% 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)

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 indicates which variables are used in the
\emph{denominator}. See \code{\link{selections}} for more details.}

\item{role}{For terms created by this step, what analysis role should
they be assigned?. By default, the function assumes that the newly created
ratios created by the original variables will be used as
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}}).  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.recipe}} is executed.}
}
\value{
An updated version of \code{recipe} with the
  new step added to the sequence of existing steps (if any).
}
\description{
\code{step_ratio} creates a a \emph{specification} of a recipe step that
  will create one or more ratios out of numeric variables.
}
\examples{
library(recipes)
data(biomass)

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_predictors(), denom = denom_vars(total)) \%>\%
  # get rid of the original predictors 
  step_rm(all_predictors(), -matches("_o_"))
  

ratio_recipe <- prep(ratio_recipe, training = biomass_tr)

ratio_data <- bake(ratio_recipe, biomass_te)
ratio_data
}
\concept{
preprocessing
}
\keyword{datagen}
