I am working with two rasters stacks: bioclim (climate data) and soil data. They have different resolution. The description of data:
bioclim
#class : RasterStack
#dimensions : 163, 319, 51997, 19 (nrow, ncol, ncell, nlayers)
#resolution : 0.1666667, 0.1666667 (x, y)
#extent : 18.83337, 72.00005, 40.99999, 68.16666 (xmin, xmax, ymin, ymax)
#crs : NA
soil
#class : RasterStack
#dimensions : 1256, 2213, 2779528, 5 (nrow, ncol, ncell, nlayers)
#resolution : 0.02259376, 0.02259376 (x, y)
#extent : 20, 69.99999, 42.62224, 71 (xmin, xmax, ymin, ymax)
#crs : NA
I tried function:
soil <- aggregate(soil, fact=7.376669487504514)
I just put this number (fact), because it equals to resolution of bioclim divided by resolution of soil.
But, they have little differences:
res(soil)
#[1] 0.1581563 0.1581563
res(bioclim)
#[1] 0.1666667 0.1666667
Also I tried other functions like aggregate, but not successful.
I need to create main stack of two stacks: bioclim and soil. Please, could anybody help me?
env <- stack(bioclim,soil)
Here is a self-contained, minimal, reproducible example:
library(raster)
bioclim <- raster(nrow=163, ncol=319, ext=extent(18.83337, 72.00005, 40.99999, 68.16666))
soil <- raster(nrow=1256, ncol=2213, ext=extent(20, 69.99999, 42.62224, 71))
values(soil) = 1:ncell(soil)
Solution: if you cannot use (dis-)aggregate, you can use resample
sb <- resample(soil, bioclim)
sb
#class : RasterLayer
#dimensions : 163, 319, 51997 (nrow, ncol, ncell)
#resolution : 0.1666667, 0.1666667 (x, y)
#extent : 18.83337, 72.00005, 40.99999, 68.16666 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +no_defs
#source : memory
#names : layer
#values : 284578.3, 2779484 (min, max)
Or use terra, if you need better performance:
library(terra)
bc <- rast(nrow=163, ncol=319, ext=ext(18.83337, 72.00005, 40.99999, 68.16666))
so <- rast(nrow=1256, ncol=2213, ext=ext(20, 69.99999, 42.62224, 71))
values(so) = 1:ncell(so)
sb <- resample(so, bc)