[R] A comment about R:

[J Dougherty]

On Thursday 05 January 2006 12:13, Achim Zeileis wrote:
> . . . snip
> Whether you find this simple or not depends on what you might want to
> have. Personally, I always find it very limiting if I've only got a switch
> to choose one or another vcov matrix when there is a multitude of vcov
> matrices in use in the literature. What if you would want to do HC3
> instead of the HC(0) that is offered by Eviews...or HC4...or HAC...or
> something bootstrapped...or...
> In my view, this is the stengths of many implementation in R: you can make
> programs very modular so that the user can easily extend the software or
> re-use it for other purposes. The price you pay for that is that it is not
> as easy to as a point-and-click software that offers some standard tools.
> Of course, both sides have advantages or disadvantages.
> . . .snip

Stata's ADO scripting language has the ability to access intermediate steps 
and local variables used by various commands.  These are typically held in 
memory until they are purged.  The difference between Stata and R is more 
that Stata has been streamlined into an application, the nuts and bolts 
hidden away, the rivet heads counter sunk and polished, so that unless you 
really need to use them, they aren't visible.  It only LOOKS like you are 
constrained to the readily available results of specific commands.  Stata 
output will tend to look very much like the standard output one becomes 
accustomed to in undergraduate stat courses.  

R assumes you _will_ want access to the nuts and bolts, and don't much care 
about visible rivets if the system is both accurate and functional.  R is 
much more a programming environment in that sense.  It is an important 
difference.  There is going to be a continuing growth in users of R as 
companies see cost savings in OS.  They will often be people who happily 
dragged .xls files into SPSS or SPSS for analysis and then printed the 
resulting reports.  (Personally, I became a strong believer in statistical 
analysis packages after receiving a _negative_ variance in Excel once upon a 
time.  I don't see how that could even be possible, but apparently it was a 
known issue.  Some ad hoc experimentation then demonstrated that no 
spreadsheet was all that precise).

One place where R and Stata have a great deal in common is in the manner in 
which graphs and charts are formatted.  Stata is perhaps slightly less 
bizantine, but only slightly.  Both systems emphasize flexibility and quality 
graphics at the price of learning to know what you are doing.  That said, you 
can still do a lot more with R in some areas than Stata, especially in 
spatial graphics and analysis.

JD