Summarise the missingness in each variable

Provide a summary for each variable of the number, percent missings, and cumulative sum of missings of the order of the variables. By default, it orders by the most missings in each variable.

Usage

miss_var_summary(data, order = FALSE, add_cumsum = FALSE, digits, ...)

Arguments

data

a data.frame

order

a logical indicating whether to order the result by n_miss. Defaults to TRUE. If FALSE, order of variables is the order input.

add_cumsum

logical indicating whether or not to add the cumulative sum of missings to the data. This can be useful when exploring patterns of nonresponse. These are calculated as the cumulative sum of the missings in the variables as they are first presented to the function.

digits

how many digits to display in pct_miss column. Useful when you are working with small amounts of missing data.

...

extra arguments

Value

a tibble of the percent of missing data in each variable

Note

n_miss_cumsum is calculated as the cumulative sum of missings in the variables in the order that they are given in the data when entering the function

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_summary(airquality)
#> # A tibble: 6 × 3
#>   variable n_miss pct_miss
#>   <chr>     <int>    <num>
#> 1 Ozone        37    24.2 
#> 2 Solar.R       7     4.58
#> 3 Wind          0     0   
#> 4 Temp          0     0   
#> 5 Month         0     0   
#> 6 Day           0     0   
miss_var_summary(oceanbuoys, order = TRUE)
#> # A tibble: 8 × 3
#>   variable   n_miss pct_miss
#>   <chr>       <int>    <num>
#> 1 humidity       93   12.6  
#> 2 air_temp_c     81   11.0  
#> 3 sea_temp_c      3    0.408
#> 4 year            0    0    
#> 5 latitude        0    0    
#> 6 longitude       0    0    
#> 7 wind_ew         0    0    
#> 8 wind_ns         0    0    

if (FALSE) {
# works with group_by from dplyr
library(dplyr)
airquality %>%
  group_by(Month) %>%
  miss_var_summary()
}