[R] Learning the R way – A Wish

[Andrew Koeser]

The book that helped me break into R and more advanced texts was 
Crawley's "Statistics: An Introduction with R."  Very light read that 
assumes no prior knowledge with stats or R. I am using it to teach my 
fellow grad students R and all agree it was worth scrimping pennies to 
get. He also has a series of exercises (for free) that may be close to 
what you need.

http://www3.imperial.ac.uk/naturalsciences/research/statisticsusingr

Andrew

On 03/04/2013 05:42 PM, 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
>
>
>
>
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