
1234

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.
R is free to download under the terms of the GNU General Public License (see
http://www.rproject.
org/). Our web site has resources on R and I have tried, sometimes in great
earnest, to learn and understand
R. I have learned and used a number of statistical packages (well over 10)
and a number of programming
languages (over 5), and I regret to say that I have had enormous diffculties
learning and using R. I know
that R has a great fan base composed of skilled and excellent statisticians,
and that includes many people
from the UCLA statistics department. However, I feel like R is not so much
of a statistical package as much
as it is a statistical programming environment that has many new and cutting
edge features. For me learning
R has been very diffcult and I have had a very hard time finding answers to
many questions about using
it. Since the R community tends to be composed of experts deeply enmeshed in
R, I often felt that I was
missing half of the pieces of the puzzle when reading information about the
use of R { it often feels like there
is an assumption that readers are also experts in R. I often found the
documentation for R quite sparse and
many essential terms or constructs were used but not defined or
crossreferenced. While there are mailing
lists regarding R where people can ask questions, there is no offcial
"technical support". Because R is free
and is based on the contributions of the R community, it is extremely
extensible and programmable and I
have been told that it has many cutting edge features, some not available
anywhere else. Although R is free,
it may be more costly in terms of your time to learn, use, and obtain
support for it.
My feeling is that R is much more suited to the sort of statistician who is
oriented towards working
very deeply with it. I think R is the kind of package that you really need
to become immersed in (like a
foreign language) and then need to use on a regular basis. I think that it
is much more diffcult to use it
casually as compared to SAS, Stata or SPSS. But by devoting time and effort
to it would give you access
to a programming environment where you can write R programs and collaborate
with others who are also
using R. Those who are able to access its power, even at an applied level,
would be able to access tools that
may not be found in other packages, but this might come with a serious
investment of time to suffciently
use R and maintain your skills with R."
Kjetil
[[alternative HTML version deleted]]
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On 01/01/06 15:36, 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: ...
Just a warning to others. If you go to the site, it asks for
comments, but if you then ask for the LaTeX style file that is
required for sending comments, you get a message saying that the
service does not deal with those outside of UCLA.
Of course I think this is wrong wrong wrong. It makes some
assumptions about "statisticians" being the ones who use
statistics programs. But there are some researchers who like to
think of themselves as empirical scientists and who do not have
the kinds of humongous grants required to hire people to do
everything except write grant proposals. People in these fields
often even do their own data analysis! Moreover, unlike
statisticians (who consult with a great variety of researchers),
they usually do the same few types of analysis over and over, so
the learning time becomes relatively small, and the other
advantages of R become more compelling. But I will try
eventually to say this as a comment on the paper itself.
Jon

Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page: http://www.sas.upenn.edu/~baron______________________________________________
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>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_.)
Eric
This email message, including any attachments, is for the so...{{dropped}}
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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 sideeffect). 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...
Best,
Philippe
..............................................<°}))><........
) ) ) ) )
( ( ( ( ( Prof. Philippe Grosjean
) ) ) ) )
( ( ( ( ( Numerical Ecology of Aquatic Systems
) ) ) ) ) MonsHainaut University, Pentagone (3D08)
( ( ( ( ( Academie Universitaire WallonieBruxelles
) ) ) ) ) 8, av du Champ de Mars, 7000 Mons, Belgium
( ( ( ( (
) ) ) ) ) phone: + 32.65.37.34.97, fax: + 32.65.37.30.54
( ( ( ( ( email: [hidden email]
) ) ) ) )
( ( ( ( ( web: http://www.umh.ac.be/~econum ) ) ) ) ) http://www.sciviews.org( ( ( ( (
..............................................................
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On 1/2/06, Philippe Grosjean < [hidden email]> 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 sideeffect). 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.rproject.org/doc/contrib/Randoctave2.txtto make it easier for Stata users to understand R. Ditto for SAS and SPSS.
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That's a good idea.
