evalMissingData: Evaluate Missing Data

In aqp: Algorithms for Quantitative Pedology

Evaluate Missing Data

Description

Evaluate missing data in a SoilProfileCollection object

Usage

evalMissingData(
  x,
  vars,
  name = hzdesgnname(x),
  p = "Cr|R|Cd",
  method = "relative"
)

Arguments

x	a SoilProfileCollection object
vars	a character vector naming horizon-level attributes in x
name	the name of a horizon-level attribute where horizon designations are stored
p	REGEX pattern used to match non-soil horizons
method	'relative' (proportion of total) or 'absolute' depths

Details

Data completeness is evaluated by profile, based on the thickness of horizons with complete horizon-level attribute values (specified in vars) divided by the total thickness. The default REGEX pattern, p, should catch most non-soil horizons which are excluded from the evaluation.

Value

A vector values ranging from 0 to 1 (method='relative') or 0 to maximum depth in specified depth units (method='absolute'), representing the quantity of non-NA data (as specified in vars) for each profile.

Author(s)

D.E. Beaudette

Examples

# example data
data(sp2)

# init SPC object
depths(sp2) <- id ~ top + bottom

# compute data completeness
sp2$data.complete <- evalMissingData(sp2, vars = c('r', 'g', 'b'), name = 'name')
sp2$data.complete.abs <- evalMissingData(sp2, vars = c('r', 'g', 'b'),
                                         name = 'name', method = 'absolute')

# rank
new.order <- order(sp2$data.complete)

# plot along data completeness ranking
plot(sp2, plot.order=new.order, name='name')

# add relative completeness axis
# note re-ordering of axis labels
axis(side=1, at=1:length(sp2), labels = round(sp2$data.complete[new.order], 2),
     line=-1.5, cex.axis=0.75)

# add absolute completeness (cm)
axis(side=1, at=1:length(sp2), labels = sp2$data.complete.abs[new.order],
     line=1, cex.axis=0.75)

aqp documentation built on Sept. 8, 2023, 5:45 p.m.