This function transforms a list of GCM stacks by subsetting it to include only the variables
specified in var_names, reprojecting it to match the CRS of study_area,
cropping and masking it to study_area, and returning a list of data frames.
Usage
transform_gcms(s, var_names = c("bio_1", "bio_12"), study_area = NULL)Arguments
- s
A list of stacks of General Circulation Models (GCMs).
- var_names
Character. A vector of names of the variables to include, or 'all' to include all variables.
- study_area
An Extent object, or any object from which an Extent object can be extracted. Defines the study area for cropping and masking the rasters.
Examples
var_names <- c("bio_1", "bio_12")
s <- import_gcms(system.file("extdata", package = "chooseGCM"), var_names = var_names)[1:5]
study_area <- terra::ext(c(-80, -70, -50, -40)) |>
terra::vect(crs="+proj=longlat +datum=WGS84 +no_defs")
transform_gcms(s, var_names, study_area)
#> CRS from s and study_area are not identical. Reprojecting study area.
#> $ae
#> bio_1 bio_12
#> 6 11.293 518.881
#> 12 11.543 408.790
#> 18 10.787 325.573
#> 24 11.479 233.496
#> 29 7.852 652.387
#> 30 10.691 154.954
#> 35 7.734 588.390
#> 36 10.522 149.629
#>
#> $cc
#> bio_1 bio_12
#> 6 14.214 514.546
#> 12 14.298 425.775
#> 18 13.489 353.329
#> 24 14.077 256.632
#> 29 9.828 690.179
#> 30 13.183 163.592
#> 35 9.632 626.963
#> 36 12.919 157.307
#>
#> $ch
#> bio_1 bio_12
#> 6 12.378 542.395
#> 12 12.680 455.871
#> 18 12.071 381.848
#> 24 13.052 273.482
#> 29 9.519 816.399
#> 30 12.926 174.900
#> 35 9.814 712.956
#> 36 12.873 165.665
#>
#> $cr
#> bio_1 bio_12
#> 6 11.793 513.440
#> 12 11.970 431.966
#> 18 11.378 356.697
#> 24 12.378 256.534
#> 29 8.963 766.791
#> 30 12.137 164.902
#> 35 9.233 687.061
#> 36 12.105 158.755
#>
#> $ev
#> bio_1 bio_12
#> 6 12.002 570.354
#> 12 12.390 458.798
#> 18 11.723 368.207
#> 24 12.520 256.564
#> 29 9.135 741.645
#> 30 12.172 164.556
#> 35 9.373 659.248
#> 36 12.203 159.130
#>