I will try to give a lexicon on Stata vs R.
======= 20060102 23:59:10 您在来信中写道：=======
>On 1/2/06, Philippe Grosjean < [hidden email]> 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 sideeffect). 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.rproject.org/doc/contrib/Randoctave2.txt>
>to make it easier for Stata users to understand R. Ditto for SAS and SPSS.
>
>______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp>PLEASE do read the posting guide! http://www.Rproject.org/postingguide.html= = = = = = = = = = = = = = = = = = = =
20060103

Deparment of Sociology
Fudan University
My new mail addres is [hidden email]
Blog: http://sociology.yculblog.com______________________________________________
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> Original Message
> From: [hidden email]
> [mailto: [hidden email]] On Behalf Of Gabor
> Grothendieck
> Sent: Monday, January 02, 2006 4:59 PM
> To: Philippe Grosjean
> Cc: Kort, Eric; Kjetil Halvorsen; [hidden email]
> Subject: Re: [R] A comment about R:
>
>
> 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.rproject.org/doc/contrib/Randoctave2.txt>
> to make it easier for Stata users to understand R. Ditto for
> SAS and SPSS.
>
>
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 "SPSSterms" (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 RProject (it would be rather a nice sideeffect).
Just a few thoughts ...
Best,
Roland
+++++
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Roland,
Yes, indeed, you are perfectly right. The problem is that R richness
means R complexity: many different data types, "sublanguages" like
regexp or the formula interface, S3/S4 objects, classical versus lattice
(versus RGL versus iplots) graphs, etc. During translation of R in
French, I was thinking of a subset of one or two hundreds of functions
that would be enough for beginners to start with, and to propose a
translation of that small subset of the online help in French. This is
still on my todo list, but I must admit it is not an easy task to decide
which function should be kept in the subset and which should not!
In fact, that idea could be, perhaps, generalized into the whole online
help. It would be sufficient to add a flag somewhere (perhaps a keyword)
telling that page is fundamental and to allow filtering index and pages
("fundamental only" or "full help"). Even for advanced users, it
should be nice to have such a filter to display only the two or three
most important functions in a new packages that proposes perhaps hundred
online help pages...
Using R Commander is also an interesting experiment. R Commander
simplifies the use of R down to the manipulation of a single data frame
(the socalled "active dataset") + optionally one or two model objects.
Just look at all you can do just with one active data frame with R
Commander, and you will realize that it is perfectly manageable to learn
R that way.
Best,
Philippe Grosjean
Rau, Roland wrote:
> > Original Message
>
>>From: [hidden email]
>>[mailto: [hidden email]] On Behalf Of Gabor
>>Grothendieck
>>Sent: Monday, January 02, 2006 4:59 PM
>>To: Philippe Grosjean
>>Cc: Kort, Eric; Kjetil Halvorsen; [hidden email]
>>Subject: Re: [R] A comment about R:
>>
>>
>>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.rproject.org/doc/contrib/Randoctave2.txt>>
>>to make it easier for Stata users to understand R. Ditto for
>>SAS and SPSS.
>>
>>
>
> 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 "SPSSterms" (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 RProject (it would be rather a nice sideeffect).
>
> Just a few thoughts ...
>
> Best,
> Roland
>
> +++++
> This mail has been sent through the MPI for Demographic Rese...{{dropped}}
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide! http://www.Rproject.org/postingguide.html>
>
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>>> "Rau, Roland" < [hidden email]> >>> 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 "SPSSterms" (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 RProject (it would be rather a nice sideeffect).
>>>>
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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On 1/3/06, Peter Flom < [hidden email]> wrote:
> >>> "Rau, Roland" < [hidden email]> >>> 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 "SPSSterms" (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 RProject (it would be rather a nice sideeffect).
> >>>>
>
>
>
> 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
Note that at least some packages do have vignettes which are lengthier
discussions of the package than the help files, e.g.
library(zoo)
vignette("zoo")
> 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.
