R help - Jun 2009 - Comparing R and SAs

Hi:
For those of you who are adept at both SAS and R, I have the following
questions:

a) What are some reasons / tasks for which you would use R over SAS and vice
versa?
b) What are some things for which R is a must have that SAS cannot fulfill the
requirements?

I am on the ramp up on both of them. The general feeling that I am getting by
following this group is that R updates to the product are at a much faster pace
and therefore, this would be better for someone who wants the bleeding edge
(correct me if I am wrong). But I am also interested in what is inherently
better in R that SAS cannot offer perhaps because of the design.

Thanks.
Satish

Satish,
  There are nearly as many opinions as people on this question.  So accept these
as my views only.
  
  1. I have written S packages, SAS procedures and SAS macros over my career.  S
was specifically designed for extensibility and it shows.  The ratio of time to 
get a new statistical idea up and running is about 1:5:20 for S:macro:proc, 
using the "therneau days" metric.  New ideas are the lifeblood for an
academic;
SAS makes no sense in that environment and so will always lag far behind.  For 
simple things like a repetive recoding, a 2-10 line R function say, SAS macro 
will be competive with R in terms of programming effort.  
  
  2. The SAS programming model could be described as "mini batch"; the
block of
statements including and following a "data" statment is executed as a
unit.  For
data manipulation this is turns out to be a very powerful programming paradym 
and many people, myself included, prefer it for data input and manipulation.  At
the time I was first using SAS (mid 70s) no other package or system was even 
close to SAS in this arena, and by the late 80s many disciplines with complex, 
large, or messy data had wedded themselves to SAS, e.g., drug company clinical 
trials.  Most are still wedded.  
  
  Terry Therneau

Satish,

 

For a comparison of SAS and S, see the document "An Introduction to S
and the Hmisc and Design Libraries" by Carlos Alzola and Frank E.
Harrell. Frank Harrell is an expert in both SAS and R. You can download
this document from http://www.r-project.org/, then click on manuals, and
then contributed documentation. You can also look at the document
written by Bob Muenchen (at http://RforSASandSPSSusers.com
<http://rforsasandspssusers.com/>  (also a book published by Springer
Verlag) for a comparison of SAS and R (and SPSS).

 

I have been using both SAS and R. While my primary expertise is mainly
in SAS, I have been using R more and more relative to SAS as my
familiarity with it grows. From my point of view, cutting edge
methodologies will always be implemented first in R (as you pointed out
as well). SAS will follow several years later with some of these
methodologies. Also, SAS has different products and users may not have
all SAS products. Many firms have SAS/STAT but not other SAS products
like SAS/ETS (economic time series), SAS/Enterpriser Miner or SAS/GRAPH.
So in these situations R may be your only option. Even if you have these
other SAS products you can do things more rapidly in R, if you take the
time to learn it well, than you can with SAS. I have SAS/Enterprise
Miner but still prefer R for neural networks, splines, decision trees,
etc., as I can program R to produce several neural networks, etc. using
for loops. SAS/Enterprise Miner cannot be programmed. R graphs are
definitely superior to SAS graphics, and can be programmed very easily.
I also use R for EDA (exploratory data analysis) prior to building
predictive models/data mining.

 

One area where SAS still excels is in processing huge files (over 30 GB
in size - online data from vendors like double click with literally
billions of records). But for statistical analysis you generally don't
need to work with such large volumes of data. A much smaller random
sample should suffice. If you have R running on Unix or Linux 64-bit
operating systems (or Windows Vista?) and huge amounts of RAM handling
large datasets in R is less of an issue. Also, if your data resides on
mainframes, SAS is probably your only choice if you cannot download the
mainframe data to your PC. I use R on a 32-bit Windows operating system
with 3 GB of RAM, and I have not had any problem doing statistical
analysis/data mining with R on around 25,000 or so records with anywhere
from 25 to 50 variables.

 

Hope this helps.

 

Jude

 

Satish wrote:

 

Hi:

For those of you who are adept at both SAS and R, I have the following
questions:

 

a) What are some reasons / tasks for which you would use R over SAS and
vice versa?

b) What are some things for which R is a must have that SAS cannot
fulfill the requirements?

 

I am on the ramp up on both of them. The general feeling that I am
getting by following this group is that R updates to the product are at
a much faster pace and therefore, this would be better for someone who
wants the bleeding edge (correct me if I am wrong). But I am also
interested in what is inherently better in R that SAS cannot offer
perhaps because of the design.

 

Thanks.

Satish

 

 

___________________________________________
Jude Ryan
Director, Client Analytical Services
Strategy & Business Development
UBS Financial Services Inc.
1200 Harbor Boulevard, 4th Floor
Weehawken, NJ 07086-6791
Tel. 201-352-1935
Fax 201-272-2914
Email: jude.ryan at ubs.com



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