This adds a column named "any_miss" (by default) that describes whether there are any missings in all of the variables (default), or whether any of the specified columns, specified using variables names or dplyr verbs, starts_with, contains, ends_with, etc. By default the added column will be called "any_miss_all", if no variables are specified, otherwise, if variables are specified, the label will be "any_miss_vars" to indicate that not all variables have been used to create the labels.

add_any_miss(data, ..., label = "any_miss")

Arguments

data

data.frame

...

Variable names to use instead of the whole dataset. By default this looks at the whole dataset. Otherwise, this is one or more unquoted expressions separated by commas. These also respect the dplyr verbs starts_with, contains, ends_with, etc. By default will add "_all" to the label if left blank, otherwise will add "_vars" to distinguish that it has not been used on all of the variables.

label

label for the column, defaults to "any_miss". By default if no additional variables are listed the label col is "any_miss_all", otherwise it is "any_miss_vars", if variables are specified.

Value

data.frame with data and the column labelling whether that row (for those variables) has any missing values - indicated by "missing" and "complete".

Details

By default the prefix "any_miss" is used, but this can be changed in the label argument.

See also

Examples

airquality %>% add_any_miss()
#> # A tibble: 153 x 7 #> Ozone Solar.R Wind Temp Month Day any_miss_all #> <int> <int> <dbl> <int> <int> <int> <chr> #> 1 41 190 7.4 67 5 1 complete #> 2 36 118 8 72 5 2 complete #> 3 12 149 12.6 74 5 3 complete #> 4 18 313 11.5 62 5 4 complete #> 5 NA NA 14.3 56 5 5 missing #> 6 28 NA 14.9 66 5 6 missing #> 7 23 299 8.6 65 5 7 complete #> 8 19 99 13.8 59 5 8 complete #> 9 8 19 20.1 61 5 9 complete #> 10 NA 194 8.6 69 5 10 missing #> # ... with 143 more rows
airquality %>% add_any_miss(Ozone)
#> # A tibble: 153 x 7 #> Ozone Solar.R Wind Temp Month Day any_miss_vars #> <int> <int> <dbl> <int> <int> <int> <chr> #> 1 41 190 7.4 67 5 1 complete #> 2 36 118 8 72 5 2 complete #> 3 12 149 12.6 74 5 3 complete #> 4 18 313 11.5 62 5 4 complete #> 5 NA NA 14.3 56 5 5 missing #> 6 28 NA 14.9 66 5 6 complete #> 7 23 299 8.6 65 5 7 complete #> 8 19 99 13.8 59 5 8 complete #> 9 8 19 20.1 61 5 9 complete #> 10 NA 194 8.6 69 5 10 missing #> # ... with 143 more rows
airquality %>% add_any_miss(Ozone, Solar.R)
#> # A tibble: 153 x 7 #> Ozone Solar.R Wind Temp Month Day any_miss_vars #> <int> <int> <dbl> <int> <int> <int> <chr> #> 1 41 190 7.4 67 5 1 complete #> 2 36 118 8 72 5 2 complete #> 3 12 149 12.6 74 5 3 complete #> 4 18 313 11.5 62 5 4 complete #> 5 NA NA 14.3 56 5 5 missing #> 6 28 NA 14.9 66 5 6 missing #> 7 23 299 8.6 65 5 7 complete #> 8 19 99 13.8 59 5 8 complete #> 9 8 19 20.1 61 5 9 complete #> 10 NA 194 8.6 69 5 10 missing #> # ... with 143 more rows