R

[UPDATE: i remade the maps in green, hope that helps…] A recent survey reported in Science (“Defeating Creationism in the Courtroom, but not in the Classroom”) found that biology teachers in high school do not often accept the basis of their discipline, as do teachers in other disciplines, and thus may not teach evolution appropriately. Read more here: New York Times. I took a little time to play with the data provided online along with the Science article. The data is available on the Science website along with the article, and the dataset I read into R is unchanged from the original. The states abbreviations file is here (as a .xls). Here goes: ...

Excel pivot tables have been how I have reorganized data…up until now. These are just a couple of examples why R is superior to Excel for reorganizing data: UPDATE: I fixed the code to use ‘dcast’ instead of ‘cast’. And library(ggplot2) instead of library(plyr) [plyr is called along with ggplot2]. Thanks Bob! Also, see another post on this topic here. library(reshape2) library(ggplot2) dataset <- data.frame(var1 = rep(c("a","b","c","d","e","f"), each = 4), var2 = rep(c("level1","level1","level2","level2"), 6), var3 = rep(c("h","m"), 12), meas = rep(1:12)) Created by Pretty R at inside-R.org # simply pivot table dcast(dataset, var1 ~ var2 + var3) Using meas as value column. Use the value argument to cast to override this choice var1 level1_h level1_m level2_h level2_m 1 a 1 2 3 4 2 b 5 6 7 8 3 c 9 10 11 12 4 d 1 2 3 4 5 e 5 6 7 8 6 f 9 10 11 12 # mean by var1 and var2 dcast(dataset, var1 ~ var2, mean) Using meas as value column. Use the value argument to cast to override this choice var1 level1 level2 1 a 1.5 3.5 2 b 5.5 7.5 3 c 9.5 11.5 4 d 1.5 3.5 5 e 5.5 7.5 6 f 9.5 11.5 # mean by var1 and var3 dcast(dataset, var1 ~ var3, mean) Using meas as value column. Use the value argument to cast to override this choice var1 h m 1 a 2 3 2 b 6 7 3 c 10 11 4 d 2 3 5 e 6 7 6 f 10 11 # mean by var1, var2 and var3 (version 1) dcast(dataset, var1 ~ var2 + var3, mean) Using meas as value column. Use the value argument to cast to override this choice var1 level1_h level1_m level2_h level2_m 1 a 1 2 3 4 2 b 5 6 7 8 3 c 9 10 11 12 4 d 1 2 3 4 5 e 5 6 7 8 6 f 9 10 11 12 # mean by var1, var2 and var3 (version 2) dcast(dataset, var1 + var2 ~ var3, mean) Using meas as value column. Use the value argument to cast to override this choice var1 var2 h m 1 a level1 1 2 2 a level2 3 4 3 b level1 5 6 4 b level2 7 8 5 c level1 9 10 6 c level2 11 12 7 d level1 1 2 8 d level2 3 4 9 e level1 5 6 10 e level2 7 8 11 f level1 9 10 12 f level2 11 12 # use package plyr to create flexible data frames... dataset_plyr <- ddply(dataset, .(var1, var2), summarise, mean = mean(meas), se = sd(meas), CV = sd(meas)/mean(meas) ) > dataset_plyr var1 var2 mean se CV 1 a level1 1.5 0.7071068 0.47140452 2 a level2 3.5 0.7071068 0.20203051 3 b level1 5.5 0.7071068 0.12856487 4 b level2 7.5 0.7071068 0.09428090 5 c level1 9.5 0.7071068 0.07443229 6 c level2 11.5 0.7071068 0.06148755 7 d level1 1.5 0.7071068 0.47140452 8 d level2 3.5 0.7071068 0.20203051 9 e level1 5.5 0.7071068 0.12856487 10 e level2 7.5 0.7071068 0.09428090 11 f level1 9.5 0.7071068 0.07443229 12 f level2 11.5 0.7071068 0.06148755 # ...to use for plotting qplot(var1, mean, colour = var2, size = CV, data = dataset_plyr, geom = "point") ...

I recently gathered fish harvest data from the U.S. National Oceanic and Atmospheric Administarion (NOAA), which I downloaded from Infochimps. The data is fish harvest by weight and value, by species for 21 years, from 1985 to 2005. Here is a link to a google document of the data I used below. I had to do some minor pocessing in Excel first; thus the link to this data. https://spreadsheets.google.com/ccc?key=0Aq6aW8n11tS_dFRySXQzYkppLXFaU2F5aC04d19ZS0E&amp;hl=en Get the original data from Infochimps here http://infochimps.com/datasets/domestic-fish-and-shellfish-catch-value-and-price-by-species-198 ...

Wikispeedia is a website trying to gather all speed limit signs on Earth. I recently created a Google Visualization for some of their data, specifically on speed limit signs that change speed throughout the day. Check it out here. Here is how to see and comment on what they are doing: website, and Google groups.

Earlier, I posted about generating networks from abundance distributions that you specify. If this post was interesting, check out Jeff Kilpatrick’s website, where he provides code he produced in R and Octave to compare real bipartite networks to ones generated based on ecological variables measured in the field (in our case it was abundance, body size, and nectar production). We used that code for a paper we published. Code was modified from code produced by Diego P. Vazquez. ...

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. ...

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. ...