Plot Missing Data — PlotMissingData • SciDataReportR

Visualize missing data patterns with variables as rows and observations as columns. Optional hover variables can be included to facilitate quality control workflows when converting the plot to an interactive Plotly figure.

Usage

PlotMissingData(
  data,
  variables = NULL,
  HoverVars = NULL,
  x_var = NULL,
  facet_by = NULL,
  Relabel = TRUE,
  show_perc = TRUE,
  show_perc_var = TRUE,
  cluster = FALSE,
  DataFrame = lifecycle::deprecated(),
  Variables = lifecycle::deprecated()
)

Arguments

data: A data frame.
variables: Character vector of variables to visualize. If NULL, all columns except HoverVars, x_var, and facet_by are used.
HoverVars: Optional character vector of columns to include in hover text. Useful for participant IDs, visit names, dates, sites, etc.
x_var: Optional single column name to use for the x-axis. Numeric and date variables retain their original scale; categorical variables use a discrete axis. Missing x values are displayed as "Missing".
facet_by: Optional single column name used to create missingness panels. Missing facet values are displayed in a "Missing" panel.
Relabel: Logical. If TRUE, variable labels are used when available.
show_perc: Logical. If TRUE, overall missingness percentages are shown in the legend.
show_perc_var: Logical. If TRUE, variable-specific missingness percentages are appended to y-axis labels.
cluster: Logical. If TRUE, variables are clustered by missingness pattern.
DataFrame: Deprecated (since 19.15.0). Use data instead.
Variables: Deprecated (since 19.15.0). Use variables instead.

Value

A ggplot object.

Examples

data(SampleData)
data(SampleVariableTypes)

Labelled <- RevalueData(SampleData, SampleVariableTypes)$RevaluedData

# The revalued data includes missingness defined in the codebook
vars <- c("age", "AXL", "Angiotensinogen", "BMP_6", "IL_6",
          "Fetuin_A", "NT_proBNP", "ENA_78")

PlotMissingData(
  Labelled,
  variables = vars,
  HoverVars = "Diagnosis"
)