You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our packages can enable your research, or about our onboarding process for contributing new packages, discuss software sustainability or tell us how we can help you do open and reproducible research. 🔗Where’s rOpenSci?
There’s a lot of work that goes in to making software: the code that does the thing itself, unit testing, examples, tutorials, documentation, and support. rOpenSci software is created and maintained both by our staff and by our (awesome) community. In keeping with our aim to build capacity of software users and developers, three interns from our academic home at UC Berkeley are now working with us as well.
randgeo generates random points and shapes in GeoJSON and WKT formats foruse in examples, teaching, or statistical applications. Points and shapes are generated in the long/lat coordinate system and withappropriate spherical geometry; random points are distributed evenly acrossthe globe, and random shapes are sized according to a maximum great-circledistance from the center of the shape.
There is no problem in science quite as frustrating as other peoples’ data . Whether it’s malformed spreadsheets, disorganized documents, proprietary file formats, data without metadata, or any other data scenario created by someone else, scientists have taken to Twitter to complain about it. As a political scientist who regularly encounters so-called “open data” in PDFs, this problem is particularly irritating.
Version 2.0 of my data set validation package assertr hit CRAN just this weekend. It has some pretty great improvements over version 1. For those new to the package, what follows is a short and new introduction. For those who are already using assertr, the text below will point out the improvements. I can (and have) go on and on about the treachery of messy/bad datasets.
As a scientist who models plant diseases, I use a lot of weather data. Often this data is not available for areas of interest. Previously, I worked with the International Rice Research Institute (IRRI) and often the countries I was working with did not have weather data available or I was working on a large area covering several countries and needed a single source of data to work from.
camsRad is a lightweight R client for the CAMS Radiation Service, that provides satellite-based time series of solar irradiation for the actual weather conditions as well as for clear-sky conditions. Satellite-based solar irradiation data have been around roughly as long our modern era satellites. But the price tag has been very high, in the range of several thousand euros per site.
After 2.5 years of development, version 1.0 of the mongolite package has been released to CRAN. The package is now stable, well documented, and will soon be submitted for peer review to be onboarded in the rOpenSci suite. 🔗MongoDB in R and mongolite I started working on mongolite in September 2014, and it was first announced at the rOpenSci unconf 2015.
I’ve worked for over 12 years in hydrology and natural hazard modelling and one of the things that still fascinates me is the variety of factors that come into play in trying to predict phenomena such as river floods.
I’ve recently released the new package ccafs, which provides accessto data from Climate Change, Agriculture and Food Security(CCAFS; http://ccafs-climate.org/) General Circulation Models (GCM) data.GCM’s are a particular type of climate model, used for weather forecasting,and climate change forecasting - read more athttps://en.wikipedia.org/wiki/General_circulation_model.
Do you fancy open data, R, and breathing? Then you might be interested in ropenaq which provides access to open air quality data via OpenAQ! Also note that in French, R and air are homophones, therefore we French speakers can make puns like the one in the title.