`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") ) # S3 method for step_window tidy(x, ...)

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

... | One or more selector functions to choose which
variables are affected by the 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 that contains the names of
columns that should be processed. These values are not
determined until |

names | An optional character string that is the same
length of the number of terms selected by |

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. |

x | A |

An updated version of `recipe`

with the new step
added to the sequence of existing steps (if any). For the
`tidy`

method, a tibble with columns `terms`

(the
selectors or variables selected) and `statistic`

(the
summary function name), and `size`

.

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])`

.

step will stop with a note about installing the package.

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")))#> [1] "y1" "y2"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()#> # A tibble: 2 x 4 #> terms statistic size id #> <chr> <chr> <int> <chr> #> 1 y1 median 7 window_MbmJQ #> 2 y2 median 7 window_MbmJQ#> # A tibble: 2 x 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()