I have a SpatRaster object in R called IDW3, estimated using IDW interpolation method. I have nlyr = 240, containing 12 months x 20 years. I need to calculate the long-term mean monthly rainfall from the layers, so that I get nlyr = 12 at the end, in which each layer represents one calendar month (Jan - Dec).
I have tried using the code below, following this thread calculating long term daily means from a RASTER in R, but I want to verify the code I used.
Any thoughts and comments please?
idw3
#> class : SpatRaster
#> dimensions : 723, 449, 240 (nrow, ncol, nlyr)
#> resolution : 100, 100 (x, y)
#> extent : 624698.7, 669598.7, 640507.8, 712807.8 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> sources : May 1998_masked_idw3.asc
#> May 1999_masked_idw3.asc
#> May 2000_masked_idw3.asc
#> ... and 237 more source(s)
#> names : Jan 1998, Jan 1999, Jan 2000, Jan 2001, #> Jan 2002, Jan 2003, ...
#> min values : ? , ? , ? , ? , ? , ? , ...
#> max values : ? , ? , ? , ? , ? , ? , ...
## CALCULATE THE LONGTERM MONTHLY MEANS
# get the months substring
month.ltm <- substr(my, 1,3)
# calculate the ltm using tapp funtion in terra
idw3.ltm <- tapp(idw3, month.ltm, mean)
names(idw3.ltm)
#> [1] "May" "Apr" "Aug" "Jan" "Sep" "Jul" "Jun" "Feb" "Dec"
#> [10] "Nov" "Oct" "Mar"
You can use tapp for that. Here are some example data
library(terra)
r <- rast(ncols=10, nrows=10, nlyr=24)
values(r) <- 1:size(r)
If the data are ordered by year, and then by month, you can now do
x <- tapp(r, 1:12, mean)
In other cases you may have to create another index to match the layers that need to be combined. If your data has a time-stamp, there are some shortcuts. In this case you can use index="months"
time(r) <- as.Date("2000-01-15") + 0:23 * 30.5
y <- tapp(r, "months", mean)