@@ -166,8 +166,8 @@ prep.step_epi_YeoJohnson <- function(x, training, info = NULL, ...) {
166166 limits = x $ limits ,
167167 num_unique = x $ num_unique ,
168168 na_rm = x $ na_rm ,
169- forecast_date = attributes (training ) $ metadata $ as_of ,
170- metadata = attributes (training ) $ metadata ,
169+ forecast_date = attr (training , " metadata" ) $ as_of ,
170+ metadata = attr (training , " metadata" ) ,
171171 columns = col_names ,
172172 skip = x $ skip ,
173173 id = x $ id
@@ -179,13 +179,13 @@ bake.step_epi_YeoJohnson <- function(object, new_data, ...) {
179179 # If not an epi_df, make it one assuming the template of training data.
180180 # If it is an epi_df, check that the keys match.
181181 # Imitating the pattern in step_adjust_latency().
182- if (! inherits(new_data , " epi_df" ) || is.null(attributes (new_data ) $ metadata $ as_of )) {
182+ if (! inherits(new_data , " epi_df" ) || is.null(attr (new_data , " metadata" ) $ as_of )) {
183183 new_data <- as_epi_df(
184184 new_data ,
185185 as_of = object $ forecast_date ,
186186 other_keys = object $ metadata $ other_keys %|| % character ()
187187 )
188- new_data % @ % metadata <- object $ metadata
188+ attr( new_data , " metadata" ) <- object $ metadata
189189 }
190190 # Check that the keys match.
191191 keys <- key_colnames(new_data , exclude = " time_value" )
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