Merge pull request #31 from kenmawer/km-pr_34_rectify_tests

Km pr 34 rectify tests

Author: kenmawer

R/step_epi_naomit.R

@@ -1,13 +1,24 @@
-#' Special NA omit step that does two steps in one
+#' Unified NA omission wrapper function for recipes
 #'
-#' @param x Recipe to be used for omission steps
+#' @param recipe Recipe to be used for omission steps
 #'
-#' @return Omits NA's from both predictors and outcomes and training time;
-#' however, only omits predictors at prediction time
+#' @return Omits NA's from both predictors and outcomes at training time;
+#'   however, only omits associated predictors at prediction time to avoid
+#'   losing data.
 #' @export
+#' @examples
+#' tibble(geo_value = rep("place",200),
+#'            time_value = as.Date("2021-01-01") + 0:199,
+#'            case_rate = 1:200,
+#'            death_rate = 1:200) %>%
+#'  as_epi_df() %>%
+#'  recipe() %>%
+#'  step_epi_naomit()
 
-step_epi_naomit <- function(x) {
-  x %>%
+
+step_epi_naomit <- function(recipe) {
+  stopifnot("recipe" %in% class(recipe))
+  recipe %>%
     recipes::step_naomit(all_predictors()) %>%
     recipes::step_naomit(all_outcomes(), skip = TRUE)
 }

man/step_epi_naomit.Rd

@@ -4,18 +4,21 @@ library(parsnip)
 library(workflows)
 
 # Random generated dataset
-set.seed(100)
 x <- tibble(geo_value = rep("nowhere",200),
             time_value = as.Date("2021-01-01") + 0:199,
-            case_rate = rpois(100,20) + 1:200,
-            death_rate = rpois(100,10) + 1:200) %>%
+            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()) %>%