[R] A comment about R:

Gabor Grothendieck ggrothendieck at gmail.com
Mon Jan 2 16:59:10 CET 2006

On 1/2/06, Philippe Grosjean <phgrosjean at sciviews.org> wrote:
> Kort, Eric wrote:
> >
> >>Kjetil Halvorsen wrote...
> >>
> >>Readers of this list might be interested in the following commenta about R.
> >>
> >>
> >>In a recent report, by Michael N. Mitchell
> >>http://www.ats.ucla.edu/stat/technicalreports/
> >>says about R:
> >>"Perhaps the most notable exception to this discussion is R, a language for
> >>statistical computing and graphics.
> >>
> >
> > -------8<-----------------------------------------
> >
> > After reading this commentary a couple of times, I can't quite figure
> > out if he is damning with faint praise, or praising with faint damnation.
> >
> > (For example, after observing how many researchers around me approach
> > statistical analysis, I'd say discouraging "casual" use is a _feature_.)
> There are numerous reasons why people tend to consider R as too
> complicate for them (or even worse, say peremptively to others that R is
> too complicate for them!). But one must decrypt the real reasons behind
> what they say. Mostly, it is because R imposes to think about the
> analysis we are doing. As Eric says, it is a _feature_ (well, not
> discouraging "casual" use, but forcing to think about what we do, which
> in turn forces to learn R a little deeper to get results... which in
> turn may discourage casual users, as an unwanted side-effect). According
> to my own experience with teaching to students and to advanced
> scientists in different environments (academic, industry, etc.), the
> main basic reason why people are reluctant to use R is lazyness. People
> are lazy by nature. They like course where they just sit and snooze.
> Unfortunatelly, this is not the right way to learn R: you have to dwell
> on the abondant litterature about R and experiment by yourself to become
> a good R user. This is the kind of thing people do not like at all!
> Someone named Dr Brian Ripley wrote once something like:
> "`They' did write documentation that told you [...], but `they'
> can't read it for you."
> It is already many years that I write and use tools supposed to help
> beginners to master R: menu/dialog boxes approach, electronic reference
> cards, graphical object explorer, code tips, completion lists, etc...
> Everytime I got the same result: either these tools are badly designed
> because they hide the 'horrible code' those casual users don't want to
> see, and they make them *happy bad R users*, or they still force them to
> write code and think at what they exactly do (but just help them a bit),
> and they make them *good R users, but unhappy, poor, tortured
> beginners*! So, I tend to agree now: there is probably no way to instil
> R into lazy and reluctant minds.
> That said, I think one should interpret Mitchell's paper in a different
> way. Obviously, he is an unconditional and happy Stata user (he even
> wrote a book about graphs programming in Stata). His claim in favor of
> Stata (versus SAS and SPSS, and also, indirectly, versus R) is to be
> interpreted the same way as unconditional lovers of Macintoshes or PCs
> would argue against the other clan. Both architectures are good and have
> strengths and weaknesses. Real arguments are more sentimental, and could
> resume in: "The more I use it, the more I like it,... and the aliens are
> bad, ugly and stupid!" Would this apply to Stata versus R? I don't know
> Stata at all, but I imagine it could be the case from what I read in
> Mitchell's paper...

Probably what is needed is for someone familiar with both Stata and R
to create a lexicon in the vein of the Octave to R lexicon


to make it easier for Stata users to understand R.  Ditto for SAS and SPSS.

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