Cumulative sum of the number of missings in each variable

Calculate the cumulative sum of number & percentage of missingness for each variable.

Usage

miss_var_cumsum(data)

Arguments

data

a data.frame

Value

a tibble of the cumulative sum of missing data in each variable

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()

Examples

miss_var_cumsum(airquality)
#> Warning: `miss_var_cumsum()` was deprecated in naniar 1.1.0.
#> ℹ Please use `miss_var_summary(data, add_cumsum = TRUE)`
#> # A tibble: 6 × 3
#>   variable n_miss n_miss_cumsum
#>   <chr>     <int>         <int>
#> 1 Ozone        37            37
#> 2 Solar.R       7            44
#> 3 Wind          0            44
#> 4 Temp          0            44
#> 5 Month         0            44
#> 6 Day           0            44
if (FALSE) { # \dontrun{
library(dplyr)

# respects dplyr::group_by

airquality %>%
  group_by(Month) %>%
  miss_var_cumsum()
} # }