[R-sig-finance] R vs. S-PLUS vs. SAS

[Pijus Virketis]

Hoon, 

Your questions were addressed to David, but I hope he won't mind if I
interject. "How to handle a large dataset" is practically a FAQ on the
R-help list: search through the archives at
http://maths.newcastle.edu.au/~rking/R/. To summarise, first it must be
noted that thoughtful use of R (scan(), avoid silently copying data in
memory, etc.) helps handle very large datasets reasonably quickly; the
limit is basically the available amount of RAM. If you need to work on
more data than that, the best practice is to put it in a database, and
to access it in segments. The R gurus also usually encourage the
research to think whether all that data is truly necessary, or if a
subset of it could be used to draw the same conclusions without too much
trouble. 

Cheers, 

Pijus

> -----Original Message-----
> From: r-sig-finance-bounces at stat.math.ethz.ch 
> [mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Hoon Kim
> Sent: Thursday, December 02, 2004 2:02 PM
> To: r-sig-finance at stat.math.ethz.ch
> Subject: Re: [R-sig-finance] R vs. S-PLUS vs. SAS
> 
> Dear David
> 
> I appreciate sharing your experience using R in
> finance.  I am also doing quant equity research and my
> main tool is SAS.  I do not have much experience with
> R at this stage and I am curious what is your opinion
> on R vs. SAS.  My perception is SAS is much better in
> handling large data sets, which is usually the case in
> quant equity modeling.  Historical backtesting data
> can be easily over several hundread megabites.  In my
> opinion, other than the capability of handling large
> data set in SAS, I think R is a more flexible
> programming language.
> 
> Could you kindly share your thoughts with me on this
> issue?  Do you have any problem in handling large data
> sets in R?  How do you deal with large data set in R? 
> Do you have recommendation on handling those large
> data in R?
> 
> Thanks again for sharing your thoughts.
> 
> Best,
> 
> Hoon Kim
> 
> 
> ------------------------------
> 
> Message: 3
> Date: Tue, 30 Nov 2004 11:06:01 -0500
> From: David Kane <dave at kanecap.com>
> Subject: Re: [R-sig-finance] R vs. S-PLUS
> To: "My Newletters Etc." <MySubs at 3wplace.com>
> Cc: r-sig-finance at stat.math.ethz.ch
> Message-ID:
> <16812.39529.463769.246947 at gargle.gargle.HOWL>
> Content-Type: text/plain; charset=us-ascii
> 
> My Newletters Etc. writes:
>  > I'm wondering if you might be willing to share some
> specifics about
>  > your use of R.  
> 
> Of course. I am a real R evangalist.
> 
>  > For example, what types of analysis have you done
>  > with R?  
> 
> I work in quantitive global equity modeling. How much
> is a share of
> IBM worth? For me, the power in R is not so much that
> the statistical
> tools are fancier than what one find in SAS or Stata
> or whatever ---
> although this is often the case --- but that the
> programming language
> is richer and the production tools (especially
> packages and test
> cases) are so easy to use.
> 
>  > Have you used pre-defined "packages" or have you
> "rolled
>  > your own?"  
> 
> I use all sorts of R packages but, for the actual
> financial analysis
> parts, have had to role my own. It is on my to-do list
> to more fully
> explore things like Rmetrics. I am unaware of any
> packages devoted to
> the sort of stuff that I need to do regularly. As an
> example, I am
> today calculating growth rates of various sorts. Which
> company has the
> fastest growing sales? The answer to this question
> depends on all
> sorts of sticky points that reasonable people can
> disagree about.
> 
>  > Do you have any pointers about how to most
> effectively
>  > approach learning R?  Hopefully these questions are
> sufficient
>  > to give you a feel for the direction of my inquiry.
> 
> The standard R documentation is a fine place to begin.
> I would start
> by reading An Introduction to R cover to cover (while
> doing the
> exercises) and then, depending on your level of
> computer experience,
> going on to Writing R Extensions.
> 
> I encourage you to use R, especially in academics. To
> the extent that
> you believe, as I do, that research should be public
> and replication
> easy (see:
> http://gking.harvard.edu/replrepl/replrepl.html), R
> provides the perfect tool.
> 
> 
> -- 
> David Kane
> Kane Capital Management
> 
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