Summarize and visualize missingness of covariates

Computes missingness summaries for a set of covariates and displays a joint missingness heatmap (upper-triangle including the diagonal).

Usage

check_covariate_missingness(data, ...)

Arguments

data

A data frame or tibble containing the covariates.

...

Columns to include in the analysis. You can specify them unquoted (e.g., `age`, `income`) or using selection helpers such as [dplyr::all_of()] or [tidyselect::starts_with()]. If left empty, all `"X_"` columns are used.

Value

Invisibly returns a list with two elements:

summary

A tibble summarizing total cases, total missing, and fraction missing per variable.

heatmap

A [ggplot2::ggplot()] object showing the upper-triangle joint missingness (including diagonal).

Details

If no variables are specified, it defaults to all variables starting with `"X_"`, excluding columns ending with `_nona` or `_missing`.

This function supports both unquoted column names and tidyselect helpers such as [dplyr::all_of()], [tidyselect::starts_with()], etc.

See also

Other per-study checks: check_attrition(), check_balance(), check_missingness_nona(), check_smd(), check_y_bounds()

Examples

dat <- data.frame(
  X_pid_3 = c("A", NA, "B", "A", NA, "B", "A", NA, "B", "A"),
  X_income = c(100, 200, NA, 400, NA, 300, 500, NA, 600, 700),
  X_age = c(25, NA, 30, 40, NA, 35, 45, NA, 50, 55)
)

# X_pid_3 and X_income have similar missingness
check_covariate_missingness(dat, X_pid_3, X_income, X_age)
#> # A tibble: 3 × 4
#>   variable total_cases total_missing_cases fraction_missing_cases
#>   <chr>          <int>               <int>                  <dbl>
#> 1 X_pid_3           10                   3                    0.3
#> 2 X_income          10                   3                    0.3
#> 3 X_age             10                   3                    0.3


# Or default to all "X_" columns
check_covariate_missingness(dat)
#> # A tibble: 3 × 4
#>   variable total_cases total_missing_cases fraction_missing_cases
#>   <chr>          <int>               <int>                  <dbl>
#> 1 X_pid_3           10                   3                    0.3
#> 2 X_income          10                   3                    0.3
#> 3 X_age             10                   3                    0.3


# Or use tidyselect helpers
vars <- c("X_pid_3", "X_income", "X_age")
check_covariate_missingness(dat, dplyr::all_of(vars))
#> # A tibble: 3 × 4
#>   variable total_cases total_missing_cases fraction_missing_cases
#>   <chr>          <int>               <int>                  <dbl>
#> 1 X_pid_3           10                   3                    0.3
#> 2 X_income          10                   3                    0.3
#> 3 X_age             10                   3                    0.3