[R] A comment about R:

Philippe Grosjean phgrosjean at sciviews.org
Mon Jan 2 12:00:08 CET 2006

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
>>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...


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( ( ( ( (    Numerical Ecology of Aquatic Systems
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