Regarding (a) note that for certain areas CRAN Task Views
addresses this, at least in part. See:
http://cran.rproject.org/src/contrib/Views/and RNews has a section on changes in CRAN which lists all new
packages since the prior issue of CRAN. See:
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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 SPLUS 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 nonstandard 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
(LispStat) 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
9055259140x23604
http://socserv.mcmaster.ca/jfox

> Original Message
> From: [hidden email]
> [mailto: [hidden email]] On Behalf Of Peter Flom
> Sent: Tuesday, January 03, 2006 6:28 AM
> To: [hidden email]; [hidden email]
> Cc: [hidden email]
> Subject: Re: [R] A comment about R:
>
> >>> "Rau, Roland" < [hidden email]> >>> 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
> "SPSSterms" (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
> RProject (it would be rather a nice sideeffect).
> >>>>
>
>
>
> 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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>>> "John Fox" < [hidden email]> 1/3/2006 9:35 am >>> as always,
raises some excellent points. I have some responses, interspersed
<<<<
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:
>>>
A lot of this probably has to do with what you learned first. I
learned SAS long
before I learned R. Had it been reversed, I would probably find SAS
hard.
<<<
(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.
>>>
True, but I was comparing SAS and R, and this originally started
with STATA and R, and all 3 of those are command driven.
<<<<
(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
(LispStat) 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.
>>>>
This is, I think, a big part of it. I think that R would be a lot
easier to learn for
someone who has learned some other computer language. I have not.
I agree that learning something difficult can often be worth it.
Peter
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I have had an email conversation with the author of the
technical report from which the quote was taken. I am
formulating a comment to the report that will be posted
with the technical report.
I would be pleased if this thread continued, so I will know
better what I want to say. Plus I should be able to reference
this thread in the comment.
Regards,
Patrick Burns
[hidden email]
+44 (0)20 8525 0696
http://www.burnsstat.com(home of S Poetry and "A Guide for the Unwilling S User")
Rau, Roland wrote:
> > Original Message
>
>
>>From: [hidden email]
>>[mailto: [hidden email]] On Behalf Of Gabor
>>Grothendieck
>>Sent: Monday, January 02, 2006 4:59 PM
>>To: Philippe Grosjean
>>Cc: Kort, Eric; Kjetil Halvorsen; [hidden email]
>>Subject: Re: [R] A comment about R:
>>
>>
>>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.rproject.org/doc/contrib/Randoctave2.txt>>
>>to make it easier for Stata users to understand R. Ditto for
>>SAS and SPSS.
>>
>>
>>
>>
>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 "SPSSterms" (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 RProject (it would be rather a nice sideeffect).
>
>Just a few thoughts ...
>
>Best,
>Roland
>
>+++++
>This mail has been sent through the MPI for Demographic Rese...{{dropped}}
>
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>
>
>
>
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On Mon, 2 Jan 2006, Philippe Grosjean wrote:
>
> 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...
I think there are good reasons why Stata is becoming much more popular in
epidemiology and biostatistics [and I'm not particularly prejudiced
against R]. In my experience people who like R also like Stata, though
clearly the reverse is not necessarily true.
Stata, like R, is readily programmable. Users can  and do  write
and distribute programs that look just like the builtin routines. There
is an active and helpful mailing list. However, Stata programming is very
different from R programming, since it is macrobased (think Tcl/Tk)
rather than functionbased.
Stata is also easier to learn: it has a very consistent syntax and even
better documentation than R. We use Stata for all our service course
teaching, and despite the fact that it is commandline based rather than
GUI the students were no more unhappy than when SPSS was used for the
lowestlevel courses and Egret for the higherlevel service courses.
[Stata now has a GUI but it is awful and quite a lot of students prefer
the commandline]
thomas
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On 3 Jan 2006 at 7:35, Thomas Lumley wrote:
> On Mon, 2 Jan 2006, Philippe Grosjean wrote:
> >
> > 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...
