This function summarizes GCM data by calculating various statistics for each variable.
Usage
summary_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)
study_area <- terra::ext(c(-80, -30, -50, 10)) |> terra::vect(crs="epsg:4326")
summary_gcms(s, var_names, study_area)
#> $ae
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 7.734 24.38925 30.4245 27.59028 32.15975 36.223
#> bio_12 13.387 865.08324 1282.8990 1307.64099 1661.11371 3153.833
#> sd NAs n_cells
#> bio_1 6.555202 0 470
#> bio_12 658.375705 0 470
#>
#> $cc
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 9.632 27.3485 32.6475 30.03756 34.59025 37.114
#> bio_12 17.049 748.7095 1239.3800 1324.42981 1842.35349 3718.158
#> sd NAs n_cells
#> bio_1 6.375763 0 470
#> bio_12 736.340840 0 470
#>
#> $ch
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 9.519 24.7940 30.745 27.86301 32.00825 33.721
#> bio_12 11.061 953.1217 1582.748 1561.86020 2079.47766 3962.162
#> sd NAs n_cells
#> bio_1 5.813717 0 470
#> bio_12 800.308736 0 470
#>
#> $cr
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 8.963 24.57275 30.8235 27.76396 32.3655 34.225
#> bio_12 10.892 940.39249 1437.9115 1431.00430 1853.5811 3904.810
#> sd NAs n_cells
#> bio_1 6.226339 0 470
#> bio_12 717.373064 0 470
#>
#> $ev
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 9.135 24.09275 29.9925 27.10791 31.32125 33.478
#> bio_12 8.491 927.21727 1518.8020 1495.06446 1995.87979 3329.857
#> sd NAs n_cells
#> bio_1 5.925051 0 470
#> bio_12 749.026990 0 470
#>
#> $gg
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 8.072 23.73725 29.465 26.57517 30.865 32.873
#> bio_12 13.857 983.01001 1629.942 1522.41924 1955.706 3891.572
#> sd NAs n_cells
#> bio_1 5.928956 0 470
#> bio_12 727.476365 0 470
#>
#> $hg
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 10.346 26.00875 32.2665 29.63256 34.93375 37.080
#> bio_12 10.447 899.70450 1414.8480 1423.15330 1762.83701 3694.793
#> sd NAs n_cells
#> bio_1 6.74503 0 470
#> bio_12 758.12557 0 470
#>
#> $`in`
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 7.136 23.0155 28.138 25.25642 29.269 30.790
#> bio_12 14.053 905.7955 1648.554 1642.04464 2330.816 3754.984
#> sd NAs n_cells
#> bio_1 5.694216 0 470
#> bio_12 872.713168 0 470
#>
#> $me
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 6.926 22.8935 28.7935 25.8296 30.1795 32.280
#> bio_12 12.646 924.1427 1524.8660 1549.1119 2004.8098 3736.639
#> sd NAs n_cells
#> bio_1 5.949899 0 470
#> bio_12 848.705135 0 470
#>
#> $ml
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 7.324 23.94325 29.1495 26.45857 30.6025 33.174
#> bio_12 8.940 885.25952 1498.8890 1462.32763 1928.1500 3534.142
#> sd NAs n_cells
#> bio_1 5.986042 0 470
#> bio_12 749.279630 0 470
#>
#> $mr
#> min quantile_0.25 median mean quantile_0.75 max
#> bio_1 7.992 23.5035 28.8605 26.03051 30.0505 32.220
#> bio_12 12.032 998.1548 1620.2320 1559.82805 2049.5742 3386.632
#> sd NAs n_cells
#> bio_1 5.666088 0 470
#> bio_12 770.164184 0 470
#>