide, as R books fall into those two categories.
If you have a specific type of work in mind (bio-informatics, data mining, data visualization, ...) I'd say to find a book that focuses on that topic. I haven't looked in a while, but I haven't seen a general R book that I like, anything I suggest there would be guessing on my part.
There are plenty of good references on the web. I'd start by looking at the material available from the R web site:
R's core manuals [1] are typically correct and reasonable to use. The "Introduction to R" guide will get you up to speed fairly well if you already know another programming language. There is also the contributed documentation [2]. I haven't gone through these, so I can't say much about them, or promise that they are up-to-date. I suspect not, as R develops rapidly. The one reference I can recommend highly is "The R Inferno" by Patrick Burns [3]. This is not a starter guide, but something you read after one. It gives excellent advice on avoiding common pitfalls in R.
[1] http://cran.r-project.org/manuals.html
So far I could satisfy most of my statistics needs with the function in numpy and scipy but occasionally I need to do something slightly more fancy and R I guess is the way to go.
And don't lump R in with Python. And good statistician would have your neck. You mention S, but again S doesn't look anything like Python either.
_delirium is merely pointing out that there are push-button packages for statistics, and that statisticians using programming languages (be they statistics-oriented or not) usually do so because they want to or because they need to (as the push-button stuff is not sufficient for their needs, for instance)
The statisticians who choose to use a programming language like R or Python typically do it because they actually do want a programming language. I mean, that's why Bell Labs statisticians invented S (R's predecessor) to begin with.