352 remove all instances of epi keys

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.19
+Version: 0.0.20
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NAMESPACE

@@ -28,11 +28,6 @@ S3method(bake,step_population_scaling)
 S3method(bake,step_training_window)
 S3method(detect_layer,frosting)
 S3method(detect_layer,workflow)
-S3method(epi_keys,data.frame)
-S3method(epi_keys,default)
-S3method(epi_keys,epi_df)
-S3method(epi_keys,epi_workflow)
-S3method(epi_keys,recipe)
 S3method(epi_recipe,default)
 S3method(epi_recipe,epi_df)
 S3method(epi_recipe,formula)
@@ -55,6 +50,9 @@ S3method(forecast,epi_workflow)
 S3method(format,dist_quantiles)
 S3method(is.na,dist_quantiles)
 S3method(is.na,distribution)
+S3method(key_colnames,epi_workflow)
+S3method(key_colnames,list)
+S3method(key_colnames,recipe)
 S3method(mean,dist_quantiles)
 S3method(median,dist_quantiles)
 S3method(predict,epi_workflow)
@@ -154,7 +152,6 @@ export(clean_f_name)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
-export(epi_keys)
 export(epi_recipe)
 export(epi_recipe_blueprint)
 export(epi_workflow)
@@ -170,7 +167,6 @@ export(flusight_hub_formatter)
 export(forecast)
 export(frosting)
 export(get_test_data)
-export(grab_names)
 export(is_epi_recipe)
 export(is_epi_workflow)
 export(is_layer)
@@ -194,6 +190,7 @@ export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
 export(prep)
 export(quantile_reg)
+export(rand_id)
 export(remove_epi_recipe)
 export(remove_frosting)
 export(remove_model)
@@ -207,6 +204,8 @@ export(step_growth_rate)
 export(step_lag_difference)
 export(step_population_scaling)
 export(step_training_window)
+export(tibble)
+export(tidy)
 export(update_epi_recipe)
 export(update_frosting)
 export(update_model)
@@ -229,30 +228,50 @@ importFrom(checkmate,assert_number)
 importFrom(checkmate,assert_numeric)
 importFrom(checkmate,assert_scalar)
 importFrom(cli,cli_abort)
+importFrom(cli,cli_warn)
 importFrom(dplyr,across)
 importFrom(dplyr,all_of)
+importFrom(dplyr,any_of)
+importFrom(dplyr,arrange)
 importFrom(dplyr,bind_cols)
+importFrom(dplyr,bind_rows)
+importFrom(dplyr,everything)
+importFrom(dplyr,filter)
+importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
-importFrom(dplyr,n)
+importFrom(dplyr,left_join)
+importFrom(dplyr,mutate)
+importFrom(dplyr,relocate)
+importFrom(dplyr,rename)
+importFrom(dplyr,select)
 importFrom(dplyr,summarise)
+importFrom(dplyr,summarize)
 importFrom(dplyr,ungroup)
 importFrom(epiprocess,epi_slide)
 importFrom(epiprocess,growth_rate)
 importFrom(generics,augment)
 importFrom(generics,fit)
 importFrom(generics,forecast)
+importFrom(generics,tidy)
+importFrom(ggplot2,aes)
 importFrom(ggplot2,autoplot)
+importFrom(ggplot2,geom_line)
+importFrom(ggplot2,geom_linerange)
+importFrom(ggplot2,geom_point)
+importFrom(ggplot2,geom_ribbon)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
 importFrom(recipes,bake)
 importFrom(recipes,prep)
+importFrom(recipes,rand_id)
 importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,"%@%")
 importFrom(rlang,"%||%")
 importFrom(rlang,":=")
 importFrom(rlang,abort)
+importFrom(rlang,arg_match)
 importFrom(rlang,as_function)
 importFrom(rlang,caller_env)
 importFrom(rlang,enquo)
@@ -264,6 +283,7 @@ importFrom(rlang,is_logical)
 importFrom(rlang,is_null)
 importFrom(rlang,is_true)
 importFrom(rlang,set_names)
+importFrom(rlang,sym)
 importFrom(stats,as.formula)
 importFrom(stats,family)
 importFrom(stats,lm)
@@ -274,9 +294,9 @@ importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
+importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
-importFrom(tidyr,drop_na)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
 importFrom(vctrs,new_rcrd)

NEWS.md

@@ -56,3 +56,4 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - add functionality to calculate weighted interval scores for `dist_quantiles()`
 - Add `step_epi_slide` to produce generic sliding computations over an `epi_df`
 - Add quantile random forests (via `{grf}`) as a parsnip engine
+- Replace `epi_keys()` with `epiprocess::key_colnames()`, #352

