Set specified values to NA in selected variables or entire data frame
Source:R/scaling.R
setna_scale.RdThis function allows users to set specific values to NA in chosen variables
within a data frame. It can handle numeric, character, and factor variables.
Arguments
- data
- ...
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