I'd like to join tabular data to a raster using the current cell values as a key. Is there an way to do this with large rasters (100M- 1B cells)? Maybe there's something obvious in terra:: but nothing is coming to mind. See my current solution below:
# Load Libs
library(data.table)
library(terra)
# -------- example data ------------
# Create Raster
r <- rast(matrix(c(1:24), nrow = 4, ncol = 6))
names(r) <- "r_key"
# Create tabular data
dt <- data.table(
r_key = c(1:50),
vals = sample(c(1000:5000), 50)
)
# -------- end example data ----------
# -------- Current Solution ----------
# Clunky Solution: This has memory limitations as ncell() in raster grows.
r_table <- data.table()
r_table[, r_key := values(r)]
# Make index ensure order for raster cells
r_table[, index := .I]
# Conventional Merge
r_table <- merge(r_table,
dt,
by = "r_key",
all.x = TRUE)
# Ensure order
setorder(r_table, "index")
# Raster with new vals
r_new <- deepcopy(r)
values(r_new) <- r_table[["vals"]]
plot(r_new)
# ---------- end current solution ------
One variation on the above was to skip merge() and use values(r_new) <- dt[r_table, , on = "r_key"][["vals"]], but I'm not sure if this puts me any further ahead as far as memory goes.
Maybe there's an obvious way to do a lookup on a vector or something clever with data.table? Or does something exist in terra?
You can do this:
x <- classify(r, dt)
or this (I do not know how well it would scale)
levels(r) <- dt
y <- as.numeric(r, 1)
It is difficult to give a more complete answer without understanding the context a bit better. That is, why do you need to do this in the first place?