There is an increasing set of R packages for interacting with the web from R, whether it be the low level tools to interact with the web via http (see RCurl and httr), parsing data from the web (like RJSONIO and XML), or wrappers to web APIs that provide data (like twitteR). Most of you probably know about CRAN Task Views that aggregate information about R packages and functions on a particular subject area into a simple web page.

Good discovery tools for sotware are important as they can facilitate the pace of software development, bugs are found and squashed and new features added more quickly, and users find software they need faster. We have a page on our website for our packages that provides an overview of the packages we have, with descriptions and links.

I recently attended ScienceOnline Climate, a conference in Washington, D.C. at AAAS. You may have heard of the ScienceOnline annual meeting in North Carolina - this was one of their topical meetings focused on Climate Change. I moderated a session on working with data from the web in R, focusing on climate data. Search Twitter for #scioClimate for tweets from the conference, and #sciordata for tweets from the session I ran.

We have started a new R package interacting with NOAA climate data called rnoaa . You can find our package in development here and documentation for NOAA web services here. It is still early days for this package, but we wanted to demo what you can do with the package.

We recently had a paper come out in a special issue on article-level metrics in the journal Information Standards Quarterly. Our paper basically compared article-level metrics provided by different aggregators. The other papers covered various article-level metrics topics from folks at PLOS, Mendeley, and more. Get our paper.

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.

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.

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?

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.

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.

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.