finalfit: missing_pairs – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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)

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.

Value

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

Examples

## Not run: 
explanatory = c("age", "nodes", "age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  missing_pairs(dependent, explanatory)

## End(Not run)

finalfit

Quickly Create Elegant Regression Results Tables and Plots when Modelling

v1.0.2

MIT + file LICENCE

Authors

Ewen Harrison [aut, cre], Tom Drake [aut], Riinu Ots [aut]

Initial release