[R] A comment about R:
042045003 at fudan.edu.cn
Mon Jan 2 17:16:35 CET 2006
That's a good idea.
I will try to give a lexicon on Stata vs R.
======= 2006-01-02 23:59:10 ÄúÔÚÀ´ÐÅÖÐÐ´µÀ£º=======
>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
>> >>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.
>R-help at stat.math.ethz.ch mailing list
>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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Deparment of Sociology
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