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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/unorder.R
\name{step_unorder}
\alias{step_unorder}
\title{Convert Ordered Factors to Unordered Factors}
\usage{
step_unorder(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("unorder")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose variables
for this step. See \code{\link[=selections]{selections()}} for more details.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{columns}{A character string of variable names that will
be populated (eventually) by the \code{terms} argument.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{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 \code{skip = TRUE} as it may affect
the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_unorder} creates a \emph{specification} of a recipe
step that will transform the data.
}
\details{
The factors level order is preserved during the transformation.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with column
\code{terms} (the columns that will be affected) is returned.
}
\section{Case weights}{
The underlying operation does not allow for case weights.
}
\examples{
lmh <- c("Low", "Med", "High")
examples <- data.frame(
X1 = factor(rep(letters[1:4], each = 3)),
X2 = ordered(rep(lmh, each = 4),
levels = lmh
)
)
rec <- recipe(~ X1 + X2, data = examples)
factor_trans <- rec \%>\%
step_unorder(all_nominal_predictors())
factor_obj <- prep(factor_trans, training = examples)
transformed_te <- bake(factor_obj, examples)
table(transformed_te$X2, examples$X2)
tidy(factor_trans, number = 1)
tidy(factor_obj, number = 1)
}
\seealso{
Other dummy variable and encoding steps:
\code{\link{step_bin2factor}()},
\code{\link{step_count}()},
\code{\link{step_date}()},
\code{\link{step_dummy_extract}()},
\code{\link{step_dummy_multi_choice}()},
\code{\link{step_dummy}()},
\code{\link{step_factor2string}()},
\code{\link{step_holiday}()},
\code{\link{step_indicate_na}()},
\code{\link{step_integer}()},
\code{\link{step_novel}()},
\code{\link{step_num2factor}()},
\code{\link{step_ordinalscore}()},
\code{\link{step_other}()},
\code{\link{step_regex}()},
\code{\link{step_relevel}()},
\code{\link{step_string2factor}()},
\code{\link{step_time}()},
\code{\link{step_unknown}()}
}
\concept{dummy variable and encoding steps}
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