styler has opinions

Author: dsweber2

R/utils-shift.R

@@ -43,14 +43,16 @@ adjust_latency <- function(object, new_data) {
       time_type <- attributes(new_data)$metadata$time_type
 
       if ((grepl("day", time_type) && (shift_amount >= 10)) ||
-            (grepl("week", time_type) && (shift_amount >= 4))||
-            ((time_type == "yearmonth") && (shift_amount >=2)) ||
-            ((time_type == "yearquarter") && (shift_amount >= 1)) ||
-            ((time_type == "year") && (shift_amount >= 1))) {
+        (grepl("week", time_type) && (shift_amount >= 4)) ||
+        ((time_type == "yearmonth") && (shift_amount >= 2)) ||
+        ((time_type == "yearquarter") && (shift_amount >= 1)) ||
+        ((time_type == "year") && (shift_amount >= 1))) {
         cli::cli_warn(c(
-          "!" = glue::glue("The ahead has been adjusted by {shift_amount}, ",
-                           "which is questionable for it's `time_type` of ",
-                           "{time_type}"),
+          "!" = glue::glue(
+            "The ahead has been adjusted by {shift_amount}, ",
+            "which is questionable for it's `time_type` of ",
+            "{time_type}"
+          ),
           "i" = "input ahead: {ahead}",
           "i" = "shifted ahead: {effective_ahead}",
           "i" = "max_time = {max_time} -> as_of = {as_of}"

tests/testthat/test-population_scaling.R

@@ -207,20 +207,22 @@ test_that("Postprocessing to get cases from case rate", {
 
 
 test_that("test joining by default columns", {
-
   jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
 
-  reverse_pop_data = data.frame(geo_value = c("ca", "ny"),
-                                values = c(1/20000, 1/30000))
+  reverse_pop_data <- data.frame(
+    geo_value = c("ca", "ny"),
+    values = c(1 / 20000, 1 / 30000)
+  )
 
   r <- epi_recipe(jhu) %>%
     step_population_scaling(case_rate,
-                            df = reverse_pop_data,
-                            df_pop_col = "values",
-                            by = NULL,
-                            suffix = "_scaled") %>%
+      df = reverse_pop_data,
+      df_pop_col = "values",
+      by = NULL,
+      suffix = "_scaled"
+    ) %>%
     step_epi_lag(case_rate_scaled, lag = c(0, 7, 14)) %>% # cases
     step_epi_ahead(case_rate_scaled, ahead = 7, role = "outcome") %>% # cases
     recipes::step_naomit(recipes::all_predictors()) %>%
@@ -234,20 +236,28 @@ test_that("test joining by default columns", {
     layer_predict() %>%
     layer_threshold(.pred) %>%
     layer_naomit(.pred) %>%
-    layer_population_scaling(.pred, df = reverse_pop_data,
-                             by =  NULL,
-                             df_pop_col = "values")
+    layer_population_scaling(.pred,
+      df = reverse_pop_data,
+      by = NULL,
+      df_pop_col = "values"
+    )
 
-  expect_snapshot(wf <- epi_workflow(r,
-                     parsnip::linear_reg()) %>%
+  expect_snapshot(wf <- epi_workflow(
+    r,
+    parsnip::linear_reg()
+  ) %>%
     fit(jhu) %>%
     add_frosting(f))
 
-  latest <- get_test_data(recipe = r,
-                          x = case_death_rate_subset %>%
-                            dplyr::filter(time_value > "2021-11-01",
-                                          geo_value %in% c("ca", "ny")) %>%
-                            dplyr::select(geo_value, time_value, case_rate))
+  latest <- get_test_data(
+    recipe = r,
+    x = case_death_rate_subset %>%
+      dplyr::filter(
+        time_value > "2021-11-01",
+        geo_value %in% c("ca", "ny")
+      ) %>%
+      dplyr::select(geo_value, time_value, case_rate)
+  )
 
 
   expect_snapshot(p <- predict(wf, latest))