From 333e220e04456fe36f5cba4e9400284a459c74f4 Mon Sep 17 00:00:00 2001
From: Hugo Gruson <10783929+Bisaloo@users.noreply.github.com>
Date: Tue, 27 Aug 2024 11:17:17 +0200
Subject: [PATCH] Use stricter check to bypass validation of x[]

This ensures that object manipulated by [ via dplyr function are still validated.
See the report in https://github.com/epiforecasts/scoringutils/pull/884#issuecomment-2293785836.
---
 R/forecast.R | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/R/forecast.R b/R/forecast.R
index e559f44a..ac17d1f4 100644
--- a/R/forecast.R
+++ b/R/forecast.R
@@ -693,10 +693,17 @@ is_forecast_nominal <- function(x) {
 
   out <- NextMethod()
 
-  # ...length() > 1: we don't need to revalidate x[]
+  # (identical(x, out) && ...length() == 1) is the best way I have found to
+  #   selectively catch x[], which we don't want to revalidate. ...length()
+  #   alone will skip cases with dplyr verbs and identical alone will skip cases
+  #   where we used data.table := operator which will turn x into out before we
+  #   arrive to this function.
+  is_dt_force_print <- identical(x, out) && ...length() == 1
+  #   ...length() as it still returns 1 in x[] and then skips validations in
+  #   undesired situation if we set ...length() > 1
   # is.data.table: when [.data.table returns an atomic vector, it's clear it
   #   cannot be a valid forecast object, and it is likely intended by the user
-  if (...length() > 1 && data.table::is.data.table(out)) {
+  if (data.table::is.data.table(out) && !is_dt_force_print) {
     # check whether subset object passes validation
     validation <- try(
       assert_forecast(forecast = out, verbose = FALSE),