On 06/03/2013 07:20, Andrew Hoerner wrote:
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
Actually I find myself sometimes thinking the same thing.
Pat
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 <pbu...@pburns.seanet.com
<mailto:pbu...@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 <mailto: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
pbu...@pburns.seanet.com <mailto:pbu...@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
--
Patrick Burns
pbu...@pburns.seanet.com
twitter: @burnsstat @portfolioprobe
http://www.portfolioprobe.com/blog
http://www.burns-stat.com
(home of:
'Impatient R'
'The R Inferno'
'Tao Te Programming')
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
