step_dummy creates a a specification of a recipe
step that will convert nominal data (e.g. character or factors)
into one or more numeric binary model terms for the levels of
the original data.
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A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose which
factor variables will be used to create the dummy variables. See
For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the binary dummy variable columns created by the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A logical. For C levels, should C dummy variables be created rather than C-1?
A single logical; should the selected column(s) be retained (in addition to the new dummy variables).
A function that defines the naming convention for new dummy columns. See Details below.
A list that contains the information needed to
create dummy variables for each variable contained in
A logical. Should the step be skipped when the
recipe is baked by
A character string that is unique to this step to identify it.
step_dummy will create a set of binary dummy
variables from a factor variable. For example, if an unordered
factor column in the data set has levels of "red", "green",
"blue", the dummy variable bake will create two additional
columns of 0/1 data for two of those three values (and remove
the original column). For ordered factors, polynomial contrasts
are used to encode the numeric values.
By default, the excluded dummy variable (i.e. the reference cell) will correspond to the first level of the unordered factor being converted.
The function allows for non-standard 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. Note that if the factor levels are
not valid variable names (e.g. "some text with spaces"), it will
be changed by
base::make.names() to be valid (see the example
below). The naming format can be changed using the
argument and the function
dummy_names() is the default. This
function will also change the names of ordinal dummy variables.
Instead of values such as "
.Q", or "
dummy variables are given simple integer suffixes such as
To change the type of contrast being used, change the global
contrast option via
When the factor being converted has a missing value, all of the corresponding dummy variables are also missing.
When data to be processed contains novel levels (i.e., not
contained in the training set), a missing value is assigned to
the results. See
step_other() for an alternative.
If no columns are selected (perhaps due to an earlier
juice() functions will return the data as-is (e.g. with no
The package vignette for dummy variables and interactions has more information.
An updated version of
recipe with the new step
added to the sequence of existing steps (if any). For the
tidy method, a tibble with columns
selectors or original variables selected) and
list of corresponding binary columns).
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library(modeldata) data(okc) okc <- okc[complete.cases(okc),] rec <- recipe(~ diet + age + height, data = okc) dummies <- rec %>% step_dummy(diet) dummies <- prep(dummies, training = okc) dummy_data <- bake(dummies, new_data = okc) unique(okc$diet) grep("^diet", names(dummy_data), value = TRUE) # Obtain the full set of dummy variables using `one_hot` option rec %>% step_dummy(diet, one_hot = TRUE) %>% prep(training = okc) %>% juice(starts_with("diet")) %>% names() %>% length() length(unique(okc$diet)) # Without one_hot length(grep("^diet", names(dummy_data), value = TRUE)) tidy(dummies, number = 1)
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