step_dummy_extract() creates a specification of a recipe step that will
convert nominal data (e.g. characters or factors) into one or more integer
model terms for the extracted levels.
step_dummy_extract(
recipe,
...,
role = "predictor",
trained = FALSE,
sep = NULL,
pattern = NULL,
threshold = 0,
other = "other",
naming = dummy_extract_names,
levels = NULL,
sparse = "auto",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("dummy_extract")
)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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
Character string containing a regular expression to use
for splitting. strsplit() is used to perform the split. sep takes
priority if pattern is also specified.
Character string containing a regular expression used
for extraction. gregexpr() and regmatches() are used to perform
pattern extraction using perl = TRUE.
A numeric value between 0 and 1, or an integer greater or
equal to one. If less than one, then factor levels with a rate of
occurrence in the training set below threshold will be pooled to other.
If greater or equal to one, then this value is treated as a frequency
and factor levels that occur less than threshold times will be pooled
to other.
A single character value for the "other" category.
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 terms. This is NULL until the
step is trained by prep().
A single string. Should the columns produced be sparse vectors.
Can take the values "yes", "no", and "auto". If sparse = "auto"
then workflows can determine the best option. Defaults to "auto".
A logical to keep the original variables in the
output. Defaults to FALSE.
A logical. Should the step be skipped when the
recipe is baked by bake()? While all operations are baked
when prep() 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 using skip = TRUE as it may affect
the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
step_dummy_extract() will create a set of integer dummy
variables from a character variable by extracting individual strings
by either splitting or extracting then counting those to create
count variables.
Note that threshold works in a very specific way for this step.
While it is possible for one label to be present multiple times in
the same row, it will only be counted once when calculating the
occurrences and frequencies.
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.
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 return value is ordered according to the frequency of columns entries in the training data set.
This step produces sparse columns if sparse = "yes" is being set. The
default value "auto" won't trigger production fo sparse columns if a recipe
is prep()ed, but allows for a workflow to toggle to "yes" or "no"
depending on whether the model supports sparse_data and if the model is
is expected to run faster with the data.
The mechanism for determining how much sparsity is produced isn't perfect,
and there will be times when you want to manually overwrite by setting
sparse = "yes" or sparse = "no".
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org.
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy(),
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(tate_text, package = "modeldata")
dummies <- recipe(~ artist + medium, data = tate_text) %>%
step_dummy_extract(artist, medium, sep = ", ") %>%
prep()
dummy_data <- bake(dummies, new_data = NULL)
dummy_data %>%
select(starts_with("medium")) %>%
names() %>%
head()
#> [1] "medium_X1.person" "medium_X1.projection.and.1.monitor"
#> [3] "medium_X100.digital.prints.on.paper" "medium_X100.works.on.paper"
#> [5] "medium_X11.photographs" "medium_X11.works.on.panel"
# More detailed splitting
dummies_specific <- recipe(~medium, data = tate_text) %>%
step_dummy_extract(medium, sep = "(, )|( and )|( on )") %>%
prep()
dummy_data_specific <- bake(dummies_specific, new_data = NULL)
dummy_data_specific %>%
select(starts_with("medium")) %>%
names() %>%
head()
#> [1] "medium_X1.monitor" "medium_X1.person"
#> [3] "medium_X1.projection" "medium_X10.light.boxes"
#> [5] "medium_X10.tranformers" "medium_X100.digital.prints"
tidy(dummies, number = 1)
#> # A tibble: 2,673 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 artist Thomas dummy_extract_mbqAp
#> 2 artist Schütte dummy_extract_mbqAp
#> 3 artist John dummy_extract_mbqAp
#> 4 artist Akram dummy_extract_mbqAp
#> 5 artist Zaatari dummy_extract_mbqAp
#> 6 artist Joseph dummy_extract_mbqAp
#> 7 artist Beuys dummy_extract_mbqAp
#> 8 artist Richard dummy_extract_mbqAp
#> 9 artist Ferrari dummy_extract_mbqAp
#> 10 artist León dummy_extract_mbqAp
#> # ℹ 2,663 more rows
tidy(dummies_specific, number = 1)
#> # A tibble: 1,216 × 3
#> terms columns id
#> <chr> <chr> <chr>
#> 1 medium paper dummy_extract_oEGyP
#> 2 medium Etching dummy_extract_oEGyP
#> 3 medium Photograph dummy_extract_oEGyP
#> 4 medium colour dummy_extract_oEGyP
#> 5 medium gelatin silver print dummy_extract_oEGyP
#> 6 medium Screenprint dummy_extract_oEGyP
#> 7 medium Lithograph dummy_extract_oEGyP
#> 8 medium on paper dummy_extract_oEGyP
#> 9 medium canvas dummy_extract_oEGyP
#> 10 medium aquatint dummy_extract_oEGyP
#> # ℹ 1,206 more rows
# pattern argument can be useful to extract harder patterns
color_examples <- tibble(
colors = c(
"['red', 'blue']",
"['red', 'blue', 'white']",
"['blue', 'blue', 'blue']"
)
)
dummies_color <- recipe(~colors, data = color_examples) %>%
step_dummy_extract(colors, pattern = "(?<=')[^',]+(?=')") %>%
prep()
dummies_data_color <- dummies_color %>%
bake(new_data = NULL)
dummies_data_color
#> # A tibble: 3 × 4
#> colors_blue colors_red colors_white colors_other
#> <int> <int> <int> <int>
#> 1 1 1 0 0
#> 2 1 1 1 0
#> 3 3 0 0 0