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. "LUCY", 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)
  
}

# }