step_count creates a specification of a recipe
step that will create a variable that counts instances of a
regular expression pattern in text.
step_count(recipe, ..., role = "predictor", trained = FALSE, pattern = ".", normalize = FALSE, options = list(), result = make.names(pattern), input = NULL, skip = FALSE, id = rand_id("count")) # S3 method for step_count tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
A single selector functions to choose which variable
will be searched for the pattern. The selector should resolve
into a single variable. See
For a variable created by this step, what analysis role should they be assigned?. By default, the function assumes that the new dummy variable column created by the original variable will be used as a predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string containing a regular
expression (or character string for
A logical; should the integer counts be divided by the total number of characters in the string?.
A list of options to
A single character value for the name of the new variable. It should be a valid column name.
A single character value for the name of the
variable being searched. This is
A logical. Should the step be skipped when the
recipe is baked by
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 existing steps (if any). For the
tidy method, a tibble with columns
selectors or variables selected) and
new column name).
data(covers) 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#> Data Recipe #> #> Inputs: #> #> role #variables #> predictor 1 #> #> Training data contained 40 data points and no missing data. #> #> Operations: #> #> Regular expression counts using `(rock|stony)` [trained] #> Regular expression counts using `famil` [trained]count_values <- bake(rec2, new_data = covers) count_values#> # A tibble: 40 x 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 #> # … with 30 more rowstidy(rec, number = 1)#> # A tibble: 1 x 3 #> terms result id #> <chr> <chr> <chr> #> 1 description <NA> count_soExAtidy(rec2, number = 1)#> # A tibble: 1 x 3 #> terms result id #> <chr> <chr> <chr> #> 1 description rocks count_soExA