Cleanup variable names, sync documentation, bump lintr hook

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
   #####
   # R
   - repo: https://github.com/lorenzwalthert/precommit
-    rev: v0.4.3.9017
+    rev: v0.4.3.9019
     hooks:
       - id: lintr
   #####

hewr/NAMESPACE

@@ -2,6 +2,7 @@
 
 S3method(process_model_samples,pyrenew)
 S3method(process_model_samples,timeseries)
+export(augment_timeseries_draws_w_obs)
 export(epiweekly_samples_from_daily)
 export(format_timeseries_output)
 export(generate_exp_growth_pois)
@@ -21,6 +22,5 @@ export(process_loc_forecast)
 export(process_model_samples)
 export(prop_from_timeseries)
 export(read_and_combine_data)
-export(to_tidy_draws_timeseries)
 importFrom(rlang,":=")
 importFrom(rlang,.data)

hewr/R/process_loc_forecast.R

@@ -33,8 +33,8 @@ load_and_aggregate_ts <- function(
     dplyr::mutate(
       data = purrr::pmap(
         list(.data$samples, .data$observed),
-        function(samples, observed, epiweekly) {
-          to_tidy_draws_timeseries(
+        function(samples, observed) {
+          augment_timeseries_draws_w_obs(
             tidy_forecast = samples,
             observed = observed
           )
@@ -239,24 +239,22 @@ read_and_combine_data <- function(model_dir) {
 #' @param value_colname Name of the column in
 #' `tidy_forecast` for the sampled values.
 #' Default `".value"`.
-#' @param epiweekly Is the timeseries epiweekly (as opposed
-#' to daily)? Boolean, default `FALSE` (i.e. daily timeseries).
 #' @export
-to_tidy_draws_timeseries <- function(
+augment_timeseries_draws_w_obs <- function(
   tidy_forecast,
   observed,
   date_colname = "date",
   sample_id_colname = ".draw",
-  value_colname = ".value",
-  epiweekly = FALSE
+  value_colname = ".value"
 ) {
   first_forecast_date <- min(tidy_forecast[[date_colname]])
   resolution <- unique(tidy_forecast$resolution)
-  day_count <- ifelse(resolution == "epiweekly", 7, 1)
+  checkmate::assert_scalar(resolution)
+  step_size_days <- dplyr::if_else(resolution == "epiweekly", 7, 1)
   n_draws <- max(tidy_forecast[[sample_id_colname]])
-
   target_variables <- unique(tidy_forecast$.variable)
-  transformed_obs <- observed |>
+
+  obs_as_samples <- observed |>
     dplyr::filter(
       .data[[date_colname]] < !!first_forecast_date,
       .data$.variable %in% target_variables
@@ -265,13 +263,13 @@ to_tidy_draws_timeseries <- function(
     dplyr::mutate(resolution = !!resolution)
 
   stopifnot(
-    max(as.Date(transformed_obs[[date_colname]])) +
-      lubridate::ddays(day_count) ==
+    max(as.Date(obs_as_samples[[date_colname]])) +
+      lubridate::ddays(step_size_days) ==
       first_forecast_date
   )
 
   dplyr::bind_rows(
-    transformed_obs,
+    obs_as_samples,
     tidy_forecast
   ) |>
     dplyr::select(!!sample_id_colname, tidyselect::everything())

hewr/tests/testthat/test_process_loc_forecast.R

@@ -49,7 +49,7 @@ example_eval_dat <- tibble::tibble(
   ) |>
   dplyr::mutate(.value = rpois(dplyr::n(), 100))
 
-test_that("to_tidy_draws_timeseries() works as expected", {
+test_that("augment_timeseries_draws_w_obs() works as expected", {
   forecast <- tibble::tibble(
     date = as.Date(c("2024-12-21", "2024-12-22")),
     resolution = "daily",
@@ -67,7 +67,7 @@ test_that("to_tidy_draws_timeseries() works as expected", {
     .variable = rep("other_ed_visits", 4L),
     .value = c(11037, 12898, 15172, 17716),
   )
-  result <- to_tidy_draws_timeseries(
+  result <- augment_timeseries_draws_w_obs(
     forecast,
     obs
   )

hewr/tests/testthat/test_timeseries_utils.R

@@ -144,7 +144,7 @@ test_that("epiweekly_samples_from_daily aggregates correctly", {
   expect_true(all(result$.variable == "observed_ed_visits"))
 })
 
-test_that("to_tidy_draws_timeseries combines forecast and observed", {
+test_that("augment_timeseries_draws_w_obs combines forecast and observed", {
   # Create minimal forecast data - 3 forecast dates x 2 draws = 6 rows
   tidy_forecast <- tibble::tibble(
     date = rep(as.Date("2024-01-08") + 0:2, each = 2),
@@ -160,7 +160,7 @@ test_that("to_tidy_draws_timeseries combines forecast and observed", {
     .value = 10:16
   )
 
-  result <- to_tidy_draws_timeseries(
+  result <- augment_timeseries_draws_w_obs(
     tidy_forecast = tidy_forecast,
     observed = observed
   )