[R-SIG-Mac] learning R

William Revelle lists at revelle.net
Mon Dec 13 00:36:38 CET 2010

I share with Carl about the misuse of the term learning curve.

The original derivation was from learning theory where one plotted 
number of correct responses on the y axis against trial number on the 
x axis.  Steep learning curves thus implied rapid learning (of easy 
material).  Flat learning curves implied slow learning (of difficult 

When I complain about this misuse of the term to my psychological 
colleagues, they smile, agree with me that their usage was incorrect, 
and then suggest I go back to worrying about how to code things in R.

When I teach R, I suggest that yes, the learning curve is steep, 
smile and then point out that therefore it is easy to learn. 
Realistically, as is true of learning any skill, the curve is 
negatively accelerated with asymptotic learning achieved only by the 
wizards of Core R.


At 8:42 AM +1100 12/13/10, John Maindonald wrote:
>Surely what is envisaged is the sheer effort involved in climbing
>a step mountain side.  It does not have a graph in mind.  If one
>wants to change the metaphor and turn it into a graph, it is not
>at all obvious what the horizontal axis ought to be, though
>various rather strained interpretations can be proposed. 
>There is a further aspect to the metaphor that deserves attention.
>The reward for negotiating the steep learning curve is to reach
>a great height, where marvellous vistas spread out before the
>John Maindonald             email: john.maindonald at anu.edu.au
>phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
>Centre for Mathematics & Its Applications, Room 1194,
>John Dedman Mathematical Sciences Building (Building 27)
>Australian National University, Canberra ACT 0200.
>On 11/12/2010, at 8:25 AM, Rolf Turner wrote:
>>  I agree with you completely about ``begging the question''.  The
>>  nearly universal misuse of this expression drives me crazy.  I'm
>>  not so sure about ``steep learning curve'' however.  My impression
>>  is that this phrase has *always* been used to convey the idea that
>>  a subject area is difficult to learn, whence to use it (as you suggest)
>>  in the sense that the subject area can be learned quickly would be to
>>  change the original meaning of the phrase.  That would be undesirable,
>>  even given that the original meaning is counter-intuitive.
>>  I recall having heard/read a ``justification'' for the original meaning
>>  to the effect that what is envisaged is plotting effort expended on
>>  the *y* axis and knowledge level on the *x* axis.  Thus a steep learning
>>  curve would entail expending a great deal of effort for a small increase
>>  in knowledge.
>>  I agree that this is a silly choice of axes --- I certainly wouldn't make
>>  such a choice.  But I don't suppose that there's any law against it.
>>	cheers,
>>		Rolf Turner
>>  On 11/12/2010, at 4:22 AM, Carl Witthoft wrote:
>>>  Next to "begging the question,"  the phrase "steep learning curve" is
>>>  probably the most misused cliche out there.
>>>  A 'learning curve' represents knowledge (or understanding) as a function
>>>  of time.  THerefore,  the steeper the better.
>>>  Please help save the English language from descent into Humpty-Dumpty
>>>  land, and train your colleagues in the correct usage of both these terms.
>>>  Carl
>>>>  Message: 2 Date: Thu, 9 Dec 2010 09:51:27 -0800 From: Payam
>>>>  Minoofar<payam.minoofar at meissner.com> To:
>>>>  "r-sig-mac at r-project.org"<r-sig-mac at r-project.org> Cc:
>>>>  "deniz.kellecioglu at gmail.com"<deniz.kellecioglu at gmail.com> Subject:
>>>>  [R-SIG-Mac] R for Mac, good enough?
>>>>  Message-ID:<53DF393B-2037-4B0D-890F-8DBAA1BA1F55 at meissner.com>
>>>>  Content-Type: text/plain; charset="us-ascii"
>>>>  The power of R is virtually unmatched, and R for Mac works extremely
>>>>  well.
>>>>  The learning curve is steep, however, and documentation is difficult
>  >>> to grasp, even though it is abundantly available. I am more partial
>>>>  to a commercial data analysis package with which I grew up, but I
>>>>  have done enough work with R on the mac platform to recommend it
>>>>  highly.
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