[R] A comment about R:

[John Fox]

Dear Peter et al.,

It's not reasonable to argue with someone's experience -- that is, if people
tell me that they found R harder to learn than SAS, say, then I believe them
-- but that's not my experience in teaching relatively inexperienced
students to use statistical software. A few points:

(1) Casual and initial  use of statistical software is easier through a GUI,
so it's not reasonable, for example, to compare learning to use SPSS via its
GUI to learning R via commands.

(2) I don't believe that it's hard to teach a useful initial subset of R
commands. Which commands are in the subset will depend somewhat on what one
is trying to do. I believe that there are several examples of this approach,
including my R and S-PLUS Companion to Applied Regression. Likewise,
starting with a simple modus operandi, such as working with a single
attached data frame, can cut through a lot of the complexity. Once someone
is comfortable with basic use of R, expanding knowledge of functions,
packages, and other ways of handling data comes naturally. 

(3) I don't find R less uniform than SAS or SPSS, particularly in the way
that statistical models are handled. Moreover, trying to do something
innovative or non-standard in SAS is relatively difficult (in my
experience), and even harder in SPSS. I'm less familiar with Stata, but
uniformity seems one of its strengths. (The Stata scripting language puts me
off, however.)

(4) Not everyone has the same experience and thinks in the same way. I've
used many different statistical packages and computing environments, and
have learned quite a few programming languages (most of which I can no
longer use). Of these, I found APL and R the easiest to learn, and Lisp
(Lisp-Stat) the hardest. Sometimes, though, it's worth expending the effort
to learn something that's difficult -- I feel that I got a lot out of
learning to program in Lisp, for example.

(5) The essential point is that how hard one finds it to learn something is
a function of the intrinsic difficulty of the thing, the person's previous
experience, preferred modes of thinking, etc., and how learning is
approached.

Regards,
 John

--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox 
-------------------------------- 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Peter Flom
> Sent: Tuesday, January 03, 2006 6:28 AM
> To: Rau at demogr.mpg.de; ggrothendieck at gmail.com
> Cc: R-help at stat.math.ethz.ch
> Subject: Re: [R] A comment about R:
> 
> >>> "Rau, Roland" <Rau at demogr.mpg.de>  >>> wrote
> <<<
> IMO this is a very good proposal but I think that the main 
> problem is not the "translation" of one function in 
> SPSS/Stata/SAS to the equivalent in R.
> Remembering my first contact with R after using SPSS for some 
> years (and having some experience with Stata and SAS) was 
> that your mental framework is different. You think in 
> "SPSS-terms" (i.e. you expect that data are automatically a 
> rectangular matrix, functions operate on columns of this 
> matrix, you have always only one dataset available, ...). 
> This is why "jumping" from SPSS to Stata is relatively easy. 
> But to jump from any of the three to R is much more difficult. 
> This mental barrier is also the main obstacle for me now when 
> I try to encourage the use of R to other people who have a 
> similar background as I had.
> What can be done about it? I guess the only answer is 
> investing time from the user which implies that R will 
> probably never become the language of choice for "casual 
> users". But popularity is probably not the main goal of the 
> R-Project (it would be rather a nice side-effect).
> >>>>
> 
> 
> 
> As someone who uses SAS qutie a bit and R somewhat less, I 
> think Roland makes some excellent points.  Going from SPSS to 
> SAS (which I once did) is like going from Spansih to French.  
> Going from SAS to R (which I am trying to do) is like going 
> from English to Chinese.
> 
> But it's more than that.  
> 
> Beyond the obvious differences in the languages is a 
> difference in how they are written about; and how they are 
> improved.  SAS documentation is much lengthier than R's.  
> Some people like the terseness of R's help.  Some like the 
> verboseness of SAS's.  SOme of this difference is doubtless 
> due to the fact that SAS is commercial, and pays people to 
> write the documentation.  I have tremednous appreciation for 
> the unpaid effort that goes into R, and nothing I say here 
> should be seen as detracting from that.
> 
> As to how they are improved, the fact that R is extended (in 
> part) by packages written by many many different people is 
> good, becuase it means that the latest techniques can be 
> written up, often by the people who invent the techniques 
> (and, again, I appreciate this tremendously), but it does 
> mean that a) It is hard to know what is out there at any 
> given time; b) the styles of pacakages difer somewhat.
> 
> In addition, I think the distinction between 'casual user' 
> and serious user is something of a false dichotomy.
> It's really a continuum, or, probably, several continua, that 
> make R harder or easier for people to learn.
> 
> I like R.  I like it a lot.  I like that it's free.  I like 
> that it's cutting edge.  I like that it can do amazing graphics.
> I like that the code is open.  I like that I can write my own 
> functions in the same language.  And, again, I am amazed at 
> the amount of time and effort people put into it.
> 
>  But I do think that the link in the original post made some 
> good points, and the writer of that post is not the only one 
> who has found R difficult to learn.
> 
> 
> Peter
> 
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