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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/naomit.R
\name{step_naomit}
\alias{step_naomit}
\alias{tidy.step_naomit}
\title{Remove observations with missing values}
\usage{
step_naomit(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("naomit")
)
\method{tidy}{step_naomit}(x, ...)
}
\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 which
variables will be used to remove observations containing \code{NA} or \code{NaN}
values. See \code{\link[=selections]{selections()}} for more details.}
\item{role}{Unused, include for consistency with other steps.}
\item{trained}{A logical to indicate if the quantities for preprocessing
have been estimated. Again included for consistency.}
\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.recipe]{bake.recipe()}}? While all operations are baked
when \code{\link[=prep.recipe]{prep.recipe()}} 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.}
\item{x}{A \code{step_naomit} object.}
}
\value{
An updated version of \code{recipe} with the
new step added to the sequence of existing steps (if any).
}
\description{
\code{step_naomit} creates a \emph{specification} of a recipe step that
will remove observations (rows of data) if they contain \code{NA}
or \code{NaN} values.
}
\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.recipe]{bake.recipe()}}. 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()}}.
}
\examples{
recipe(Ozone ~ ., data = airquality) \%>\%
step_naomit(Solar.R) \%>\%
prep(airquality, verbose = FALSE) \%>\%
bake(new_data = NULL)
}
\seealso{
\code{\link[=step_filter]{step_filter()}} \code{\link[=step_sample]{step_sample()}} \code{\link[=step_slice]{step_slice()}}
}
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