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.
Usage
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")
)Arguments
- 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
selections()for more details. The selected variables must be factors.- 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
keep_original_colsto specify whether the selected column(s) should be retained (in addition to the new dummy variables).- 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
terms. This isNULLuntil the step is trained byprep().- keep_original_cols
A logical to keep the original variables in the output. Defaults to
FALSE.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
Details
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.
Tidying
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.
See also
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()
Examples
data(Sacramento, package = "modeldata")
# Original data: city has 37 levels
length(unique(Sacramento$city))
#> [1] 37
unique(Sacramento$city) %>% sort()
#> [1] ANTELOPE AUBURN CAMERON_PARK CARMICHAEL
#> [5] CITRUS_HEIGHTS COOL DIAMOND_SPRINGS EL_DORADO
#> [9] EL_DORADO_HILLS ELK_GROVE ELVERTA FAIR_OAKS
#> [13] FOLSOM FORESTHILL GALT GARDEN_VALLEY
#> [17] GOLD_RIVER GRANITE_BAY GREENWOOD LINCOLN
#> [21] LOOMIS MATHER MEADOW_VISTA NORTH_HIGHLANDS
#> [25] ORANGEVALE PENRYN PLACERVILLE POLLOCK_PINES
#> [29] RANCHO_CORDOVA RANCHO_MURIETA RIO_LINDA ROCKLIN
#> [33] ROSEVILLE SACRAMENTO WALNUT_GROVE WEST_SACRAMENTO
#> [37] WILTON
#> 37 Levels: ANTELOPE AUBURN CAMERON_PARK CARMICHAEL ... WILTON
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
#> [1] "city_AUBURN" "city_CAMERON_PARK" "city_CARMICHAEL"
#> [4] "city_CITRUS_HEIGHTS" "city_COOL" "city_DIAMOND_SPRINGS"
#> [7] "city_EL_DORADO" "city_EL_DORADO_HILLS" "city_ELK_GROVE"
#> [10] "city_ELVERTA" "city_FAIR_OAKS" "city_FOLSOM"
#> [13] "city_FORESTHILL" "city_GALT" "city_GARDEN_VALLEY"
#> [16] "city_GOLD_RIVER" "city_GRANITE_BAY" "city_GREENWOOD"
#> [19] "city_LINCOLN" "city_LOOMIS" "city_MATHER"
#> [22] "city_MEADOW_VISTA" "city_NORTH_HIGHLANDS" "city_ORANGEVALE"
#> [25] "city_PENRYN" "city_PLACERVILLE" "city_POLLOCK_PINES"
#> [28] "city_RANCHO_CORDOVA" "city_RANCHO_MURIETA" "city_RIO_LINDA"
#> [31] "city_ROCKLIN" "city_ROSEVILLE" "city_SACRAMENTO"
#> [34] "city_WALNUT_GROVE" "city_WEST_SACRAMENTO" "city_WILTON"
# 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
#> [1] "city_ANTELOPE" "city_AUBURN" "city_CAMERON_PARK"
#> [4] "city_CARMICHAEL" "city_CITRUS_HEIGHTS" "city_COOL"
#> [7] "city_DIAMOND_SPRINGS" "city_EL_DORADO" "city_EL_DORADO_HILLS"
#> [10] "city_ELK_GROVE" "city_ELVERTA" "city_FAIR_OAKS"
#> [13] "city_FOLSOM" "city_FORESTHILL" "city_GALT"
#> [16] "city_GARDEN_VALLEY" "city_GOLD_RIVER" "city_GRANITE_BAY"
#> [19] "city_GREENWOOD" "city_LINCOLN" "city_LOOMIS"
#> [22] "city_MATHER" "city_MEADOW_VISTA" "city_NORTH_HIGHLANDS"
#> [25] "city_ORANGEVALE" "city_PENRYN" "city_PLACERVILLE"
#> [28] "city_POLLOCK_PINES" "city_RANCHO_CORDOVA" "city_RANCHO_MURIETA"
#> [31] "city_RIO_LINDA" "city_ROCKLIN" "city_ROSEVILLE"
#> [34] "city_SACRAMENTO" "city_WALNUT_GROVE" "city_WEST_SACRAMENTO"
#> [37] "city_WILTON"
tidy(dummies, number = 1)
#> # A tibble: 36 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 city AUBURN dummy_ypfCg
#> 2 city CAMERON_PARK dummy_ypfCg
#> 3 city CARMICHAEL dummy_ypfCg
#> 4 city CITRUS_HEIGHTS dummy_ypfCg
#> 5 city COOL dummy_ypfCg
#> 6 city DIAMOND_SPRINGS dummy_ypfCg
#> 7 city EL_DORADO dummy_ypfCg
#> 8 city EL_DORADO_HILLS dummy_ypfCg
#> 9 city ELK_GROVE dummy_ypfCg
#> 10 city ELVERTA dummy_ypfCg
#> # … with 26 more rows
tidy(dummies_one_hot, number = 1)
#> # A tibble: 37 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 city ANTELOPE dummy_ONQD4
#> 2 city AUBURN dummy_ONQD4
#> 3 city CAMERON_PARK dummy_ONQD4
#> 4 city CARMICHAEL dummy_ONQD4
#> 5 city CITRUS_HEIGHTS dummy_ONQD4
#> 6 city COOL dummy_ONQD4
#> 7 city DIAMOND_SPRINGS dummy_ONQD4
#> 8 city EL_DORADO dummy_ONQD4
#> 9 city EL_DORADO_HILLS dummy_ONQD4
#> 10 city ELK_GROVE dummy_ONQD4
#> # … with 27 more rows