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Add an epi_recipe to a workflow

Source: R/epi_recipe.R
add_epi_recipe.Rd

Add an epi_recipe to a workflow

Usage

add_epi_recipe(x, recipe, ..., blueprint = default_epi_recipe_blueprint())

remove_epi_recipe(x)

update_epi_recipe(x, recipe, ..., blueprint = default_epi_recipe_blueprint())

Arguments

x

A workflow or epi_workflow

recipe

An epi recipe or recipe

...

Not used

blueprint

A hardhat blueprint used for fine tuning the preprocessing

default_epi_recipe_blueprint() is used.

Note that preprocessing done here is separate from preprocessing that might be done automatically by the underlying model.

Value

x, updated with a new recipe preprocessor.

Details

add_epi_recipe has the same behaviour as workflows::add_recipe() but sets a different default blueprint to automatically handle epiprocess::epi_df data.

See also

workflows::add_recipe()

  • add_recipe() specifies the terms of the model and any preprocessing that is required through the usage of a recipe.

  • remove_recipe() removes the recipe as well as any downstream objects

  • update_recipe() first removes the recipe, then replaces the previous recipe with the new one.

Examples

library(dplyr)
library(recipes)
#> 
#> Attaching package: ‘recipes’
#> The following object is masked from ‘package:stats’:
#> 
#>     step

jhu <- covid_case_death_rates %>%
  filter(time_value > "2021-08-01") %>%
  arrange(geo_value, time_value)

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7) %>%
  step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
  step_naomit(all_predictors()) %>%
  step_naomit(all_outcomes(), skip = TRUE)

workflow <- epi_workflow() %>%
  add_epi_recipe(r)

workflow
#> 
#> ══ Epi Workflow ════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: None
#> Postprocessor: None
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 
#> 5 Recipe steps.
#> 1. step_epi_lag()
#> 2. step_epi_ahead()
#> 3. step_epi_lag()
#> 4. step_naomit()
#> 5. step_naomit()
#> 
#> 

r2 <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7)

workflow <- update_epi_recipe(workflow, r2)

workflow <- remove_epi_recipe(workflow)

workflow
#> 
#> ══ Epi Workflow ════════════════════════════════════════════════════════════════
#> Preprocessor: None
#> Model: None
#> Postprocessor: None
#> 
#>