Moving Window FunctionsSource:
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.
step_window( recipe, ..., role = NA, trained = FALSE, size = 3, na_rm = TRUE, statistic = "mean", columns = NULL, names = NULL, skip = FALSE, id = rand_id("window") )
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.
For model terms created by this step, what analysis role should they be assigned? If
namesis left to be
NULL, the rolling statistics replace the original columns and the roles are left unchanged. If
namesis set, those new columns will have a role of
NULLunless this argument has a value.
A logical to indicate if the quantities for preprocessing have been estimated.
An odd integer
>= 3for the window size.
A logical for whether missing values should be removed from the calculations within each window.
A character string for the type of statistic that should be calculated for each moving window. Possible values are:
A character string that contains the names of columns that should be processed. These values are not determined until
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
summaryfunction (see the example below). These will be the names of the new columns created by the step.
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 = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
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
step will stop with a note about installing the package.
tidy() this step, a tibble with columns
terms (the selectors or variables selected),
summary function name), and
size is returned.
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) #> # A tibble: 2 × 4 #> terms statistic size id #> <chr> <chr> <int> <chr> #> 1 y1 median 7 window_MbmJQ #> 2 y2 median 7 window_MbmJQ tidy(rec, number = 2) #> # A tibble: 2 × 4 #> terms statistic size id #> <chr> <chr> <int> <chr> #> 1 y1 mean 3 window_uDjJs #> 2 y2 mean 3 window_uDjJs # 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()