Wow, Python much faster than MatLab

[Ramon Diaz-Uriarte]

On 12/31/06, gblais at cox.net <gblais at cox.net> wrote:
> R is the free version of the S language.  S-PLUS is a commercial version.
> Both are targeted at statisticians per se.  Their strengths are in
> exploratory data analysis (in my opinion).
>
> SAS has many statistical featues, and is phenomenally well-documented and
> supported.  One of its great strengths is the robustness of its data model
> -- very well suited to large sizes, repetitive inputs, industrial-strength
> data processing with a statistics slant.  Well over 200 SAS books,for
> example.
>
> I think of SAS and R as being like airliners and helicopters -- airlines get
> the job done, and well, as long as it's well-defined and nearly the same job
> all the time.  Helicopters can go anywhere, do anything, but a moment's
> inattention leads to a crash.
> --

inattention leading to a crash? I don't get it. I used SAS for about 3
or 4 years, and have used S-Plus and then R for 10 years (R for 8
years now). I've never noticed inattention leading to a crash. I've
noticed I cannot get away in R without a careful definition of what I
want (which is good), and the immediate interactivity of R is very
helpful with mistakes. And of course, programming in R is, well,
programming in a reasonable language. Programming in SAS is ... well,
programming in SAS (which is about as fun as programming in SPSS).

(Another email somehow suggested that the stability/instability
analogy of airplanes vs. helicopters does apply to SAS vs. R. Again, I
don't really get it. Sure, SAS is very stable. But so is R ---one
common complaint is getting seg faults because package whatever has
memory leaks, but that is not R's fault, but rather the package's
fault).

But then, this might start looking a lot like a flame war, which is
actually rather off-topic for this list.


Anyway, for a Python programmer, picking up R should be fairly easy.
And rpy is really a great way of getting R and Python to talk to each
other. We do this sort of thing quite a bit on our applications.

And yes, R is definitely available for both Linux and Windows (and
Mac), has excellent support from several editors in those platforms
(e.g., emacs + ess, tinn-R, etc), and seems to be becoming a de facto
standard at least in statistical research and is extremely popular in
bioinformatics and among statisticians who do bioinformatics (look at
bioconductor.org).


Ramon


-- 
Ramon Diaz-Uriarte
Statistical Computing Team
Structural Biology and Biocomputing Programme
Spanish National Cancer Centre (CNIO)
http://ligarto.org/rdiaz