Merge pull request #349 from cmu-delphi/ds/step-lag-example

doc: fix step_lag_difference example

Author: dshemetov

R/step_growth_rate.R

@@ -39,8 +39,8 @@
 #' r
 #'
 #' r %>%
-#'   recipes::prep() %>%
-#'   recipes::bake(case_death_rate_subset)
+#'   prep(case_death_rate_subset) %>%
+#'   bake(case_death_rate_subset)
 step_growth_rate <-
   function(recipe,
            ...,

R/step_lag_difference.R

@@ -16,12 +16,13 @@
 #' @export
 #' @examples
 #' r <- epi_recipe(case_death_rate_subset) %>%
-#'   step_lag_difference(case_rate, death_rate, horizon = c(7, 14))
+#'   step_lag_difference(case_rate, death_rate, horizon = c(7, 14)) %>%
+#'   step_epi_naomit()
 #' r
 #'
 #' r %>%
-#'   recipes::prep() %>%
-#'   recipes::bake(case_death_rate_subset)
+#'   prep(case_death_rate_subset) %>%
+#'   bake(case_death_rate_subset)
 step_lag_difference <-
   function(recipe,
            ...,

man/step_growth_rate.Rd

@@ -19,23 +19,23 @@ test_that("check_enough_train_data works on pooled data", {
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = 2 * n, drop_na = FALSE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
   # Check both column don't have enough data
   expect_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL),
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL),
     regexp = "The following columns don't have enough data"
   )
   # Check drop_na works
   expect_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
 })
 
@@ -44,32 +44,32 @@ test_that("check_enough_train_data works on unpooled data", {
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = n, epi_keys = "geo_value", drop_na = FALSE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
   # Check one column don't have enough data
   expect_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL),
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL),
     regexp = "The following columns don't have enough data"
   )
   # Check drop_na works
   expect_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
 })
 
 test_that("check_enough_train_data outputs the correct recipe values", {
   expect_no_error(
     p <- epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = 2 * n - 2) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
 
   expect_equal(nrow(p), 2 * n)
@@ -93,15 +93,15 @@ test_that("check_enough_train_data only checks train data", {
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(x, y, n = n - 2, epi_keys = "geo_value") %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = toy_test_data)
+      prep(toy_epi_df) %>%
+      bake(new_data = toy_test_data)
   )
   # Same thing, but skip = FALSE
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       check_enough_train_data(y, n = n - 2, epi_keys = "geo_value", skip = FALSE) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = toy_test_data)
+      prep(toy_epi_df) %>%
+      bake(new_data = toy_test_data)
   )
 })
 
@@ -111,14 +111,14 @@ test_that("check_enough_train_data works with all_predictors() downstream of con
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
       check_enough_train_data(all_predictors(), y, n = 2 * n - 6) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
   expect_error(
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
       check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>%
-      recipes::prep(toy_epi_df) %>%
-      recipes::bake(new_data = NULL)
+      prep(toy_epi_df) %>%
+      bake(new_data = NULL)
   )
 })

man/step_lag_difference.Rd

@@ -11,8 +11,8 @@ toy_epi_df <- tibble::tibble(
 test_that("step_training_window works with default n_recent", {
   p <- epi_recipe(y ~ x, data = toy_epi_df) %>%
     step_training_window() %>%
-    recipes::prep(toy_epi_df) %>%
-    recipes::bake(new_data = NULL)
+    prep(toy_epi_df) %>%
+    bake(new_data = NULL)
 
   expect_equal(nrow(p), 100L)
   expect_equal(ncol(p), 4L)
@@ -28,8 +28,8 @@ test_that("step_training_window works with default n_recent", {
 test_that("step_training_window works with specified n_recent", {
   p2 <- epi_recipe(y ~ x, data = toy_epi_df) %>%
     step_training_window(n_recent = 5) %>%
-    recipes::prep(toy_epi_df) %>%
-    recipes::bake(new_data = NULL)
+    prep(toy_epi_df) %>%
+    bake(new_data = NULL)
 
   expect_equal(nrow(p2), 10L)
   expect_equal(ncol(p2), 4L)
@@ -48,8 +48,8 @@ test_that("step_training_window does not proceed with specified new_data", {
   # testing data.
   p3 <- epi_recipe(y ~ x, data = toy_epi_df) %>%
     step_training_window(n_recent = 3) %>%
-    recipes::prep(toy_epi_df) %>%
-    recipes::bake(new_data = toy_epi_df[1:10, ])
+    prep(toy_epi_df) %>%
+    bake(new_data = toy_epi_df[1:10, ])
 
   expect_equal(nrow(p3), 10L)
   expect_equal(ncol(p3), 4L)
@@ -78,8 +78,8 @@ test_that("step_training_window works with multiple keys", {
 
   p4 <- epi_recipe(y ~ x, data = toy_epi_df2) %>%
     step_training_window(n_recent = 3) %>%
-    recipes::prep(toy_epi_df2) %>%
-    recipes::bake(new_data = NULL)
+    prep(toy_epi_df2) %>%
+    bake(new_data = NULL)
 
   expect_equal(nrow(p4), 12L)
   expect_equal(ncol(p4), 5L)
@@ -112,20 +112,20 @@ test_that("step_training_window and step_naomit interact", {
 
   e1 <- epi_recipe(y ~ x, data = tib) %>%
     step_training_window(n_recent = 3) %>%
-    recipes::prep(tib) %>%
-    recipes::bake(new_data = NULL)
+    prep(tib) %>%
+    bake(new_data = NULL)
 
   e2 <- epi_recipe(y ~ x, data = tib) %>%
-    recipes::step_naomit() %>%
+    step_naomit() %>%
     step_training_window(n_recent = 3) %>%
-    recipes::prep(tib) %>%
-    recipes::bake(new_data = NULL)
+    prep(tib) %>%
+    bake(new_data = NULL)
 
   e3 <- epi_recipe(y ~ x, data = tib) %>%
     step_training_window(n_recent = 3) %>%
-    recipes::step_naomit() %>%
-    recipes::prep(tib) %>%
-    recipes::bake(new_data = NULL)
+    step_naomit() %>%
+    prep(tib) %>%
+    bake(new_data = NULL)
 
   expect_identical(e1, e2)
   expect_identical(e2, e3)