missing_pairs: Missing values pairs plot

In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

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 documentation built on Nov. 17, 2023, 1:09 a.m.