R/arx_classifier.R

@@ -26,8 +26,9 @@
 #' @seealso [arx_class_epi_workflow()], [arx_class_args_list()]
 #'
 #' @examples
+#' library(dplyr)
 #' jhu <- case_death_rate_subset %>%
-#'   dplyr::filter(time_value >= as.Date("2021-11-01"))
+#'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
 #'
@@ -45,23 +46,23 @@ arx_classifier <- function(
     epi_data,
     outcome,
     predictors,
-    trainer = parsnip::logistic_reg(),
+    trainer = logistic_reg(),
     args_list = arx_class_args_list()) {
   if (!is_classification(trainer)) {
-    cli::cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
+    cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
   }
 
   wf <- arx_class_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
-  wf <- generics::fit(wf, epi_data)
+  wf <- fit(wf, epi_data)
 
   preds <- forecast(
     wf,
     fill_locf = TRUE,
     n_recent = args_list$nafill_buffer,
     forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
   ) %>%
-    tibble::as_tibble() %>%
-    dplyr::select(-time_value)
+    as_tibble() %>%
+    select(-time_value)
 
   structure(
     list(
@@ -95,17 +96,17 @@ arx_classifier <- function(
 #' @export
 #' @seealso [arx_classifier()]
 #' @examples
-#'
+#' library(dplyr)
 #' jhu <- case_death_rate_subset %>%
-#'   dplyr::filter(time_value >= as.Date("2021-11-01"))
+#'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' arx_class_epi_workflow(jhu, "death_rate", c("case_rate", "death_rate"))
 #'
 #' arx_class_epi_workflow(
 #'   jhu,
 #'   "death_rate",
 #'   c("case_rate", "death_rate"),
-#'   trainer = parsnip::multinom_reg(),
+#'   trainer = multinom_reg(),
 #'   args_list = arx_class_args_list(
 #'     breaks = c(-.05, .1), ahead = 14,
 #'     horizon = 14, method = "linear_reg"
@@ -119,18 +120,18 @@ arx_class_epi_workflow <- function(
     args_list = arx_class_args_list()) {
   validate_forecaster_inputs(epi_data, outcome, predictors)
   if (!inherits(args_list, c("arx_class", "alist"))) {
-    rlang::abort("args_list was not created using `arx_class_args_list().")
+    cli_abort("`args_list` was not created using `arx_class_args_list()`.")
   }
   if (!(is.null(trainer) || is_classification(trainer))) {
-    rlang::abort("`trainer` must be a `{parsnip}` model of mode 'classification'.")
+    cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
   }
   lags <- arx_lags_validator(predictors, args_list$lags)
 
   # --- preprocessor
   # ------- predictors
   r <- epi_recipe(epi_data) %>%
     step_growth_rate(
-      tidyselect::all_of(predictors),
+      dplyr::all_of(predictors),
       role = "grp",
       horizon = args_list$horizon,
       method = args_list$method,
@@ -173,26 +174,24 @@ arx_class_epi_workflow <- function(
   o2 <- rlang::sym(paste0("ahead_", args_list$ahead, "_", o))
   r <- r %>%
     step_epi_ahead(!!o, ahead = args_list$ahead, role = "pre-outcome") %>%
-    step_mutate(
+    recipes::step_mutate(
       outcome_class = cut(!!o2, breaks = args_list$breaks),
       role = "outcome"
     ) %>%
     step_epi_naomit() %>%
-    step_training_window(n_recent = args_list$n_training) %>%
-    {
-      if (!is.null(args_list$check_enough_data_n)) {
-        check_enough_train_data(
-          .,
-          all_predictors(),
-          !!outcome,
-          n = args_list$check_enough_data_n,
-          epi_keys = args_list$check_enough_data_epi_keys,
-          drop_na = FALSE
-        )
-      } else {
-        .
-      }
-    }
+    step_training_window(n_recent = args_list$n_training)
+
+  if (!is.null(args_list$check_enough_data_n)) {
+    r <- check_enough_train_data(
+      r,
+      recipes::all_predictors(),
+      recipes::all_outcomes(),
+      n = args_list$check_enough_data_n,
+      epi_keys = args_list$check_enough_data_epi_keys,
+      drop_na = FALSE
+    )
+  }
+
 
   forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
@@ -264,7 +263,7 @@ arx_class_args_list <- function(
     outcome_transform = c("growth_rate", "lag_difference"),
     breaks = 0.25,
     horizon = 7L,
-    method = c("rel_change", "linear_reg", "smooth_spline", "trend_filter"),
+    method = c("rel_change", "linear_reg"),
     log_scale = FALSE,
     additional_gr_args = list(),
     nafill_buffer = Inf,
@@ -274,8 +273,8 @@ arx_class_args_list <- function(
   rlang::check_dots_empty()
   .lags <- lags
   if (is.list(lags)) lags <- unlist(lags)
-  method <- match.arg(method)
-  outcome_transform <- match.arg(outcome_transform)
+  method <- rlang::arg_match(method)
+  outcome_transform <- rlang::arg_match(outcome_transform)
 
   arg_is_scalar(ahead, n_training, horizon, log_scale)
   arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
@@ -287,12 +286,11 @@ arx_class_args_list <- function(
   if (is.finite(n_training)) arg_is_pos_int(n_training)
   if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
   if (!is.list(additional_gr_args)) {
-    cli::cli_abort(
-      c("`additional_gr_args` must be a {.cls list}.",
-        "!" = "This is a {.cls {class(additional_gr_args)}}.",
-        i = "See `?epiprocess::growth_rate` for available arguments."
-      )
-    )
+    cli_abort(c(
+      "`additional_gr_args` must be a {.cls list}.",
+      "!" = "This is a {.cls {class(additional_gr_args)}}.",
+      i = "See `?epiprocess::growth_rate` for available arguments."
+    ))
   }
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)