Recology HAS MOVED TO http://recology.info/. To get to the same blog post on the new site replace the http://r-ecology.blogspot.ca/ with http://recology.info/, but with the same ending, e,g. /2011/12/weecology-can-has-new-mammal-dataset.html (except remove the .html at the end)
Carl Boettiger, a graduate student at UC Davis, just got two packages on CRAN. One is treebase, which which handshakes with the Treebase API. The other is rfishbase, which connects with the Fishbase, although I believe just scrapes XML content as there is no API. See development on GitHub for treebase here, and for rfishbase here. Carl has some tutorials on treebase and rfishbase at his website here, and we have an official rOpenSci tutorial for treebase here.
Basically, these two R packages let you search and pull down data from Treebase and Fishbase - pretty awesome. This improves workflow, and puts your data search and acquisition component into your code, instead of being a bunch of mouse clicks in a browser.
Tom Miller (a prof here at Rice) and Brian Inouye have a paper out in Ecology (paper, appendices) that confronts two-sex models of dispersal with empirical data.
They conducted the first confrontation of two-sex demographic models with empirical data on lab populations of bean beetles Callosobruchus.
Their R code for the modeling work is available at Ecological Archives (link here).
Here is a figure made from running the five blocks of code in 'Miller_and_Inouye_figures.txt' that reproduces Fig. 4 (A-E) in their Ecology paper (p = proportion female, Nt = density). Nice!
A: Saturating density dependence
B: Over-compensatory density dependence
C: Sex-specific gamma's (but bM=bF=0.5)
D: Sex-specific b's (but gammaM=gammaF=1)
E: Sex-specific b's (but gammaM=gammaF=2)
So, there is a new food web dataset out that was put in Ecological Archives here, and I thought I would play with it. The food web is from Otago Harbour, an intertidal mudflat ecosystem in New Zealand. The web contains 180 nodes, with 1,924 links.
As I said in the original post, part of the power of the PGLMM (phylogenetic generalized linear mixed models) approach is that you don't have to conduct quite so many separate statistical tests as with the previous null model/randomization approach.
Their original code was written in Matlab. Here I provide the R code that Matt has so graciously shared with me. There are four functions and a fifth file has an example use case. The example and output are shown below.
Look for the inclusion of Matt's PGLMM to the picante R package in the future.
Regular expressions are a powerful in any language to manipulate, search, etc. data.
> fruit <- c("apple","banana","pear","pineapple")
> grep("a", fruit) # there is an "a" in each of the words1234
> strsplit("a string","s") # strsplit splits the string on the "s"[]"a ""tring"
R base has many functions for regular expressions, see slide 9 of Ed's talk below. The package stringr, created by Hadley Wickham, is a nice alternative that wraps the base regex functions for easier use. I highly recommend stringr.
Ed Goodwin, the coordinator of the Houston R Users group, gave a presentation to the group last night on regular expressions in R. It was a great talk, and he is allowing me to post his talk here.