Exactly. You shouldn't have to be a programmer to do statistics. Just like you shouldn't have to be a network engineer to share files. What if DropBox had stuff in there about http, ports, levels of service, bandwidth, etc... You'd probably say, "Great! I always wnated to specify that DropBox use SSL4.7 draft B over CDMA EvoX.1 -- who wouldn't?"
When you're doing a DSL make it is as simple as possible. And if you have time, in v2, give it hooks to just break out and do crazy stuff... but the 90% case should be simple as pi.
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.
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)
If you just need graphs and pivot tables, use some GUI tool.
(Eg: the weird SAS code gets replaced by something as simple as tabulate x y)
I have to agree (with your criticism). I spend most of my day in SAS and R, and my Python is limited to tweaking code from my colleagues, but I don't see how either the SAS or Python listed is better or worse than the other.
I actually like the quote in the article reg. DropBox's simplicity, but I don't get the relationship to statistical programming languages.
I've been involved the last few years with creating better data structures and tools for doing statistics in Python-- with excellent results (http://pandas.sourceforge.net and http://statsmodels.sourceforge.net). So I think the author should take a closer look at some of the libraries and tools out there.
MBA- and business types have Excel. As a researcher, I flex both Python and R regulary -- but I want the full power of a programming language, not a couple of macros to generate a pivot table.
Also, none of this has anything to do with Python, which is an absolutely beautiful language.
However, I don't think the programming concepts one needs to understand to make effective use of a well designed Python library are too much to ask. I've only dabbled in R, but when I did, it required me to exercise my general programming knowledge to understand list, matrices and functions. I think the author is also falling in the trap of what is obvious to him is obvious to everyone. I'm actually not sure of what the SAS code is doing, and much prefer the Python.