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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sample.R
\name{step_sample}
\alias{step_sample}
\title{Sample rows using dplyr}
\usage{
step_sample(
recipe,
...,
role = NA,
trained = FALSE,
size = NULL,
replace = FALSE,
skip = TRUE,
id = rand_id("sample")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{Argument ignored; included for consistency with other step
specification functions.}
\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{size}{An integer or fraction. If the value is within (0, 1),
\code{\link[dplyr:sample_n]{dplyr::sample_frac()}} is applied to the data. If an integer
value of 1 or greater is used, \code{\link[dplyr:sample_n]{dplyr::sample_n()}} is applied.
The default of \code{NULL} uses \code{\link[dplyr:sample_n]{dplyr::sample_n()}} with the size
of the training set (or smaller for smaller \code{new_data}).}
\item{replace}{Sample with or without replacement?}
\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 = FALSE}.}
\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_sample} creates a \emph{specification} of a recipe step
that will sample rows using \code{\link[dplyr:sample_n]{dplyr::sample_n()}} or
\code{\link[dplyr:sample_n]{dplyr::sample_frac()}}.
}
\section{Row Filtering}{
This step can entirely remove observations (rows of data), which can have
unintended and/or problematic consequences when applying the step to new
data later via \code{\link[=bake]{bake()}}. Consider whether \code{skip = TRUE} or
\code{skip = FALSE} is more appropriate in any given use case. In most instances
that affect the rows of the data being predicted, this step probably should
not be applied at all; instead, execute operations like this outside and
before starting a preprocessing \code{\link[=recipe]{recipe()}}.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with columns
\code{size}, \code{replace}, and \code{id} is returned.
}
\section{Case weights}{
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in \link{case_weights} and the examples on
\code{tidymodels.org}.
}
\examples{
# Uses `sample_n`
recipe(~., data = mtcars) \%>\%
step_sample(size = 1) \%>\%
prep(training = mtcars) \%>\%
bake(new_data = NULL) \%>\%
nrow()
# Uses `sample_frac`
recipe(~., data = mtcars) \%>\%
step_sample(size = 0.9999) \%>\%
prep(training = mtcars) \%>\%
bake(new_data = NULL) \%>\%
nrow()
# Uses `sample_n` and returns _at maximum_ 20 samples.
smaller_cars <-
recipe(~., data = mtcars) \%>\%
step_sample() \%>\%
prep(training = mtcars \%>\% slice(1:20))
bake(smaller_cars, new_data = NULL) \%>\% nrow()
bake(smaller_cars, new_data = mtcars \%>\% slice(21:32)) \%>\% nrow()
}
\seealso{
Other row operation steps:
\code{\link{step_arrange}()},
\code{\link{step_filter}()},
\code{\link{step_impute_roll}()},
\code{\link{step_lag}()},
\code{\link{step_naomit}()},
\code{\link{step_shuffle}()},
\code{\link{step_slice}()}
Other dplyr steps:
\code{\link{step_arrange}()},
\code{\link{step_filter}()},
\code{\link{step_mutate_at}()},
\code{\link{step_mutate}()},
\code{\link{step_rename_at}()},
\code{\link{step_rename}()},
\code{\link{step_select}()},
\code{\link{step_slice}()}
}
\concept{dplyr steps}
\concept{row operation steps}
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