[Aqwis]

As a student of statistics, I'm kind of split on R. On one hand, it's just not a very well-designed language. The fact that it has three (!) independent object systems is a testament to this. On the other hand, as vegabook also mentions, working with vectors and matrices is just a lot more natural in R than in general-purpose languages like Python, because R's syntax has been built from the ground up to work with the kind of structures you usually work with in science.

I'm hoping Julia might become a good alternative to R and Python, but I can't see it catching on in the statistical community anytime soon given how many people are still using relics like SAS and Stata. The raw fact is that statisticians (considered as a group) just aren't very good at programming (and many older statisticians can't program at all), which means that a well-designed programming language may not necessarily be easy to use for a member of the statistical community used to point-and-click statistics suites.

[hadley]

I think R is a well-designed language. It definitely has its quirks (what language doesn't?), but by-and-large they are problems with the standard library, not the language. This is admittedly a subtle distinction, but it's much easier to fix problems with the standard library than it is with the language.

Three aspects of the language that make R particularly well suited for statistical programming are:

1) Missing values built in at a fundamental level.

2) Metaprogramming capabilities. The best way to solve many categories of data analysis problems is to design a domain specific language which allows you to easily combine independent pieces. R's incredible flexibility is great for this.

3) Fundamentally vectorised and functional. This allows you to elegantly express many common data analysis tasks.

[jph00]

Could you describe what facilities of R help with metaprogramming and make it good for designing DSLs?

[hadley]

http://adv-r.had.co.nz/dsl.html and the two prior chapters