step_window {recipes} | R Documentation |
Moving window functions
Description
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 |
role |
For model terms created by this step, what analysis role should
they be assigned? If |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
size |
An odd integer |
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: |
columns |
A character string of the selected variable names. This field
is a placeholder and will be populated once |
names |
An optional character string that is the same length of the
number of terms selected by |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
skip |
A logical. Should the step be skipped when the recipe is baked by
|
id |
A character string that is unique to this step to identify it. |
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.
This step requires the RcppRoll package. If not installed, the step will stop with a note about installing the package.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
, statistic
, size
, and id
:
- terms
character, the selectors or variables selected
- statistic
character, the summary function name
- size
integer, window size
- id
character, id of this step
Tuning Parameters
This step has 2 tuning parameters:
-
statistic
: Rolling Summary Statistic (type: character, default: mean) -
size
: Window Size (type: integer, default: 3)
Case weights
The underlying operation does not allow for case weights.
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)
smoothed_dat <- bake(rec, sim_dat)
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)
ggplot(smoothed_dat, aes(x = original, y = y1)) +
geom_point() +
theme_bw()