step_num2factor {recipes} | 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 |
role |
Not used by this step since no new variables are created. |
transform |
A function taking a single argument |
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 |
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
|
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
NA
s 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.
See Also
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy()
,
step_dummy_extract()
,
step_dummy_multi_choice()
,
step_factor2string()
,
step_holiday()
,
step_indicate_na()
,
step_integer()
,
step_novel()
,
step_ordinalscore()
,
step_other()
,
step_regex()
,
step_relevel()
,
step_string2factor()
,
step_time()
,
step_unknown()
,
step_unorder()
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)