Merge pull request #364 from cmu-delphi/275-step_epi_slide

275 step epi slide

Author: dshemetov

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.17
+Version: 0.0.18
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NAMESPACE

@@ -21,6 +21,7 @@ S3method(bake,check_enough_train_data)
 S3method(bake,epi_recipe)
 S3method(bake,step_epi_ahead)
 S3method(bake,step_epi_lag)
+S3method(bake,step_epi_slide)
 S3method(bake,step_growth_rate)
 S3method(bake,step_lag_difference)
 S3method(bake,step_population_scaling)
@@ -62,6 +63,7 @@ S3method(prep,check_enough_train_data)
 S3method(prep,epi_recipe)
 S3method(prep,step_epi_ahead)
 S3method(prep,step_epi_lag)
+S3method(prep,step_epi_slide)
 S3method(prep,step_growth_rate)
 S3method(prep,step_lag_difference)
 S3method(prep,step_population_scaling)
@@ -90,6 +92,7 @@ S3method(print,layer_threshold)
 S3method(print,layer_unnest)
 S3method(print,step_epi_ahead)
 S3method(print,step_epi_lag)
+S3method(print,step_epi_slide)
 S3method(print,step_growth_rate)
 S3method(print,step_lag_difference)
 S3method(print,step_naomit)
@@ -143,6 +146,7 @@ export(bake)
 export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
+export(clean_f_name)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
@@ -194,6 +198,7 @@ export(smooth_quantile_reg)
 export(step_epi_ahead)
 export(step_epi_lag)
 export(step_epi_naomit)
+export(step_epi_slide)
 export(step_growth_rate)
 export(step_lag_difference)
 export(step_population_scaling)
@@ -221,10 +226,12 @@ importFrom(checkmate,assert_scalar)
 importFrom(cli,cli_abort)
 importFrom(dplyr,across)
 importFrom(dplyr,all_of)
+importFrom(dplyr,bind_cols)
 importFrom(dplyr,group_by)
 importFrom(dplyr,n)
 importFrom(dplyr,summarise)
 importFrom(dplyr,ungroup)
+importFrom(epiprocess,epi_slide)
 importFrom(epiprocess,growth_rate)
 importFrom(generics,augment)
 importFrom(generics,fit)
@@ -244,6 +251,8 @@ importFrom(rlang,":=")
 importFrom(rlang,abort)
 importFrom(rlang,as_function)
 importFrom(rlang,caller_env)
+importFrom(rlang,enquo)
+importFrom(rlang,enquos)
 importFrom(rlang,global_env)
 importFrom(rlang,inject)
 importFrom(rlang,is_logical)
@@ -262,6 +271,7 @@ importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
 importFrom(tibble,tibble)
+importFrom(tidyr,crossing)
 importFrom(tidyr,drop_na)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)

NEWS.md

@@ -52,3 +52,4 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `...` args intended for `predict.model_fit()`
 - `bake.epi_recipe()` will now re-infer the geo and time type in case baking the
   steps has changed the appropriate values
+- Add `step_epi_slide` to produce generic sliding computations over an `epi_df`

R/epipredict-package.R

@@ -1,7 +1,7 @@
 ## usethis namespace: start
 #' @importFrom tibble tibble
 #' @importFrom rlang := !! %||% as_function global_env set_names !!!
-#' @importFrom rlang is_logical is_true inject
+#' @importFrom rlang is_logical is_true inject enquo enquos
 #' @importFrom stats poly predict lm residuals quantile
 #' @importFrom cli cli_abort
 #' @importFrom checkmate assert assert_character assert_int assert_scalar

R/get_test_data.R

@@ -65,20 +65,18 @@ get_test_data <- function(
       i = "The following required columns are missing: {check$missing_names}"
     ))
   }
-
   if (class(forecast_date) != class(x$time_value)) {
     cli::cli_abort("`forecast_date` must be the same class as `x$time_value`.")
   }
-
-
   if (forecast_date < max(x$time_value)) {
     cli::cli_abort("`forecast_date` must be no earlier than `max(x$time_value)`")
   }
 
   min_lags <- min(map_dbl(recipe$steps, ~ min(.x$lag %||% Inf)), Inf)
   max_lags <- max(map_dbl(recipe$steps, ~ max(.x$lag %||% 0)), 0)
   max_horizon <- max(map_dbl(recipe$steps, ~ max(.x$horizon %||% 0)), 0)
-  min_required <- max_lags + max_horizon
+  max_slide <- max(map_dbl(recipe$steps, ~ max(.x$before %||% 0)), 0)
+  min_required <- max_lags + max_horizon + max_slide
   if (is.null(n_recent)) n_recent <- min_required + 1 # one extra for filling
   if (n_recent <= min_required) n_recent <- min_required + n_recent