This function transforms specified categorical variables into dummy variables. Each level of the categorical variable is represented by a new dummy variable. Missing values are retained. These new dummy variables are appended to the original data frame. This function does not allow specifying new column names for the dummy variables. Instead, it follows a consistent naming pattern: the new dummy variables are named using the original variable name with the category value appended. For example, if a categorical variable named "autonomy" with levels "low", "medium", "high" is dummified, the new dummy variables will be named "autonomy_low", "autonomy_medium", "autonomy_high".
dummify_scale(data, ..., overwrite = FALSE)Categorical variables to be transformed into dummy variables. Category names will be automatically appended to the newly created dummy variables.
Logical. If TRUE, it overwrites the original variable(s)
with the dummy variables. If FALSE (default), new variables are created.
A tdcmm model with the dummy variables appended.
Other scaling:
categorize_scale(),
center_scale(),
minmax_scale(),
recode_cat_scale(),
reverse_scale(),
setna_scale(),
z_scale()
WoJ %>% dplyr::select(temp_contract) %>% dummify_scale(temp_contract)
#> # A tibble: 1,200 × 3
#> temp_contract temp_contract_permanent temp_contract_temporary
#> * <fct> <int> <int>
#> 1 Permanent 1 0
#> 2 Permanent 1 0
#> 3 Permanent 1 0
#> 4 Permanent 1 0
#> 5 Permanent 1 0
#> 6 NA NA NA
#> 7 Permanent 1 0
#> 8 Permanent 1 0
#> 9 Permanent 1 0
#> 10 Permanent 1 0
#> # ℹ 1,190 more rows
WoJ %>% categorize_scale(autonomy_emphasis, breaks = c(2, 3),
labels = c('low', 'medium', 'high')) %>%
dummify_scale(autonomy_emphasis_cat) %>% dplyr::select(starts_with('autonomy_emphasis'))
#> Warning: Lower and/or upper end missing. Based on the minimum and maximum values observed in the data, the original scale (autonomy_emphasis) is assumed to range from 1 to 5. To prevent this warning, please provide the lower_end and upper_end values as arguments when calling the function.
#> # A tibble: 1,200 × 5
#> autonomy_emphasis autonomy_emphasis_cat autonomy_emphasis_cat_low
#> <dbl> <fct> <int>
#> 1 4 high 0
#> 2 4 high 0
#> 3 4 high 0
#> 4 5 high 0
#> 5 4 high 0
#> 6 4 high 0
#> 7 4 high 0
#> 8 3 medium 0
#> 9 5 high 0
#> 10 4 high 0
#> # ℹ 1,190 more rows
#> # ℹ 2 more variables: autonomy_emphasis_cat_medium <int>,
#> # autonomy_emphasis_cat_high <int>