random sampling from a large raster using clusterR - r

Question

I need to take a 5% random sample from a very large raster and return a new raster. I am trying to use sampleRandom from the raster package, but the process is very slow (I only have 8GB RAM on my machine, running 64-bit R). The raster has been cropped/masked to match an irregularly shaped study area boundary, as well - so has NA values in the rectangular extent around the polygon boundary and some internal NA values - I'm trying to sample only from the non-NA values. I've tried both sampling 5% and reversing that to sampling 95% - both ran for >2 hours without producing a result, at which point I terminated the process.
I am trying to speed it up by running it in parallel using the clusterR command, but I'm new to both the sampleRandom command and to using clusterR. My code runs, but I get all of the non-NA pixels returned, so the sample doesn't seem to working. Is this a problem with my code or is it that sampleRandom can't run with clusterR?
Here is a description of my raster layer:
conv.mod
class : RasterLayer
dimensions : 23828, 19095, 454995660 (nrow, ncol, ncell)
resolution : 56, 56 (x, y)
extent : -1220192, -150872, 87580, 1421948 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=aea +lat_1=44.75 +lat_2=55.75 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : C:\GIS\carbon_cows\Intact\conv_mod.tif
names : conv_mod
values : 1, 1 (min, max)
And here is the code I have tried:
library(raster)
library(parallel)
tot<-cellStats(conv.mod,'sum', na.rm=TRUE) #get the total pixels in conv.mod
sampsize<-tot * 0.05 #calculate how many pixels would represent 5%
removeTmpFiles() #clear some memory
numcores<-detectCores() -1
start<-Sys.time()
beginCluster(numcores)
cl<-getCluster()
clusterExport(cl,"sampsize", envir = .GlobalEnv)
conv.perc <- clusterR(conv.mod,sampleRandom,args=list(size=sampsize,na.rm=TRUE,asRaster=TRUE))
endCluster()
end<-Sys.time()
difftime(end,start)
Here are the total non-NA cells in the original raster layer:
tot<-cellStats(conv.mod,'sum', na.rm=TRUE)
tot
105193858
and the number that should be a 5% sample:
sampsize<-tot * 0.05
sampsize
5259693
But, the resulting raster has the same number of non-NA pixels as the original raster:
tot_convperc<-cellStats(conv.perc,'sum',na.rm=T)
tot_convperc
105193858
I've also tried reversing the sample size calculation and running sampleRandom, so that I'm requesting a 95% sample. But, I get the same result.
I'd appreciate any help in understanding why this code is not running as expected. Thanks!

Answer 1

Never mind. I was able to take advantage of this post: https://gis.stackexchange.com/questions/17255/random-sampling-of-raster-using-r and the reply by whuber.
The following code solved my problem, without the use of a cluster:
col.conv <- ncol(conv.mod)
row.conv<-nrow(conv.mod)
r<-conv.mod
start<-Sys.time()
r[runif(col.conv*row.conv) >= 0.95] <- NA # Randomly *unselect* 5% of the data
end<-Sys.time()
difftime(end,start)
That code ran in ~3 minutes, as opposed to over an hour for putting the simpleRandom code in the clusterR command. I still wonder why simpleRandom was not actually taking a sample and also why this new code is so much more efficient, but happy to have the problem solved.