% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mutate.R
\name{step_mutate}
\alias{step_mutate}
\title{Add new variables using dplyr}
\usage{
step_mutate(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  inputs = NULL,
  skip = FALSE,
  id = rand_id("mutate")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}

\item{...}{Name-value pairs of expressions. See \code{\link[dplyr:mutate]{dplyr::mutate()}}.}

\item{role}{For model terms created by this step, what analysis role should
they be assigned? By default, the new columns created by this step from
the original variables will be used as \emph{predictors} in a model.}

\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}

\item{inputs}{Quosure(s) of \code{...}.}

\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_mutate()} creates a \emph{specification} of a recipe step
that will add variables using \code{\link[dplyr:mutate]{dplyr::mutate()}}.
}
\details{
When using this flexible step, use extra care to avoid data leakage in your
preprocessing. Consider, for example, the transformation \code{x = w > mean(w)}.
When applied to new data or testing data, this transformation would use the
mean of \code{w} from the \emph{new} data, not the mean of \code{w} from the training data.

When an object in the user's global environment is
referenced in the expression defining the new variable(s),
it is a good idea to use quasiquotation (e.g. \verb{!!}) to embed
the value of the object in the expression (to be portable
between sessions). See the examples.

If a preceding step removes a column that is selected by name in
\code{step_mutate()}, the recipe will error when being estimated with \code{\link[=prep]{prep()}}.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with column
\code{values}, which contains the \code{mutate()} expressions as character
strings (and are not reparsable), is returned.
}

\section{Case weights}{


The underlying operation does not allow for case weights.
}

\examples{
rec <-
  recipe(~., data = iris) \%>\%
  step_mutate(
    dbl_width = Sepal.Width * 2,
    half_length = Sepal.Length / 2
  )

prepped <- prep(rec, training = iris \%>\% slice(1:75))

library(dplyr)

dplyr_train <-
  iris \%>\%
  as_tibble() \%>\%
  slice(1:75) \%>\%
  mutate(
    dbl_width = Sepal.Width * 2,
    half_length = Sepal.Length / 2
  )

rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)

dplyr_test <-
  iris \%>\%
  as_tibble() \%>\%
  slice(76:150) \%>\%
  mutate(
    dbl_width = Sepal.Width * 2,
    half_length = Sepal.Length / 2
  )
rec_test <- bake(prepped, iris \%>\% slice(76:150))
all.equal(dplyr_test, rec_test)

# Embedding objects:
const <- 1.414

qq_rec <-
  recipe(~., data = iris) \%>\%
  step_mutate(
    bad_approach = Sepal.Width * const,
    best_approach = Sepal.Width * !!const
  ) \%>\%
  prep(training = iris)

bake(qq_rec, new_data = NULL, contains("appro")) \%>\% slice(1:4)

# The difference:
tidy(qq_rec, number = 1)
}
\seealso{
Other individual transformation steps: 
\code{\link{step_BoxCox}()},
\code{\link{step_YeoJohnson}()},
\code{\link{step_bs}()},
\code{\link{step_harmonic}()},
\code{\link{step_hyperbolic}()},
\code{\link{step_inverse}()},
\code{\link{step_invlogit}()},
\code{\link{step_logit}()},
\code{\link{step_log}()},
\code{\link{step_ns}()},
\code{\link{step_percentile}()},
\code{\link{step_poly}()},
\code{\link{step_relu}()},
\code{\link{step_sqrt}()}

Other dplyr steps: 
\code{\link{step_arrange}()},
\code{\link{step_filter}()},
\code{\link{step_mutate_at}()},
\code{\link{step_rename_at}()},
\code{\link{step_rename}()},
\code{\link{step_sample}()},
\code{\link{step_select}()},
\code{\link{step_slice}()}
}
\concept{dplyr steps}
\concept{individual transformation steps}
