Create Counts of Patterns using Regular ExpressionsSource:
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") )
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.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string containing a regular expression (or character string for
fixed = TRUE) to be matched in the given character vector. Coerced by
as.characterto a character string if possible.
A logical; should the integer counts be divided by the total number of characters in the string?.
A list of options to
gregexpr()that should not include
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
NULLuntil computed by
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 = TRUEas 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.
tidy() this step, a tibble with columns
terms (the selectors or variables selected) and
new column name) is returned.
Other dummy variable and encoding steps:
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 #> # … with 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