All posts (Page 99 of 121)
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. From local observations of meteorological and hydrological variables and their spatio-temporal patterns to the type and condition of soils and vegetation/land use as well as the geometry and state of river channels and engineering structures affecting the flow....
I’ve recently released the new package ccafs, which provides access to 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 at https://en.wikipedia.org/wiki/General_circulation_model.
ccafs falls in the data client camp - its focus is on getting users
data - many rOpenSci packages
fall into this area. These kinds of packages are important so that
scientists don’t have to recreate the wheel themselves every time, but
instead use one client that everyone else uses.
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. Please re-read it with a French accent and don’t judge me.
In this post I’ll motivate the existence of the package, then show you the basics of its use, and finally show off with some pretty figures. You can skip any part but if I were you I wouldn’t!
...Our Community Call on Tuesday, March 7th, 8-9 AM PST, will cover “How to ask questions so they get answered! Possibly by yourself!”. Asking questions about programming is a skill you can develop - we’re not just born with it. The speakers will cover some of the background and skills you’ll need to increase your chances of having your questions answered by your peers or by a busy expert.
...ifelses to the plater packageAs a lab scientist, I do almost all of my experiments in microtiter plates. These tools are an efficient means of organizing many parallel experimental conditions. It’s not always easy, however, to translate between the physical plate and a useful data structure for analysis. My first attempts to solve this problem–nesting one ifelse call inside of the next to describe which well was which–were very unsatisfying. Over time, my attempts at solving the problem grew more sophisticated, and eventually, the plater package was born. Here I will tell the story of how with the help of R Packages and the amazing reviewers (Julia Gustavsen and Dean Attali) and editors at rOpenSci, I ended up with a package that makes it easy to work with plate-based data....
