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Detect a regular expression

Source: R/regex.R
step_regex.Rd

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,
  sparse = "auto",
  keep_original_cols = TRUE,
  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 by as.character to a character string if possible.

options

A list of options to grepl() that should not include x or pattern.

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 NULL until computed by prep().

sparse

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".

keep_original_cols

A logical to keep the original variables in the output. Defaults to TRUE.

skip

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.

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 is returned with columns terms, result , and id:

terms

character, the selectors or variables selected

result

character, new column name

id

character, id of this step

Sparse data

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".

Case weights

The underlying operation does not allow for case weights.

See also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy(), 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_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 
#> Number of variables by role
#> predictor: 1
#> 
#> ── Training information 
#> Training data contained 40 data points and no incomplete rows.
#> 
#> ── 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 ratake.families
#>    <fct>                                          <int>           <int>
#>  1 1,cathedral family,rock outcrop complex,extre…     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,r…     1               0
#>  6 6,vanet,wetmore families,rock outcrop complex…     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 c…     1               0
#> # ℹ 30 more rows
tidy(rec, number = 1)
#> # A tibble: 1 × 3
#>   terms       result id         
#>   <chr>       <chr>  <chr>      
#> 1 description NA     regex_0cUH6
tidy(rec2, number = 1)
#> # A tibble: 1 × 3
#>   terms       result id         
#>   <chr>       <chr>  <chr>      
#> 1 description rocks  regex_0cUH6