In SCAR-sandpit/quantarcticR: Access to Quantarctica Data Sets

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
library(quantarcticR)
## for vignette purposes, and if this is Ben's computer, use a persistent cache_dir
if (grepl("ben_ray", qa_cache_dir())) qa_cache_dir("c:/data/Quantarctica3")

Overview

The quantarcticR package provides access to Quantarctica data sets for R users, without needing QGIS to be installed.

This vignette:

explains what Quantarctica data is
shows how the datasets can be used in R
provides examples of how you can work with them

What is Quantarctica?

Quantarctica is a collection of Antarctic geographical datasets which works with the free, cross-platform, open-source software QGIS. It includes community-contributed, peer-reviewed data from ten different scientific themes and a professionally-designed basemap.

Quantarctica is is published and made available under under a Creative Commons Attribution 4.0 International License.

If you use it, please cite it:

Matsuoka K, Skoglund A, Roth G (2018) Quantarctica dataset. Norwegian Polar Institute. https://doi.org/10.21334/npolar.2018.8516e961

In addition, published works produced using Quantarctica are asked to cite each dataset that was used in the work. Please consult the abstract of each data set for the relevant citation.

Caching datasets

The quantarcticR R package provides you with flexibility to either temporarily or persistently store the data that is downloaded from Quantarctica. By default a temporary directory is used, which will only persist for the current R session. This means that data will not be re-used from session to session, and you may end up re-downloading the same data if you run the same script in different sessions.

You can instead choose to save the data to a persistent directory, by issuing the command qa_cache_dir("persistent") after loading the quantarcticR package. This will use a standard user data directory (e.g. under the user's AppData directory on Windows operating systems). You could also specify a particular directory to use, if you prefer:

qa_cache_dir("c:/my/data/directory/")

You can switch cache directories at any time, and you can find out the current cache directory by calling qa_cache_dir() with no arguments.

Quantarctica Data Sets

Start by loading the package:

library(quantarcticR)

In order to return a list of the datasets available, use the qa_datasets function.

datasets <- qa_datasets()
head(datasets)

In the datasets object we can see the following:

layername which is the name of the dataset
main_file is the primary data file associated with each dataset
type which is the object type (currently "shapefile" or "raster")
cached whether it has been downloaded to the local cache or not
download_size which is the size of the dataset.

Dataset details

In order to view the details of a dataset use the qa_dataset function. This function gives more information about the dataset (but does not download or return the actual data). For example, with the simple basemap called "ADD Simple basemap":

basemap <- qa_dataset("ADD Simple basemap")
basemap

Fetch a dataset

To actually fetch the data, use the qa_get function. You can provide it with either the name of the dataset (i.e. layername as returned by qa_datasets()) or the dataset object (as returned by qa_dataset()).

Here we'll fetch a dataset called "AntGG Free-air gravity anomaly (10km)".

ga_info <- qa_dataset("AntGG Free-air gravity anomaly (10km)") ## the dataset info
ga_data <- qa_get(ga_info, verbose = TRUE) ## fetch the actual data
class(ga_data)

Raster Plot

The gravity anomaly data set is a raster, so we can use the raster package to plot it.

library(raster)
plot(ga_data)

The Quantarctica project maintainers have gone to quite a lot of effort to create nice visual representations of the data layers, by defining colour palettes and similar. The full range of visual information is not yet available through quantarcticR, but it is a work in progress. In this case, there is a colour palette for the gravity anomaly layer:

cmap <- ga_info$palette[[1]]
cmap

We need to make some tweaks to the palette to cope with the differences in how QGIS defines them compared to how raster expects them (we will endeavour to automatically deal with such differences in future versions of quantarcticR):

breaks <- c(cmap$value, cellStats(ga_data, "max"))
breaks[1] <- cellStats(ga_data, "min")

Re-plot using that colour map, and with a land layer underneath:

basemap <- qa_get("ADD Simple basemap")
plot(basemap)
plot(ga_data, breaks = breaks, col = cmap$color, add = TRUE, legend = FALSE)

which is a little closer to the Quantarctica-rendered version of the same data layer:

Using datasets with sf

Read in the simple basemap "ADD Simple basemap" data as an sf object and use the ggplot2 and sf packages to create a plot.

library(sf)
library(ggplot2)

surface_sf <- qa_get("ADD Simple basemap", shapefile_reader = sf::st_read)

class(surface_sf)
ggplot(surface_sf) + geom_sf()