missingDataGrid: Missing Data Grid

In aqp: Algorithms for Quantitative Pedology

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 evaulation 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

## visualizing missing data
# 10 random profiles
require(plyr)
s <- ldply(1:10, random_profile)

# 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 May 2, 2019, 4:51 p.m.