Summarize soil color data, weighted by occurrence and horizon thickness.
aggregateColor(
x,
groups = "genhz",
col = "soil_color",
colorSpace = "CIE2000",
k = NULL,
profile_wt = NULL,
mixingMethod = c("estimate", "exact")
)a SoilProfileCollection object
the name of a horizon or site attribute used to group horizons, see examples
the name of a horizon-level attribute with soil color specified in hexadecimal (i.e. "#rrggbb")
(now deprecated, removed in aqp 2.1) 'CIE2000' used for all cases
single integer specifying the number of colors discretized via PAM (cluster::pam()), see details
the name of a site-level attribute used to modify weighting, e.g. area
method used to estimate "aggregate" soil colors, see mixMunsell()
A list with the following components:
a list of colors and associated weights, one item for each generalized horizon label with at least one color specified in the source data
a data.frame of weighted-mean colors, one row for each generalized horizon label with at least one color specified in the source data
Weights are computed by:
w_i = sqrt(sum(thickness_i)) * n_i
where w_i is the weight associated with color i, thickness_i is the total thickness of all horizons associated with the color i, and n_i is the number of horizons associated with color i. Weights are computed within groups specified by groups.
# keep examples from using more than 2 cores
data.table::setDTthreads(Sys.getenv("OMP_THREAD_LIMIT", unset = 2))
# load some example data
data(sp1, package='aqp')
# upgrade to SoilProfileCollection and convert Munsell colors
sp1$soil_color <- with(sp1, munsell2rgb(hue, value, chroma))
depths(sp1) <- id ~ top + bottom
site(sp1) <- ~ group
# generalize horizon names
n <- c('O', 'A', 'B', 'C')
p <- c('O', 'A', 'B', 'C')
sp1$genhz <- generalize.hz(sp1$name, n, p)
# aggregate colors over horizon-level attribute: 'genhz'
a <- aggregateColor(sp1, groups = 'genhz', col = 'soil_color')
# check results
str(a)
#> List of 2
#> $ scaled.data :List of 4
#> ..$ O:'data.frame': 3 obs. of 5 variables:
#> .. ..$ soil_color: chr [1:3] "#3C2C22FF" "#3A2D20FF" "#564436FF"
#> .. ..$ weight : num [1:3] 0.678 0.177 0.145
#> .. ..$ n.hz : int [1:3] 2 1 1
#> .. ..$ munsell : chr [1:3] "7.5YR 2/2" "10YR 2/2" "7.5YR 3/2"
#> .. ..$ .id : Factor w/ 4 levels "O","A","B","C": 1 1 1
#> ..$ A:'data.frame': 9 obs. of 5 variables:
#> .. ..$ soil_color: chr [1:9] "#3A2D20FF" "#564436FF" "#745C40FF" "#3C2C22FF" ...
#> .. ..$ weight : num [1:9] 0.3515 0.2342 0.1067 0.0754 0.0621 ...
#> .. ..$ n.hz : int [1:9] 4 3 1 2 1 1 1 1 1
#> .. ..$ munsell : chr [1:9] "10YR 2/2" "7.5YR 3/2" "10YR 4/3" "7.5YR 2/2" ...
#> .. ..$ .id : Factor w/ 4 levels "O","A","B","C": 2 2 2 2 2 2 2 2 2
#> ..$ B:'data.frame': 14 obs. of 5 variables:
#> .. ..$ soil_color: chr [1:14] "#564436FF" "#745C40FF" "#544535FF" "#58432CFF" ...
#> .. ..$ weight : num [1:14] 0.2842 0.2033 0.1265 0.0821 0.0552 ...
#> .. ..$ n.hz : int [1:14] 5 3 3 2 2 2 2 1 1 1 ...
#> .. ..$ munsell : chr [1:14] "7.5YR 3/2" "10YR 4/3" "10YR 3/2" "10YR 3/3" ...
#> .. ..$ .id : Factor w/ 4 levels "O","A","B","C": 3 3 3 3 3 3 3 3 3 3 ...
#> ..$ C:'data.frame': 9 obs. of 5 variables:
#> .. ..$ soil_color: chr [1:9] "#564436FF" "#725C50FF" "#8E775BFF" "#795B36FF" ...
#> .. ..$ weight : num [1:9] 0.279 0.152 0.131 0.131 0.089 ...
#> .. ..$ n.hz : int [1:9] 3 2 2 2 1 1 1 1 1
#> .. ..$ munsell : chr [1:9] "7.5YR 3/2" "5YR 4/2" "10YR 5/3" "10YR 4/4" ...
#> .. ..$ .id : Factor w/ 4 levels "O","A","B","C": 4 4 4 4 4 4 4 4 4
#> $ aggregate.data:'data.frame': 4 obs. of 9 variables:
#> ..$ genhz : Factor w/ 4 levels "O","A","B","C": 1 2 3 4
#> ..$ hue : chr [1:4] "7.5YR" "7.5YR" "7.5YR" "7.5YR"
#> ..$ value : num [1:4] 2 3 3 4
#> ..$ chroma : num [1:4] 2 2 3 3
#> ..$ munsell : chr [1:4] "7.5YR 2/2" "7.5YR 3/2" "7.5YR 3/3" "7.5YR 4/3"
#> ..$ distance: num [1:4] 1.16 2.2 2.74 2.88
#> ..$ col : chr [1:4] "#3C2C22FF" "#564436FF" "#5B422EFF" "#775B44FF"
#> ..$ n : int [1:4] 3 9 14 9
#> ..$ H : num [1:4] 1.23 2.66 3.14 2.91
if (FALSE) {
# aggregate colors over site-level attribute: 'group'
a <- aggregateColor(sp1, groups = 'group', col = 'soil_color')
# aggregate colors over site-level attribute: 'group'
# discretize colors to 4 per group
a <- aggregateColor(sp1, groups = 'group', col = 'soil_color', k = 4)
# aggregate colors over depth-slices
s <- dice(sp1, c(5, 10, 15, 25, 50, 100, 150) ~ soil_color)
s$slice <- paste0(s$top, ' cm')
s$slice <- factor(s$slice, levels=guessGenHzLevels(s, 'slice')$levels)
a <- aggregateColor(s, groups = 'slice', col = 'soil_color')
# optionally plot with helper function
if(require(sharpshootR))
aggregateColorPlot(a)
# a more interesting example
data(loafercreek, package = 'soilDB')
# generalize horizon names using REGEX rules
n <- c('Oi', 'A', 'BA','Bt1','Bt2','Bt3','Cr','R')
p <- c('O', '^A$|Ad|Ap|AB','BA$|Bw',
'Bt1$|^B$','^Bt$|^Bt2$','^Bt3|^Bt4|CBt$|BCt$|2Bt|2CB$|^C$','Cr','R')
loafercreek$genhz <- generalize.hz(loafercreek$hzname, n, p)
# remove non-matching generalized horizon names
loafercreek$genhz[loafercreek$genhz == 'not-used'] <- NA
loafercreek$genhz <- factor(loafercreek$genhz)
a <- aggregateColor(loafercreek, 'genhz')
# plot results with helper function
par(mar=c(1,4,4,1))
aggregateColorPlot(a, print.n.hz = TRUE)
# inspect aggregate data
a$aggregate.data
}