[Rd] Ross Ihaka's reflections on Common Lisp and R

Wed May 7 10:47:26 CEST 2008

I came across a quite interesting post from Ross Ihaka, thought would be good
to share it and get the opinion of folks around here. I am not sure where to
post this for the R community but since it has to do with development I
thought or R-devel

Ross Ihaka 
Newsgroups: comp.lang.lisp
From: Ross Ihaka <ih... at stat.auckland.ac.nz>
Date: Wed, 23 Jan 2008 10:35:26 +1300
Local: Tues, Jan 22 2008 11:35 pm 
Subject: Re: Is Xlisp-Stat Dead?

Rainer Joswig wrote: 
> In article 
> <78de26c1-fb05-4479-ac5f-97046de54... at i72g2000hsd.googlegroups.com>, 
>  Robert <irishhac... at gmail.com> wrote: 
>> Did the founders of R cut it's head off? 
>> Did SAS and SPSS chop it to pieces? 
>> What happened to it? 
> See: http://repositories.cdlib.org/uclastat/papers/2004062201/ 
> On Abandoning Xlisp-Stat 
> Jan de Leeuw, UCLA Department of Statistics 

I'm one of the two originators of R.  After reading Jan's 
paper I wrote to him and said I thought it was interesting 
that he was choosing to jump from Lisp to R at the same 
time I was jumping from R to Common Lisp. 
Building something like R is a big task though.  The 
capabilities in R reflect the specialist contributions 
of hundreds of research statisticians. Currently there 
is a very small group of us scoping out ways to create 
a Lisp-based framework in which similar contributions 
could be made.

... A a little further:

Ken Tilton wrote: 
> So how come an originator of something with the momentum and mindshare 
> of R is swimming against the current, and one he helped set in motion to 
> boot? 

We started work on R in the early '90s.  At the time 
decent Lisp implementations required much more resources 
than our target machines had.  We therefore wrote a small 
scheme-like interpreter and implemented over that. 
Being rank amateurs we didn't do a great job of the 
implementation and the semantics of the S language which 
we borrowed also don't lead to efficiency (there is a 
lot of copying of big objects). 
R is now being applied to much bigger problems than we 
ever anticipated and efficiency is a real issue.  What 
we're looking at now is implementing a thin syntax over 
Common Lisp.  The reason for this is that while Lisp is 
great for  programming it is not good for carrying out 
interactive data analysis.  That requires a mindset better 
expressed by standard math notation.  We do plan to make 
the syntax thin enough that it is possible to still work 
at the Lisp level.  (I believe that the use of Lisp syntax 
was partially responsible for why XLispStat failed to gain 
a large user community). 
The payoff (we hope) will be much greater flexibility and 
a big boost in performance (we are working with SBCL so 
we gain from compilation).  For some simple calculations 
we are seeing orders of magnitude increases in performance 
over R, and quite big gains over Python. 
There is lots to do.  We're experimenting with syntax and 
making a start on assembling quality numerics libraries. 
Creating a fully-featured system will require buy-in from 
the statistical specialists who can contribute implementations 
of their methodology, so we also thinking about issues 
associated with community building (eg. licensing). 
-- 
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