Set specified values to NA in selected variables or entire data frame

This function allows users to set specific values to NA in chosen variables within a data frame. It can handle numeric, character, and factor variables.

setna_scale(data, ..., value, name = NULL, overwrite = FALSE)

Arguments

data

A tibble or a tdcmm model.

...

One or more variables where specified values will be set to NA. If no variables are provided, the function is applied to the entire data frame.

value

A value (or vector of values) that needs to be set to NA.

name

The name of the new variable(s). By default, this is the same name as the provided variable(s) but suffixed with _na.

overwrite

Logical. If TRUE, it overwrites the original variable(s). You cannot specify both 'name' and 'overwrite' parameters simultaneously.

Value

A tdcmm model or a tibble.

See also

Other scaling: categorize_scale(), center_scale(), dummify_scale(), minmax_scale(), recode_cat_scale(), reverse_scale(), z_scale()

Examples

WoJ %>%
dplyr::select(autonomy_emphasis) %>%
setna_scale(autonomy_emphasis, value = 5)
#> # A tibble: 1,200 × 2
#>    autonomy_emphasis autonomy_emphasis_na
#>  *             <dbl>                <dbl>
#>  1                 4                    4
#>  2                 4                    4
#>  3                 4                    4
#>  4                 5                   NA
#>  5                 4                    4
#>  6                 4                    4
#>  7                 4                    4
#>  8                 3                    3
#>  9                 5                   NA
#> 10                 4                    4
#> # ℹ 1,190 more rows
WoJ %>%
dplyr::select(autonomy_emphasis) %>%
setna_scale(autonomy_emphasis, value = 5, name = "new_na_autonomy")
#> # A tibble: 1,200 × 2
#>    autonomy_emphasis new_na_autonomy
#>  *             <dbl>           <dbl>
#>  1                 4               4
#>  2                 4               4
#>  3                 4               4
#>  4                 5              NA
#>  5                 4               4
#>  6                 4               4
#>  7                 4               4
#>  8                 3               3
#>  9                 5              NA
#> 10                 4               4
#> # ℹ 1,190 more rows
WoJ %>%
setna_scale(value = c(2, 3, 4), overwrite = TRUE)
#> NOTE: No variables provided. All columns that fit the value type will receive updates to their missing values (NA).FALSE
#> # A tibble: 1,200 × 15
#>    country   reach employment temp_contract autonomy_selection autonomy_emphasis
#>  * <fct>     <fct> <chr>      <fct>                      <dbl>             <dbl>
#>  1 Germany   Nati… Full-time  Permanent                      5                NA
#>  2 Germany   Nati… Full-time  Permanent                     NA                NA
#>  3 Switzerl… Regi… Full-time  Permanent                     NA                NA
#>  4 Switzerl… Local Part-time  Permanent                     NA                 5
#>  5 Austria   Nati… Part-time  Permanent                     NA                NA
#>  6 Switzerl… Local Freelancer NA                            NA                NA
#>  7 Germany   Local Full-time  Permanent                     NA                NA
#>  8 Denmark   Nati… Full-time  Permanent                     NA                NA
#>  9 Switzerl… Local Full-time  Permanent                      5                 5
#> 10 Denmark   Nati… Full-time  Permanent                     NA                NA
#> # ℹ 1,190 more rows
#> # ℹ 9 more variables: ethics_1 <dbl>, ethics_2 <dbl>, ethics_3 <dbl>,
#> #   ethics_4 <dbl>, work_experience <dbl>, trust_parliament <dbl>,
#> #   trust_government <dbl>, trust_parties <dbl>, trust_politicians <dbl>
WoJ %>%
dplyr::select(country) %>% setna_scale(country, value = "Germany")
#> # A tibble: 1,200 × 2
#>    country     country_na 
#>  * <fct>       <fct>      
#>  1 Germany     NA         
#>  2 Germany     NA         
#>  3 Switzerland Switzerland
#>  4 Switzerland Switzerland
#>  5 Austria     Austria    
#>  6 Switzerland Switzerland
#>  7 Germany     NA         
#>  8 Denmark     Denmark    
#>  9 Switzerland Switzerland
#> 10 Denmark     Denmark    
#> # ℹ 1,190 more rows
WoJ %>%
dplyr::select(country) %>% setna_scale(country, value = c("Germany", "Switzerland"))
#> # A tibble: 1,200 × 2
#>    country     country_na
#>  * <fct>       <fct>     
#>  1 Germany     NA        
#>  2 Germany     NA        
#>  3 Switzerland NA        
#>  4 Switzerland NA        
#>  5 Austria     Austria   
#>  6 Switzerland NA        
#>  7 Germany     NA        
#>  8 Denmark     Denmark   
#>  9 Switzerland NA        
#> 10 Denmark     Denmark   
#> # ℹ 1,190 more rows