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

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()). 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.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and ordered is returned.

See also

Examples

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

attrition %>%
  group_by(StockOptionLevel) %>%
  count()
#> # A tibble: 4 × 2
#> # Groups:   StockOptionLevel [4]
#>   StockOptionLevel     n
#>              <int> <int>
#> 1                0   631
#> 2                1   596
#> 3                2   158
#> 4                3    85

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)
#>          
#>             0   1   2   3
#>   nothin  631   0   0   0
#>   meh       0 596   0   0
#>   some      0   0 158   0
#>   copious   0   0   0  85


# 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))
#>       
#>          1   2   3
#>   low  749   0   0
#>   med    0 440   0
#>   high   0   0 281

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

bake(rec, ceo)
#> # A tibble: 1 × 2
#>   MonthlyIncome Attrition
#>   <ord>         <fct>    
#> 1 NA            Yes