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Calculate a rolling window transformation

Source: R/step_epi_slide.R
step_epi_slide.Rd

step_epi_slide() creates a specification of a recipe step that will generate one or more new columns of derived data by "sliding" a computation along existing data.

Usage

step_epi_slide(
  recipe,
  ...,
  .f,
  .window_size = NULL,
  .align = c("right", "center", "left"),
  role = "predictor",
  prefix = "epi_slide_",
  f_name = clean_f_name(.f),
  skip = FALSE,
  id = rand_id("epi_slide")
)

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 recipes::selections() for more details.

.f

A function in one of the following formats:

  1. An unquoted function name with no arguments, e.g., mean

  2. A character string name of a function, e.g., "mean". Note that this can be difficult to examine for mistakes (so the misspelling "maen" won't produce an error until you try to actually fit the model)

  3. A base R lambda function, e.g., function(x) mean(x, na.rm = TRUE)

  4. A new-style base R lambda function, e.g., \(x) mean(x, na.rm = TRUE)

  5. A one-sided formula like ~ mean(.x, na.rm = TRUE).

Note that in cases 3 and 4, x can be any variable name you like (for example \(dog) mean(dog, na.rm = TRUE) will work). But in case 5, the argument must be named .x. A common, though very difficult to debug error is using something like function(x) mean. This will not work because it returns the function mean, rather than mean(x)

.window_size

the size of the sliding window, required. Usually a non-negative integer will suffice (e.g. for data indexed by date, but more restrictive in other time_type cases (see epiprocess::epi_slide() for details). For example, set to 7 for a 7-day window.

.align

a character string indicating how the window should be aligned. By default, this is "right", meaning the slide_window will be anchored with its right end point on the reference date. (see epiprocess::epi_slide() for details).

role

For model terms created by this step, what analysis role should they be assigned? lag is default a predictor while ahead is an outcome.

prefix

A character string that will be prefixed to the new column.

f_name

a character string of at most 20 characters that describes the function. This will be combined with prefix and the columns in ... to name the result using {prefix}{f_name}_{column}. By default it will be determined automatically using clean_f_name().

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 unique identifier for the step

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Examples

library(dplyr)
jhu <- case_death_rate_subset %>%
  filter(time_value >= as.Date("2021-01-01"), geo_value %in% c("ca", "ny"))
rec <- epi_recipe(jhu) %>%
  step_epi_slide(case_rate, death_rate,
    .f = \(x) mean(x, na.rm = TRUE),
    .window_size = 7L
  )
bake(prep(rec, jhu), new_data = NULL)
#> An `epi_df` object, 730 x 6 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 730 × 6
#>    geo_value time_value case_rate death_rate epi_slide__.f_case_rate
#>  * <chr>     <date>         <dbl>      <dbl>                   <dbl>
#>  1 ca        2021-01-01      104.      0.850                    104.
#>  2 ca        2021-01-02      103.      0.857                    103.
#>  3 ca        2021-01-03      102.      0.860                    103.
#>  4 ca        2021-01-04      102.      0.911                    103.
#>  5 ca        2021-01-05      101.      0.908                    102.
#>  6 ca        2021-01-06      108.      0.893                    103.
#>  7 ca        2021-01-07      106.      0.941                    104.
#>  8 ca        2021-01-08      106.      1.02                     104.
#>  9 ca        2021-01-09      107.      1.11                     105.
#> 10 ca        2021-01-10      110.      1.22                     106.
#> # ℹ 720 more rows
#> # ℹ 1 more variable: epi_slide__.f_death_rate <dbl>