Making maps in R

Published

December 24, 2022

I recently shifted from using the raster package for spatial data to using the incredibly versatile and fast terra package. Since, tmap does not provide direct integration with terra objects, I wrote this post to show how terra allows for plotting nice graphics using it’s inbuilt functions.

I am going to create a map of continental United States with the spatial distribution of annual $P M 2.5$ concentration for 2019. I use $P M 2.5$ concentration data from Atmospheric Composition Analysis Group (Donkelaar et al., 2021).

We begin by loading our required packages.

rm(list=ls())
gc()

#load required packages
require(data.table)
require(here)
require(pbapply) 
library(terra)

# check root directory
here::here()

I use the United States Census Bureau shapefile which can be downloaded from here. I now import the shapefile.

us.shp <- terra::vect(x = here::here('data', 'shapefile', 'cb_2018_us_state_500k', 'cb_2018_us_state_500k.shp'))

I subset the shapefile by removing the states that are not part of continental United States.

us.shp <- us.shp[us.shp$NAME %in% us.shp$NAME[!us.shp$NAME %in% c("Puerto Rico", "American Samoa", "United States Virgin Islands", "Hawaii", "Guam", "Commonwealth of the Northern Mariana Islands", "Alaska")],]
us.shp <- terra::project(x = us.shp, y = 'EPSG:5070')

I now import the $P M 2.5$ concentration data for 2019.

poll.dt <- terra::rast(x = here::here('data', 'pollution', 'V5GL03.HybridPM25.NorthAmerica.201901-201912.nc'),
                       subds = c('GWRPM25'),
                       lyrs = 1L)

I now subset the $P M 2.5$ concentration raster layer for the continental United States.

poll.dt <- terra::project(x = poll.dt,
                          y = 'EPSG:5070')
poll.dt <- terra::crop(x = poll.dt,
                       y = us.shp)
poll.dt <- terra::mask(x = poll.dt,
                       mask = us.shp)

I now plot the map.

terra::plot(x = poll.dt, type = 'continuous', pax = list(labels = FALSE, tick = FALSE), maxcell = 300000000000000000)

References

Donkelaar, A. van, Hammer, M.S., Bindle, L., Brauer, M., Brook, J.R., Garay, M.J., Hsu, N.C., Kalashnikova, O.V., Kahn, R.A., Lee, C., Levy, R.C., Lyapustin, A., Sayer, A.M., Martin, R.V., 2021. Monthly global estimates of fine particulate matter and their uncertainty. Environmental Science & Technology 55, 15287–15300. https://doi.org/10.1021/acs.est.1c05309