Create counts of patterns using regular expressions

step_count() creates a specification of a recipe step that will create a variable that counts instances of a regular expression pattern in text.

Usage

step_count(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  pattern = ".",
  normalize = FALSE,
  options = list(),
  result = make.names(pattern),
  input = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("count")
)

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.

normalize

A logical; should the integer counts be divided by the total number of characters in the string?.

options

A list of options to gregexpr() 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().

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, the new column names

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

Other dummy variable and encoding steps: step_bin2factor(), 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_regex(), step_relevel(), step_string2factor(), step_time(), step_unknown(), step_unorder()

Examples

data(covers, package = "modeldata")

rec <- recipe(~description, covers) %>%
  step_count(description, pattern = "(rock|stony)", result = "rocks") %>%
  step_count(description, pattern = "famil", normalize = TRUE)

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 counts using: description | Trained
#> • Regular expression counts using: description | Trained

count_values <- bake(rec2, new_data = covers)
count_values
#> # A tibble: 40 × 3
#>    description                                                rocks  famil
#>    <fct>                                                      <int>  <dbl>
#>  1 1,cathedral family,rock outcrop complex,extremely stony        2 0.0182
#>  2 2,vanet,ratake families complex,very stony                     1 0.0238
#>  3 3,haploborolis,rock outcrop complex,rubbly                     1 0     
#>  4 4,ratake family,rock outcrop complex,rubbly                    1 0.0233
#>  5 5,vanet family,rock outcrop complex complex,rubbly             1 0.02  
#>  6 6,vanet,wetmore families,rock outcrop complex,stony            2 0.0196
#>  7 7,gothic family                                                0 0.0667
#>  8 8,supervisor,limber families complex                           0 0.0278
#>  9 9,troutville family,very stony                                 1 0.0333
#> 10 10,bullwark,catamount families,rock outcrop complex,rubbly     1 0.0172
#> # ℹ 30 more rows

tidy(rec, number = 1)
#> # A tibble: 1 × 3
#>   terms       result id         
#>   <chr>       <chr>  <chr>      
#> 1 description NA     count_HX7KJ
tidy(rec2, number = 1)
#> # A tibble: 1 × 3
#>   terms       result id         
#>   <chr>       <chr>  <chr>      
#> 1 description rocks  count_HX7KJ