This function calls similar_soils() iteratively to estimate the proportions
of observations corresponding to distinct groups of similar soils. Use it
exhaustively group all observations (for example, within a map unit or other
conceptual unit of interest)
Usage
design_mapunit(
x,
mapping,
idname = "id",
component_labels = SOILmilaR::greekletters[[1]],
...
)Arguments
- x
A data.frame or a SoilProfileCollection.
- mapping
A named list of rating functions.
- idname
The name of the ID column in
x. Default:"id".- component_labels
A character vector of labels to assign to components. Default:
greekletters[[1]]- ...
Additional arguments passed on to
similar_soils(), such asthreshold_singleorthreshold_all.
Value
A data.frame containing the original data along with a new 'compname' column that assigns a map unit component label to each group of similar soils.
Details
This function automates the iterative process of calling
similar_soils() to build out an estimate of map unit composition.
As a special case, if threshold_single and threshold_all are set to
0, the function will use a more efficient method. It will directly group
all soils that share the exact same combination of rated properties, as
this is the logical outcome of a zero-distance threshold. The component
labels are assigned based on the size of the groups, with the largest group
being assigned the first label.
Examples
data("loamy", package = "SOILmilaR")
rate_taxpartsize <- function(x) {
dplyr::case_match(x,
c("sandy-skeletal") ~ 1,
c("sandy") ~ 3,
c("loamy", "coarse-loamy", "coarse-silty") ~ 5,
c("fine-loamy", "fine-silty") ~ 7,
c("clayey", "fine") ~ 9,
c("very-fine") ~ 11,
c("loamy-skeletal", "clayey-skeletal") ~ 13,
"fragmental" ~ 15)
}
rate_depthclass <- function(x,
breaks = c(
`very shallow` = 25,
`shallow` = 50,
`moderately deep` = 100,
`deep` = 150,
`very deep` = 1e4
),
...) {
res <- cut(x, c(0, breaks))
factor(res, levels = levels(res), labels = names(breaks))
}
m <- list(taxpartsize = rate_taxpartsize, depth = rate_depthclass)
mapunit_composition <- design_mapunit(loamy, m)
mapunit_composition[order(mapunit_composition$component), ]
#> id taxpartsize depth component
#> 1 A1 fine-loamy 68.07141 Alpha
#> 2 B1 fine-loamy 125.65509 Alpha
#> 3 C1 fine-loamy 82.03235 Alpha
#> 4 D1 fine-loamy 136.54700 Alpha
#> 11 A2 fine-loamy 145.74779 Alpha
#> 12 B2 fine-loamy 138.76439 Alpha
#> 13 C2 fine-loamy 114.43111 Alpha
#> 14 D2 fine-loamy 126.47875 Alpha
#> 21 A3 fine-loamy 111.48825 Alpha
#> 23 C3 fine-loamy 79.15651 Alpha
#> 24 D3 fine-loamy 66.55412 Alpha
#> 8 H1 loamy-skeletal 137.62819 Beta
#> 9 I1 loamy-skeletal 98.41503 Beta
#> 10 J1 loamy-skeletal 87.51069 Beta
#> 18 H2 loamy-skeletal 59.88691 Beta
#> 20 J2 loamy-skeletal 61.63697 Beta
#> 28 H3 loamy-skeletal 128.42479 Beta
#> 30 J3 loamy-skeletal 85.58064 Beta
#> 6 F1 loamy 40.23900 Delta
#> 15 E2 loamy 37.83057 Delta
#> 22 B3 loamy 45.90668 Delta
#> 19 I2 fragmental 71.59082 Epsilon
#> 29 I3 fragmental 126.34937 Epsilon
#> 5 E1 coarse-loamy 143.15374 Gamma
#> 7 G1 coarse-loamy 95.73213 Gamma
#> 16 F2 coarse-loamy 89.94654 Gamma
#> 17 G2 coarse-loamy 122.22285 Gamma
#> 25 E3 coarse-loamy 128.68360 Gamma
#> 26 F3 coarse-loamy 86.57938 Gamma
#> 27 G3 coarse-loamy 128.15740 Gamma
sort(prop.table(table(mapunit_composition$component)), decreasing = TRUE)
#>
#> Alpha Beta Gamma Delta Epsilon
#> 0.36666667 0.23333333 0.23333333 0.10000000 0.06666667