diagnosticPropertyPlot: Diagnostic Property Plot

In sharpshootR: A Soil Survey Toolkit

Description

Generate a graphical description of the presence/absence of soil diagnostic properties.

Usage

diagnosticPropertyPlot(f, v, k, grid.label='pedon_id', 
dend.label='pedon_id', sort.vars=TRUE)
diagnosticPropertyPlot2(f, v, k, grid.label='pedon_id', sort.vars=TRUE)

Arguments

f	a SoilProfileCollection object
v	a character vector of site-level attribute names that are boolean (e.g. TRUE/FALSE) data
k	an integer, number of groups to highlight
grid.label	the name of a site-level attribute (usually unique) annotating the y-axis of the grid
dend.label	the name of a site-level attribute (usually unique) annotating dendrogram terminal leaves
sort.vars	sort variables according to natural clustering (TRUE), or use supplied ordering in v (FALSE)

Details

This function attempts to display several pieces of information within a single figure. First, soil profiles are sorted according to the presence/absence of diagnostic features named in v. Second, these diagnostic features are sorted according to their distribution among soil profiles. Third, a binary grid is established with row-ordering of profiles based on step 1 and column-ordering based on step 2. Blue cells represent the presence of a diagnostic feature. Soils with similar diagnostic features should 'clump' together. See examples below.

Value

a list is silently returned by this function, containing:

rd

a data.frame containing IDs and grouping code

profile.order

a vector containing the order of soil profiles (row-order in figure), according to diagnostic property values

var.order

a vector containing the order of variables (column-order in figure), according to their distribution among profiles

Author(s)

D.E. Beaudette and J.M. Skovlin

See Also

multinominal2logical

Examples

## Not run: 
library(aqp)

# sample data, an SPC
data(gopheridge, package='soilDB')

# get depth class
sdc <- getSoilDepthClass(gopheridge)
site(gopheridge) <- sdc

# diagnostic properties to consider, no need to convert to factors
v <- c('lithic.contact', 'paralithic.contact', 'argillic.horizon', 
'cambic.horizon', 'ochric.epipedon', 'mollic.epipedon', 'very.shallow',
'shallow', 'mod.deep', 'deep', 'very.deep')

# base graphics
x <- diagnosticPropertyPlot(gopheridge, v, k=5)

# lattice graphics
x <- diagnosticPropertyPlot2(gopheridge, v, k=3)

# check output
str(x)

## End(Not run)

Example output

This is aqp 1.25

Attaching package: ‘aqp’

The following object is masked from ‘package:stats’:

    filter

List of 3
 $ rd           :'data.frame':	52 obs. of  3 variables:
  ..$ peiid   : chr [1:52] "1137354" "1147151" "1147190" "242808" ...
  ..$ pedon_id: chr [1:52] "08DWB028" "07RJV098" "07RJV099" "S2007CA009002" ...
  ..$ g       : int [1:52] 1 2 2 1 1 1 1 1 1 1 ...
 $ profile.order: int [1:52] 1 4 6 7 8 9 15 16 17 21 ...
 $ var.order    : int [1:8] 1 3 5 8 2 4 6 7

sharpshootR documentation built on May 2, 2019, 4:46 p.m.