% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/window.R
\name{step_window}
\alias{step_window}
\title{Moving Window Functions}
\usage{
step_window(recipe, ..., role = NA, trained = FALSE, size = 3,
  na.rm = TRUE, statistic = "mean", columns = NULL, names = NULL)
}
\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 are 
affected by the step. See \code{\link{selections}} for more details.}

\item{role}{For model terms created by this step, what analysis role should
they be assigned? If \code{names} is left to be \code{NULL}, the rolling
statistics replace the original columns and the roles are left unchanged.
If \code{names} is set, those new columns will have a role of \code{NULL}
unless this argument has a value.}

\item{trained}{A logical to indicate if the quantities for preprocessing 
have been estimated.}

\item{size}{An odd integer \code{>= 3} for the window size.}

\item{na.rm}{A logical for whether missing values should be removed from the
calculations within each window.}

\item{statistic}{A character string for the type of statistic that should
be calculated for each moving window. Possible values are: \code{'max'},
\code{'mean'}, \code{'median'}, \code{'min'}, \code{'prod'}, \code{'sd'},
\code{'sum'}, \code{'var'}}

\item{columns}{A character string that contains the names of columns that
should be processed. These values are not determined until
\code{\link{prep.recipe}} is called.}

\item{names}{An optional character string that is the same length of the
number of terms selected by \code{terms}. If you are not sure what columns
will be selected, use the \code{summary} function (see the example below).
These will be the names of the new columns created by the step.}
}
\value{
An updated version of \code{recipe} with the
  new step added to the sequence of existing steps (if any).
}
\description{
\code{step_window} creates a \emph{specification} of a recipe step that will
  create new columns that are the results of functions that compute
  statistics across moving windows.
}
\details{
The calculations use a somewhat atypical method for handling the
  beginning and end parts of the rolling statistics. The process starts
  with the center justified window calculations and the beginning and
  ending parts of the rolling values are determined using the first and
  last rolling values, respectively. For example if a column \code{x} with
  12 values is smoothed with a 5-point moving median, the first three
  smoothed values are estimated by \code{median(x[1:5])} and the fourth
  uses \code{median(x[2:6])}.
}
\examples{
library(recipes)
library(dplyr)
library(rlang)
library(ggplot2, quietly = TRUE)

set.seed(5522)
sim_dat <- data.frame(x1 = (20:100) / 10)
n <- nrow(sim_dat)
sim_dat$y1 <- sin(sim_dat$x1) + rnorm(n, sd = 0.1)
sim_dat$y2 <- cos(sim_dat$x1) + rnorm(n, sd = 0.1)
sim_dat$x2 <- runif(n)
sim_dat$x3 <- rnorm(n)

rec <- recipe(y1 + y2 ~ x1 + x2 + x3, data = sim_dat) \%>\%
  step_window(starts_with("y"), size = 7, statistic = "median",
              names = paste0("med_7pt_", 1:2),
              role = "outcome") \%>\%
  step_window(starts_with("y"),
              names = paste0("mean_3pt_", 1:2),
              role = "outcome")
rec <- prep(rec, training = sim_dat)

# If you aren't sure how to set the names, see which variables are selected
# and the order that they are selected:
terms_select(info = summary(rec), terms = quos(starts_with("y")))

smoothed_dat <- bake(rec, sim_dat, everything())

ggplot(data = sim_dat, aes(x = x1, y = y1)) +
  geom_point() +
  geom_line(data = smoothed_dat, aes(y = med_7pt_1)) +
  geom_line(data = smoothed_dat, aes(y = mean_3pt_1), col = "red") +
  theme_bw()

# If you want to replace the selected variables with the rolling statistic
# don't set `names`
sim_dat$original <- sim_dat$y1
rec <- recipe(y1 + y2 + original ~ x1 + x2 + x3, data = sim_dat) \%>\%
  step_window(starts_with("y"))
rec <- prep(rec, training = sim_dat)
smoothed_dat <- bake(rec, sim_dat, everything())
ggplot(smoothed_dat, aes(x = original, y = y1)) + 
  geom_point() + 
  theme_bw()
}
\concept{
preprocessing moving_windows
}
\keyword{datagen}
