All posts (Page 125 of 128)
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.
In this example, we search for stations that collect climate data, then get the data for those stations, pull out only the precipitation data, then get latitude/longitude coordinates for each station, and plot data on a map.
...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.
To get data from the article-level metrics providers we used one R package we created to get DOIs for PLOS articles (rplos) and three R packages we created to get metrics: alm, rImpactStory, and rAltmetric. Here, we will show how we produced visualizations in the paper. The code here is basically that used in the paper - but modified to make it useable by you hopefully.
...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....
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....
