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") ) # S3 method for step_dummy tidy(x, ...)
| recipe | 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
|
| role | 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. |
| 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. |
| x | A |
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 terms (the
selectors or original variables selected) and columns (the
list of corresponding binary columns).
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 "a"
and "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 naming
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 ".L", ".Q", or "^4", ordinal
dummy variables are given simple integer suffixes such as
"_1", "_2", etc.
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.
The package vignette for dummy variables and interactions has more information.
step_factor2string(), step_string2factor(),
dummy_names(), step_regex(), step_count(),
step_ordinalscore(), step_unorder(), step_other()
step_novel()
library(modeldata) data(okc) okc <- okc[complete.cases(okc),] # Original data: diet has 18 levels length(unique(okc$diet))#> [1] 18#> [1] "anything" "halal" "kosher" #> [4] "mostly anything" "mostly halal" "mostly kosher" #> [7] "mostly other" "mostly vegan" "mostly vegetarian" #> [10] "other" "strictly anything" "strictly halal" #> [13] "strictly kosher" "strictly other" "strictly vegan" #> [16] "strictly vegetarian" "vegan" "vegetarian"rec <- recipe(~ diet + age + height, data = okc) # Default dummy coding: 17 dummy variables dummies <- rec %>% step_dummy(diet) %>% prep(training = okc) dummy_data <- bake(dummies, new_data = NULL) dummy_data %>% select(starts_with("diet")) %>% names() # level "anything" is the reference level#> [1] "diet_halal" "diet_kosher" #> [3] "diet_mostly.anything" "diet_mostly.halal" #> [5] "diet_mostly.kosher" "diet_mostly.other" #> [7] "diet_mostly.vegan" "diet_mostly.vegetarian" #> [9] "diet_other" "diet_strictly.anything" #> [11] "diet_strictly.halal" "diet_strictly.kosher" #> [13] "diet_strictly.other" "diet_strictly.vegan" #> [15] "diet_strictly.vegetarian" "diet_vegan" #> [17] "diet_vegetarian"# Obtain the full set of 18 dummy variables using `one_hot` option dummies_one_hot <- rec %>% step_dummy(diet, one_hot = TRUE) %>% prep(training = okc) dummy_data_one_hot <- bake(dummies_one_hot, new_data = NULL) dummy_data_one_hot %>% select(starts_with("diet")) %>% names() # no reference level#> [1] "diet_anything" "diet_halal" #> [3] "diet_kosher" "diet_mostly.anything" #> [5] "diet_mostly.halal" "diet_mostly.kosher" #> [7] "diet_mostly.other" "diet_mostly.vegan" #> [9] "diet_mostly.vegetarian" "diet_other" #> [11] "diet_strictly.anything" "diet_strictly.halal" #> [13] "diet_strictly.kosher" "diet_strictly.other" #> [15] "diet_strictly.vegan" "diet_strictly.vegetarian" #> [17] "diet_vegan" "diet_vegetarian"#> # A tibble: 17 x 3 #> terms columns id #> <chr> <chr> <chr> #> 1 diet halal dummy_fCgON #> 2 diet kosher dummy_fCgON #> 3 diet mostly anything dummy_fCgON #> 4 diet mostly halal dummy_fCgON #> 5 diet mostly kosher dummy_fCgON #> 6 diet mostly other dummy_fCgON #> 7 diet mostly vegan dummy_fCgON #> 8 diet mostly vegetarian dummy_fCgON #> 9 diet other dummy_fCgON #> 10 diet strictly anything dummy_fCgON #> 11 diet strictly halal dummy_fCgON #> 12 diet strictly kosher dummy_fCgON #> 13 diet strictly other dummy_fCgON #> 14 diet strictly vegan dummy_fCgON #> 15 diet strictly vegetarian dummy_fCgON #> 16 diet vegan dummy_fCgON #> 17 diet vegetarian dummy_fCgON#> # A tibble: 18 x 3 #> terms columns id #> <chr> <chr> <chr> #> 1 diet anything dummy_QD4mb #> 2 diet halal dummy_QD4mb #> 3 diet kosher dummy_QD4mb #> 4 diet mostly anything dummy_QD4mb #> 5 diet mostly halal dummy_QD4mb #> 6 diet mostly kosher dummy_QD4mb #> 7 diet mostly other dummy_QD4mb #> 8 diet mostly vegan dummy_QD4mb #> 9 diet mostly vegetarian dummy_QD4mb #> 10 diet other dummy_QD4mb #> 11 diet strictly anything dummy_QD4mb #> 12 diet strictly halal dummy_QD4mb #> 13 diet strictly kosher dummy_QD4mb #> 14 diet strictly other dummy_QD4mb #> 15 diet strictly vegan dummy_QD4mb #> 16 diet strictly vegetarian dummy_QD4mb #> 17 diet vegan dummy_QD4mb #> 18 diet vegetarian dummy_QD4mb