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Comment by klochner | Hacker News Reader

MBA: Excel

CS: {R, Python, Matlab, Mathematica}

Where do the Statisticians go? With the CS group, I'm sure, but more specifically, my understanding is that a statistician created R, and thus it suits the statistical mind better than Matlab and Mathematica.

Applied Statisticians: {R, SAS, SPSS}

Basic Stats Research: Same as CS people.

There's a ton of overlap between stats faculty and cs faculty these days - machine learning (usu. within AI) being the field of overlap in CS.

CS people might be put off by the fact R is really a pretty lousy language if you've seen any other language (like, for example, Python). It's obvious that it wasn't a programmer who designed the language R uses.

This blog posts points out a list of specific gripes about the language: http://tjic.com/?p=10739 There is some real weirdness to this language.

The R Programming Language for Programmers: http://www.johndcook.com/R_language_for_programmers.html does a lot to ameliorate this, but the fact the weirdness is there at all makes a programmer a little uneasy.

Think of it as a DSL, statisticians like to use. While it has its dark sides, its vector-orientation is well suited for a language whose main purpose is data manipulation.

> Think of it as a DSL

I can't let this stand as a blanket defense of crappy languages. We have Lua. We have Python. We have Scheme. We have multiple other languages that can be used as a good way to access the goodies provided by your wonderful hand-crafted libraries regardless of what they do. There is no reason for you to invent your own language just so people will be able to use your library code from a REPL.

Sadly, back in the dark ages, this was not true. However, in the future, anyone who tries to defend the horrible design of a new language with "Think of it as a DSL" gets to debug a 1000 KLOC application written in ANS COBOL 1968, which is a DSL for fixed-field database munging.

Let's put things in perspective: R is S and S had it's first appearance in the 1970s. It's current reincarnation is from the late 1980s. I remember that other statistical packages back then had a similar looking syntax. This is also the reason why lua/python stand no chance against R: they simply lack the vast abundance of statistical packages and tutorials. The best thing they can do is use R as an inferior process (IIRC there is a package for incanter that does that).

The point is though that S/R isn't that badly suited for what it was created for. There are a few pitfalls but those are explained in the langage definition. But unfortunately nobody RTFM nowadays (like people used to do back then in what you call the dark ages) because it's easier to start screaming for Mommy and write stupid blog posts that prove nothing but that those people haven't read the language definition.

And BTW, python sucks.

The first article is incorrect in saying that "user_info" will not behave as you'd expect. (I guess the author didn't try it out?) There are definitely some deeper issues, but it seems wrong to call out something as not being programmed by a "programmer" based on syntax. (Underscore (drawn as arrow) is shared by Squeak Smalltalk, at least.)

The second article probably would've done better to use m <- matrix(1:6, nrow=2) and m <- matrix(1:6, nrow=2, byrow=TRUE) as examples. Maybe matrices and arrays are implemented as contiguous ranges of memory underneath -- vectors -- but the language/standard library has plenty of functions dealing with them at a higher level, and you can live entirely within the abstraction if you like (although you may pay a price in speed, especially when constructing the initial structure). ("array" does not take a "by.row" argument.)

I guess I agree with the overall sentiment, but I find most languages to be lousy for one reason or another. (Python certainly has some warts, some in common with R -- lack of consistent naming conventions in the standard library for one.) The vector-oriented functions, convenient syntax for regression, the large library of stat functions, the packages, etc., make the pain worth it. And if R is from non-programmers, wait till you see SAS.

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