step_dummy | R Documentation |
step_dummy()
creates a specification of a recipe step that will convert
nominal data (e.g. factors) into one or more numeric binary model terms
corresponding to the levels of the original data.
step_dummy(
recipe,
...,
role = "predictor",
trained = FALSE,
one_hot = FALSE,
preserve = deprecated(),
naming = dummy_names,
levels = NULL,
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("dummy")
)
recipe |
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 |
role |
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. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
one_hot |
A logical. For C levels, should C dummy variables be created rather than C-1? |
preserve |
This argument has been deprecated. Please use
|
naming |
A function that defines the naming convention for new dummy columns. See Details below. |
levels |
A list that contains the information needed to create dummy
variables for each variable contained in |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
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.
step_relevel()
can be used to create a new reference level by setting the
ref_level
argument.
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.
To change the type of contrast being used, change the global contrast option
via options
.
When the factor being converted has a missing value, all of the corresponding
dummy variables are also missing. See step_unknown()
for a solution.
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 step_zv()
), bake()
will return the data as-is (e.g. with no dummy variables).
Note that, by default, the new dummy variable column names obey the naming
rules for columns. If there are levels such as "0", dummy_names()
will put
a leading "X" in front of the level (since it uses make.names()
). This can
be changed by passing in a different function to the naming
argument for
this step.
Also, there are a number of contrast methods that return fractional values. The columns returned by this step are doubles (not integers).
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 any existing operations.
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
The underlying operation does not allow for case weights.
dummy_names()
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy_extract()
,
step_dummy_multi_choice()
,
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()
data(Sacramento, package = "modeldata")
# Original data: city has 37 levels
length(unique(Sacramento$city))
unique(Sacramento$city) %>% sort()
rec <- recipe(~ city + sqft + price, data = Sacramento)
# Default dummy coding: 36 dummy variables
dummies <- rec %>%
step_dummy(city) %>%
prep(training = Sacramento)
dummy_data <- bake(dummies, new_data = NULL)
dummy_data %>%
select(starts_with("city")) %>%
names() # level "anything" is the reference level
# Obtain the full set of 37 dummy variables using `one_hot` option
dummies_one_hot <- rec %>%
step_dummy(city, one_hot = TRUE) %>%
prep(training = Sacramento)
dummy_data_one_hot <- bake(dummies_one_hot, new_data = NULL)
dummy_data_one_hot %>%
select(starts_with("city")) %>%
names() # no reference level
tidy(dummies, number = 1)
tidy(dummies_one_hot, number = 1)
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