>
>
> I think there are good reasons why Stata is becoming much more popular in
> epidemiology and biostatistics [and I'm not particularly prejudiced
> against R]. In my experience people who like R also like Stata, though
> clearly the reverse is not necessarily true.
>
> Stata, like R, is readily programmable. Users can  and do  write
> and distribute programs that look just like the builtin routines. There
> is an active and helpful mailing list. However, Stata programming is very
> different from R programming, since it is macrobased (think Tcl/Tk)
> rather than functionbased.
>
> Stata is also easier to learn: it has a very consistent syntax and even
> better documentation than R. We use Stata for all our service course
> teaching, and despite the fact that it is commandline based rather than
> GUI the students were no more unhappy than when SPSS was used for the
> lowestlevel courses and Egret for the higherlevel service courses.
> [Stata now has a GUI but it is awful and quite a lot of students prefer
> the commandline]
>
>
> thomas
>
I'll offer a Second to Thomas's motion.
I like R but I find Stata much easier to teach in service courses. For most of my students, the Stata learning curve is much more
tolerable than that of R (at a reduction in capability, of course). I state on Day 1 that I think R is the world's best package,
and that Stata is my choice for a very acceptable compromise  for most purposes. A few students go on to write their own Stata
programs, and a few go on to learn R and love it.
But the vast majority of my students learn enough Stata to get through the courses, and afterward they do whatever their advisor
wants them to do (the First Law of Graduate School). For a sizable fraction (maybe 25%), that also proves to be Stata, as there is
a solid core of Stata users among the faculty here.
I'l also agree that Stata's GUI is ghastly; most of my students (both during courses and any later use) quickly adapt to using
Stata's command line, and they use it quite effectively.
JRG
John R. Gleason
Associate Professor
Syracuse University
430 Huntington Hall Voice: 3154433107
Syracuse, NY 132442340 USA FAX: 3154434085
PGP public key at keyservers
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Patrick Burns < [hidden email]> writes:
> I have had an email conversation with the author of the
> technical report from which the quote was taken. I am
> formulating a comment to the report that will be posted
> with the technical report.
>
> I would be pleased if this thread continued, so I will know
> better what I want to say. Plus I should be able to reference
> this thread in the comment.
One thing that is often overlooked, and hasn't yet been mentioned in
the thread, is how much *simpler* R can be for certain completely
basic tasks of practical or pedagogical relevance: Calculate a simple
derived statistic, confidence intervals from estimate and SE,
percentage points of the binomial distribution  using dbinom or from
the formula, take the sum of each of 10 random samples from a set of
numbers, etc. This is where other packages get stuck in the
procedure+dataset mindset.
For much the same reason, those packages make you tend to treat
practical data analysis as something distinct from theoretical
understanding of the methods: You just don't use SAS or SPSS or Stata
to illustrate the concept of a random sample by setting up a small
simulation study as the first thing you do in a statistics class,
whereas you could quite conceivably do it in R. (What *is* the
equivalent of rnorm(25) in those languages, actually?)
Even when using SAS in teaching, I sometimes fire up R just to
calculate simple things like
pbar < (p1+p2)/2
sqrt(pbar*(1pbar))
which you need to cheat SAS Analyst's sample size calculator to work
with proportions rather than means. SAS leaves you no way to do this
short of setting up a new data set. The Windows calculator will do it,
of course, but the students can't see what you are doing then.

O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*)  University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~  ( [hidden email]) FAX: (+45) 35327907
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Another big difference between R and other computing language such as
SPSS/SAS/STATA.
You can easily get a job using SPSS/SAS/STATA. But it is extremely difficult
to find a job using R. ^_^.
On 03 Jan 2006 17:53:40 +0100, Peter Dalgaard < [hidden email]>
wrote:
>
> Patrick Burns < [hidden email]> writes:
>
> > I have had an email conversation with the author of the
> > technical report from which the quote was taken. I am
> > formulating a comment to the report that will be posted
> > with the technical report.
