remove / revise some silent tests

Author: dajmcdon

tests/testthat/test-population_scaling.R

@@ -5,18 +5,25 @@ test_that("Column names can be passed with and without the tidy way", {
     value = c(1000, 2000, 3000, 4000, 5000, 6000)
   )
 
-  newdata <- case_death_rate_subset %>% filter(geo_value %in% c("ak", "al", "ar", "as", "az", "ca"))
+  pop_data2 <- pop_data %>% dplyr::rename(geo_value = states)
+
+  newdata <- case_death_rate_subset %>%
+    filter(geo_value %in% c("ak", "al", "ar", "as", "az", "ca"))
 
   r1 <- epi_recipe(newdata) %>%
-    step_population_scaling(c("case_rate", "death_rate"),
+    step_population_scaling(
+      case_rate, death_rate,
       df = pop_data,
-      df_pop_col = "value", by = c("geo_value" = "states")
+      df_pop_col = "value",
+      by = c("geo_value" = "states")
     )
 
   r2 <- epi_recipe(newdata) %>%
-    step_population_scaling(case_rate, death_rate,
-      df = pop_data,
-      df_pop_col = "value", by = c("geo_value" = "states")
+    step_population_scaling(
+      case_rate, death_rate,
+      df = pop_data2,
+      df_pop_col = "value",
+      by = "geo_value"
     )
 
   prep1 <- prep(r1, newdata)
@@ -56,9 +63,9 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
       suffix = "_rate"
     )
 
-  prep <- prep(r, newdata)
+  p <- prep(r, newdata)
 
-  expect_silent(b <- bake(prep, newdata))
+  b <- bake(p, newdata)
   expect_equal(ncol(b), 7L)
   expect_true("case_rate" %in% colnames(b))
   expect_true("death_rate" %in% colnames(b))
@@ -75,15 +82,15 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
       create_new = FALSE
     )
 
-  expect_warning(prep <- prep(r, newdata))
+  expect_warning(p <- prep(r, newdata))
 
-  expect_warning(b <- bake(prep, newdata))
+  expect_warning(b <- bake(p, newdata))
   expect_equal(ncol(b), 5L)
 })
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- epiprocess::jhu_csse_daily_subset %>%
+  jhu <- jhu_csse_daily_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 
@@ -207,9 +214,17 @@ test_that("test joining by default columns", {
     recipes::step_naomit(recipes::all_predictors()) %>%
     recipes::step_naomit(recipes::all_outcomes(), skip = TRUE)
 
-  expect_silent(prep(r, jhu))
+  p <- prep(r, jhu)
+  b <- bake(p, new_data = NULL)
+  expect_named(
+    b,
+    c("geo_value", "time_value", "case_rate", "case_rate_scaled",
+      paste0("lag_", c(0,7,14), "_case_rate_scaled"),
+      "ahead_7_case_rate_scaled"
+    )
+  )
+
 
-  expect_silent(bake(prep(r, jhu), new_data = NULL))
 
   f <- frosting() %>%
     layer_predict() %>%
@@ -221,13 +236,13 @@ test_that("test joining by default columns", {
       df_pop_col = "values"
     )
 
-  expect_silent(
-    wf <- epi_workflow(r, parsnip::linear_reg()) %>%
-      fit(jhu) %>%
-      add_frosting(f)
-  )
+  wf <- epi_workflow(r, parsnip::linear_reg()) %>%
+    fit(jhu) %>%
+    add_frosting(f)
 
-  expect_silent(forecast(wf))
+  fc <- forecast(wf)
+  expect_named(fc, c("geo_value", "time_value", ".pred", ".pred_scaled"))
+  expect_equal(fc$.pred_scaled, fc$.pred * c(1 / 20000, 1 / 30000))
 })