refactor: minor epieval package

* don't reexport pipe
* import %>% and %<>%
* bring back %<>%
* consolidate dplyr imports in one place
* update <name>-package.R name
* document

Author: dshemetov

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-export("%>%")
 export(absolute_error)
 export(add_id)
 export(arx_postprocess)
@@ -74,6 +73,7 @@ importFrom(epipredict,step_population_scaling)
 importFrom(epipredict,step_training_window)
 importFrom(epiprocess,epix_slide)
 importFrom(here,here)
+importFrom(magrittr,"%<>%")
 importFrom(magrittr,"%>%")
 importFrom(purrr,map)
 importFrom(purrr,map2_vec)

R/epieval-package.R

@@ -0,0 +1,13 @@
+#' @importFrom magrittr %>% %<>%
+#' @importFrom dplyr select rename inner_join join_by mutate relocate any_of group_by reframe summarize left_join across filter rowwise everything ungroup
+#' @importFrom purrr transpose map map2_vec
+#' @keywords internal
+"_PACKAGE"
+.onLoad <- function(libname, pkgname) {
+  epidatr::set_cache(
+    cache_dir = ".exploration_cache",
+    days = 14,
+    max_size = 4,
+    confirm = FALSE
+  )
+}

R/exploration-tooling-package.R

@@ -84,7 +84,7 @@ arx_preprocess <- function(rec, outcome, predictors, args_list) {
     p <- predictors[l]
     rec <- rec %>% step_epi_lag(!!p, lag = lags[[l]])
   }
-  rec <- rec %>%
+  rec %<>%
     step_epi_ahead(!!outcome, ahead = args_list$ahead) %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training)
@@ -112,22 +112,22 @@ arx_postprocess <- function(postproc,
                             args_list,
                             forecast_date = NULL,
                             target_date = NULL) {
-  postproc <- postproc %>% layer_predict()
+  postproc %<>% layer_predict()
   if (inherits(trainer, "quantile_reg")) {
-    postproc <- postproc %>%
+    postproc %<>%
       layer_quantile_distn(quantile_levels = args_list$quantile_levels) %>%
       layer_point_from_distn()
   } else {
-    postproc <- postproc %>% layer_residual_quantiles(
+    postproc %<>% layer_residual_quantiles(
       quantile_levels = args_list$quantile_levels, symmetrize = args_list$symmetrize,
       by_key = args_list$quantile_by_key
     )
   }
   if (args_list$nonneg) {
-    postproc <- postproc %>% layer_threshold(dplyr::starts_with(".pred"))
+    postproc %<>% layer_threshold(dplyr::starts_with(".pred"))
   }
 
-  postproc <- postproc %>%
+  postproc %<>%
     layer_naomit(dplyr::starts_with(".pred")) %>%
     layer_add_target_date(target_date = target_date)
   return(postproc)
@@ -177,7 +177,6 @@ run_workflow_and_format <- function(preproc, postproc, trainer, epi_data) {
 #' @param forecaster_args_names a bit of a hack around targets, it contains
 #'   the names of the `forecaster_args`.
 #' @importFrom epiprocess epix_slide
-#' @importFrom dplyr select rename inner_join join_by
 #' @importFrom cli cli_abort
 #' @importFrom rlang !!
 #' @export
@@ -235,14 +234,14 @@ forecaster_pred <- function(data,
     before = before,
     ref_time_values = valid_predict_dates,
   )
-  res <- res %>% select(-time_value)
+  res %<>% select(-time_value)
   names(res) <- sub("^slide_value_", "", names(res))
 
   # append the truth data
   true_value <- archive$as_of(archive$versions_end) %>%
     select(geo_value, time_value, !!outcome) %>%
     rename(true_value = !!outcome)
-  res <- res %>%
+  res %<>%
     inner_join(true_value,
       by = join_by(geo_value, target_end_date == time_value)
     )

R/forecaster.R

@@ -9,7 +9,6 @@
 #'   making the forecast, rather than the last day of available data
 #' @param target_end_date the date of the prediction
 #' @importFrom epipredict nested_quantiles
-#' @importFrom dplyr mutate select rename relocate any_of
 #' @importFrom tidyr unnest
 #' @export
 format_storage <- function(pred, true_forecast_date, target_end_date) {
@@ -35,7 +34,6 @@ format_storage <- function(pred, true_forecast_date, target_end_date) {
 #'   making the forecast, rather than the last day of available data
 #' @param target_end_date the date of the prediction
 #' @param quantile_levels the quantile levels
-#' @importFrom dplyr group_by rename reframe mutate
 format_covidhub <- function(pred, true_forecast_date, target_end_date, quantile_levels) {
   pred %<>%
     group_by(forecast_date, geo_value, target_date) %>%

R/formatters.R

@@ -40,7 +40,6 @@
 #'
 #' @importFrom rlang quo .data
 #' @importFrom assertthat assert_that
-#' @importFrom dplyr group_by summarize inner_join left_join mutate relocate across
 #' @export
 evaluate_predictions <- function(
     predictions_cards,
@@ -106,7 +105,6 @@ evaluate_predictions <- function(
 #'
 #' @importFrom assertthat assert_that
 #' @importFrom tidyr pivot_wider
-#' @importFrom dplyr filter mutate relocate select
 #'
 #' @export
 collapse_cards <- function(cards) {

R/scoring_functions.R

@@ -16,7 +16,6 @@ covidhub_probs <- function(type = c("standard", "inc_case")) {
 #' and append the `ahead` at the end.
 #' @param df the df to add a column to. everything should be convertable to a string
 #' @param n_adj the number of adjectives to use; default of 2.
-#' @importFrom dplyr select rowwise mutate across everything ungroup
 #' @importFrom cli hash_animal
 #' @export
 add_id <- function(df, n_adj = 2) {
@@ -72,8 +71,6 @@ lookup_ids <- function() {
 #' @inheritParams add_id
 #' @importFrom tidyr expand_grid
 #' @importFrom tibble tibble
-#' @importFrom purrr map2_vec
-#' @importFrom dplyr rowwise mutate
 #' @export
 id_ahead_ensemble_grid <- function(ensemble_grid, aheads, n_adj = 2) {
   ensemble_grid <- expand_grid(

R/small_utils.R

@@ -5,11 +5,9 @@
 #' @param param_grid the tibble of parameters. Must have forecaster and trainer, everything else is optional
 #' @export
 #' @importFrom rlang syms
-#' @importFrom purrr map
-#' @importFrom dplyr mutate
 make_target_param_grid <- function(param_grid) {
-  param_grid <- param_grid %>% mutate(forecaster = syms(forecaster))
-  param_grid <- param_grid %>% mutate(trainer = syms(trainer))
+  param_grid %<>% mutate(forecaster = syms(forecaster))
+  param_grid %<>% mutate(trainer = syms(trainer))
   list_of_params <- lists_of_real_values(param_grid)
   list_names <- map(list_of_params, names)
   tibble(
@@ -22,8 +20,6 @@ make_target_param_grid <- function(param_grid) {
 
 #' helper function for `make_target_param_grid`
 #' @keywords internal
-#' @importFrom purrr transpose map
-#' @importFrom dplyr select
 lists_of_real_values <- function(param_grid) {
   full_lists <- transpose(param_grid %>% select(-forecaster, -id))
   filter_nonvalues <- function(x) {