> >
> > I would be pleased if this thread continued, so I will know
> > better what I want to say. Plus I should be able to reference
> > this thread in the comment.
>
> One thing that is often overlooked, and hasn't yet been mentioned in
> the thread, is how much *simpler* R can be for certain completely
> basic tasks of practical or pedagogical relevance: Calculate a simple
> derived statistic, confidence intervals from estimate and SE,
> percentage points of the binomial distribution  using dbinom or from
> the formula, take the sum of each of 10 random samples from a set of
> numbers, etc. This is where other packages get stuck in the
> procedure+dataset mindset.
>
> For much the same reason, those packages make you tend to treat
> practical data analysis as something distinct from theoretical
> understanding of the methods: You just don't use SAS or SPSS or Stata
> to illustrate the concept of a random sample by setting up a small
> simulation study as the first thing you do in a statistics class,
> whereas you could quite conceivably do it in R. (What *is* the
> equivalent of rnorm(25) in those languages, actually?)
>
> Even when using SAS in teaching, I sometimes fire up R just to
> calculate simple things like
>
> pbar < (p1+p2)/2
> sqrt(pbar*(1pbar))
>
> which you need to cheat SAS Analyst's sample size calculator to work
> with proportions rather than means. SAS leaves you no way to do this
> short of setting up a new data set. The Windows calculator will do it,
> of course, but the students can't see what you are doing then.
>
>
> 
> O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
> c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*)  University of Copenhagen Denmark Ph: (+45)
> 35327918
> ~~~~~~~~~~  ( [hidden email]) FAX: (+45)
> 35327907
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide!
> http://www.Rproject.org/postingguide.html>

WenSui Liu
( http://statcompute.blogspot.com)
Senior Decision Support Analyst
Health Policy and Clinical Effectiveness
Cincinnati Children Hospital Medical Center
[[alternative HTML version deleted]]
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One implicit point in Kjetil's message is the difficulty of learning
enough of R to make its use a natural and desired "first choice
alternative," which I see as the point at which real progress and
learning commence with any new language. I agree that the long learning
curve is a serious problem, and in the past I have discussed, off list,
with one of the very senior contributors to this list the possibility of
splitting the list into sections for newcomers and for advanced users.
He gave some very cogent reasons for not splitting, such as the
possibility of newcomers' getting bad advice from others only slightly
more advanced than themselves. And yet I suspect that a newcomers'
section would encourage the kind of mutually helpful collegiality among
newcomers that now characterizes the exchanges of the more experienced
users on this list. I know that I have occasionally been reluctant to
post issues that seem too elementary or trivial to vex the others on the
list with and so have stumbled around for an hour or so seeking the
solution to a simple problem. Had I the counsel of others similarly
situated progress might have been far faster. Have other newcomers or
occasional users had the same experience?
Is it time to reconsider splitting this list into two sections?
Certainly the volume of traffic could justify it.
Ben Fairbank
Original Message
From: [hidden email]
[mailto: [hidden email]] On Behalf Of Kjetil Halvorsen
Sent: Sunday, January 01, 2006 8:37 AM
To: [hidden email]
Subject: [R] A comment about R:
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. R is free to download under the
terms of the GNU General Public License (see http://www.rproject.
org/). Our web site has resources on R and I have tried, sometimes in
great earnest, to learn and understand R. I have learned and used a
number of statistical packages (well over 10) and a number of
programming languages (over 5), and I regret to say that I have had
enormous diffculties learning and using R. I know that R has a great fan
base composed of skilled and excellent statisticians, and that includes
many people from the UCLA statistics department. However, I feel like R
is not so much of a statistical package as much as it is a statistical
programming environment that has many new and cutting edge features. For
me learning R has been very diffcult and I have had a very hard time
finding answers to many questions about using it. Since the R community
tends to be composed of experts deeply enmeshed in R, I often felt that
I was missing half of the pieces of the puzzle when reading information
about the use of R { it often feels like there is an assumption that
readers are also experts in R. I often found the documentation for R
quite sparse and many essential terms or constructs were used but not
defined or crossreferenced. While there are mailing lists regarding R
where people can ask questions, there is no offcial "technical support".
