All posts (Page 127 of 130)
One of our primary goals at ROpenSci is to wrap as many science API’s as possible. While each package can be used as a standalone interface, there’s lots of ways our packages can overlap and complement each other. Sure He-Man usually rode Battle Cat, but there’s no reason he couldn’t ride a my little pony sometimes too. That’s the case with our packages for GBIF and the worldbank climate data api. Both packages will give you lots and lots of data, but a shared feature of both is the ability to plot spatial information. The rWBclimate package provides a robust mapping ability on top of access to climate data. At it’s most bare bones, it can be used as alternative to the built in mapping facilities included in rgbif. Building on the example in the rgbif tutorial we’ll plot data for two species in the US and Mexico, the dark eyed junco (Junco hyemalis) and the wood duck (Aix sponsa). Here’s how you can use the kml interface from rWBclimate to download a map of the US and Mexico and overlay it with data from rgbif....
A recent video on the PBS Ideas Channel posited that the discovery of climate change is humanities greatest scientific achievement. It took synthesizing generations of data from thousands of scientists, hundreds of thousands (if not more) of hours of computer time to run models at institutions all over the world. But how can the individual researcher get their hands of some this data? Right now the World Bank provides access to global circulation model (GCM) output from between 1900 and 2100 in 20 year intervals via their climate data api. Using our new package rWBclimate you can access model output from 15 different GCM’s, ensemble data from all GCM’s aggregated, and historical climate data. This data is available at two different spatial scales, individual countries or watershed basins. On top of access to all this data, the API provides a way to download KML definitions for each corresponding spatial element (country or basin). This means with our package it’s easy to download climate data and create maps of any of the thousands of datapoints you have access to via the API....
Previously on this blog and on my own personal blog, I have discussed how easy it is to create interactive maps on Github using a combination of R, git and Github. This is done using a file format called geojson, a file format based on JSON (JavaScript Object Notation) in which you can specify geographic data along with any other metadata.
In my previous post on this blog about geojson, I described how you could get data from the USGS BISON API using our rbison package, then make a geojson file, then push to Github. Here, I describe briefly how you can style your map. This time, we’ll get data from GBIF using the rgbif package.
...
We have a number of packages for getting species occurrence data: rgbif and rbison. The power of R is that you can pull down this occurrence data, manipulate the data, do some analyses, and visualize the data - all in one open source framework.
However, when dealing with occurrence data on maps, it is often useful to be able to interact with the visualization. Github, a code hosting and collaboration site, now renders a particular type of map file format as an interactive map. This file format is called .geojson. Here is an example of an interactive map hosted on Github, embedded here:
R has a reputation of not playing nice on the web. At rOpenSci, we write R pacakages to bring data from around the web into R on your local machine - so we mostly don’t do any dev for the web. However, the United States Geological Survey (USGS) recenty held an app competition - it was a good opportunity to play with R on the web. We won best overall app as described in an earlier post on this blog. Check out our app TaxaViewer at https://ropensci.shinyapps.io/taxaviewer/. Last week we presented the app to the USGS - a video of the presentation will be coming soon. A screenshot:...
