vizAnnualClimate: Annual Climate Summaries for Soil Series Data

Description

Annual climate summaries for soil series, based on latticeExtra::segplot, based on 5th, 25th, 50th, 75th, and 95th percentiles. Input data should be from soilDB::fetchOSD.

Usage

vizAnnualClimate(climate.data, IQR.cex=1, s=NULL, s.col='firebrick', ...)

Arguments

climate.data

Annual climate summaries, as returned from soilDB::fetchOSD(..., extended=TRUE)

IQR.cex

scaling factor for bar representing interquartile range

a soil series name, e.g. "AMADOR", to highlight

s.col

color for highlighted soil series

…

further arguments passed to latticeExtra::segplot

Value

a list with the following elements:

fig

lattice object (the figure)

clust

clustering object returned by cluster::diana

Details

This function was designed for use with soilDB::fetchOSD. It might be possible to use with other sources of data but your mileage may vary.

Examples

Run this code

# NOT RUN {
if(requireNamespace("curl") &
   curl::has_internet() &
   require(soilDB) & 
   require(aqp) & 
   require(latticeExtra)
) {
  
  # soil series of interest
soil <- 'ARBUCKLE'

# get competing series
sdata <- fetchOSD(soil, extended = TRUE)

# get competing series' data
sdata.competing <- fetchOSD(c(soil, sdata$competing$competing))

# only use established series
idx <- which(sdata.competing$series_status == 'established')

# subset as needed
if(length(idx) < length(sdata.competing)) {
  sdata.competing <- sdata.competing[idx, ]
  
}

# now get the extended data
sdata.competing.full <- fetchOSD(site(sdata.competing)$id, extended = TRUE)

# extract SPC
spc <- sdata.competing.full$SPC

# full set of series names
s.names <- unique(site(spc)$id)

# todo: probably better ways to do this...
# note: need to load lattice for this to work
trellis.par.set(plot.line=list(col='RoyalBlue'))

# control center symbol and size here
res <- vizAnnualClimate(sdata.competing.full$climate.annual, s=soil, IQR.cex = 1.1, cex=1.1, pch=18)

# plot figure
print(res$fig)

# check clustering
str(res$clust)

# do something with clustering
par(mar=c(0,0,1,1))
plotProfileDendrogram(spc, clust = res$clust, scaling.factor = 0.075, width = 0.2, y.offset = 0.5)
mtext('sorted by annual climate summaries', side = 3, at = 0.5, adj = 0, line = -1.5, font=3)

}


# }