From 40a7508348c5fa511916e7e50a602aa732533f0a Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 11:20:10 -0700
Subject: [PATCH 01/15] Add new_data to slather signature

---
 R/epi_workflow.R      | 2 +-
 R/frosting.R          | 4 ++--
 R/layer_naomit.R      | 3 ++-
 R/layers.R            | 4 +++-
 man/apply_frosting.Rd | 5 ++++-
 man/create_layer.Rd   | 7 ++-----
 man/slather.Rd        | 5 ++++-
 7 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index c3eb409e6..2f18a4e5a 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -120,7 +120,7 @@ predict.epi_workflow <- function(object, new_data, ...) {
                                       blueprint = components$mold$blueprint)
   components$keys <- grab_forged_keys(components$forged,
                                       components$mold, new_data)
-  components <- apply_frosting(object, components, the_fit, the_recipe, ...)
+  components <- apply_frosting(object, components, the_fit, the_recipe, new_data, ...)
   components$predictions
 }
 
diff --git a/R/frosting.R b/R/frosting.R
index c8f30a2cb..41546d57b 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -227,7 +227,7 @@ apply_frosting.default <- function(workflow, components, ...) {
 #' @importFrom rlang abort
 #' @export
 apply_frosting.epi_workflow <-
-  function(workflow, components, the_fit, the_recipe, ...) {
+  function(workflow, components, the_fit, the_recipe, new_data, ...) {
 
     if (!has_postprocessor(workflow)) {
       components$predictions <- predict(
@@ -260,7 +260,7 @@ apply_frosting.epi_workflow <-
 
     for (l in seq_along(layers)) {
       la <- layers[[l]]
-      components <- slather(la, components, the_fit, the_recipe)
+      components <- slather(la, components, the_fit, the_recipe, new_data)
     }
 
     return(components)
diff --git a/R/layer_naomit.R b/R/layer_naomit.R
index 24c276507..86ce46e6b 100644
--- a/R/layer_naomit.R
+++ b/R/layer_naomit.R
@@ -46,7 +46,8 @@ layer_naomit_new <- function(terms, id) {
 }
 
 #' @export
-slather.layer_naomit <- function(object, components, the_fit, the_recipe, ...) {
+slather.layer_naomit <- function(object, components, the_fit, the_recipe, new_data, ...) {
+  newd1 <<- new_data
   exprs <- rlang::expr(c(!!!object$terms))
   pos <- tidyselect::eval_select(exprs, components$predictions)
   col_names <- names(pos)
diff --git a/R/layers.R b/R/layers.R
index 781c96089..64d481bb6 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -125,11 +125,13 @@ detect_layer.workflow <- function(x, name, ...) {
 #'     here for ease.
 #' @param the_fit the fitted model object as returned by calling `parsnip::fit()`
 #' @param the_recipe the `epi_recipe` preprocessor
+#' @param new_data A data frame containing the new predictors to preprocess
+#'   and predict on
 #'
 #' @param ... additional arguments used by methods. Currently unused.
 #'
 #' @return The `components` list. In the same format after applying any updates.
 #' @export
-slather <- function(object, components, the_fit, the_recipe, ...) {
+slather <- function(object, components, the_fit, the_recipe, new_data, ...) {
   UseMethod("slather")
 }
diff --git a/man/apply_frosting.Rd b/man/apply_frosting.Rd
index c9b636f45..b0d685afb 100644
--- a/man/apply_frosting.Rd
+++ b/man/apply_frosting.Rd
@@ -11,7 +11,7 @@ apply_frosting(workflow, ...)
 
 \method{apply_frosting}{default}(workflow, components, ...)
 
-\method{apply_frosting}{epi_workflow}(workflow, components, the_fit, the_recipe, ...)
+\method{apply_frosting}{epi_workflow}(workflow, components, the_fit, the_recipe, new_data, ...)
 }
 \arguments{
 \item{workflow}{An object of class workflow}
@@ -35,6 +35,9 @@ here for ease.
 \item{the_fit}{the fitted model object as returned by calling \code{parsnip::fit()}}
 
 \item{the_recipe}{the \code{epi_recipe} preprocessor}
+
+\item{new_data}{A data frame containing the new predictors to preprocess
+and predict on}
 }
 \description{
 This function is intended for internal use. It implements postprocessing
diff --git a/man/create_layer.Rd b/man/create_layer.Rd
index 81f5e33b0..399d62efa 100644
--- a/man/create_layer.Rd
+++ b/man/create_layer.Rd
@@ -7,11 +7,8 @@
 create_layer(name = NULL, open = rlang::is_interactive())
 }
 \arguments{
-\item{name}{Either a name without extension, or \code{NULL} to create the
-paired file based on currently open file in the script editor. If
-the \verb{R/} file is open, \code{use_test()} will create/open the corresponding
-test file; if the test file is open, \code{use_r()} will create/open the
-corresponding \verb{R/} file.}
+\item{name}{Either a string giving a file name (without directory) or
+\code{NULL} to take the name from the currently open file in RStudio.}
 
 \item{open}{Whether to open the file for interactive editing.}
 }
diff --git a/man/slather.Rd b/man/slather.Rd
index 8fb357e2a..f40784610 100644
--- a/man/slather.Rd
+++ b/man/slather.Rd
@@ -4,7 +4,7 @@
 \alias{slather}
 \title{Spread a layer of frosting on a fitted workflow}
 \usage{
-slather(object, components, the_fit, the_recipe, ...)
+slather(object, components, the_fit, the_recipe, new_data, ...)
 }
 \arguments{
 \item{object}{a workflow with \code{frosting} postprocessing steps}
@@ -27,6 +27,9 @@ here for ease.
 
 \item{the_recipe}{the \code{epi_recipe} preprocessor}
 
+\item{new_data}{A data frame containing the new predictors to preprocess
+and predict on}
+
 \item{...}{additional arguments used by methods. Currently unused.}
 }
 \value{

From 827d106c998911f30bb18e058d9ea1a127ebb86b Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 12:08:07 -0700
Subject: [PATCH 02/15] Add code to include workflow in slather signature

---
 R/frosting.R          | 2 +-
 R/layer_naomit.R      | 5 ++++-
 R/layers.R            | 5 +++--
 man/apply_frosting.Rd | 2 +-
 man/slather.Rd        | 6 ++++--
 5 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/R/frosting.R b/R/frosting.R
index 41546d57b..0a6f841eb 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -260,7 +260,7 @@ apply_frosting.epi_workflow <-
 
     for (l in seq_along(layers)) {
       la <- layers[[l]]
-      components <- slather(la, components, the_fit, the_recipe, new_data)
+      components <- slather(la, components, the_fit, the_recipe, workflow, new_data)
     }
 
     return(components)
diff --git a/R/layer_naomit.R b/R/layer_naomit.R
index 86ce46e6b..597308cad 100644
--- a/R/layer_naomit.R
+++ b/R/layer_naomit.R
@@ -46,8 +46,11 @@ layer_naomit_new <- function(terms, id) {
 }
 
 #' @export
-slather.layer_naomit <- function(object, components, the_fit, the_recipe, new_data, ...) {
+slather.layer_naomit <- function(object, components, the_fit, the_recipe, workflow, new_data, ...) {
   newd1 <<- new_data
+  obj2 <<- object
+  compon <<- components
+  wf2 <<- workflow
   exprs <- rlang::expr(c(!!!object$terms))
   pos <- tidyselect::eval_select(exprs, components$predictions)
   col_names <- names(pos)
diff --git a/R/layers.R b/R/layers.R
index 64d481bb6..1f3ec97e3 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -125,13 +125,14 @@ detect_layer.workflow <- function(x, name, ...) {
 #'     here for ease.
 #' @param the_fit the fitted model object as returned by calling `parsnip::fit()`
 #' @param the_recipe the `epi_recipe` preprocessor
-#' @param new_data A data frame containing the new predictors to preprocess
+#' @param workflow an object of class workflow
+#' @param new_data a data frame containing the new predictors to preprocess
 #'   and predict on
 #'
 #' @param ... additional arguments used by methods. Currently unused.
 #'
 #' @return The `components` list. In the same format after applying any updates.
 #' @export
-slather <- function(object, components, the_fit, the_recipe, new_data, ...) {
+slather <- function(object, components, the_fit, the_recipe, workflow, new_data, ...) {
   UseMethod("slather")
 }
diff --git a/man/apply_frosting.Rd b/man/apply_frosting.Rd
index b0d685afb..6221e8f59 100644
--- a/man/apply_frosting.Rd
+++ b/man/apply_frosting.Rd
@@ -36,7 +36,7 @@ here for ease.
 
