Adaikalavan Ramasamy wrote:
I agree! The best way to learn (and remember for longer) is to teach
someone else about it.
And there is not reason not to repeat some of the anlysis done on SAS
with R. That way you can verify your outputs or compare the
presentations. If you consistently find differences in the outputs,
then trying to figure out the reason may lead you to better understand
the methods (e.g. different optimization or estimation procedures).
My take on this:
I have repeatedly found that it is surprisingly easy to improve on
existing (non-R) implementations
of statistical and non-statistical computation, when working in R.
Something about the structure of the language, something about the
package mechanism,
something about R-help, something about R-core, something about
open-source, something
about JSS or R-news, whatever it is, there is SOMETHING ABOUT R which
lends itself
to straightforward production of quality software. And that something
is missing from other
programming languages, IMO.
rksh
Regards, Adai
Barry Rowlingson wrote:
2008/9/19 Wensui Liu <[EMAIL PROTECTED]>:
Dear Listers,
I've been a big fan of R since graduate school. After working in the
industry for years, I haven't had many opportunities to use R and am
mainly
using SAS. However, I am still forcing myself really hard to stay
close to R
by reading R-help and books and writing R code by myself for fun.
But by and
by, I start realizing I have hard time to keep up with R and am
afraid that
I would totally forget how to program in R.
I really like it and am very unwilling to give it up. Is there any
idea how
I might keep touch with R without using it in work on daily basis? I
really
appreciate it.
--
Robin K. S. Hankin
Senior Research Associate
Cambridge Centre for Climate Change Mitigation Research (4CMR)
Faculty of Economics
The University of Cambridge
[EMAIL PROTECTED]
01223-764877
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