Replace NA's with 0 for large raster data using R?

Question

I want to replace all NA values in a large raster with 0. I found this answer: Replace NA's with 0 for raster data using R?

#getting a raster
library(raster)
f <- system.file("external/test.grd", package="raster")
f
r <- raster(f) #r is the object of class 'raster'.

# replacing NA's by zero
r[is.na(r[])] <- 0 

but it's not working for my case because the raster is too large thus the computer runs out of memory. Is there an other solution which is more memory efficient?

Answer 1

"external/test.grd" isn't the best way to test this. Is a very small raster, so results can't be applied in a large raster.

Here I present a comparison with 4 different approaches, the file used is a mosaic of 12 tiles of ALOS DEM 30m (size per tile: 1x1 degree). Options a and b are the most suitable for small rasters, let's see if they are good for big ones:

library(raster)
library(microbenchmark)

r <- raster('~/path/to/mosaic.tif')

NAvalue(r) <- -9999

r <- setMinMax(r)

plot(r)

plot(is.na(r))

microbenchmark(a = r[is.na(r[])] <- 0,
               b = values(r)[is.na(values(r))] <- 0,
               c = raster::mask(r,is.na(r),maskvalue = 1, updatevalue = 0),
               d = reclassify(r, cbind(NA, NA, 0), right=FALSE),
               times = 100L)

## Unit: seconds
##  expr      min       lq     mean   median       uq      max neval cld
##     a 1.653149 1.898718 2.031415 1.971430 2.120584 3.352690   100 ab 
##     b 1.707620 1.938079 2.126679 2.089398 2.235046 3.033048   100  b 
##     c 4.362750 5.176214 5.413074 5.471538 5.660152 6.599903   100   c
##     d 1.424628 1.791048 1.935061 1.860629 2.044008 2.753409   100 a 

Option d seems to be a slightly efficient, but there isn't significant differences in this case. Maybe in bigger rasters could be.

Answer 2

I think you can use raster::calc() as perhaps a more memory efficient method. So, guessing that you have a single-layer raster you can do the following to replace NA values (used some test data here):

library(raster)

r <- raster(nrow=1E3, ncol=1E3)
values(r) <- NA


replaceNA <- function(x, na.rm, ...){ 
  if(is.na(x[1]))
    return(0)
  else
    return(x)
} 

r <- calc(r, fun = replaceNA)

I was able to run this replacement in a test raster layer with 1E8 NA pixels without running out of memory.