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", missing = "missing", complete = "complete")
| 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
|
| 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" |
data.frame with data and the column labelling whether that row (for those variables) has any missing values - indicated by "missing" and "complete".
By default the
prefix "any_miss" is used, but this can be changed in the label argument.
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()
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 rowsairquality %>% 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 rowsairquality %>% 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