Okay, so this isn’t ecology related at all, but I like exploring data sets. So here goes… Propublica has some awesome data sets available at their website: http://www.propublica.org/tools/ I played around with their data set on Recovery.gov (see hyperlink below in code). Here’s some figures: Mean award amount, ranked by mean amount, and also categorized by number of grants received (“nfund”) by state (by size and color of point). Yes, there are 56 “states”, which includes things like Northern Marian Islands (MP). Notice that California got the largest number of awards, but the mean award size was relatively small. ...

Another grad student and I tried recently to make a contribution to our understanding of the relationship between ecological network structure (e.g., nestedness) and community structure (e.g., evenness)… …Alas, I had no luck making new insights. However, I am providing the code used for this failed attempt in hopes that someone may find it useful. This is very basic code. It was roughly based off of the paper by Bluthgen et al. 2008 Ecology (here). In my code the number of interactions is set to 600, and there are 30 plant species, and 10 animal species. This assumes they share the same abundance distributions and sigma values. ...

Just a quick FYI note in case you haven’t seen this site. R-bloggers is an awesome site, bringing together more than 140 blogs (including mine) about R in a single location. See Tal Galili’s motivation for creating the site, and his notes on the site here.

Anthony Ives, of University of Wisconsin-Madison, and Matthew Helmus of the Xishuangbanna Tropical Botanical Garden, present a new statistical method for analyzing phylogenetic community structure in an early view paper in Ecological Monographs. See the abstract here. Up to now, most phylogenetic community structure papers have calculated metrics and used randomization tests to determine if observed metrics are different from random. The approach of Ives and Helmus fits models to observed data, instead of calculating single metrics. ...

The recent availability of google ngram data is a great source of data on language use. Here are some terms from ecology from 1890 to 2000 (from here: https://books.google.com/ngrams/). Note that the word “ecology” doesn’t appear at all until about 1890. Notice the close alignment of “ecology” and “ecosystem”, while “predation” and “facilitation” put in somewhat equal showings. “Parasitism” is almost constant through time, while “mutualism” falls way below predation and facilitation and parasitism. ...

Here is some code to run Phylometa from R. Phylometa is a program that conducts phylogenetic meta-analyses. The great advantage of the approach below is that you can easily run Phylometa from R, and manipulate the output from Phylometa in R. Phylometa was created by Marc Lajeunesse at University of South Florida, and is described in his 2009 AmNat paper. Phylometa can be downloaded free here. Save phylometa_fxn.R (get here) to your working directory. Then use the block of code below to call the functions within phylometa_fxn.R. ...

I am starting this blog not because I am a seasoned code writer, but because I am learning how to use R specifically for ecology and evolution, and figured many others might have the same questions I have. If I find cool solutions I will post them here for all to view, criticize, improve, etc.