Many of us generate more data than we know what to do with (speaking of which: keep an eye out for the 2016 NGS Field Guide, coming soon!), so it’s easy to forget about the piles of data already at our fingertips. Research potential is hidden in online resources like GBIF, iDigBio, VertNet, and the IUCN database, and some great R packages make it possible to batch download enormous amounts of data at once.
Here’s an introduction to a few R packages that make it easy to mine data on species occurrences, conservation statuses, and more.
Species conservation data
library(devtools) #used to download packages from github devtools::install_github('ropenscilabs/rredlist') devtools::install_github('ropensci/taxize') library(taxize) library(rredlist) iucn_summary('Gulo gulo')
The output from “iucn_summary(‘Gulo gulo’)” tells us that:
1) The status of Gulo gulo is “Least Concern”
2) Gulo gulo was reassessed as “Near Threatened” in 2008
3) Gulo gulo is found in 9 countries: Canada, China, Estonia, Finland, Mongolia, Norway, Russia, Sweden, and the United States
4) Gulo gulo‘s population trend is decreasing
Also note that the package taxize has several additional tools for taxonomists, such as species name verification and taxonomic hierarchy information.
Species occurrence data
Retrieving and visualizing species occurrence data is easy with the R packages dismo and maptools:
install.packages(c('dismo','maptools')) library(dismo) library(maptools) Soug<-gbif("Sorex", "ugyunak", geo=T, removeZeros=T, args=c('originisocountrycode=US', 'originisocountrycode=CA')) #retrieve all occurrences of Sorex ugyunak from the U.S. and Canada Soug_xy<-subset(Soug, lat !=0 & lon !=0) #retain only the georeferenced records. This can also be done by including geo=T in the previous command coordinates(Soug_xy) = c("lon", "lat") #Set coordinates to a Spatial data frame data(wrld_simpl) plot(wrld_simpl, axes=TRUE, col='light green', las=1) #plot points on a world map zoom(wrld_simpl, axes=TRUE, las=1, col='light green') #zoom to a specific region points(Soug_xy, col='orange', pch=20, cex=0.75)
Which gives the following result:
Other helpful R packages include ridigbio, rvertnet, and rgbif, which allow you to download data from (you guessed it!) iDigBio, VertNet, and GBIF. These are particularly useful for obtaining museum collection information, including record counts by year or by institution.
What’s your favorite tool for data mining in R? Let us know in the comments below.