`# Option 1: shorter but slower`

rasterstack_meansd_slow <- function(x) {mean <- raster::mean(x) sd <- raster::calc(x, sd)

list(mean=mean, sd=sd)

} # Option 2: faster but more code rasterstack_meansd_fast <- function(x) { s0 <- nlayers(x) s1 <- raster(x, layer=1) s2 <- s1^2 for(ri in 2:s0) { r <- raster(x, layer=ri) s1 <- s1 + r s2 <- s2 + r^2 } list(mean=s1/s0, sd=sqrt((s0 * s2 - s1 * s1)/(s0 * (s0 - 1)))) }

As a small example I calculate the mean and standard deviation of the sea surface temperature for different climate scenarios for 2050. The fast version only takes 10 seconds on my machine while the slow version takes 225 seconds to calculate the mean and standard deviation.

library(sdmpredictors) sstfuture <- load_layers(c('BO2_RCP26_2050_tempmean_ss', 'BO2_RCP45_2050_tempmean_ss', 'BO2_RCP60_2050_tempmean_ss', 'BO2_RCP85_2050_tempmean_ss')) system.time({ fast <- rasterstack_meansd_fast(sstfuture) }) # 10 seconds system.time({ slow <- rasterstack_meansd_slow(sstfuture) }) # 225 seconds

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