bind_shadow should only add columns for variables that have missing values

[dicook]

When a variable has NO missing there is no need to add a shadow column

[njtierney]

Good point!

[njtierney]

I've added an option in bind_shadow to take care of this, but default this is turned off. This is because other flavours of missing values #50 will make use of variables that might be "present", but could be values like -99. So you will want to have the full range of variables available to you to do this.

Example behaviour

library(naniar)

bind_shadow(airquality)
#> # A tibble: 153 x 12
#>    Ozone Solar.R  Wind  Temp Month   Day Ozone_NA Solar.R_NA Wind_NA
#>    <int>   <int> <dbl> <int> <int> <int> <fct>    <fct>      <fct>  
#>  1    41     190  7.40    67     5     1 !NA      !NA        !NA    
#>  2    36     118  8.00    72     5     2 !NA      !NA        !NA    
#>  3    12     149 12.6     74     5     3 !NA      !NA        !NA    
#>  4    18     313 11.5     62     5     4 !NA      !NA        !NA    
#>  5    NA      NA 14.3     56     5     5 NA       NA         !NA    
#>  6    28      NA 14.9     66     5     6 !NA      NA         !NA    
#>  7    23     299  8.60    65     5     7 !NA      !NA        !NA    
#>  8    19      99 13.8     59     5     8 !NA      !NA        !NA    
#>  9     8      19 20.1     61     5     9 !NA      !NA        !NA    
#> 10    NA     194  8.60    69     5    10 NA       !NA        !NA    
#> # ... with 143 more rows, and 3 more variables: Temp_NA <fct>, Month_NA
#> #   <fct>, Day_NA <fct>
# bind only the variables that contain missing values
bind_shadow(airquality, only_miss = TRUE)
#> # A tibble: 153 x 8
#>    Ozone Solar.R  Wind  Temp Month   Day Ozone_NA Solar.R_NA
#>    <int>   <int> <dbl> <int> <int> <int> <fct>    <fct>     
#>  1    41     190  7.40    67     5     1 !NA      !NA       
#>  2    36     118  8.00    72     5     2 !NA      !NA       
#>  3    12     149 12.6     74     5     3 !NA      !NA       
#>  4    18     313 11.5     62     5     4 !NA      !NA       
#>  5    NA      NA 14.3     56     5     5 NA       NA        
#>  6    28      NA 14.9     66     5     6 !NA      NA        
#>  7    23     299  8.60    65     5     7 !NA      !NA       
#>  8    19      99 13.8     59     5     8 !NA      !NA       
#>  9     8      19 20.1     61     5     9 !NA      !NA       
#> 10    NA     194  8.60    69     5    10 NA       !NA       
#> # ... with 143 more rows