pass local checks

Author: dajmcdon

R/cdc_baseline_forecaster.R

@@ -34,7 +34,7 @@
 #'   filter(weekdays(time_value) == "Saturday")
 #'
 #' cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
-#' preds <- pivot_quantiles(cdc$predictions, .pred_distn)
+#' preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)
 #'
 #' if (require(ggplot2)) {
 #' forecast_date <- unique(preds$forecast_date)

R/layer_cdc_flatline_quantiles.R

@@ -72,7 +72,7 @@
 #'
 #' preds <- preds %>%
 #'   unnest(.pred_distn_all) %>%
-#'   pivot_quantiles(.pred_distn) %>%
+#'   pivot_quantiles_wider(.pred_distn) %>%
 #'   mutate(target_date = forecast_date + ahead)
 #'
 #' if (require("ggplot2")) {

R/pivot_quantiles.R

@@ -25,7 +25,7 @@ nested_quantiles <- function(x) {
 #'
 #' Selected columns that contain `dist_quantiles` will be "lengthened" with
 #' the quantile levels serving as 1 column and the values as another. If
-#' multiple columns are selected, these will be prefixed the the column name.
+#' multiple columns are selected, these will be prefixed with the column name.
 #'
 #' @param .data A data frame, or a data frame extension such as a tibble or
 #'   epi_df.
@@ -39,7 +39,7 @@ nested_quantiles <- function(x) {
 #'   has 7, then the only option would be to recycle everything, creating a
 #'   _very_ long result. By default, this would throw an error. But if this is
 #'   really the goal, then the error can be bypassed by setting this argument
-#'   to `TRUE`. The first selected column will vary fastest.
+#'   to `TRUE`. The quantiles in the first selected column will vary the fastest.
 #'
 #' @return An object of the same class as `.data`.
 #' @export

man/cdc_baseline_forecaster.Rd

@@ -129,7 +129,7 @@ fc <- bind_rows(
     )
   ) %>% list_rbind()
 ) %>%
-  pivot_quantiles(fc_.pred_distn)
+  pivot_quantiles_wider(fc_.pred_distn)
 ```
 
 Here, `arx_forecaster()` does all the heavy lifting. It creates leads of the
@@ -225,7 +225,7 @@ can_fc <- bind_rows(
     )
   ) %>% list_rbind()
 ) %>%
-  pivot_quantiles(fc_.pred_distn)
+  pivot_quantiles_wider(fc_.pred_distn)
 ```
 
 The figures below shows the results for all of the provinces. 
@@ -327,7 +327,7 @@ k_week_version_aware <- function(ahead = 7, version_aware = TRUE) {
 fc <- bind_rows(
   map(aheads, ~ k_week_version_aware(.x, TRUE)) %>% list_rbind(),
   map(aheads, ~ k_week_version_aware(.x, FALSE)) %>% list_rbind()
-) %>% pivot_quantiles(fc_.pred_distn)
+) %>% pivot_quantiles_wider(fc_.pred_distn)
 ```
 
 Now we can plot the results on top of the latest case rates. As before, we will only display and focus on the results for FL and CA for simplicity.