Analyze Missing Data Patterns — missing_analysis • Saqrmisc

Provides comprehensive missing data analysis including patterns, Little's MCAR test, and visualizations.

Usage

missing_analysis(
  data,
  Vars = NULL,
  pattern_plot = TRUE,
  mcar_test = TRUE,
  correlations = FALSE,
  digits = 2
)

Arguments

data: A data frame to analyze.
Vars: Character vector of variable names. If NULL (default), all variables are included.
pattern_plot: Logical. Create missing data pattern visualization? Default TRUE.
mcar_test: Logical. Perform Little's MCAR test? Default TRUE.
correlations: Logical. Show correlations between missingness indicators? Default FALSE.
digits: Integer. Number of decimal places. Default 2.

Value

A list containing:

summary: gt table with missing data summary
patterns: Data frame of missing patterns
mcar: MCAR test results (if requested)
plot: Missing pattern plot (if requested)

Examples

if (FALSE) { # \dontrun{
# Create data with missing values
df <- mtcars
df$mpg[c(1, 5, 10)] <- NA
df$hp[c(2, 5, 15)] <- NA

missing_analysis(df)
} # }