step_regex creates a specification of a recipe step that will
create a new dummy variable based on a regular expression.
Usage
step_regex(
recipe,
...,
role = "predictor",
trained = FALSE,
pattern = ".",
options = list(),
result = make.names(pattern),
input = NULL,
skip = FALSE,
id = rand_id("regex")
)Arguments
- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.
- ...
A single selector function to choose which variable will be searched for the regex pattern. The selector should resolve to a single variable. See
selections()for more details.- 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.
- pattern
A character string containing a regular expression (or character string for
fixed = TRUE) to be matched in the given character vector. Coerced byas.characterto a character string if possible.- options
A list of options to
grepl()that should not includexorpattern.- result
A single character value for the name of the new variable. It should be a valid column name.
- input
A single character value for the name of the variable being searched. This is
NULLuntil computed byprep().- 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.
Tidying
When you tidy() this step, a tibble with columns
terms (the selectors or variables selected) and result (the
new column name) is returned.
See also
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy_extract(),
step_dummy_multi_choice(),
step_dummy(),
step_factor2string(),
step_holiday(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_ordinalscore(),
step_other(),
step_relevel(),
step_string2factor(),
step_time(),
step_unknown(),
step_unorder()
Examples
data(covers, package = "modeldata")
rec <- recipe(~description, covers) %>%
step_regex(description, pattern = "(rock|stony)", result = "rocks") %>%
step_regex(description, pattern = "ratake families")
rec2 <- prep(rec, training = covers)
rec2
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> predictor 1
#>
#> Training data contained 40 data points and no missing data.
#>
#> Operations:
#>
#> Regular expression dummy variable using "(rock|stony)" [trained]
#> Regular expression dummy variable using "ratake families" [trained]
with_dummies <- bake(rec2, new_data = covers)
with_dummies
#> # A tibble: 40 × 3
#> description rocks ratak…¹
#> <fct> <int> <int>
#> 1 1,cathedral family,rock outcrop complex,extremely stony 1 0
#> 2 2,vanet,ratake families complex,very stony 1 1
#> 3 3,haploborolis,rock outcrop complex,rubbly 1 0
#> 4 4,ratake family,rock outcrop complex,rubbly 1 0
#> 5 5,vanet family,rock outcrop complex complex,rubbly 1 0
#> 6 6,vanet,wetmore families,rock outcrop complex,stony 1 0
#> 7 7,gothic family 0 0
#> 8 8,supervisor,limber families complex 0 0
#> 9 9,troutville family,very stony 1 0
#> 10 10,bullwark,catamount families,rock outcrop complex,rubb… 1 0
#> # … with 30 more rows, and abbreviated variable name ¹ratake.families
tidy(rec, number = 1)
#> # A tibble: 1 × 3
#> terms result id
#> <chr> <chr> <chr>
#> 1 description NA regex_nOqFi
tidy(rec2, number = 1)
#> # A tibble: 1 × 3
#> terms result id
#> <chr> <chr> <chr>
#> 1 description rocks regex_nOqFi