# The magic of downsampling

It’s time for another mapping post! For a recent project, I wanted to plot some very highly resolved raster data with ggplot2, but I ran into a hiccup. In case it’s useful to someone else, here’s what I learned.

First, some setup:

library(terra) # Raster package, successor to raster
library(tidyverse) # Data manipulation and visualization (includes ggplot2)
library(sf) # Spatial work
library(cowplot) # theme_map
library(rnaturalearth) # World shapefile

knitr::opts_knit$set(message = F, warning = F, cache = T) theme_set(theme_map()) # We're only going to be making maps, so just set the theme here. Note that I load the new-ish terra rather than the usual, and now venerable, raster package. terra is the successor to raster, and in general it’s much, much faster (because its written in C++). But everything I do below with terra could be done with raster instead. For this exercise, we’ll use the Natural Earth II (downloaded here and described in detail here). It’s a raster of what the earth might look like without humans. Hopefully not a projection. It’s a 1:10m, world-wide raster, which means that each cell represents a very small chunk of the earth. So there are a LOT of chunks. More than 230 million to be exact. First, we’ll load the raster and try to plot it. Will it work? tr <- terra::rast("~/Downloads/large-raster/NE2_HR_LC/NE2_HR_LC.tif")[[1]] plot(tr) Wow, looks great! And it plotted in less than a second. How did plot (actually terra::plot) display hundreds of millions of pieces of information so quickly? It didn’t, it turns out. Through the magic of downsampling (or aggregating, or downscaling, or …), the function cleverly takes averages over larger areas, effectively creating a coarser raster with fewer cells. This makes no difference visually, since neither the human eye nor your monitor is capable of detecting or displaying the differences between the very tiny cells represented by the true data at this scale. So what is downsamping actually doing? Borrowing a visual from here: You can think of the downsampling operation as converting a high-resolution raster like (a), above, into (b) by taking some summary (above, a mean) over sets of cells. By summarizing many cells into fewer cells, it leaves plot with a lot less work to do. But, let’s say you’ve taken great pains to produce all of your other figures using the consistent visual styling offered by ggplot2. You might feel inexplicably compelled to try to want to plot this map using ggplot as well. Fortunately, this is doable, at least in principle: you can simply convert, or “fortify”, the raster to data.frame that ggplot can use. Here’s how we would do that: # tr_df <- as.data.frame(tr, xy = T) # nrow(tr_df)  Why is that commented out? Because it takes a long time (and screws up my knitr process). What happened? Remember those 230m+ cells? If we fortify that raster, it becomes a massive, 230 million row data.frame. This is a big problem, because ggplot is not going to be able to plot all of these in a reasonable amount of time (take my word for it…). So what can we do? One option is to take a page out of the terra playbook and downsample ourselves. We’ll use the function terra::aggregate with a factor of 10 both horizontally and vertically, which should reduce the total cells by a factor of 100. tr_a <- terra::aggregate(tr, fact = 10) tr_a_df <- as.data.frame(tr_a, xy = T) nrow(tr_a_df) ## [1] 2332800 Now we have 2 rather than 233 million rows. This is a lot, but actually well within the capabilities of ggplot2::geom_raster. Let’s take a look: ggplot(tr_a_df, aes(x = x, y = y, fill = NE2_1)) + geom_raster() + scale_fill_viridis_c(direction = -1) To see how this can be useful, we’ll download a shapefile of the countries of the world. world <- rnaturalearth::ne_countries(scale = "small", returnclass = "sf") ggplot(world) + geom_sf() Next, we’ll reproject the raster to the same projection as as the world shapefile and aggregate and fortify again. tr_p <- terra::project(tr, st_crs(world)$proj4string)
tr_p_a <- terra::aggregate(tr_p, fact = 10)
tr_p_a_df <- as.data.frame(tr_p_a, xy = T)

And plot, this time with country boundaries.

ggplot(world) +
geom_raster(data = tr_p_a_df, mapping = aes(x = x, y = y, fill = NE2_1)) +
geom_sf(colour = alpha("white", 0.5), fill = NA) +
scale_fill_viridis_c(direction = -1)

Converting the raster into a ggplot-compatible object opens up a whole world of visualization, but we’ll stop here for now. I hope this has given you a sense for how to smoothly integrate large spatial rasters with your usual ggplot2 and sf spatial work. Happy mapping!