aqp (version 2.1.0)

aggregateColor: Summarize Soil Colors

Description

Summarize soil color data, weighted by occurrence and horizon thickness.

Usage

aggregateColor(
  x,
  groups = "genhz",
  col = "soil_color",
  colorSpace = "CIE2000",
  k = NULL,
  profile_wt = NULL,
  mixingMethod = c("estimate", "exact")
)

Value

A list with the following components:

scaled.data

a list of colors and associated weights, one item for each generalized horizon label with at least one color specified in the source data

aggregate.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

Arguments

x

a SoilProfileCollection object

groups

the name of a horizon or site attribute used to group horizons, see examples

col

the name of a horizon-level attribute with soil color specified in hexadecimal (i.e. "#rrggbb")

colorSpace

(now deprecated, removed in aqp 2.1) 'CIE2000' used for all cases

k

single integer specifying the number of colors discretized via PAM (cluster::pam()), see details

profile_wt

the name of a site-level attribute used to modify weighting, e.g. area

mixingMethod

method used to estimate "aggregate" soil colors, see mixMunsell()

Author

D.E. Beaudette

Details

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.

See Also

generalize.hz()

Examples

# 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)

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
}