recipes (version 0.1.4)

step_num2factor: Convert Numbers to Factors

Description

step_num2factor will convert one or more numeric vectors to factors (ordered or unordered). This can be useful when categories are encoded as integers.

Usage

step_num2factor(recipe, ..., role = NA, transform = function(x) x,
  trained = FALSE, levels = NULL, ordered = FALSE, skip = FALSE,
  id = rand_id("num2factor"))
# S3 method for step_num2factor
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables will converted to factors. See selections() for more details. For the tidy method, these are not currently used.

role

Not used by this step since no new variables are created.

transform

A function taking a single argument x that can be used to modify the numeric values prior to determining the levels (perhaps using base::paste() or base::format()).

trained

A logical to indicate if the quantities for preprocessing have been estimated.

levels

A list of values that will be used as the levels. These are the numeric data converted to character and ordered. This is NULL until computed by prep.recipe().

ordered

A single logical value; should the factor(s) be ordered?

skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.

x

A step_num2factor object.

Value

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 terms (the selectors or variables selected) and ordered.

See Also

step_factor2string(), step_string2factor(), step_dummy()

Examples

# NOT RUN {
iris2 <- iris
iris2$Species <- as.numeric(iris2$Species)

rec <- recipe(~ ., data = iris2)

make_factor <- rec %>% step_num2factor(Species)
make_factor <- prep(make_factor,
                    training = iris2,
                    retain = TRUE)

# note that `diet` is a factor
juice(make_factor) %>% head
okc %>% head
tidy(make_factor, number = 1)

# When novel values are exposed
with_transform <- rec %>%
  step_num2factor(Species, transform = function(x) paste0("val_", x))

with_transform <- prep(with_transform,
                       training = iris2[1:75,])
new_values <- bake(with_transform, new_data = iris2, Species)
table(new_values[["Species"]], iris2$Species, useNA = "ifany")
# }