`step_window()`

creates a *specification* of a recipe step that will create
new columns that are the results of functions that compute statistics across
moving windows.

## Usage

```
step_window(
recipe,
...,
role = NA,
trained = FALSE,
size = 3,
na_rm = TRUE,
statistic = "mean",
columns = NULL,
names = NULL,
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("window")
)
```

## Arguments

- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.

- ...
One or more selector functions to choose variables for this step. See

`selections()`

for more details.- role
For model terms created by this step, what analysis role should they be assigned? If

`names`

is left to be`NULL`

, the rolling statistics replace the original columns and the roles are left unchanged. If`names`

is set, those new columns will have a role of`NULL`

unless this argument has a value.- trained
A logical to indicate if the quantities for preprocessing have been estimated.

- size
An odd integer

`>= 3`

for the window size.- na_rm
A logical for whether missing values should be removed from the calculations within each window.

- statistic
A character string for the type of statistic that should be calculated for each moving window. Possible values are:

`'max'`

,`'mean'`

,`'median'`

,`'min'`

,`'prod'`

,`'sd'`

,`'sum'`

,`'var'`

- columns
A character string of the selected variable names. This field is a placeholder and will be populated once

`prep()`

is used.- names
An optional character string that is the same length of the number of terms selected by

`terms`

. If you are not sure what columns will be selected, use the`summary`

function (see the example below). These will be the names of the new columns created by the step.- keep_original_cols
A logical to keep the original variables in the output. Defaults to

`FALSE`

.- skip
A logical. Should the step be skipped when the recipe is baked by

`bake()`

? While all operations are baked when`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`skip = TRUE`

as it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.

## Value

An updated version of `recipe`

with the new step added to the
sequence of any existing operations.

## 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 `x`

with 12 values is
smoothed with a 5-point moving median, the first three smoothed
values are estimated by `median(x[1:5])`

and the fourth
uses `median(x[2:6])`

.

`keep_original_cols`

also applies to this step if `names`

is specified.

step will stop with a note about installing the package.

## Tidying

When you `tidy()`

this step, a tibble with columns
`terms`

(the selectors or variables selected), `statistic`

(the
summary function name), and `size`

is returned.

## Tuning Parameters

This step has 2 tuning parameters:

`statistic`

: Rolling Summary Statistic (type: character, default: mean)`size`

: Window Size (type: integer, default: 3)

## Examples

```
if (FALSE) { # rlang::is_installed(c("RcppML", "ggplot2"))
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)
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()
tidy(rec, number = 1)
tidy(rec, number = 2)
# 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()
}
```