 \item{the_recipe}{the \code{epi_recipe} preprocessor}
 
-\item{new_data}{A data frame containing the new predictors to preprocess
+\item{new_data}{a data frame containing the new predictors to preprocess
 and predict on}
 }
 \description{
diff --git a/man/slather.Rd b/man/slather.Rd
index f40784610..6e748372f 100644
--- a/man/slather.Rd
+++ b/man/slather.Rd
@@ -4,7 +4,7 @@
 \alias{slather}
 \title{Spread a layer of frosting on a fitted workflow}
 \usage{
-slather(object, components, the_fit, the_recipe, new_data, ...)
+slather(object, components, the_fit, the_recipe, workflow, new_data, ...)
 }
 \arguments{
 \item{object}{a workflow with \code{frosting} postprocessing steps}
@@ -27,7 +27,9 @@ here for ease.
 
 \item{the_recipe}{the \code{epi_recipe} preprocessor}
 
-\item{new_data}{A data frame containing the new predictors to preprocess
+\item{workflow}{an object of class workflow}
+
+\item{new_data}{a data frame containing the new predictors to preprocess
 and predict on}
 
 \item{...}{additional arguments used by methods. Currently unused.}

From a16dd380f50952ac92400efff89c02e4a8468fef Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 12:10:53 -0700
Subject: [PATCH 03/15] change wf test obj

---
 R/layer_naomit.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/layer_naomit.R b/R/layer_naomit.R
index 597308cad..7c61fb7f9 100644
--- a/R/layer_naomit.R
+++ b/R/layer_naomit.R
@@ -50,7 +50,7 @@ slather.layer_naomit <- function(object, components, the_fit, the_recipe, workfl
   newd1 <<- new_data
   obj2 <<- object
   compon <<- components
-  wf2 <<- workflow
+  wf3 <<- workflow
   exprs <- rlang::expr(c(!!!object$terms))
   pos <- tidyselect::eval_select(exprs, components$predictions)
   col_names <- names(pos)

From d9fc1688f017518b9a946649d3c1d335f07df5e1 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 12:27:09 -0700
Subject: [PATCH 04/15] Remove instances of the_recipe, the_fit

---
 R/epi_workflow.R             | 4 +---
 R/frosting.R                 | 6 ++++--
 R/layer_add_forecast_date.R  | 2 +-
 R/layer_add_target_date.R    | 4 +++-
 R/layer_naomit.R             | 6 +-----
 R/layer_point_from_distn.R   | 2 +-
 R/layer_population_scaling.R | 2 +-
 R/layer_predict.R            | 4 +++-
 R/layer_predictive_distn.R   | 4 +++-
 R/layer_quantile_distn.R     | 2 +-
 R/layer_residual_quantiles.R | 5 ++++-
 R/layer_threshold_preds.R    | 2 +-
 R/layer_unnest.R             | 2 +-
 R/layers.R                   | 4 +---
 man/apply_frosting.Rd        | 6 +-----
 man/slather.Rd               | 6 +-----
 16 files changed, 28 insertions(+), 33 deletions(-)

diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 2f18a4e5a..ddd40fc9d 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -113,14 +113,12 @@ predict.epi_workflow <- function(object, new_data, ...) {
         i = "Do you need to call `fit()`?"))
   }
   components <- list()
-  the_fit <- workflows::extract_fit_parsnip(object)
-  the_recipe <- workflows::extract_recipe(object)
   components$mold <- workflows::extract_mold(object)
   components$forged <- hardhat::forge(new_data,
                                       blueprint = components$mold$blueprint)
   components$keys <- grab_forged_keys(components$forged,
                                       components$mold, new_data)
-  components <- apply_frosting(object, components, the_fit, the_recipe, new_data, ...)
+  components <- apply_frosting(object, components, new_data, ...)
   components$predictions
 }
 
diff --git a/R/frosting.R b/R/frosting.R
index 0a6f841eb..dc407d23b 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -227,7 +227,9 @@ apply_frosting.default <- function(workflow, components, ...) {
 #' @importFrom rlang abort
 #' @export
 apply_frosting.epi_workflow <-
-  function(workflow, components, the_fit, the_recipe, new_data, ...) {
+  function(workflow, components, new_data, ...) {
+
+    the_fit <- workflows::extract_fit_parsnip(object)
 
     if (!has_postprocessor(workflow)) {
       components$predictions <- predict(
@@ -260,7 +262,7 @@ apply_frosting.epi_workflow <-
 
     for (l in seq_along(layers)) {
       la <- layers[[l]]
-      components <- slather(la, components, the_fit, the_recipe, workflow, new_data)
+      components <- slather(la, components, workflow, new_data)
     }
 
     return(components)
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 34f34303e..d39e3612e 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -74,7 +74,7 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 }
 
 #' @export
-slather.layer_add_forecast_date <- function(object, components, the_fit, the_recipe, ...) {
+slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
 
   if (is.null(object$forecast_date)) {
     max_time_value <- max(components$keys$time_value)
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 3429b735e..aec425a24 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -63,7 +63,9 @@ layer_add_target_date_new <- function(id = id, target_date = target_date) {
 }
 
 #' @export
-slather.layer_add_target_date <- function(object, components, the_fit, the_recipe, ...) {
+slather.layer_add_target_date <- function(object, components, workflow, new_data, ...) {
+
+  the_recipe <- workflows::extract_recipe(workflow)
 
   if (is.null(object$target_date)) {
     max_time_value <- max(components$keys$time_value)
diff --git a/R/layer_naomit.R b/R/layer_naomit.R
index 7c61fb7f9..ba1081e8d 100644
--- a/R/layer_naomit.R
+++ b/R/layer_naomit.R
@@ -46,11 +46,7 @@ layer_naomit_new <- function(terms, id) {
 }
 
 #' @export
-slather.layer_naomit <- function(object, components, the_fit, the_recipe, workflow, new_data, ...) {
-  newd1 <<- new_data
-  obj2 <<- object
-  compon <<- components
-  wf3 <<- workflow
+slather.layer_naomit <- function(object, components, workflow, new_data, ...) {
   exprs <- rlang::expr(c(!!!object$terms))
   pos <- tidyselect::eval_select(exprs, components$predictions)
   col_names <- names(pos)
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index 7861cacf1..855d8b194 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -76,7 +76,7 @@ layer_point_from_distn_new <- function(type, name, id) {
 
 #' @export
 slather.layer_point_from_distn <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
 
     dstn <- components$predictions$.pred
     if (!inherits(dstn, "distribution")) {
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 2fb6dfa13..448812bd8 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -129,7 +129,7 @@ layer_population_scaling_new <-
 
 #' @export
 slather.layer_population_scaling <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
     stopifnot("Only one population column allowed for scaling" =
                 length(object$df_pop_col) == 1)
 
diff --git a/R/layer_predict.R b/R/layer_predict.R
index bfe8d219f..e60f0595c 100644
--- a/R/layer_predict.R
+++ b/R/layer_predict.R
@@ -62,7 +62,9 @@ layer_predict_new <- function(type, opts, dots_list, id) {
 }
 
 #' @export
-slather.layer_predict <- function(object, components, the_fit, the_recipe, ...) {
+slather.layer_predict <- function(object, components, workflow, new_data, ...) {
+
+  the_fit <- workflows::extract_fit_parsnip(workflow)
 
   components$predictions <- predict(
     the_fit,
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 9c03a488a..815ff883e 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -71,7 +71,9 @@ layer_predictive_distn_new <- function(dist_type, truncate, name, id) {
 
 #' @export
 slather.layer_predictive_distn <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
+
+    the_fit <- workflows::extract_fit_parsnip(object)
 
     m <- components$predictions$.pred
     r <- grab_residuals(the_fit, components)
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index bc9f4a448..97d546ed1 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -71,7 +71,7 @@ layer_quantile_distn_new <- function(levels, truncate, name, id) {
 
 #' @export
 slather.layer_quantile_distn <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
 
     dstn <- components$predictions$.pred
     if (!inherits(dstn, "distribution")) {
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index d4edca457..fddc4ce20 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -72,7 +72,10 @@ layer_residual_quantiles_new <- function(probs, symmetrize, by_key, name, id) {
 
 #' @export
 slather.layer_residual_quantiles <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
+
+    the_fit <- workflows::extract_fit_parsnip(object)
+
     if (is.null(object$probs)) return(components)
 
     s <- ifelse(object$symmetrize, -1, NA)
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 8528d20e1..eb1cb0577 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -99,7 +99,7 @@ snap.dist_quantiles <- function(x, lower, upper, ...) {
 
 #' @export
 slather.layer_threshold <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
     exprs <- rlang::expr(c(!!!object$terms))
     pos <- tidyselect::eval_select(exprs, components$predictions)
     col_names <- names(pos)
diff --git a/R/layer_unnest.R b/R/layer_unnest.R
index ef82e1308..8b545c9cd 100644
--- a/R/layer_unnest.R
+++ b/R/layer_unnest.R
@@ -27,7 +27,7 @@ layer_unnest_new <- function(terms, id) {
 
