Provide a summary for each case in the data 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.

miss_case_summary(data, order = TRUE, ...)

Arguments

data

a data.frame

order

a logical indicating whether or not to order the result by n_miss. Defaults to TRUE. If FALSE, order of cases is the order input.

...

extra arguments

Value

a tibble of the percent of missing data in each case.

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

Examples

# works with group_by from dplyr library(dplyr) airquality %>% group_by(Month) %>% miss_case_summary()
#> # A tibble: 153 x 5 #> Month case n_miss pct_miss n_miss_cumsum #> <int> <int> <int> <dbl> <int> #> 1 5 5 2 40 2 #> 2 5 27 2 40 9 #> 3 5 6 1 20 3 #> 4 5 10 1 20 4 #> 5 5 11 1 20 5 #> 6 5 25 1 20 6 #> 7 5 26 1 20 7 #> 8 5 1 0 0 0 #> 9 5 2 0 0 0 #> 10 5 3 0 0 0 #> # ... with 143 more rows
miss_case_summary(airquality)
#> # A tibble: 153 x 4 #> case n_miss pct_miss n_miss_cumsum #> <int> <int> <dbl> <int> #> 1 5 2 33.3 2 #> 2 27 2 33.3 9 #> 3 6 1 16.7 3 #> 4 10 1 16.7 4 #> 5 11 1 16.7 5 #> 6 25 1 16.7 6 #> 7 26 1 16.7 7 #> 8 32 1 16.7 10 #> 9 33 1 16.7 11 #> 10 34 1 16.7 12 #> # ... with 143 more rows