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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/missing.r
\name{check_missing}
\alias{check_missing}
\alias{tidy.check_missing}
\title{Check for Missing Values}
\usage{
check_missing(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("missing")
)
\method{tidy}{check_missing}(x, ...)
}
\arguments{
\item{recipe}{A recipe object. The check will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose which
variables are checked in the check See \code{\link[=selections]{selections()}}
for more details. For the \code{tidy} method, these are not
currently used.}
\item{role}{Not used by this check since no new variables are
created.}
\item{trained}{A logical for whether the selectors in \code{...}
have been resolved by \code{\link[=prep]{prep()}}.}
\item{columns}{A character string of variable names that will
be populated (eventually) by the terms argument.}
\item{skip}{A logical. Should the check 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 = TRUE} as it may affect
the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
\item{x}{A \code{check_missing} object.}
}
\value{
An updated version of \code{recipe} with the new check
added to the sequence of existing operations (if any). For the
\code{tidy} method, a tibble with columns \code{terms} (the
selectors or variables selected).
}
\description{
\code{check_missing} creates a \emph{specification} of a recipe
operation that will check if variables contain missing values.
}
\details{
This check will break the \code{bake} function if any of the checked
columns does contain \code{NA} values. If the check passes, nothing is changed
to the data.
}
\examples{
library(modeldata)
data(credit_data)
is.na(credit_data) \%>\% colSums()
# If the test passes, `new_data` is returned unaltered
recipe(credit_data) \%>\%
check_missing(Age, Expenses) \%>\%
prep() \%>\%
bake(credit_data)
# If your training set doesn't pass, prep() will stop with an error
\dontrun{
recipe(credit_data) \%>\%
check_missing(Income) \%>\%
prep()
}
# If `new_data` contain missing values, the check will stop bake()
train_data <- credit_data \%>\% dplyr::filter(Income > 150)
test_data <- credit_data \%>\% dplyr::filter(Income <= 150 | is.na(Income))
rp <- recipe(train_data) \%>\%
check_missing(Income) \%>\%
prep()
bake(rp, train_data)
\dontrun{
bake(rp, test_data)
}
}
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