 #' @export
 slather.layer_unnest <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
     exprs <- rlang::expr(c(!!!object$terms))
     pos <- tidyselect::eval_select(exprs, components$predictions)
     col_names <- names(pos)
diff --git a/R/layers.R b/R/layers.R
index 1f3ec97e3..ab3167609 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -123,8 +123,6 @@ detect_layer.workflow <- function(x, name, ...) {
 #'     `keys`).
 #'   * `keys` - we put the keys (`time_value`, `geo_value`, and any others)
 #'     here for ease.
-#' @param the_fit the fitted model object as returned by calling `parsnip::fit()`
-#' @param the_recipe the `epi_recipe` preprocessor
 #' @param workflow an object of class workflow
 #' @param new_data a data frame containing the new predictors to preprocess
 #'   and predict on
@@ -133,6 +131,6 @@ detect_layer.workflow <- function(x, name, ...) {
 #'
 #' @return The `components` list. In the same format after applying any updates.
 #' @export
-slather <- function(object, components, the_fit, the_recipe, workflow, new_data, ...) {
+slather <- function(object, components, workflow, new_data, ...) {
   UseMethod("slather")
 }
diff --git a/man/apply_frosting.Rd b/man/apply_frosting.Rd
index 6221e8f59..fc01a3461 100644
--- a/man/apply_frosting.Rd
+++ b/man/apply_frosting.Rd
@@ -11,7 +11,7 @@ apply_frosting(workflow, ...)
 
 \method{apply_frosting}{default}(workflow, components, ...)
 
-\method{apply_frosting}{epi_workflow}(workflow, components, the_fit, the_recipe, new_data, ...)
+\method{apply_frosting}{epi_workflow}(workflow, components, new_data, ...)
 }
 \arguments{
 \item{workflow}{An object of class workflow}
@@ -32,10 +32,6 @@ have three components \code{predictors}, \code{outcomes} (if these were in the
 here for ease.
 }}
 
-\item{the_fit}{the fitted model object as returned by calling \code{parsnip::fit()}}
-
-\item{the_recipe}{the \code{epi_recipe} preprocessor}
-
 \item{new_data}{a data frame containing the new predictors to preprocess
 and predict on}
 }
diff --git a/man/slather.Rd b/man/slather.Rd
index 6e748372f..dd556b629 100644
--- a/man/slather.Rd
+++ b/man/slather.Rd
@@ -4,7 +4,7 @@
 \alias{slather}
 \title{Spread a layer of frosting on a fitted workflow}
 \usage{
-slather(object, components, the_fit, the_recipe, workflow, new_data, ...)
+slather(object, components, workflow, new_data, ...)
 }
 \arguments{
 \item{object}{a workflow with \code{frosting} postprocessing steps}
@@ -23,10 +23,6 @@ have three components \code{predictors}, \code{outcomes} (if these were in the
 here for ease.
 }}
 
-\item{the_fit}{the fitted model object as returned by calling \code{parsnip::fit()}}
-
-\item{the_recipe}{the \code{epi_recipe} preprocessor}
-
 \item{workflow}{an object of class workflow}
 
 \item{new_data}{a data frame containing the new predictors to preprocess

From d99133710e58645aa4fbea30d966898986164c64 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 12:31:28 -0700
Subject: [PATCH 05/15] Changed default forecast and target dates

---
 R/layer_add_forecast_date.R | 2 +-
 R/layer_add_target_date.R   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index d39e3612e..e8dbabd20 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -77,7 +77,7 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
 
   if (is.null(object$forecast_date)) {
-    max_time_value <- max(components$keys$time_value)
+    max_time_value <- max(new_data$time_value)
     object$forecast_date <- max_time_value
   }
 
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index aec425a24..29a3558df 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -68,7 +68,7 @@ slather.layer_add_target_date <- function(object, components, workflow, new_data
   the_recipe <- workflows::extract_recipe(workflow)
 
   if (is.null(object$target_date)) {
-    max_time_value <- max(components$keys$time_value)
+    max_time_value <- max(new_data$time_value)
     ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
 
     if (is.null(ahead)){

From bdb27e75480a6b3dc3c1d5f0abf41911fcfca84c Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 12:45:15 -0700
Subject: [PATCH 06/15] Update tests and documentaiton

---
 R/frosting.R                                  | 2 +-
 R/layer_add_forecast_date.R                   | 7 +++++++
 man/layer_add_forecast_date.Rd                | 7 +++++++
 tests/testthat/test-layer_add_forecast_date.R | 2 +-
 tests/testthat/test-layer_add_target_date.R   | 2 +-
 5 files changed, 17 insertions(+), 3 deletions(-)

diff --git a/R/frosting.R b/R/frosting.R
index dc407d23b..f0c554935 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -229,7 +229,7 @@ apply_frosting.default <- function(workflow, components, ...) {
 apply_frosting.epi_workflow <-
   function(workflow, components, new_data, ...) {
 
-    the_fit <- workflows::extract_fit_parsnip(object)
+    the_fit <- workflows::extract_fit_parsnip(workflow)
 
     if (!has_postprocessor(workflow)) {
       components$predictions <- predict(
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index e8dbabd20..19d63ef24 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -28,6 +28,13 @@
 #' latest <- jhu %>%
 #'   dplyr::filter(time_value >= max(time_value) - 14)
 #'
+#'  # Don't specify `forecast_date` (by default, this should be last date in latest)
+#' f <- frosting() %>% layer_predict() %>%
+#'    layer_naomit(.pred)
+#' wf0 <- wf %>% add_frosting(f)
+#' p0 <- predict(wf0, latest)
+#' p0
+#'
 #' # Specify a `forecast_date` that is greater than or equal to `as_of` date
 #' f <- frosting() %>% layer_predict() %>%
 #'   layer_add_forecast_date(forecast_date = "2022-05-31") %>%
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index 52af21866..96558e403 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -45,6 +45,13 @@ wf <- epi_workflow(r, parsnip::linear_reg()) \%>\% fit(jhu)
 latest <- jhu \%>\%
   dplyr::filter(time_value >= max(time_value) - 14)
 
+ # Don't specify `forecast_date` (by default, this should be last date in latest)
+f <- frosting() \%>\% layer_predict() \%>\%
+   layer_naomit(.pred)
+wf0 <- wf \%>\% add_frosting(f)
+p0 <- predict(wf0, latest)
+p0
+
 # Specify a `forecast_date` that is greater than or equal to `as_of` date
 f <- frosting() \%>\% layer_predict() \%>\%
   layer_add_forecast_date(forecast_date = "2022-05-31") \%>\%
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 06d65da48..5d965e7b3 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -65,6 +65,6 @@ test_that("Do not specify a forecast_date in `layer_add_forecast_date()`", {
   expect_equal(ncol(p3), 4L)
   expect_s3_class(p3, "epi_df")
   expect_equal(nrow(p3), 3L)
-  expect_equal(p3$forecast_date, rep(as.Date("2022-01-14"), times = 3))
+  expect_equal(p3$forecast_date, rep(as.Date("2021-12-31"), times = 3))
   expect_named(p3, c("geo_value", "time_value", ".pred", "forecast_date"))
 })
diff --git a/tests/testthat/test-layer_add_target_date.R b/tests/testthat/test-layer_add_target_date.R
index 2c80fe657..49ba1d997 100644
--- a/tests/testthat/test-layer_add_target_date.R
+++ b/tests/testthat/test-layer_add_target_date.R
@@ -19,7 +19,7 @@ test_that("Use ahead from preprocessing", {
   expect_equal(ncol(p), 4L)
   expect_s3_class(p, "epi_df")
   expect_equal(nrow(p), 3L)
-  expect_equal(p$target_date, rep(as.Date("2022-01-21"), times = 3))
+  expect_equal(p$target_date, rep(as.Date("2022-01-07"), times = 3))
   expect_named(p, c("geo_value", "time_value", ".pred", "target_date"))
 })
 

From 47d7518c44536d660010ad914bbf8f0aeb42df01 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 17 Jul 2023 12:50:02 -0700
Subject: [PATCH 07/15] object to workflow

---
 R/layer_predictive_distn.R   | 2 +-
 R/layer_residual_quantiles.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 815ff883e..c951d9ccd 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -73,7 +73,7 @@ layer_predictive_distn_new <- function(dist_type, truncate, name, id) {
 slather.layer_predictive_distn <-
   function(object, components, workflow, new_data, ...) {
 
-    the_fit <- workflows::extract_fit_parsnip(object)
+    the_fit <- workflows::extract_fit_parsnip(workflow)
 
     m <- components$predictions$.pred
     r <- grab_residuals(the_fit, components)
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index fddc4ce20..c97525b41 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -74,7 +74,7 @@ layer_residual_quantiles_new <- function(probs, symmetrize, by_key, name, id) {
 slather.layer_residual_quantiles <-
   function(object, components, workflow, new_data, ...) {
 
