Tabulate the missings in the variables

Provide a tidy table of the number of variables with 0, 1, 2, up to n, missing values and the proportion of the number of variables those variables make up.

Usage

miss_var_table(data)

Arguments

data

a dataframe

Value

a dataframe

See also

pct_miss_case() prop_miss_case() pct_miss_var() prop_miss_var() pct_complete_case() prop_complete_case() pct_complete_var() prop_complete_var() miss_prop_summary() miss_case_summary() miss_case_table() miss_summary() miss_var_prop() miss_var_run() miss_var_span() miss_var_summary() miss_var_table() n_complete() n_complete_row() n_miss() n_miss_row() pct_complete() pct_miss() prop_complete() prop_complete_row() prop_miss()

Examples

miss_var_table(airquality)
#> # A tibble: 3 × 3
#>   n_miss_in_var n_vars pct_vars
#>           <int>  <int>    <dbl>
#> 1             0      4     66.7
#> 2             7      1     16.7
#> 3            37      1     16.7
if (FALSE) {
library(dplyr)
airquality %>%
  group_by(Month) %>%
  miss_var_table()
}