R-packages to access, manage and analyze biodiversity data.
This article is about R packages that are relevant to Antarctic and Southern Ocean science. It includes packages in various states of maturity, including some in planning or very early stages of development.
Antarctic and Southern Ocean science covers a diverse range of topics within the geosciences, life sciences, physical sciences, and humanities and social sciences. This article is not intended to be an exhaustive list of packages that are relevant to all of those fields, but rather a synopsis of packages that are for one reason or another of particular interest to Antarctic and Southern Ocean researchers. The definition of “particular interest” is of course largely arbitrary. Packages listed here are generally expected to be at a useful stage of development, or if not, are seeking engagement/input from the wider community.
Contributions are welcome! Please submit an issue, or make a contribution (see the contribution guidelines). If you have an issue with one of the packages discussed below, please contact the maintainer of that package.
Many thanks to contributors, including Scott Chamberlain, Michael Sumner, Grant Humphries and Hsun-yi Hsieh
The Register of Antarctic Marine Species (RAMS) is the authoritative taxonomic database for Antarctic marine organisms. RAMS is part of the World Register of Marine Species (WoRMS).
RAMS is currently being extended to cover non-marine taxa, which will become the Register of Antarctic Species (RAS). Hopefully this will remain covered by
worrms and the server-side infrastructure hosted by VLIZ. There is also the biotaxa package in development for working with RAS (visualising and predicting the growth in taxonomic diversity over time).
For more detail on R packages dealing with taxonomy in general, see the rOpenSci taxonomy task view.
Mapping is a very common task, and in an Antarctic/Southern Ocean context brings with it particular issues including dealing with projection properties at high latitudes, coping with data that crosses the 180°E line, adding commonly-desired features such as ocean fronts, management boundaries, sea ice extent, stations and other geographic features, and common contextual layers such as bathymetry.
Tracking of animals using satellite, GPS, or light-level geolocation tags is common, and there are many R packages that can help with this. See the spatiotemporal task view for a more complete list. Of particular interest may be:
Packages that may be of interest but don’t yet fit neatly into another category.