mutate_profile() is a function used for transforming SoilProfileCollections. Each expression is applied to site or horizon level attributes of individual profiles. This distinguishes this function from transform, which is applied to all values in a collection, regardless of which profile they came from.
mutate_profile(object, ..., col_names = NULL, horizon_level = NULL)
mutate_profile_raw(object, expr, col_names = NULL, horizon_level = NULL)A SoilProfileCollection
A set of comma-delimited R expressions that resolve to a transformation to be applied to a single profile e.g mutate_profile(hzdept = max(hzdept) - hzdept)
character. Optional column names. Should match the number of expressions in ....
logical. If TRUE results of expressions are added to the SoilProfileCollection's horizon slot, if FALSE the results are added to the site slot. If NULL (default) the results are stored in the site or horizon slot based on the number of rows in each slot compared to the length of the result calculated from the first and last profile in the collection.
A list of expressions in terms of column names in site or horizon table of object
A SoilProfileCollection.
If the length an expression's result matches the number of horizons, the result is stored as a horizon-level variable. If the result has length 1, it is stored as a site-level variable. In the ambiguous case where the first and last profile have only one horizon, the results are stored in the horizon slot by default. To force results into site slot use horizon_level = FALSE.
data(sp4)
depths(sp4) <- id ~ top + bottom
mutate_profile(sp4, clay_wtd_average = weighted.mean(clay, bottom - top))
#> SoilProfileCollection with 10 profiles and 30 horizons
#> profile ID: id | horizon ID: hzID
#> Depth range: 16 - 49 cm
#>
#> ----- Horizons (6 / 30 rows | 10 / 14 columns) -----
#> id hzID top bottom name K Mg Ca CEC_7 ex_Ca_to_Mg
#> colusa 1 0 3 A 0.3 25.7 9.0 23.0 0.35
#> colusa 2 3 8 ABt 0.2 23.7 5.6 21.4 0.23
#> colusa 3 8 30 Bt1 0.1 23.2 1.9 23.7 0.08
#> colusa 4 30 42 Bt2 0.1 44.3 0.3 43.0 0.01
#> glenn 5 0 9 A 0.2 21.9 4.4 18.8 0.20
#> glenn 6 9 34 Bt 0.3 18.9 4.5 27.5 0.20
#> [... more horizons ...]
#>
#> ----- Sites (6 / 10 rows | 2 / 2 columns) -----
#> id clay_wtd_average
#> colusa 37.19048
#> glenn 31.61765
#> kings 21.90000
#> mariposa 30.32653
#> mendocino 21.93333
#> napa 16.40000
#> [... more sites ...]
#>
#> Spatial Data:
#> [EMPTY]
data(jacobs2000)
set.seed(123)
## col_names allows for column names to be calculated
x <- mutate_profile(jacobs2000, bottom - top / sum(bottom - top),
col_names = paste0("relthk", floor(runif(1, 0, 100))))
x$relthk28
#> [1] 18.00000 42.91549 78.79812 129.62911 152.38967 155.28169 212.26761
#> [8] 18.00000 45.91549 83.78404 121.60563 144.42723 212.31925 15.00000
#> [15] 24.91429 63.85714 83.63429 111.52000 164.36000 174.05714 20.00000
#> [22] 52.90148 78.73892 129.61084 164.35961 184.18719 202.08867 28.00000
#> [29] 60.84699 108.66667 134.40437 182.26230 18.00000 45.89286 75.72619
#> [36] 103.54762 118.38095 167.29167 15.00000 40.90132 47.73026 60.68421
#> [43] 90.59868 131.40132 139.13158 151.07895
# mutate_profile_raw allows for lists of expressions to be evaluated
master_desgn <- c("O", "A", "E", "B", "C", "R", "L", "M")
thk_names <- paste0("thk_", master_desgn)
# calculate thickness for each horizon
x$thk <- x$bottom - x$top
## construct an arbitrary number of expressions using variable inputs
ops <- lapply(master_desgn, function(x) {
substitute(sum(thk[grepl(PATTERN, name)], na.rm = TRUE), list(PATTERN = x))
})
names(ops) <- thk_names
# do mutation
y <- mutate_profile_raw(x, ops)
site(y)[c(idname(y), thk_names)]
#> id thk_O thk_A thk_E thk_B thk_C thk_R thk_L thk_M
#> 1 92-1 0 18 25 113 60 0 0 0
#> 2 92-2 0 18 28 99 68 0 0 0
#> 3 92-3 0 25 49 111 0 0 0 0
#> 4 92-4 0 20 0 0 183 0 0 0
#> 5 92-5 0 28 81 26 48 0 0 0
#> 6 92-6 0 46 86 64 0 0 0 0
#> 7 92-7 0 15 97 28 12 0 0 0