[R] A comment about R:

[Peter Dalgaard]

Patrick Burns <pburns at pburns.seanet.com> writes:

> I have had an email conversation with the author of the
> technical report from which the quote was taken.  I am
> formulating a comment to the report that will be posted
> with the technical report.
> 
> I would be pleased if this thread continued, so I will know
> better what I want to say.  Plus I should be able to reference
> this thread in the comment.

One thing that is often overlooked, and hasn't yet been mentioned in
the thread, is how much *simpler* R can be for certain completely
basic tasks of practical or pedagogical relevance: Calculate a simple
derived statistic, confidence intervals from estimate and SE,
percentage points of the binomial distribution - using dbinom or from
the formula, take the sum of each of 10 random samples from a set of
numbers, etc. This is where other packages get stuck in the
procedure+dataset mindset.

For much the same reason, those packages make you tend to treat
practical data analysis as something distinct from theoretical
understanding of the methods: You just don't use SAS or SPSS or Stata
to illustrate the concept of a random sample by setting up a small
simulation study as the first thing you do in a statistics class,
whereas you could quite conceivably do it in R. (What *is* the
equivalent of rnorm(25) in those languages, actually?)

Even when using SAS in teaching, I sometimes fire up R just to
calculate simple things like

  pbar <- (p1+p2)/2
  sqrt(pbar*(1-pbar))

which you need to cheat SAS Analyst's sample size calculator to work
with proportions rather than means. SAS leaves you no way to do this
short of setting up a new data set. The Windows calculator will do it,
of course, but the students can't see what you are doing then.


-- 
   O__  ---- Peter Dalgaard             Øster Farimagsgade 5, Entr.B
  c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
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