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Plot Missing Data Overview (Barplot or Longitudinal Heatmap)

Source: R/rd_missplot.R
rd_missplot.Rd

[Experimental]

Generates a visual summary of missing data for variables in a REDCap dataset or list structure. For cross-sectional (single-event) studies, it displays a barplot showing the proportion or count of missing values per variable. For longitudinal (multi-event) studies, it generates a heatmap illustrating missingness across events and variables.

Usage

rd_missplot(
  data,
  sort_by_missingness = TRUE,
  max_vars = NULL,
  show_percentages = TRUE,
  use_field_labels = TRUE,
  drop_complete = TRUE
)

Arguments

data

A data frame or a list containing a data element retrieved from REDCap.

sort_by_missingness

Logical. Variables are ordered from highest to lowest missingness. Default TRUE.

max_vars

Integer. If specified, only the top max_vars variables are plotted (by missingness, or by original column order if sort_by_missingness = FALSE). Default NULL (show all variables).

show_percentages

Logical. Displays missingness as percentages. Default TRUE.

use_field_labels

Logical. Uses field labels from the dictionary for axis labels instead of variable names. Default TRUE.

drop_complete

Logical. Variables with 0 missing values are excluded from the plot. Default TRUE.

Value

A ggplot2 object representing either a missingness barplot or a longitudinal missingness heatmap.

Details

The function automatically detects whether the input dataset contains longitudinal event information. If redcap_event_name is detected in the data columns, a missingness heatmap grouped by event is produced.