-    the_fit <- workflows::extract_fit_parsnip(object)
+    the_fit <- workflows::extract_fit_parsnip(workflow)
 
     if (is.null(object$probs)) return(components)
 

From 06c71d9dad8d4e4b92460917040223f0d2f7b6ee Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Sat, 22 Jul 2023 17:57:20 -0700
Subject: [PATCH 08/15] Doc. for fit.epi_workflow and trying different things
 for mtv

---
 NAMESPACE                   |  1 +
 R/epi_recipe.R              |  2 ++
 R/epi_workflow.R            | 43 +++++++++++++++++++++++++++++++++++++
 R/frosting.R                | 18 ++++++++++++++++
 R/layer_add_forecast_date.R |  4 ++--
 man/fit-epi_workflow.Rd     | 42 ++++++++++++++++++++++++++++++++++++
 6 files changed, 108 insertions(+), 2 deletions(-)
 create mode 100644 man/fit-epi_workflow.Rd

diff --git a/NAMESPACE b/NAMESPACE
index e35124d55..de3a11e3e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -32,6 +32,7 @@ S3method(extract_layers,workflow)
 S3method(extrapolate_quantiles,dist_default)
 S3method(extrapolate_quantiles,dist_quantiles)
 S3method(extrapolate_quantiles,distribution)
+S3method(fit,epi_workflow)
 S3method(format,dist_quantiles)
 S3method(is.na,dist_quantiles)
 S3method(is.na,distribution)
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index a6fb616ed..9b8568689 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -131,6 +131,7 @@ epi_recipe.epi_df <-
       term_info = var_info,
       steps = NULL,
       template = x[1,],
+      mtv = max(x$time_value), #%%
       levels = NULL,
       retained = NA
     )
@@ -374,6 +375,7 @@ prep.epi_recipe <- function(
   } else {
     x$template <- training[0, ]
   }
+  x$mtv <- max(training$time_value) #%%
   x$tr_info <- tr_data
   x$levels <- lvls
   x$orig_lvls <- orig_lvls
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index ddd40fc9d..7952893ba 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -58,6 +58,47 @@ is_epi_workflow <- function(x) {
   inherits(x, "epi_workflow")
 }
 
+#' Fit an `epi_workflow` object
+#'
+#' @description
+#' This is the `fit()` method for an `epi_workflow` object that
+#' estimates parameters for a given model from a set of data.
+#' Fitting an `epi_workflow` involves two main steps, which are
+#' preprocessing the data and fitting the underlying parsnip model.
+#'
+#' @inheritParams workflows::fit.workflow
+#'
+#' @param object an `epi_workflow` object
+#'
+#' @param x an `epi_df` of predictors and outcomes to use when
+#' fitting the `epi_workflow`
+#'
+#' @return The `epi_workflow` object, updated with a fit parsnip
+#' model in the `object$fit$fit` slot.
+#'
+#' @seealso workflows::fit-workflow
+#'
+#' @name fit-epi_workflow
+#' @export
+#' @examples
+#' jhu <- case_death_rate_subset %>%
+#' filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
+#'
+#' r <- epi_recipe(jhu) %>%
+#'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
+#'   step_epi_ahead(death_rate, ahead = 7)
+#'
+#' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
+#' wf
+#'
+#' @export
+fit.epi_workflow <- function(object, x, ...){
+
+  object$fit$meta <- list(mtv = max(x$time_value))
+  #object$fit$as_of <- attributes(x)$metadata$as_of
+
+  NextMethod()
+}
 
 #' Predict from an epi_workflow
 #'
@@ -112,6 +153,7 @@ predict.epi_workflow <- function(object, new_data, ...) {
       c("Can't predict on an untrained epi_workflow.",
         i = "Do you need to call `fit()`?"))
   }
+
   components <- list()
   components$mold <- workflows::extract_mold(object)
   components$forged <- hardhat::forge(new_data,
@@ -119,6 +161,7 @@ predict.epi_workflow <- function(object, new_data, ...) {
   components$keys <- grab_forged_keys(components$forged,
                                       components$mold, new_data)
   components <- apply_frosting(object, components, new_data, ...)
+
   components$predictions
 }
 
diff --git a/R/frosting.R b/R/frosting.R
index f0c554935..62be492cb 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -229,6 +229,9 @@ apply_frosting.default <- function(workflow, components, ...) {
 apply_frosting.epi_workflow <-
   function(workflow, components, new_data, ...) {
 
+    #%% wf1$post$meta <<- list(mtv = max(new_data$time_value)) #%% change wf1 and possibly <<-
+    # assign("workflow$post$meta", list(mtv = max(new_data$time_value)), envir = .GlobalEnv)
+
     the_fit <- workflows::extract_fit_parsnip(workflow)
 
     if (!has_postprocessor(workflow)) {
@@ -268,6 +271,21 @@ apply_frosting.epi_workflow <-
     return(components)
   }
 
+#%% change_workflow = function(x){
+#  assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
+#}
+
+#%% add_meta_post <- function(workflow, new_data){
+#
+#  workflow$post$meta <- list(mtv = max(new_data$time_value))
+#
+#  workflow
+#}
+
+#%% changeMe = function(x){
+#  assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
+#}
+
 #' @export
 print.frosting <- function(x, form_width = 30, ...) {
   cli::cli_div(
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 19d63ef24..89ea2dd30 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -82,9 +82,9 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 
 #' @export
 slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
-
+  wf <<- workflow
   if (is.null(object$forecast_date)) {
-    max_time_value <- max(new_data$time_value)
+    max_time_value <- max(workflows::extract_preprocessor(wf)$mtv, wf$fit$meta$mtv, max(new_data$time_value))#wf$post$meta$mtv) # workflow$fit$max_train_time #max(new_data$time_value)
     object$forecast_date <- max_time_value
   }
 
diff --git a/man/fit-epi_workflow.Rd b/man/fit-epi_workflow.Rd
new file mode 100644
index 000000000..a5dcdee79
--- /dev/null
+++ b/man/fit-epi_workflow.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi_workflow.R
+\name{fit-epi_workflow}
+\alias{fit-epi_workflow}
+\alias{fit.epi_workflow}
+\title{Fit an \code{epi_workflow} object}
+\usage{
+\method{fit}{epi_workflow}(object, x, ...)
+}
+\arguments{
+\item{object}{an \code{epi_workflow} object}
+
+\item{x}{an \code{epi_df} of predictors and outcomes to use when
+fitting the \code{epi_workflow}}
+
+\item{...}{Not used}
+}
+\value{
+The \code{epi_workflow} object, updated with a fit parsnip
+model in the \code{object$fit$fit} slot.
+}
+\description{
+This is the \code{fit()} method for an \code{epi_workflow} object that
+estimates parameters for a given model from a set of data.
+Fitting an \code{epi_workflow} involves two main steps, which are
+preprocessing the data and fitting the underlying parsnip model.
+}
+\examples{
+jhu <- case_death_rate_subset \%>\%
+filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
+
+r <- epi_recipe(jhu) \%>\%
+  step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
+  step_epi_ahead(death_rate, ahead = 7)
+
+wf <- epi_workflow(r, parsnip::linear_reg()) \%>\% fit(jhu)
+wf
+
+}
+\seealso{
+workflows::fit-workflow
+}

From 106b2ad67aa916bfbf89cdbe7eefcd63c72895b3 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Mon, 24 Jul 2023 23:32:35 -0700
Subject: [PATCH 09/15] Update doc + code to pick max pre/fit/post

---
 R/epi_workflow.R               | 11 +++++------
 R/frosting.R                   |  9 +++++++++
 R/layer_add_forecast_date.R    | 22 ++++++++++++++--------
 R/layer_add_target_date.R      |  7 ++++---
 man/fit-epi_workflow.Rd        |  6 ++++--
 man/layer_add_forecast_date.Rd | 11 ++++++-----
 man/layer_add_target_date.Rd   |  5 +++--
 7 files changed, 45 insertions(+), 26 deletions(-)

diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 7952893ba..97171b55b 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -70,9 +70,11 @@ is_epi_workflow <- function(x) {
 #'
 #' @param object an `epi_workflow` object
 #'
-#' @param x an `epi_df` of predictors and outcomes to use when
+#' @param data an `epi_df` of predictors and outcomes to use when
 #' fitting the `epi_workflow`
 #'
+#' @param control A [workflows::control_workflow()] object
+#'
 #' @return The `epi_workflow` object, updated with a fit parsnip
 #' model in the `object$fit$fit` slot.
 #'
@@ -92,10 +94,9 @@ is_epi_workflow <- function(x) {
 #' wf
 #'
 #' @export
-fit.epi_workflow <- function(object, x, ...){
+fit.epi_workflow <- function(object, data, ..., control = workflows::control_workflow()){
 