Because R is free and is based on the contributions of the R community,
it is extremely extensible and programmable and I have been told that it
has many cutting edge features, some not available anywhere else.
Although R is free, it may be more costly in terms of your time to
learn, use, and obtain support for it. My feeling is that R is much more
suited to the sort of statistician who is oriented towards working very
deeply with it. I think R is the kind of package that you really need to
become immersed in (like a foreign language) and then need to use on a
regular basis. I think that it is much more diffcult to use it casually
as compared to SAS, Stata or SPSS. But by devoting time and effort to it
would give you access to a programming environment where you can write R
programs and collaborate with others who are also using R. Those who are
able to access its power, even at an applied level, would be able to
access tools that may not be found in other packages, but this might
come with a serious investment of time to suffciently use R and maintain
your skills with R."
Kjetil
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>>> "Ben Fairbank" < [hidden email]> 1/3/2006 12:42 pm >>> wrote
<<<
One implicit point in Kjetil's message is the difficulty of learning
enough of R to make its use a natural and desired "first choice
alternative," which I see as the point at which real progress and
learning commence with any new language. I agree that the long
learning
curve is a serious problem, and in the past I have discussed, off
list,
with one of the very senior contributors to this list the possibility
of
splitting the list into sections for newcomers and for advanced users.
He gave some very cogent reasons for not splitting, such as the
possibility of newcomers' getting bad advice from others only slightly
more advanced than themselves. And yet I suspect that a newcomers'
section would encourage the kind of mutually helpful collegiality
among
newcomers that now characterizes the exchanges of the more experienced
users on this list. I know that I have occasionally been reluctant to
post issues that seem too elementary or trivial to vex the others on
the
list with and so have stumbled around for an hour or so seeking the
solution to a simple problem. Had I the counsel of others similarly
situated progress might have been far faster. Have other newcomers or
occasional users had the same experience?
>>>
I, for one, have had this experience. I am usually hesitant to post
elementary questions here.
However, I think that the 'cogent reasons' given by 'one of the very
senior contributors' are valid.
I think that a 'newcomers list' would only really be useful if it
included some experts who could respond,
out of generosity. I don't think the R community lacks generosity 
obviously not, given all the thousands of
hours people have spent writing the language and all the packages and
so on.
But these generous people have different abilities and get pleasure in
different ways. Some people get a thrill
out of answering complex questions that require them to come up with
novel solutions involving complex code.
Some people get a thrill out of helping newbies over the humps.
Dividing the lists might help the experts, as much as it helps the
beginners.
Peter
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Ummmm....
I cannot say how easy or hard R is to learn, but in response to the UCLA
commentary:
> However, I
> feel like R
> is not so much of a statistical package as much as it is a statistical
> programming environment that has many new and cutting edge
> features.
Please note: the first sentence of the Preface of THE Green Book
(PROGRAMMING WITH DATA: A GUIDE TO THE S LANGUAGE) by John Chambers, the
inventor of the S Language, explicitly states:
"S is a programming language and environment for all kinds of computing
involving data."
I think this says that R is **not** meant to be a statistical package in the
conventional sense and should not be considered one. As computing involving
data is a complex and frequently messy business on both technical
(statistics), practical (messy data), and aesthetic (graphics, tables)
levels, it is perhaps to be expected that "a programming language and
environment for all kinds of computing involving data" is complex.
Personally, I find that (Chambers's next sentence) R's ability "To turn
ideas into software, quickly and faithfully," to be a boon. But, then again,
I'm a statistical professional and not a "casual user."
Cheers,
 Bert Gunter
Genentech NonClinical Statistics
South San Francisco, CA
"The business of the statistician is to catalyze the scientific learning
process."  George E. P. Box
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