Data Analysts Captivated by Power of R

I am baffled that a <i>programming language</i> is getting coverage in the NYT. I'd like to figure out what, exactly, it is about this one that merits mainstream coverage, but I'm afraid I've been at the office too long and I think my perspective on the matter is permanently skewed.<p>As an aside, I used R briefly for a econometrics/philosophy course I took. I recognized immediately it was a powerful, functional language. What I wonder, though, is if the scientific programming libraries in Python might eventually be a better environment. Surely there must be some R users here who can comment.

having programmed R on the job for some heavy statistics, I will say this: good for quick analyses but burdened by legacy functionality from the S+ days. I switched to Python/NumPy and rewrote all the R code I had, could not be happier with the results. Of course, you have to create your own data structures if you want something like R's data frame, but at least you have a rich language to do that with.<p>however, if you need to do anything systematic, do NOT use R, bugs are elusive and extremely tedious to debug

"We have customers who build engines for aircraft. I am happy they are not using freeware when I get on a jet." - Ugh, SAS isn't about science, it's about administration.

Meanwhile the creator of R wants to return to a Lisp-based statistics environment:<p><a href="http://books.google.com/books?id=8Cf16JkKz30C&#38;pg=PA21&#38;lpg=PA21" rel="nofollow">http://books.google.com/books?id=8Cf16JkKz30C&#38;pg=PA21&#3...</a>

In the life science space, R dominates research informatics. A large chunk of molecular profiling methods and techniques use R, or quite often the Bioconductor package, <a href="http://www.bioconductor.org/" rel="nofollow">http://www.bioconductor.org/</a> (from Gentleman's group). Most commercial bioinformatics apps also implement a number of methods using R and provide ability to implement R-based classifiers, etc.<p>In the clinical space, it's all SAS. Pretty much the de facto standard.

I can't believe nobody here has mentioned PDL, the Perl Data Language: <a href="http://pdl.perl.org" rel="nofollow">http://pdl.perl.org</a><p>Admittedly, i think R and PDL do different things... (I have never played with R).

Is it just me, or was this article not very well written? I felt it was all over the place. It brings up S, then mentions that S isn't open source. It mentions open source and brings up things like apache, the web, and Microsoft, I don't see how it relates much to R. Though, I'm probably spoiled due to the usual quality of news I get here.

"The co-creators of R express satisfaction that such companies profit from the fruits of their labor and that of hundreds of volunteers."<p>That's an interesting reaction from the first designers of the program.

Interesting. My girlfriend is a statistician for the WHO and they definitely still use SAS, at least in her area (calculating global burden of disease). I'm going to ask her if anyone there uses R.

My 2 cents: Numpy+Scipy+Matplotlib and other packages, which u can download together in a convenient package at Enthought. That enthought distribution also comes with Ipython, which, is, REALLY nice. I checked out R, Sage and am still sometimes forced into Matlab, but u just can't beat a programming language (Python) which can be used OUTSIDE of whtever problem space you happen to be working on.

At least it's a functional language, and you can do things like manipulating code symbolically, showing its lisp heritage.<p>The tooling, library integration, and debuggers aren't as good as, say, Python, though.

My 2 cents: Numpy+Scipy+Matplotlib and other packages, which u can download together in a convenient package at Enthought. That enthought distribution also comes with Ipython, which, is, REALLY nice. I checked out R, Sage and am still sometimes forced into Matlab, but u just can't beat a programming language (Python) which can be used OUTSIDE of whtever problem space you happen to be working on.

"But R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use."<p>whaaaa?

Could someone explain how this differs from Matlab, which is the most popular language for statistics and machine learning at my university?

The power of R (speaking as a very heavy user who has deployed it in multiple production environments and been using it for 5 years) is that it makes it very fast, easy, and natural to do statistics. It also has the nicest data structure I've ever seen for manipulating table data, called a data frame -- I'll elaborate, if anybody cares. In addition, it encourages people to create packages to extend the functionality. There are extant packages to do almost every analysis you can think of -- time series, kmeans, other clustering techniques, cox-box style analyses, regular maximum likelihood style GLM, hierarchical regression, HB, etc. Further, the amount of knowledge and the open source nature of the language, base, and packages encourage additional development and widespread adoption. See: <a href="http://cran.r-project.org/" rel="nofollow">http://cran.r-project.org/</a> and <a href="http://cran.r-project.org/web/views/" rel="nofollow">http://cran.r-project.org/web/views/</a> ^ is task views. Explore it -- it's well worth your time.<p>The downsides are, well, it's slow for large data sets and debugging can be difficult. But as a desktop / rapid development platform for statistics it is without peer, IMO.<p>ps -- unlike Matlab, which often costs thousands of dollars, and the Statistics Toolbox, more thousands, R is free. This is pretty important on its own -- instead of $5k per server and workstation and home pc, install it on any linux, Mac, or windows box you have and get to work for $0.00.