-  object$fit$meta <- list(mtv = max(x$time_value))
-  #object$fit$as_of <- attributes(x)$metadata$as_of
+  object$fit$meta <- list(mtv = max(data$time_value), as_of = attributes(data)$metadata$as_of)
 
   NextMethod()
 }
@@ -153,7 +154,6 @@ predict.epi_workflow <- function(object, new_data, ...) {
       c("Can't predict on an untrained epi_workflow.",
         i = "Do you need to call `fit()`?"))
   }
-
   components <- list()
   components$mold <- workflows::extract_mold(object)
   components$forged <- hardhat::forge(new_data,
@@ -161,7 +161,6 @@ predict.epi_workflow <- function(object, new_data, ...) {
   components$keys <- grab_forged_keys(components$forged,
                                       components$mold, new_data)
   components <- apply_frosting(object, components, new_data, ...)
-
   components$predictions
 }
 
diff --git a/R/frosting.R b/R/frosting.R
index 62be492cb..7622002c3 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -267,10 +267,19 @@ apply_frosting.epi_workflow <-
       la <- layers[[l]]
       components <- slather(la, components, workflow, new_data)
     }
+    #%% mtv <- max(new_data$time_value)
+    #%% update_workflow_post(workflow, mtv)
 
     return(components)
   }
 
+#%% #' @export
+# update_workflow_post <- function(x, mtv) {
+#  substitute(x) <- "changed"
+#  #assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
+#  #workflow$post$meta <- list(mtv = max(new_data$time_value))
+# }
+
 #%% change_workflow = function(x){
 #  assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
 #}
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 89ea2dd30..3884eacd7 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -4,17 +4,18 @@
 #' @param forecast_date The forecast date to add as a column to the `epi_df`.
 #' For most cases, this should be specified in the form "yyyy-mm-dd". Note that
 #' when the forecast date is left unspecified, it is set to the maximum time
-#' value in the test data after any processing (ex. leads and lags) has been
-#' applied.
+#' value from the data used in pre-processing, fitting the model, and
+#' postprocessing.
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
 #'
 #' @details To use this function, either specify a forecast date or leave the
 #'  forecast date unspecifed here. In the latter case, the forecast date will
-#'  be set as the maximum time value in the processed test data. In any case,
-#'  when the forecast date is less than the most recent update date of the data
-#'  (ie. the `as_of` value), an appropriate warning will be thrown.
+#'  be set as the maximum time value from the data used in pre-processing,
+#'  fitting the model, and postprocessing. In any case, when the forecast date is
+#'  less than the maximum `as_of` value (from the data used pre-processing,
+#'  model fitting, and postprocessing), an appropriate warning will be thrown.
 #'
 #' @export
 #' @examples
@@ -82,14 +83,19 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 
 #' @export
 slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
-  wf <<- workflow
+  #%% wf <<- workflow
+  #%% comp <<- components
   if (is.null(object$forecast_date)) {
-    max_time_value <- max(workflows::extract_preprocessor(wf)$mtv, wf$fit$meta$mtv, max(new_data$time_value))#wf$post$meta$mtv) # workflow$fit$max_train_time #max(new_data$time_value)
+    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv, workflow$fit$meta$mtv, max(new_data$time_value)) #%% wf$post$meta$mtv) # workflow$fit$max_train_time #max(new_data$time_value)
     object$forecast_date <- max_time_value
   }
+  as_of_pre <- attributes(workflows::extract_preprocessor(workflow)$template)$metadata$as_of
+  as_of_fit <- workflow$fit$meta$as_of
+  as_of_post <- attributes(new_data)$metadata$as_of
 
-  as_of_date <- as.Date(attributes(components$keys)$metadata$as_of)
+  as_of_date <- as.Date(max(as_of_pre, as_of_fit, as_of_post)) #%% as.Date(attributes(components$keys)$metadata$as_of)
 
+  # It would be nice to say that forecast_date is >= to the max of all of them.
   if (object$forecast_date < as_of_date) {
     cli_warn(
       c("The forecast_date is less than the most ",
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 29a3558df..e9178737d 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -2,8 +2,9 @@
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param target_date The target date to add as a column to the `epi_df`.
-#' By default, this is the maximum `time_value` from the processed test
-#' data plus `ahead`, where `ahead` has been specified in preprocessing
+#' By default, this is the maximum `time_value` (from the data used in
+#' pre-processing, fitting the model, and postprocessing) plus `ahead`,
+#' where `ahead` has been specified in preprocessing
 #' (most likely in `step_epi_ahead`). The user may override this with a
 #' date of their own (that will usually be in the form "yyyy-mm-dd").
 #' @param id a random id string
@@ -68,7 +69,7 @@ slather.layer_add_target_date <- function(object, components, workflow, new_data
   the_recipe <- workflows::extract_recipe(workflow)
 
   if (is.null(object$target_date)) {
-    max_time_value <- max(new_data$time_value)
+    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv, workflow$fit$meta$mtv, max(new_data$time_value))
     ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
 
     if (is.null(ahead)){
diff --git a/man/fit-epi_workflow.Rd b/man/fit-epi_workflow.Rd
index a5dcdee79..fb1c3af28 100644
--- a/man/fit-epi_workflow.Rd
+++ b/man/fit-epi_workflow.Rd
@@ -5,15 +5,17 @@
 \alias{fit.epi_workflow}
 \title{Fit an \code{epi_workflow} object}
 \usage{
-\method{fit}{epi_workflow}(object, x, ...)
+\method{fit}{epi_workflow}(object, data, ..., control = workflows::control_workflow())
 }
 \arguments{
 \item{object}{an \code{epi_workflow} object}
 
-\item{x}{an \code{epi_df} of predictors and outcomes to use when
+\item{data}{an \code{epi_df} of predictors and outcomes to use when
 fitting the \code{epi_workflow}}
 
 \item{...}{Not used}
+
+\item{control}{A \code{\link[workflows:control_workflow]{workflows::control_workflow()}} object}
 }
 \value{
 The \code{epi_workflow} object, updated with a fit parsnip
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index 96558e403..421978eb5 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -16,8 +16,8 @@ layer_add_forecast_date(
 \item{forecast_date}{The forecast date to add as a column to the \code{epi_df}.
 For most cases, this should be specified in the form "yyyy-mm-dd". Note that
 when the forecast date is left unspecified, it is set to the maximum time
-value in the test data after any processing (ex. leads and lags) has been
-applied.}
+value from the data used in pre-processing, fitting the model, and
+postprocessing.}
 
 \item{id}{a random id string}
 }
@@ -30,9 +30,10 @@ Postprocessing step to add the forecast date
 \details{
 To use this function, either specify a forecast date or leave the
 forecast date unspecifed here. In the latter case, the forecast date will
-be set as the maximum time value in the processed test data. In any case,
-when the forecast date is less than the most recent update date of the data
-(ie. the \code{as_of} value), an appropriate warning will be thrown.
+be set as the maximum time value from the data used in pre-processing,
+fitting the model, and postprocessing. In any case, when the forecast date is
+less than the maximum \code{as_of} value (from the data used pre-processing,
+model fitting, and postprocessing), an appropriate warning will be thrown.
 }
 \examples{
 jhu <- case_death_rate_subset \%>\%
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index db73066fb..c75f480ba 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -14,8 +14,9 @@ layer_add_target_date(
 \item{frosting}{a \code{frosting} postprocessor}
 
 \item{target_date}{The target date to add as a column to the \code{epi_df}.
-By default, this is the maximum \code{time_value} from the processed test
-data plus \code{ahead}, where \code{ahead} has been specified in preprocessing
+By default, this is the maximum \code{time_value} (from the data used in
+pre-processing, fitting the model, and postprocessing) plus \code{ahead},
+where \code{ahead} has been specified in preprocessing
 (most likely in \code{step_epi_ahead}). The user may override this with a
 date of their own (that will usually be in the form "yyyy-mm-dd").}
 

From a42de5b82c3d30763a943e5eb695b45f50e5eeb0 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Tue, 25 Jul 2023 21:59:33 -0700
Subject: [PATCH 10/15] Fix layer_add_target date and clean up

