On 1/2/06, Philippe Grosjean <[EMAIL PROTECTED]> 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 http://cran.r-project.org/doc/contrib/R-and-octave-2.txt to make it easier for Stata users to understand R. Ditto for SAS and SPSS. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html