It’s the last week in July and this means that ecologists across North America (and elsewhere) are busy returning from the field and preparing their presentations and posters in anticipation of the annual Ecological Society of America meeting. The entire rOpenSci dev team will be in attendance this year and we have several workshops, talks, and events planned out. The topics range from half-day workshops on open data, data visualization, reproducible research, to an entire symposium on open science.

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.

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.

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.

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.