---
 R/epi_recipe.R                              |  4 +-
 R/frosting.R                                | 27 ---------
 R/layer_add_forecast_date.R                 | 10 +--
 R/layer_add_target_date.R                   | 52 +++++++++++-----
 man/layer_add_target_date.Rd                | 30 ++++++---
 tests/testthat/test-layer_add_target_date.R | 67 +++++++++++++++++++--
 6 files changed, 125 insertions(+), 65 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 9b8568689..d157b0d62 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -131,7 +131,7 @@ epi_recipe.epi_df <-
       term_info = var_info,
       steps = NULL,
       template = x[1,],
-      mtv = max(x$time_value), #%%
+      mtv = max(x$time_value),
       levels = NULL,
       retained = NA
     )
@@ -375,7 +375,7 @@ prep.epi_recipe <- function(
   } else {
     x$template <- training[0, ]
   }
-  x$mtv <- max(training$time_value) #%%
+  x$mtv <- max(training$time_value)
   x$tr_info <- tr_data
   x$levels <- lvls
   x$orig_lvls <- orig_lvls
diff --git a/R/frosting.R b/R/frosting.R
index 7622002c3..f0c554935 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -229,9 +229,6 @@ apply_frosting.default <- function(workflow, components, ...) {
 apply_frosting.epi_workflow <-
   function(workflow, components, new_data, ...) {
 
-    #%% wf1$post$meta <<- list(mtv = max(new_data$time_value)) #%% change wf1 and possibly <<-
-    # assign("workflow$post$meta", list(mtv = max(new_data$time_value)), envir = .GlobalEnv)
-
     the_fit <- workflows::extract_fit_parsnip(workflow)
 
     if (!has_postprocessor(workflow)) {
@@ -267,34 +264,10 @@ apply_frosting.epi_workflow <-
       la <- layers[[l]]
       components <- slather(la, components, workflow, new_data)
     }
-    #%% mtv <- max(new_data$time_value)
-    #%% update_workflow_post(workflow, mtv)
 
     return(components)
   }
 
-#%% #' @export
-# update_workflow_post <- function(x, mtv) {
-#  substitute(x) <- "changed"
-#  #assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
-#  #workflow$post$meta <- list(mtv = max(new_data$time_value))
-# }
-
-#%% change_workflow = function(x){
-#  assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
-#}
-
-#%% add_meta_post <- function(workflow, new_data){
-#
-#  workflow$post$meta <- list(mtv = max(new_data$time_value))
-#
-#  workflow
-#}
-
-#%% changeMe = function(x){
-#  assign(deparse(substitute(x)), "changed", env=.GlobalEnv)
-#}
-
 #' @export
 print.frosting <- function(x, form_width = 30, ...) {
   cli::cli_div(
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 3884eacd7..e512cf159 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -83,19 +83,19 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 
 #' @export
 slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
-  #%% wf <<- workflow
-  #%% comp <<- components
+
   if (is.null(object$forecast_date)) {
-    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv, workflow$fit$meta$mtv, max(new_data$time_value)) #%% wf$post$meta$mtv) # workflow$fit$max_train_time #max(new_data$time_value)
+    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv,
+                          workflow$fit$meta$mtv,
+                          max(new_data$time_value))
     object$forecast_date <- max_time_value
   }
   as_of_pre <- attributes(workflows::extract_preprocessor(workflow)$template)$metadata$as_of
   as_of_fit <- workflow$fit$meta$as_of
   as_of_post <- attributes(new_data)$metadata$as_of
 
-  as_of_date <- as.Date(max(as_of_pre, as_of_fit, as_of_post)) #%% as.Date(attributes(components$keys)$metadata$as_of)
+  as_of_date <- as.Date(max(as_of_pre, as_of_fit, as_of_post))
 
-  # It would be nice to say that forecast_date is >= to the max of all of them.
   if (object$forecast_date < as_of_date) {
     cli_warn(
       c("The forecast_date is less than the most ",
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index e9178737d..d49e2481c 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -2,11 +2,10 @@
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param target_date The target date to add as a column to the `epi_df`.
-#' By default, this is the maximum `time_value` (from the data used in
-#' pre-processing, fitting the model, and postprocessing) plus `ahead`,
-#' where `ahead` has been specified in preprocessing
-#' (most likely in `step_epi_ahead`). The user may override this with a
-#' date of their own (that will usually be in the form "yyyy-mm-dd").
+#' By default, this is the forecast date plus `ahead` (from `step_epi_ahead`
+#' in the `epi_recipe`) if there is a `layer_add_forecast_date` in the
+#' `epi_workflow`. If there's no such layer, then the user may specify
+#' their own target date with a date (of the form "yyyy-mm-dd").
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
@@ -28,8 +27,9 @@
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 #' latest <- get_test_data(r, jhu)
 #'
-#' # Use ahead from preprocessing
+#' # Use ahead + forecast date
 #' f <- frosting() %>% layer_predict() %>%
+#'   layer_add_forecast_date(forecast_date = "2022-05-31") %>%
 #'   layer_add_target_date() %>%
 #'   layer_naomit(.pred)
 #' wf1 <- wf %>% add_frosting(f)
@@ -37,15 +37,25 @@
 #' p <- predict(wf1, latest)
 #' p
 #'
-#' # Override default behaviour by specifying own target date
-#' f2 <- frosting() %>%
-#'   layer_predict() %>%
-#'   layer_add_target_date(target_date = "2022-01-08") %>%
+#' # Use ahead + max time value from pre, fit, post
+#' # which is the same if include `layer_add_forecast_date()`
+#' f <- frosting() %>% layer_predict() %>%
+#'   layer_add_target_date() %>%
 #'   layer_naomit(.pred)
 #' wf2 <- wf %>% add_frosting(f2)
 #'
 #' p2 <- predict(wf2, latest)
 #' p2
+#'
+#' # Specify own target date
+#' f3 <- frosting() %>%
+#'   layer_predict() %>%
+#'   layer_add_target_date(target_date = "2022-01-08") %>%
+#'   layer_naomit(.pred)
+#' wf3 <- wf %>% add_frosting(f3)
+#'
+#' p3 <- predict(wf3, latest)
+#' p3
 layer_add_target_date <-
   function(frosting, target_date = NULL, id = rand_id("add_target_date")) {
     target_date <- arg_to_date(target_date, allow_null = TRUE)
@@ -67,14 +77,26 @@ layer_add_target_date_new <- function(id = id, target_date = target_date) {
 slather.layer_add_target_date <- function(object, components, workflow, new_data, ...) {
 
   the_recipe <- workflows::extract_recipe(workflow)
+  the_frosting <- extract_frosting(workflow)
+
+  if (detect_layer(the_frosting, "layer_add_forecast_date") &&
+      !is.null(extract_argument(the_frosting,
+                       "layer_add_forecast_date", "forecast_date"))) {
+    forecast_date <- extract_argument(the_frosting,
+                                       "layer_add_forecast_date", "forecast_date")
+
+    ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
+
+    target_date = forecast_date + ahead
+
+  } else if (is.null(object$target_date) ||
+             detect_layer(the_frosting, "layer_add_forecast_date")) {
+    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv,
+                          workflow$fit$meta$mtv,
+                          max(new_data$time_value))
 
-  if (is.null(object$target_date)) {
-    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv, workflow$fit$meta$mtv, max(new_data$time_value))
     ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
 
-    if (is.null(ahead)){
-      stop("`ahead` must be specified in preprocessing.")
-    }
     target_date = max_time_value + ahead
   } else{
     target_date = as.Date(object$target_date)
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index c75f480ba..b09a2f05a 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -14,11 +14,10 @@ layer_add_target_date(
 \item{frosting}{a \code{frosting} postprocessor}
 
 \item{target_date}{The target date to add as a column to the \code{epi_df}.
-By default, this is the maximum \code{time_value} (from the data used in
-pre-processing, fitting the model, and postprocessing) plus \code{ahead},
-where \code{ahead} has been specified in preprocessing
-(most likely in \code{step_epi_ahead}). The user may override this with a
-date of their own (that will usually be in the form "yyyy-mm-dd").}
+By default, this is the forecast date plus \code{ahead} (from \code{step_epi_ahead}
+in the \code{epi_recipe}) if there is a \code{layer_add_forecast_date} in the
+\code{epi_workflow}. If there's no such layer, then the user may specify
+their own target date with a date (of the form "yyyy-mm-dd").}
 
 \item{id}{a random id string}
 }
@@ -45,8 +44,9 @@ r <- epi_recipe(jhu) \%>\%
 wf <- epi_workflow(r, parsnip::linear_reg()) \%>\% fit(jhu)
 latest <- get_test_data(r, jhu)
 
-# Use ahead from preprocessing
+# Use ahead + forecast date
 f <- frosting() \%>\% layer_predict() \%>\%
+  layer_add_forecast_date(forecast_date = "2022-05-31") \%>\%
   layer_add_target_date() \%>\%
   layer_naomit(.pred)
 wf1 <- wf \%>\% add_frosting(f)
@@ -54,13 +54,23 @@ wf1 <- wf \%>\% add_frosting(f)
 p <- predict(wf1, latest)
 p
 
