Convert Numbers to Factors

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")
)

# 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 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()
#> # A tibble: 4 x 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 x 2
#>   MonthlyIncome Attrition
#>   <ord>         <fct>    
#> 1 NA            Yes

Arguments

Value

See also

Examples