step_num2factor: Convert numbers to factors
In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

step_num2factor

R Documentation

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,
  ordered = FALSE,
  skip = FALSE,
  id = rand_id("num2factor")
)

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 variables for this step. See `selections()` for more details.
`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::as.integer()` or `base::as.factor()`). The output of a function should be an integer that corresponds to the value of `levels` that should be assigned. If not an integer, the value will be converted to an integer during `bake()`.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`levels`	A character vector of values that will be used as the levels. These are the numeric data converted to character and ordered. This is modified once `prep()` is executed.
`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()`? 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.

Details

Note that since the numeric variables will be used for indexing into levels it will need to take values between 1 and length(levels) to avoid getting NAs as results. Using transform = base::as.factor can be used to shrink values to smaller domain.

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, ordered , and id:

terms: character, the selectors or variables selected
ordered: logical, were the factor(s) ordered
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

Examples


library(dplyr)
data(attrition, package = "modeldata")

attrition %>%
  group_by(StockOptionLevel) %>%
  count()

amnt <- c("nothin", "meh", "some", "copious")

rec <-
  recipe(Attrition ~ StockOptionLevel, data = attrition) %>%
  step_num2factor(
    StockOptionLevel,
    transform = function(x) x + 1,
    levels = amnt
  )

encoded <- rec %>%
  prep() %>%
  bake(new_data = NULL)

table(encoded$StockOptionLevel, attrition$StockOptionLevel)


# an example for binning

binner <- function(x) {
  x <- cut(x, breaks = 1000 * c(0, 5, 10, 20), include.lowest = TRUE)
  # now return the group number
  as.numeric(x)
}

inc <- c("low", "med", "high")

rec <-
  recipe(Attrition ~ MonthlyIncome, data = attrition) %>%
  step_num2factor(
    MonthlyIncome,
    transform = binner,
    levels = inc,
    ordered = TRUE
  ) %>%
  prep()

encoded <- bake(rec, new_data = NULL)

table(encoded$MonthlyIncome, binner(attrition$MonthlyIncome))

# What happens when a value is out of range?
ceo <- attrition %>%
  slice(1) %>%
  mutate(MonthlyIncome = 10^10)

bake(rec, ceo)

tidymodels/recipes documentation built on April 17, 2025, 6:17 p.m.