step_dummy | R Documentation |
step_dummy()
creates 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.
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 |
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.
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 with columns
terms
(the selectors or original variables selected) and columns
(the list of corresponding binary columns) is returned.
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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