Re: st: R and Stata efficiency

[Phil Schumm <pschumm@uchicago.edu>]

On Jul 10, 2007, at 9:42 AM, David Airey wrote:
> One other comments from the R user regarding efficiency was about  
> nesting functions:
>
> > One other issue is that the result of a command (sic in Stata)  
> > cannot be used directly as the argument of another commmand.  In  
> > R, I can do something like
> >
> > dhdbeta = A %*% diag(exp(aacoef))
> >
> > in which, exp() gives the exponential values of the elements in  
> > aacoef, then diag() makes a matrix with the diagonal being those  
> > exponential values, then the matrix is multiplied on the left by  
> > another matrix A. The exp() and diag() are calculated on the fly  
> > and discarded afterwards. Their values are not used elsewhere and  
> > thus there is no need to save them explicitly just for the next  
> > step.  While in Stata, I have save them.

That's true, and it's because in Stata, we do most things with  
commands rather than function calls; e.g., the difference between

est replay results

and

summary(results)

You can use the constructions `= ' and `: ' to get inline access to  
Stata's expression evaluator and extended macro functions,  
respectively (see [P] macro), but those are only a small part of what  
one does at the command line.

Perhaps this issue surfaces most frequently when users complain that  
they can't type

reg log(y) x

I have no idea whether StataCorp has considered making this possible,  
however, even if it did, it would not remove the fundamental  
distinction between a Stata command and a function call.

Note that there are good arguments for the way Stata works.  One  
important one is ease of use; all Stata commands (if they're written  
according to StataCorp's specs) follow a common, simple syntax, which  
makes them very easy to use interactively.  When using R  
interactively I am constantly having to consult the man pages to  
remind myself of the signatures for various functions (though, in  
fairness, if I used it more frequently I'd remember more).

Finally, if you start using Mata, you'll see that it's a lot more  
like R in this respect (i.e., most statements are either assignment  
and/or function calls, and you can nest function calls arbitrarily).   
I do most of my statistical programming in Mata these days (except  
for rather thin wrappers in Stata to implement the UI), and recently  
have done a bit of working porting an existing R package into Stata.   
In that context, I find that I am able to write just as "efficient"  
code (in the sense of fewer lines) in Mata (or even more so) than the  
original in R.


-- Phil

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