plot_missing_data: Plot Missing Group Combinations

In MAPCtools: Multivariate Age-Period-Cohort (MAPC) Modeling for Health Data

Plot Missing Group Combinations

Description

Creates a tile plot highlighting combinations of grouping variables that are expected but missing from the data. Allows for faceting.

Usage

plot_missing_data(
  data,
  x,
  y,
  stratify_by = NULL,
  for_each = NULL,
  facet_labeller = NULL,
  title = "Missing data",
  subtitle = NULL,
  x_lab = NULL,
  y_lab = NULL
)

Arguments

data	Data frame.
x	Variable in data whose values define the x-axis.
y	Variable in data whose values define the y-axis.
stratify_by	(Optional) Stratification variable. If supplied, missing data is examined separately for each leves of stratify_by, and each level gets its own panel.
for_each	(Optional) Additional stratification variable. If supplied, separate plot windows are created per level of for_each.
facet_labeller	A labeller function (e.g. labeller), or a named list where names match facet variables and values are named vectors/lists mapping levels to labels (optional).
title	Character string for the plot title. Defaults to "Missing data".
subtitle	Character string for the plot subtitle. Defaults to NULL.
x_lab	Character string for the x-axis label. Defaults to the name of x_var.
y_lab	Character string for the y-axis label. Defaults to the name of y_var.

Value

A ggplot object, or NULL if no missing combinations found.

See Also

ggplot

Examples

data("toy_data")

# Plot missing data across age and period, stratified by education, for each sex
plot_missing_data (data        = toy_data,
                   x           = period,
                   y           = age,
                   stratify_by = education,
                   for_each    = sex)

MAPCtools documentation built on June 25, 2025, 5:09 p.m.