Add a column describing presence of any missing values

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.

Usage

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

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.

missing

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

complete

character character a label for when values are complete - defaults to "complete"

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

bind_shadow() add_any_miss() add_label_missings() add_label_shadow() add_miss_cluster() add_n_miss() add_prop_miss() add_shadow_shift() cast_shadow()

Examples

airquality %>% add_any_miss()
#> # A tibble: 153 × 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     
#> # ℹ 143 more rows
airquality %>% add_any_miss(Ozone, Solar.R)
#> # A tibble: 153 × 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      
#> # ℹ 143 more rows