The Power of the R Programming Language

BartlebyScrivener writes "The New York Times has an article on the R programming language. The Times describes it as: 'a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age, whether being used to set ad prices, find new drugs more quickly or fine-tune financial models. Companies as diverse as Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.'"

Comments

SAS strikes out ^H^H^H er, "back"

[enilnomi]

FTFA:

She [Anne H. Milley, director of technology product marketing at SAS] adds, "We have customers who build engines for aircraft. I am happy they are not using freeware when I get on a jet."

Good thing Boeing's not using fere software for aircraft simulation tools, space station labs, sub hunters, or moon rockets ;-)

Re:SAS strikes out ^H^H^H er, "back"

[jd]

Good thing NASA likewise never uses Open Source to design engines and aircraft alongside companies like Boeing. (*This product may contain nuts^H^H^H^Hsarcasm.)

Re:Only for certain kind of analyst...

[jaxtherat]

Sorry, but R is not relatively new, it's been around for at least 10 years, I was taught how to use R at University back in 2001, and S and later S+ (which R is a FOSS version of) has been around for even longer, since the mid 70's.

Re:Show me some example code

[lt. slock]

I use R a great deal. Think of it as an alternative to MATLAB, or Excel, rather than C or perl or lisp or whatever you like to use as a general purpose language. So, compared to MATLAB, functions are first class objects (rather like lisp), so, you can write functions that take functions as arguments, and return them as well, just as though
they were simple variables. It handles
vectors rather easily, and has decent plotting tools.

#quick example

# function, which, given numerical arguments a and b, and a function g, returns a function of x
f - function(a,b, g){
    function(x){ a * x + g(b * x)}
}

f1 - f(1,2.5,sin)
x - seq(-pi,pi,l=100)
plot(x,f1(x),type='l')

Re:Freak your colleagues out with "no loop" code..

[Anonymous Coward]

"The worse thing about R programming is its name. Googling for "R" turns up way to much noise and way too little signal"

Try searching from http://rseek.org/ instead of directly from Google.

The R language and its uses

[golodh]

I'll pitch in because R deserves better than the usual Slashdot cocktail of random ignorance and immature jokes.

The R language (yes, it's a language; an interpreted languages is a language too) has developed as the language of choice by statisticians (both academics and sundry statistical researchers) around the world as their main computer language. It is used in those cases where researchers feel the need for customized computations rather than the use of a package like SAS or SPSS.

The reason that R has become popular is due to a snowball effect and history. It started as a FOSS re-implementation-from-scratch of the "S" language designed for statistical work at Bell labs (see http://en.wikipedia.org/wiki/S_(programming_language). Some academics and researchers of repute used it (the S language) because at that time (1975) it was very innovative and far better than most alternatives, and others followed. The S language gained a measure of acceptance among statisticians. Then when R became available the cycle intensified because of the much improved availability of the interpretor and its libraries. This cycle continued to the point that by now probably most professional statisticians use it.

As far as I can see, the R language isn't especially sophisticated or elegant, and may strike people used to more modern languages as a bit repugnant. It does however excel in three respects:

(a) it allows for easy access of Fortran and C library routines

(b) it allows you to pass large blobs of data by name

(c) it makes it easy to pass data to and from your own compiled C and Fortran routines

The first reason is particularly important because it allows one to use e.g. pre-compiled linear algebra package like LAPACK, or Fourier Transforms, or special function evaluations and thereby gain execution speeds comparable to C despite being an interpreted language (just like Matlab, Octave, Scilab, Gauss, Ox and suchlike): the hard work is carried out by a compiled library routine which is made easily accessible through the interpreted language. Any algorithm needed in statistics that's available as C or Fortran code can be linked in and called without too much effort.

The second reason is important because it slows down execution much less than any pass-by-value interpreted language would, and it allows you to change data that is passed into a function.

The third reason is particularly important because it helps researchers be more productive. Reading in your data, examining it, graphing it, tracing outliers and cleaning them up is best done in an interactive environment in an interpreted language. Coding such things in C or Fortran is an awful waste of time, and besides, researchers aren't code-monkeys and don't enjoy coding inane for-loops to read, clean, and display data. Vector and matrix primitives are far more powerful, and usually preferable unless they are so inefficient that you have to wait for the result. However, there are times when you just need to carry out standard algorithms (linear algebra, calculation of mathematical or statistical functions) or simply time-consuming repetitive algorithms that run so much faster in a genuine compiled language. You could start out by coding the algorithm in an interpreted language to check if it's working, and then isolate the computationally expensive part and code it up in C or Fortran. Using R (or Matlab or Scilab) you can *call* the compiled subroutine, pass it your (cleaned) data, and get the result back in an environment where you can easily analyze it.

That's why languages like R, Matlab, Scilab, Octave, Gauss, and Ox are so productive: you get the best of both worlds. Both the convenience, interactiveness, and terseness of a high-level interpreted language and the speed of compiled languages.

So why R, and why not Gauss or Matlab or whatever?

Well, part of that is cultural. If you're an econometrician you'll have been weane

Re:The R language and its uses

[verySmartApe]

I second that. R is terribly useful for the wide variety of libraries available and esoteric statistical procedures. But you would *never* want to write a long/complex program in R.

As you say, it's most convenient to work in some other language that's actually designed to be scaleable, object-oriented, and easy to debug. It's usually straightforward to call R libraries when you need them. I find that python+scipy+rpy is an almost ideal environment for day to day scientific programming.

Re:Based on S

[spud603]

Tell me about it. Try this:
http://www.rseek.org/

It is a pain in the ass to change.

[pavon]

Say you realize that you need to check for another corner case that you forgot, or need to extend a function for another purpose, or whatever. In any other language, you would type a few lines of code and be done with it. Not with labview. With labview you have to move things around to make room for the new code, disconnect wires and reconnect them. NI has added stuff into the newer version to help with this (auto growing, etc) but it still turns into a mess in short order.

Other things are just easier to type than to draw, and also easier to read in text then as a schematic, like equations. So much so that they have added the ability to type portions of the code, but the amount of setup that you need to do with a code block often defeats the time benefit you get from using it.

As someone who likes "clean code" I find LabView much more tedious and time consuming to keep neat, and when dealing with other coders that are not as picky, I find that their LabView code is much messier and harder to read than Java or C code by the same developer.