R/dummy_multi_choice.R
step_dummy_multi_choice.Rdstep_dummy_multi_choice() creates a specification of a recipe step that
will convert multiple nominal data (e.g. characters 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,
sparse = "auto",
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 threshold will 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 threshold times will be pooled to
other.
A list that contains the information needed to create dummy
variables for each variable contained in terms. This is NULL until the
step is trained by prep().
A character vector containing the names of the columns used.
This is NULL until the step is trained by prep().
A single character value for the other category, default to
"other".
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 single string. Should the columns produced be sparse vectors.
Can take the values "yes", "no", and "auto". If sparse = "auto"
then workflows can determine the best option. Defaults to "auto".
A logical to keep the original variables in the
output. Defaults to FALSE.
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 = TRUE as 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
threshold.
This step produces a number of columns, based on the number of categories it
finds. The naming of the columns is determined by the function based on the
naming argument. The default is to return <prefix>_<category name>. By
default prefix is NULL, which means the name of the first column selected
will be used in place.
This recipe step allows for flexible naming of the resulting
variables. For an unordered factor named x, with levels "a"
and "b", the default naming convention would be to create a
new variable called x_b. The naming format can be changed using
the naming argument; the function dummy_names() is the
default.
This step has 1 tuning parameters:
threshold: Threshold (type: double, default: 0)
When you tidy() this step, a tibble is returned with
columns terms, columns , and id:
character, the selectors or variables selected
character, names of resulting columns
character, id of this step
This step produces sparse columns if sparse = "yes" is being set. The
default value "auto" won't trigger production fo sparse columns if a recipe
is prep()ed, but allows for a workflow to toggle to "yes" or "no"
depending on whether the model supports sparse_data and if the model is
is expected to run faster with the data.
The mechanism for determining how much sparsity is produced isn't perfect,
and there will be times when you want to manually overwrite by setting
sparse = "yes" or sparse = "no".
The underlying operation does not allow for case weights.
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy(),
step_dummy_extract(),
step_factor2string(),
step_holiday(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_ordinalscore(),
step_other(),
step_regex(),
step_relevel(),
step_string2factor(),
step_time(),
step_unknown(),
step_unorder()
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 lang_1_Spanish
#> <int> <int> <int> <int> <int>
#> 1 0 1 0 1 0
#> 2 0 0 1 0 1
#> 3 1 1 1 0 0
#> 4 0 0 0 0 0
tidy(dummy_multi_choice_rec, number = 1)
#> # A tibble: 5 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 lang_1 Armenian dummy_multi_choice_Cb4Qi
#> 2 lang_1 English dummy_multi_choice_Cb4Qi
#> 3 lang_1 French dummy_multi_choice_Cb4Qi
#> 4 lang_1 Italian dummy_multi_choice_Cb4Qi
#> 5 lang_1 Spanish dummy_multi_choice_Cb4Qi
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 × 2
#> lang_English lang_other
#> <int> <int>
#> 1 1 1
#> 2 0 1
#> 3 1 1
#> 4 0 0
tidy(dummy_multi_choice_rec2, number = 1)
#> # A tibble: 2 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 lang_1 English dummy_multi_choice_z73sI
#> 2 lang_1 other dummy_multi_choice_z73sI