
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")
)
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.
Not used by this step since no new variables are created.
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()
.
A logical to indicate if the quantities for preprocessing have been estimated.
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.
A single logical value; should the factor(s) be ordered?
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.
A character string that is unique to this step to identify it.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected) and ordered
is returned.
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy_extract()
,
step_dummy_multi_choice()
,
step_dummy()
,
step_factor2string()
,
step_holiday()
,
step_indicate_na()
,
step_integer()
,
step_novel()
,
step_ordinalscore()
,
step_other()
,
step_regex()
,
step_relevel()
,
step_string2factor()
,
step_unknown()
,
step_unorder()
# NOT RUN {
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)
# }
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