recipes: step_num2factor – R documentation

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

## S3 method for class '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 be 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::as.integer()`). 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.recipe()` 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.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.

Examples

library(dplyr)
library(modeldata)
data(attrition)

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)

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release