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, add_cumsum = FALSE, ...)

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.

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.

...

extra arguments

Value

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

See also

Examples

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