finalfit (version 1.0.8)

missing_pairs: Missing values pairs plot

Description

Compare the occurence of missing values in all variables by each other. Suggest limit the number of variables to a maximum of around six. Dependent and explanatory are for convenience of variable selection, are optional, and have no other specific function.

Usage

missing_pairs(
  .data,
  dependent = NULL,
  explanatory = NULL,
  use_labels = TRUE,
  title = NULL,
  position = "stack",
  showXAxisPlotLabels = TRUE,
  showYAxisPlotLabels = FALSE
)

Value

A plot matrix comparing missing values in all variables against each other.

Arguments

.data

Data frame.

dependent

Character vector. Optional name of dependent variable.

explanatory

Character vector. Optional name(s) of explanatory variables.

use_labels

Use variable label names in plot labelling.

title

Character vector. Optional title for plot.

position

For discrete variables, choose "stack" or "fill" to show counts or proportions.

showXAxisPlotLabels

Show x-axis plot labels.

showYAxisPlotLabels

Show y-axis plot labels.

Examples

if (FALSE) {
explanatory = c("age", "nodes", "age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  missing_pairs(dependent, explanatory)
}