Handle levels in multiple predictors togetherSource:
step_dummy_multi_choice() creates a specification of a recipe
step that will convert multiple nominal data (e.g. character or factors)
into one or more numeric binary model terms for the levels of
the original data.
step_dummy_multi_choice( recipe, ..., role = "predictor", trained = FALSE, threshold = 0, levels = NULL, input = NULL, other = "other", naming = dummy_names, prefix = NULL, keep_original_cols = FALSE, skip = FALSE, id = rand_id("dummy_multi_choice") )
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this step. See
selections()for more details. The selected variables must be factors.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A numeric value between 0 and 1, or an integer greater or equal to one. If less than one, then factor levels with a rate of occurrence in the training set below
thresholdwill be pooled to
other. If greater or equal to one, then this value is treated as a frequency and factor levels that occur less than
thresholdtimes will be pooled to
A list that contains the information needed to create dummy variables for each variable contained in
terms. This is
NULLuntil the step is trained by
A character vector containing the names of the columns used. This is
NULLuntil the step is trained by
A single character value for the "other" category.
A function that defines the naming convention for new dummy columns. See Details below.
A character string for the prefix of the resulting new variables. See notes below.
A logical to keep the original variables in the output. Defaults to
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
The overall proportion (or total counts) of the categories are
computed. The "other" category is used in place of any categorical levels
whose individual proportion (or frequency) in the training set is less than
This recipe step allows for flexible naming of the resulting
variables. For an unordered factor named
x, with levels
"b", the default naming convention would be to create a
new variable called
x_b. The naming format can be changed using
naming argument; the function
dummy_names() is the
Other dummy variable and encoding steps:
library(tibble) languages <- tribble( ~lang_1, ~lang_2, ~lang_3, "English", "Italian", NA, "Spanish", NA, "French", "Armenian", "English", "French", NA, NA, NA ) dummy_multi_choice_rec <- recipe(~ ., data = languages) %>% step_dummy_multi_choice(starts_with("lang")) %>% prep() bake(dummy_multi_choice_rec, new_data = NULL) #> # A tibble: 4 × 5 #> lang_1_Armenian lang_1_English lang_1_French lang_1_Italian #> <int> <int> <int> <int> #> 1 0 1 0 1 #> 2 0 0 1 0 #> 3 1 1 1 0 #> 4 0 0 0 0 #> # … with 1 more variable: lang_1_Spanish <int> tidy(dummy_multi_choice_rec, number = 1) #> # A tibble: 5 × 3 #> terms columns id #> <chr> <chr> <chr> #> 1 lang_1 Armenian dummy_multi_choice_kJsyR #> 2 lang_1 English dummy_multi_choice_kJsyR #> 3 lang_1 French dummy_multi_choice_kJsyR #> 4 lang_1 Italian dummy_multi_choice_kJsyR #> 5 lang_1 Spanish dummy_multi_choice_kJsyR dummy_multi_choice_rec2 <- recipe(~ ., data = languages) %>% step_dummy_multi_choice(starts_with("lang"), prefix = "lang", threshold = 0.2) %>% prep() bake(dummy_multi_choice_rec2, new_data = NULL) #> # A tibble: 4 × 3 #> lang_English lang_French lang_other #> <int> <int> <int> #> 1 1 0 1 #> 2 0 1 1 #> 3 1 1 1 #> 4 0 0 0 tidy(dummy_multi_choice_rec2, number = 1) #> # A tibble: 3 × 3 #> terms columns id #> <chr> <chr> <chr> #> 1 lang_1 English dummy_multi_choice_Cb4Qi #> 2 lang_1 French dummy_multi_choice_Cb4Qi #> 3 lang_1 other dummy_multi_choice_Cb4Qi