-# Override default behaviour by specifying own target date
-f2 <- frosting() \%>\%
-  layer_predict() \%>\%
-  layer_add_target_date(target_date = "2022-01-08") \%>\%
+# Use ahead + max time value from pre, fit, post
+# which is the same if include `layer_add_forecast_date()`
+f <- frosting() \%>\% layer_predict() \%>\%
+  layer_add_target_date() \%>\%
   layer_naomit(.pred)
 wf2 <- wf \%>\% add_frosting(f2)
 
 p2 <- predict(wf2, latest)
 p2
+
+# Specify own target date
+f3 <- frosting() \%>\%
+  layer_predict() \%>\%
+  layer_add_target_date(target_date = "2022-01-08") \%>\%
+  layer_naomit(.pred)
+wf3 <- wf \%>\% add_frosting(f3)
+
+p3 <- predict(wf3, latest)
+p3
 }
diff --git a/tests/testthat/test-layer_add_target_date.R b/tests/testthat/test-layer_add_target_date.R
index 49ba1d997..a8878b9b8 100644
--- a/tests/testthat/test-layer_add_target_date.R
+++ b/tests/testthat/test-layer_add_target_date.R
@@ -9,10 +9,12 @@ wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 latest <- jhu %>%
   dplyr::filter(time_value >= max(time_value) - 14)
 
-test_that("Use ahead from preprocessing", {
+test_that("Use ahead + max time value from pre, fit, post", {
 
-  f <- frosting() %>% layer_predict() %>%
-    layer_add_target_date() %>% layer_naomit(.pred)
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_add_target_date() %>%
+    layer_naomit(.pred)
   wf1 <- wf %>% add_frosting(f)
 
   expect_silent(p <- predict(wf1, latest))
@@ -21,12 +23,48 @@ test_that("Use ahead from preprocessing", {
   expect_equal(nrow(p), 3L)
   expect_equal(p$target_date, rep(as.Date("2022-01-07"), times = 3))
   expect_named(p, c("geo_value", "time_value", ".pred", "target_date"))
+
+  # Should be same dates as above
+  f2 <- frosting() %>%
+    layer_predict() %>%
+    layer_add_forecast_date() %>%
+    layer_add_target_date() %>%
+    layer_naomit(.pred)
+  wf2 <- wf %>% add_frosting(f2)
+
+  expect_warning(p2 <- predict(wf2, latest))
+  expect_equal(ncol(p2), 5L)
+  expect_s3_class(p2, "epi_df")
+  expect_equal(nrow(p2), 3L)
+  expect_equal(p2$target_date, rep(as.Date("2022-01-07"), times = 3))
+  expect_named(p2, c("geo_value", "time_value", ".pred", "forecast_date", "target_date"))
+
 })
 
-test_that("Override default behaviour and specify own target date", {
+test_that("Use ahead + specified forecast date", {
 
-  f <- frosting() %>% layer_predict() %>%
-    layer_add_target_date(target_date = "2022-01-08") %>% layer_naomit(.pred)
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_add_forecast_date(forecast_date = "2022-05-31") %>%
+    layer_add_target_date() %>%
+    layer_naomit(.pred)
+  wf1 <- wf %>% add_frosting(f)
+
+  expect_silent(p <- predict(wf1, latest))
+  expect_equal(ncol(p), 5L)
+  expect_s3_class(p, "epi_df")
+  expect_equal(nrow(p), 3L)
+  expect_equal(p$target_date, rep(as.Date("2022-06-07"), times = 3))
+  expect_named(p, c("geo_value", "time_value", ".pred", "forecast_date", "target_date"))
+
+})
+
+test_that("Specify own target date", {
+
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_add_target_date(target_date = "2022-01-08") %>%
+    layer_naomit(.pred)
   wf1 <- wf %>% add_frosting(f)
 
   expect_silent(p2 <- predict(wf1, latest))
@@ -36,3 +74,20 @@ test_that("Override default behaviour and specify own target date", {
   expect_equal(p2$target_date, rep(as.Date("2022-01-08"), times = 3))
   expect_named(p2, c("geo_value", "time_value", ".pred", "target_date"))
 })
+
+test_that("Specify own target date, but have a forecast date layer", {
+
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_add_forecast_date() %>%
+    layer_add_target_date(target_date = "2022-01-08") %>%
+    layer_naomit(.pred)
+  wf1 <- wf %>% add_frosting(f)
+
+  expect_warning(p2 <- predict(wf1, latest))
+  expect_equal(ncol(p2), 5L)
+  expect_s3_class(p2, "epi_df")
+  expect_equal(nrow(p2), 3L)
+  expect_equal(p2$target_date, rep(as.Date("2022-01-07"), times = 3))
+  expect_named(p2, c("geo_value", "time_value", ".pred", "forecast_date", "target_date"))
+})

From 506b81f8387b2eb1473333ff0734c3a4b12c3005 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Tue, 25 Jul 2023 22:06:40 -0700
Subject: [PATCH 11/15] Update some doc

---
 R/layer_add_target_date.R    | 10 +++++-----
 man/layer_add_target_date.Rd | 10 +++++-----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index d49e2481c..fce8d8490 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -1,11 +1,11 @@
 #' Postprocessing step to add the target date
 #'
 #' @param frosting a `frosting` postprocessor
-#' @param target_date The target date to add as a column to the `epi_df`.
-#' By default, this is the forecast date plus `ahead` (from `step_epi_ahead`
-#' in the `epi_recipe`) if there is a `layer_add_forecast_date` in the
-#' `epi_workflow`. If there's no such layer, then the user may specify
-#' their own target date with a date (of the form "yyyy-mm-dd").
+#' @param target_date The target date to add as a column to the
+#' `epi_df`. By default, this is the forecast date plus `ahead`
+#' (from `step_epi_ahead` in the `epi_recipe`). If there's no
+#' forecast date layer, then the user can specify their own
+#' target date (of the form "yyyy-mm-dd").
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index b09a2f05a..c07bc4d14 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -13,11 +13,11 @@ layer_add_target_date(
 \arguments{
 \item{frosting}{a \code{frosting} postprocessor}
 
-\item{target_date}{The target date to add as a column to the \code{epi_df}.
-By default, this is the forecast date plus \code{ahead} (from \code{step_epi_ahead}
-in the \code{epi_recipe}) if there is a \code{layer_add_forecast_date} in the
-\code{epi_workflow}. If there's no such layer, then the user may specify
-their own target date with a date (of the form "yyyy-mm-dd").}
+\item{target_date}{The target date to add as a column to the
+\code{epi_df}. By default, this is the forecast date plus \code{ahead}
+(from \code{step_epi_ahead} in the \code{epi_recipe}). If there's no
+forecast date layer, then the user can specify their own
+target date (of the form "yyyy-mm-dd").}
 
 \item{id}{a random id string}
 }

From ec7314bf76a127ac4b446560ae072731a21a0cba Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Tue, 25 Jul 2023 22:14:16 -0700
Subject: [PATCH 12/15] f2

---
 R/layer_add_target_date.R    | 2 +-
 man/layer_add_target_date.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index fce8d8490..dd5ae189f 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -39,7 +39,7 @@
 #'
 #' # Use ahead + max time value from pre, fit, post
 #' # which is the same if include `layer_add_forecast_date()`
-#' f <- frosting() %>% layer_predict() %>%
+#' f2 <- frosting() %>% layer_predict() %>%
 #'   layer_add_target_date() %>%
 #'   layer_naomit(.pred)
 #' wf2 <- wf %>% add_frosting(f2)
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index c07bc4d14..747637616 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -56,7 +56,7 @@ p
 
 # Use ahead + max time value from pre, fit, post
 # which is the same if include `layer_add_forecast_date()`
-f <- frosting() \%>\% layer_predict() \%>\%
+f2 <- frosting() \%>\% layer_predict() \%>\%
   layer_add_target_date() \%>\%
   layer_naomit(.pred)
 wf2 <- wf \%>\% add_frosting(f2)

From 195a816e0601b0ac882e1aaa015f773c3a74ab30 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Thu, 27 Jul 2023 21:11:27 -0700
Subject: [PATCH 13/15] Update layer_add_target_date

---
 R/layer_add_target_date.R                   | 45 +++++++++++----------
 man/layer_add_target_date.Rd                | 11 +++--
 tests/testthat/test-layer_add_target_date.R | 29 +++++++------
 3 files changed, 45 insertions(+), 40 deletions(-)

diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index dd5ae189f..652951e10 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -2,10 +2,13 @@
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param target_date The target date to add as a column to the
-#' `epi_df`. By default, this is the forecast date plus `ahead`
-#' (from `step_epi_ahead` in the `epi_recipe`). If there's no
-#' forecast date layer, then the user can specify their own
-#' target date (of the form "yyyy-mm-dd").
+#' `epi_df`. If there's a forecast date specified in a layer, then
+#' it is the forecast date plus `ahead` (from `step_epi_ahead` in
+#' the `epi_recipe`). Otherwise, it is the maximum `time_value`
+#' (from the data used in pre-processing, fitting the model, and
+#' postprocessing) plus `ahead`, where `ahead` has been specified in
+#'  preprocessing. The user may override these by specifying a
+#' target date of their own (of the form "yyyy-mm-dd").
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
@@ -79,27 +82,27 @@ slather.layer_add_target_date <- function(object, components, workflow, new_data
   the_recipe <- workflows::extract_recipe(workflow)
   the_frosting <- extract_frosting(workflow)
 
