missingDataGrid: Missing Data Grid

In aqp: Algorithms for Quantitative Pedology

Missing Data Grid

Description

Generate a levelplot of missing data from a SoilProfileCollection object.

Usage

missingDataGrid(
  s,
  max_depth,
  vars,
  filter.column = NULL,
  filter.regex = NULL,
  cols = NULL,
  ...
)

Arguments

s	a SoilProfileCollection object
max_depth	integer specifying the max depth of analysis
vars	character vector of column names over which to evaluate missing data
filter.column	a character string naming the column to apply the filter REGEX to
filter.regex	a character string with a regular expression used to filter horizon data OUT of the analysis
cols	a vector of colors
...	additional arguments passed on to levelplot

Details

This function evaluates a ⁠missing data fraction⁠ based on slice-wise evaluation of named variables in a SoilProfileCollection object.

Value

A data.frame describing the percentage of missing data by variable.

Note

A lattice graphic is printed to the active output device.

Author(s)

D.E. Beaudette

See Also

slice

Examples

# 10 random profiles
set.seed(10101)
s <- lapply(as.character(1:10), random_profile)
s <- do.call('rbind', s)

# randomly sprinkle some missing data
s[sample(nrow(s), 5), 'p1'] <- NA
s[sample(nrow(s), 5), 'p2'] <- NA
s[sample(nrow(s), 5), 'p3'] <- NA

# set all p4 and p5 attributes of `soil 1' to NA
s[which(s$id == '1'), 'p5'] <- NA
s[which(s$id == '1'), 'p4'] <- NA

# upgrade to SPC
depths(s) <- id ~ top + bottom

# plot missing data via slicing + levelplot
missingDataGrid(
  s,
  max_depth = 100,
  vars = c('p1', 'p2', 'p3', 'p4', 'p5'),
  main='Missing Data Fraction'
)

aqp documentation built on Oct. 19, 2024, 5:06 p.m.