[R] Making a case for using R in Academia

[Tamas K Papp]

On Wed, Nov 08, 2006 at 09:24:38PM -0500, Charilaos Skiadas wrote:

Hi Charilaos,

> I would particularly like to hear from people who were not "hard-core  
> programmers" before taking up R, so perhaps had originally some  
> difficulties with it. How hard was it, and how quickly did it start  
> paying off? Our main stumbling block I feel would be to get our  
> colleagues to switch to using R, at least for their teaching, and  
> most of them would probably have never really seen a programming  
> language before.

I am an economics graduate student at Princeton.  I have used R before
coming here, but I noticed that many of my classmates started using R
in graduate school.  I think this happened because they observed a few
of our professors (eg Christopher Sims) using R not only for
statistics, but for more general programming tasks (solving dynamic
programming problems, perturbation methods, etc).

The main advantage of R for me is that besides having amazing
libraries, it is a very nice general language to program in for any
kind of numerical calculations.  Once you program your own algorithms
(eg in Bayesian statistics, solving functional equations, etc) the
need for a general programming language becomes apparent.

My impression is that R is very popular among Bayesians (see textbooks
An Introduction to Modern Bayesian Econometrics by Tony Lancaster and
Bayesian Data Analysis, Second Edition by Andrew Gelman, John
B. Carlin, Hal S. Stern, and Donald B. Rubin, both use R), and is
slowly gaining acceptance among finance people and macroeconomists
(packages for former are showing up on CRAN).  I never had professors
discouraging me from R, except once when a professor warned me that
anything except STATA will choke on huge cross-sectional datasets (but
he turned out to be wrong, R works find with SQL databases for that
purpose).

I think that the best way to persuade your colleagues to switch to R
is by example.  Demonstrate that while other languages are OK if you
are using standard canned methods, programming something new and
innovative is best done in R, and it is a pain in many widely used
languages (STATA comes to my mind, but I may not know it well enough).
Don't push R, because if they try it and get frustrated, they might
abandon it for good.  Just make them curious enough so that they will
try it because of the amazing things you can do in R.

HTH,

Tamas