Dear Patrick--
After the official Core Team's R manuals and the individual function help
pages, I have found "The R Inferno" to be the single most useful piece of
documentation when I have gotten stuck with a R problems. It is the only
introduction that seems to be aware of the ambiguities present in the
official documentation and of some of the ways one can get stuck in traps
of misunderstanding. Plus, it is enjoyably witty.

When I first started using it, I found it ranged from very useful to pretty
frustrating. I did not always understand what the examples you presented
were trying to say. It is still true that I occasionally wish for a little
more discursive explanatory style, but as time goes by I find that I am
increasingly likely to get the point just from the example.

Many thanks, Andrew


On Tue, Mar 5, 2013 at 1:46 AM, Patrick Burns <pburns@pburns.seanet.com>wrote:

> Andrew,
>
> That sounds like a sensible document you propose.
> Perhaps I'll do a few blog posts along that vein -- thanks.
>
> I presume you know of 'The R Inferno', which does
> a little of what you want.
>
> Pat
>
>
>
> On 04/03/2013 23:42, andrewH wrote:
>
>> There is something that I wish I had that I think would help me a lot to
>> be a
>> better R programmer, that I think would probably help many others as well.
>> I put the wish out there in the hopes that someone might think it was
>> worth
>> doing at some point.
>>
>> I wish I had the code of some substantial, widely used package – lm, say –
>> heavily annotated and explained at roughly the level of R knowledge of
>> someone who has completed an intro statistics course using R and picked up
>> some R along the way.  The idea is that you would say what the various
>> blocks of code are doing, why the authors chose to do it this way rather
>> than some other way, point out coding techniques that save time or memory
>> or
>> prevent errors relative to alternatives, and generally, to explain what it
>> does and point out and explain as many of the smarter features as
>> possible.
>> Ideally, this would include a description at least at the conceptual level
>> if not at the code level of the major C functions that the package calls,
>> so
>> that you understand at least what is happening at that level, if not the
>> nitty-gritty details of coding.
>>
>> I imagine this as a piece of annotated code, but maybe it could be a video
>> of someone, or some couple of people, scrolling through the code and
>> talking
>> about it. Or maybe something more like a wiki page, with various people
>> contributing explanations for different lines, sections, and practices.
>>
>> I am learning R on my own from books and the internet, and I think I would
>> learn a lot from a chatty line-by-line description of some substantial
>> block
>> of code by someone who really knows what he or she is doing – perhaps
>> with a
>> little feedback from some people who are new about where they get lost in
>> the description.
>>
>> There are a couple of particular things that I personally would hope to
>> get
>> out of this.  First, there are lots of instances of good coding practice
>> that I think most people pick up from other programmers or by having
>> individual bits of code explained to them that are pretty hard to get from
>> books and help files.  I think this might be a good way to get at them.
>>
>> Second, there are a whole bunch of functions in R that I call
>> meta-programming functions – don’t know if they have a more proper name.
>> These are things that are intended primarily to act on R language objects
>> or
>> to control how R objects are evaluated. They include functions like call,
>> match.call, parse and deparse, deparen, get, envir, substitute, eval, etc.
>> Although I have read the individual documentation for many of these
>> command,
>> and even used most of them, I don’t think I have any fluency with them, or
>> understand well how and when to code with them.  I think reading a
>> good-sized hunk of code that uses these functions to do a lot of things
>> that
>> packages often need to do in the best-practice or standard R way, together
>> with comments that describe and explain them would help a lot with that.
>> (There is a good smaller-scale example of this in Friedrich Leisch’s
>> tutorial on creating R packages).
>>
>> These are things I think I probably share with many others. I actually
>> have
>> an ulterior motive for suggesting lm in particular that is more peculiar
>> to
>> me, though not unique I am sure. I would like to understand how formulas
>> work well enough to use them in my own functions. I do not think there is
>> any way to get that from the help documentation. I have been working on a
>> piece of code that I suspect is reinventing, but in an awkward and kludgey
>> way, a piece of the functionality of formulas. So far as I have been able
>> to
>> gather, the only place they are really explained in detail is in chapters
>> 2
>> & 3 of the White Book, “Statistical Models in S”. Unfortunately, I do not
>> have ready access to a major research library and I have way, way outspent
>> my book budget. Someday I’ll probably buy a copy, but for the time being,
>> I
>> am stuck without it. So it would be great to have a piece of code that
>> uses
>> them explained in detail.
>>
>> Warmest regards to all,  andrewH
>>
>>
>>
>>
>> --
>> View this message in context: http://r.789695.n4.nabble.com/**
>> Learning-the-R-way-A-Wish-**tp4660287.html<http://r.789695.n4.nabble.com/Learning-the-R-way-A-Wish-tp4660287.html>
>> Sent from the R help mailing list archive at Nabble.com.
>>
>> ______________________________**________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help>
>> PLEASE do read the posting guide http://www.R-project.org/**
>> posting-guide.html <http://www.R-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
> --
> Patrick Burns
> pburns@pburns.seanet.com
> twitter: @burnsstat @portfolioprobe
> http://www.portfolioprobe.com/**blog <http://www.portfolioprobe.com/blog>
> http://www.burns-stat.com
> (home of:
>  'Impatient R'
>  'The R Inferno'
>  'Tao Te Programming')
>



-- 
J. Andrew Hoerner
Director, Sustainable Economics Program
Redefining Progress
(510) 507-4820

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