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, default to
"other".
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_oEGyP
#> 2 artist Schütte dummy_extract_oEGyP
#> 3 artist John dummy_extract_oEGyP
#> 4 artist Akram dummy_extract_oEGyP
#> 5 artist Zaatari dummy_extract_oEGyP
#> 6 artist Joseph dummy_extract_oEGyP
#> 7 artist Beuys dummy_extract_oEGyP
#> 8 artist Richard dummy_extract_oEGyP
#> 9 artist Ferrari dummy_extract_oEGyP
#> 10 artist León dummy_extract_oEGyP
#> # 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_2Slih
#> 2 medium Etching dummy_extract_2Slih
#> 3 medium Photograph dummy_extract_2Slih
#> 4 medium colour dummy_extract_2Slih
#> 5 medium gelatin silver print dummy_extract_2Slih
#> 6 medium Screenprint dummy_extract_2Slih
#> 7 medium Lithograph dummy_extract_2Slih
#> 8 medium on paper dummy_extract_2Slih
#> 9 medium canvas dummy_extract_2Slih
#> 10 medium aquatint dummy_extract_2Slih
#> # 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