Thanks, Andrew! I'll put it on my list. I have not been through much of it yet, but the exercises on count data are excelent and at least one of them is immediately helpful to a current project. With appreciation, andrewH
On Mon, Mar 4, 2013 at 7:28 PM, Andrew Koeser <arborkoe...@yahoo.com> wrote: > 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<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 dont 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 dont 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 Leischs >> 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 Ill 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. >> > > -- J. Andrew Hoerner Director, Sustainable Economics Program Redefining Progress (510) 507-4820 [[alternative HTML version deleted]]
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