-  if (detect_layer(the_frosting, "layer_add_forecast_date") &&
-      !is.null(extract_argument(the_frosting,
-                       "layer_add_forecast_date", "forecast_date"))) {
-    forecast_date <- extract_argument(the_frosting,
-                                       "layer_add_forecast_date", "forecast_date")
-
-    ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
+  if (!is.null(object$target_date)) {
+    target_date = as.Date(object$target_date)
+  } else { # null target date case
+    if (detect_layer(the_frosting, "layer_add_forecast_date") &&
+        !is.null(extract_argument(the_frosting,
+                                  "layer_add_forecast_date", "forecast_date"))) {
+      forecast_date <- extract_argument(the_frosting,
+                                        "layer_add_forecast_date", "forecast_date")
 
-    target_date = forecast_date + ahead
+      ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
 
-  } else if (is.null(object$target_date) ||
-             detect_layer(the_frosting, "layer_add_forecast_date")) {
-    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv,
-                          workflow$fit$meta$mtv,
-                          max(new_data$time_value))
+      target_date = forecast_date + ahead
+    } else {
+      max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv,
+                            workflow$fit$meta$mtv,
+                            max(new_data$time_value))
 
-    ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
+      ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
 
-    target_date = max_time_value + ahead
-  } else{
-    target_date = as.Date(object$target_date)
+      target_date = max_time_value + ahead
+    }
   }
 
   components$predictions <- dplyr::bind_cols(components$predictions,
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index 747637616..58ff7770f 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -14,10 +14,13 @@ layer_add_target_date(
 \item{frosting}{a \code{frosting} postprocessor}
 
 \item{target_date}{The target date to add as a column to the
-\code{epi_df}. By default, this is the forecast date plus \code{ahead}
-(from \code{step_epi_ahead} in the \code{epi_recipe}). If there's no
-forecast date layer, then the user can specify their own
-target date (of the form "yyyy-mm-dd").}
+\code{epi_df}. If there's a forecast date specified in a layer, then
+it is the forecast date plus \code{ahead} (from \code{step_epi_ahead} in
+the \code{epi_recipe}). Otherwise, it is the maximum \code{time_value}
+(from the data used in pre-processing, fitting the model, and
+postprocessing) plus \code{ahead}, where \code{ahead} has been specified in
+preprocessing. The user may override these by specifying a
+target date of their own (of the form "yyyy-mm-dd").}
 
 \item{id}{a random id string}
 }
diff --git a/tests/testthat/test-layer_add_target_date.R b/tests/testthat/test-layer_add_target_date.R
index a8878b9b8..b8627571c 100644
--- a/tests/testthat/test-layer_add_target_date.R
+++ b/tests/testthat/test-layer_add_target_date.R
@@ -61,33 +61,32 @@ test_that("Use ahead + specified forecast date", {
 
 test_that("Specify own target date", {
 
+  # No forecast date layer
   f <- frosting() %>%
     layer_predict() %>%
     layer_add_target_date(target_date = "2022-01-08") %>%
     layer_naomit(.pred)
   wf1 <- wf %>% add_frosting(f)
 
-  expect_silent(p2 <- predict(wf1, latest))
-  expect_equal(ncol(p2), 4L)
-  expect_s3_class(p2, "epi_df")
-  expect_equal(nrow(p2), 3L)
-  expect_equal(p2$target_date, rep(as.Date("2022-01-08"), times = 3))
-  expect_named(p2, c("geo_value", "time_value", ".pred", "target_date"))
-})
-
-test_that("Specify own target date, but have a forecast date layer", {
+  expect_silent(p1 <- predict(wf1, latest))
+  expect_equal(ncol(p1), 4L)
+  expect_s3_class(p1, "epi_df")
+  expect_equal(nrow(p1), 3L)
+  expect_equal(p1$target_date, rep(as.Date("2022-01-08"), times = 3))
+  expect_named(p1, c("geo_value", "time_value", ".pred", "target_date"))
 
-  f <- frosting() %>%
+  # Include forecast date layer - should be same results as previous
+  f2 <- frosting() %>%
     layer_predict() %>%
     layer_add_forecast_date() %>%
     layer_add_target_date(target_date = "2022-01-08") %>%
     layer_naomit(.pred)
-  wf1 <- wf %>% add_frosting(f)
+  wf2 <- wf %>% add_frosting(f2)
 
-  expect_warning(p2 <- predict(wf1, latest))
-  expect_equal(ncol(p2), 5L)
+  expect_silent(p2 <- predict(wf1, latest))
+  expect_equal(ncol(p2), 4L)
   expect_s3_class(p2, "epi_df")
   expect_equal(nrow(p2), 3L)
-  expect_equal(p2$target_date, rep(as.Date("2022-01-07"), times = 3))
-  expect_named(p2, c("geo_value", "time_value", ".pred", "forecast_date", "target_date"))
+  expect_equal(p2$target_date, rep(as.Date("2022-01-08"), times = 3))
+  expect_named(p2, c("geo_value", "time_value", ".pred", "target_date"))
 })

From e26894f2f3d08b91786bf98dd2cd6c0ac59b3fde Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Thu, 27 Jul 2023 22:24:53 -0700
Subject: [PATCH 14/15] mtv --> max_time_value

---
 R/epi_recipe.R              | 4 ++--
 R/epi_workflow.R            | 2 +-
 R/layer_add_forecast_date.R | 4 ++--
 R/layer_add_target_date.R   | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index d157b0d62..4caea7476 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -131,7 +131,7 @@ epi_recipe.epi_df <-
       term_info = var_info,
       steps = NULL,
       template = x[1,],
-      mtv = max(x$time_value),
+      max_time_value = max(x$time_value),
       levels = NULL,
       retained = NA
     )
@@ -375,7 +375,7 @@ prep.epi_recipe <- function(
   } else {
     x$template <- training[0, ]
   }
-  x$mtv <- max(training$time_value)
+  x$max_time_value <- max(training$time_value)
   x$tr_info <- tr_data
   x$levels <- lvls
   x$orig_lvls <- orig_lvls
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 97171b55b..1379fef86 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -96,7 +96,7 @@ is_epi_workflow <- function(x) {
 #' @export
 fit.epi_workflow <- function(object, data, ..., control = workflows::control_workflow()){
 
-  object$fit$meta <- list(mtv = max(data$time_value), as_of = attributes(data)$metadata$as_of)
+  object$fit$meta <- list(max_time_value = max(data$time_value), as_of = attributes(data)$metadata$as_of)
 
   NextMethod()
 }
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index e512cf159..0b522ef65 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -85,8 +85,8 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
 
   if (is.null(object$forecast_date)) {
-    max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv,
-                          workflow$fit$meta$mtv,
+    max_time_value <- max(workflows::extract_preprocessor(workflow)$max_time_value,
+                          workflow$fit$meta$max_time_value,
                           max(new_data$time_value))
     object$forecast_date <- max_time_value
   }
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 652951e10..1fe151bce 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -95,8 +95,8 @@ slather.layer_add_target_date <- function(object, components, workflow, new_data
 
       target_date = forecast_date + ahead
     } else {
-      max_time_value <- max(workflows::extract_preprocessor(workflow)$mtv,
-                            workflow$fit$meta$mtv,
+      max_time_value <- max(workflows::extract_preprocessor(workflow)$max_time_value,
+                            workflow$fit$meta$max_time_value,
                             max(new_data$time_value))
 
       ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")

From 7566fcbaa0aa8c581281ada88411cdca9c403568 Mon Sep 17 00:00:00 2001
From: rachlobay <rachel.lobay@gmail.com>
Date: Fri, 11 Aug 2023 16:35:37 -0700
Subject: [PATCH 15/15] Update inst/templates/layer.R

---
 inst/templates/layer.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inst/templates/layer.R b/inst/templates/layer.R
index 14dc4a82b..3fecb3c33 100644
--- a/inst/templates/layer.R
+++ b/inst/templates/layer.R
@@ -28,7 +28,7 @@ layer_{{{ name }}}_new <- function(terms, args, more_args, id) {
 
 #' @export
 slather.layer_{{{ name }}} <-
-  function(object, components, the_fit, the_recipe, ...) {
+  function(object, components, workflow, new_data, ...) {
 
     # if layer_ used ... in tidyselect, we need to evaluate it now
     exprs <- rlang::expr(c(!!!object$terms))