Instead of focussing on labelling whether there are missings, we instead focus on whether there have been any shadows created. This can be useful when data has been imputed and you need to determine which rows contained missing values when the shadow was bound to the dataset.

add_label_shadow(data, ..., missing = "Missing",
  complete = "Not Missing")

Arguments

data

data.frame

...

extra variable to label

missing

character a label for when values are missing - defaults to "Missing"

complete

character character a label for when values are complete - defaults to "Not Missing"

Value

data.frame with a column, "any_missing", which describes whether or not there are any rows that have a shadow value.

See also

Examples

airquality %>% add_shadow(Ozone, Solar.R) %>% add_label_shadow()
#> # A tibble: 153 x 9 #> Ozone Solar.R Wind Temp Month Day Ozone_NA Solar.R_NA any_missing #> <int> <int> <dbl> <int> <int> <int> <fct> <fct> <chr> #> 1 41 190 7.4 67 5 1 !NA !NA Not Missing #> 2 36 118 8 72 5 2 !NA !NA Not Missing #> 3 12 149 12.6 74 5 3 !NA !NA Not Missing #> 4 18 313 11.5 62 5 4 !NA !NA Not Missing #> 5 NA NA 14.3 56 5 5 NA NA Missing #> 6 28 NA 14.9 66 5 6 !NA NA Missing #> 7 23 299 8.6 65 5 7 !NA !NA Not Missing #> 8 19 99 13.8 59 5 8 !NA !NA Not Missing #> 9 8 19 20.1 61 5 9 !NA !NA Not Missing #> 10 NA 194 8.6 69 5 10 NA !NA Missing #> # ... with 143 more rows