% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ratio.R
\name{step_ratio}
\alias{step_ratio}
\alias{denom_vars}
\title{Ratio Variable Creation}
\usage{
step_ratio(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  denom = denom_vars(),
  naming = function(numer, denom) {
     make.names(paste(numer, denom, sep = "_o_"))
 },
  columns = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("ratio")
)

denom_vars(...)
}
\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 in the \emph{numerator} of the ratio.
When used with \code{denom_vars}, the dots indicate which
variables are used in the \emph{denominator}. See
\code{\link[=selections]{selections()}} for more details.}

\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{denom}{A call to \code{denom_vars} to specify which
variables are used in the denominator that can include specific
variable names separated by commas or different selectors (see
\code{\link[=selections]{selections()}}). If a column is included in both lists
to be numerator and denominator, it will be removed from the
listing.}

\item{naming}{A function that defines the naming convention for
new ratio columns.}

\item{columns}{The column names used in the ratios. This
argument is not populated until \code{\link[=prep]{prep()}} is
executed.}

\item{keep_original_cols}{A logical to keep the original variables in the
output. Defaults to \code{TRUE}.}

\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_ratio} creates a \emph{specification} of a recipe
step that will create one or more ratios out of numeric
variables.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with columns
\code{terms} (the selectors or variables selected) and \code{denom} is returned.
}

\section{Case weights}{


The underlying operation does not allow for case weights.
}

\examples{
\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
library(recipes)
data(biomass, package = "modeldata")

biomass$total <- apply(biomass[, 3:7], 1, sum)
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen +
  sulfur + total,
data = biomass_tr
)

ratio_recipe <- rec \%>\%
  # all predictors over total
  step_ratio(all_numeric_predictors(), denom = denom_vars(total)) \%>\%
  # get rid of the original predictors
  step_rm(all_predictors(), -ends_with("total"))

ratio_recipe <- prep(ratio_recipe, training = biomass_tr)

ratio_data <- bake(ratio_recipe, biomass_te)
ratio_data
\dontshow{\}) # examplesIf}
}
\seealso{
Other multivariate transformation steps: 
\code{\link{step_classdist}()},
\code{\link{step_depth}()},
\code{\link{step_geodist}()},
\code{\link{step_ica}()},
\code{\link{step_isomap}()},
\code{\link{step_kpca_poly}()},
\code{\link{step_kpca_rbf}()},
\code{\link{step_kpca}()},
\code{\link{step_mutate_at}()},
\code{\link{step_nnmf_sparse}()},
\code{\link{step_nnmf}()},
\code{\link{step_pca}()},
\code{\link{step_pls}()},
\code{\link{step_spatialsign}()}
}
\concept{multivariate transformation steps}
