step_epi_naomit branched off frosting.

NAMESPACE

@@ -70,6 +70,7 @@ export(smooth_arx_args_list)
 export(smooth_arx_forecaster)
 export(step_epi_ahead)
 export(step_epi_lag)
+export(step_epi_naomit)
 export(validate_layer)
 import(recipes)
 importFrom(generics,augment)

R/step_epi_naomit.R

@@ -0,0 +1,20 @@
+#' Unified NA omission wrapper function for recipes
+#'
+#' @param recipe Recipe to be used for omission steps
+#'
+#' @return Omits NA's from both predictors and outcomes at training time
+#'   to fit the model. Also only omits associated predictors and not
+#'   outcomes at prediction time due to lack of response and avoidance
+#'   of data loss.
+#' @export
+#' @examples
+#' case_death_rate_subset %>%
+#'  epi_recipe() %>%
+#'  step_epi_naomit()
+
+step_epi_naomit <- function(recipe) {
+  stopifnot("recipe" %in% class(recipe))
+  recipe %>%
+    recipes::step_naomit(all_predictors()) %>%
+    recipes::step_naomit(all_outcomes(), skip = TRUE)
+}

tests/testthat/test-step_epi_naomit.R

@@ -0,0 +1,38 @@
+library(dplyr)
+library(epiprocess)
+library(parsnip)
+library(workflows)
+
+# Random generated dataset
+x <- tibble(geo_value = rep("nowhere",200),
+            time_value = as.Date("2021-01-01") + 0:199,
+            case_rate = 1:200,
+            death_rate = 1:200) %>%
+  as_epi_df()
+
+# Preparing the datasets to be used for comparison
+r <- epi_recipe(x) %>%
+  step_epi_ahead(death_rate, ahead = 7) %>%
+  step_epi_lag(death_rate, lag = c(0,7,14))
+
+test_that("Argument must be a recipe", {
+  expect_error(step_epi_naomit(x))
+})
+
+z1 <- step_epi_naomit(r)
+z2 <- r %>%
+  step_naomit(all_predictors()) %>%
+  step_naomit(all_outcomes(), skip = TRUE)
+
+# Checks the behaviour of a step function, omitting the quosure and id that
+# differ from one another, even with identical behaviour
+behav <- function(recipe,step_num) recipe$steps[[step_num]][-1][-5]
+# Checks the class type of an object
+step_class <- function(recipe,step_num) class(recipe$steps[step_num])
+
+test_that("Check that both functions behave the same way", {
+  expect_identical(behav(z1,3),behav(z2,3))
+  expect_identical(behav(z1,4),behav(z2,4))
+  expect_identical(step_class(z1,3),step_class(z2,3))
+  expect_identical(step_class(z1,4),step_class(z2,4))
+})