fix: plot.gg_variable default multi-class render error (yvar not found)

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: ggRandomForests
 Type: Package
 Title: Visually Exploring Random Forests
-Version: 2.7.3.9006
+Version: 2.7.3.9007
 Date: 2026-05-21
 Authors@R: person("John", "Ehrlinger",
   role = c("aut", "cre"),

NEWS.md

@@ -1,8 +1,24 @@
 Package: ggRandomForests
-Version: 2.7.3.9006
+Version: 2.7.3.9007
 
 ggRandomForests v2.8.0 (development) — continued
 =================================================
+* `plot.gg_variable()`: fix render error on the default multi-class
+  classification plot. The default-xvar selection was treating `yvar` (the
+  observed-class column) and `outcome` (the multi-class pivot facet) as
+  predictors; pivoting them into `var` then dropped the column the
+  downstream `geom_jitter(aes(color = yvar))` referenced, and the patchwork
+  errored when actually rendered. CI did not catch this because the existing
+  test only asserted the patchwork class (lazy) and snapshots run with
+  `VDIFFR_RUN_TESTS = false`. New test exercises a real build of every
+  sub-plot.
+* `plot.gg_variable()`: the same default-xvar selection used substring
+  `grep("time", ...)` / `grep("event", ...)`, which silently dropped any
+  predictor whose name contained those substrings -- e.g. the documented
+  veteran-data survival predictor `diagtime`. Switch to exact matching for
+  `event` / `time` / `yvar` / `outcome` and an anchored prefix for `yhat`
+  (`yhat` or `yhat.<class>`). New test exercises `diagtime` on the veteran
+  survival forest.
 * `gg_roc()`: per-class one-vs-rest ROC curves (#88, closes #72).
   - New `per_class` argument, default `FALSE`. With `per_class = TRUE` on a
     forest of more than two classes, `gg_roc()` returns a long-format

R/plot.gg_variable.R

@@ -180,11 +180,17 @@ plot.gg_variable <- function(x, # nolint: cyclocomp_linter
 
   ## ---- Default xvar: all predictor columns -----------------------------
   if (missing(xvar)) {
-    # Remove response-side columns (yhat, event, time) to isolate predictors
+    # Remove response-side and structural columns to isolate predictors.
+    # `event`, `time`, `yvar`, `outcome` are matched by exact name -- a
+    # substring match (the prior code's `grep("time", ...)`) would silently
+    # drop a documented predictor like the veteran data's `diagtime`. `yhat`
+    # matches the bare `yhat` column (regression / single-class survival) and
+    # also `yhat.<classname>` columns produced by classification gg_variable,
+    # so the regex anchors at the start and either ends or transitions on a
+    # literal dot.
     cls <- c(
-      grep("yhat", colnames(gg_dta)),
-      grep("event", colnames(gg_dta)),
-      grep("time", colnames(gg_dta))
+      grep("^yhat(\\.|$)", colnames(gg_dta)),
+      which(colnames(gg_dta) %in% c("event", "time", "yvar", "outcome"))
     )
     xvar <- colnames(gg_dta)[-cls]
   }

tests/testthat/test_gg_variable.R

@@ -411,6 +411,57 @@ test_that("gg_variable.randomForest classification: layer_data works on single-x
   expect_no_error(ggplot2::layer_data(p, 1L))
 })
 
+test_that("gg_variable.randomForest classification: default plot renders every panel", {
+  # yvar / outcome must not be treated as predictors by the default-xvar pick.
+  skip_if_not_installed("randomForest")
+  # Regression test: the default-xvar selection in plot.gg_variable used to
+  # include yvar and outcome (the response factor and the multi-class pivot's
+  # facet column), pivoting them into `var` and dropping them from the
+  # panel data. The geom_jitter aes then errored at render time with
+  # "Column `yvar` not found". This test exercises a real build of every
+  # patchwork sub-plot, which is what catches the regression — checking the
+  # patchwork class alone does not, because patchwork is lazy.
+  set.seed(42L)
+  rf <- randomForest::randomForest(Species ~ ., data = iris, ntree = 50L)
+  gg <- gg_variable(rf)
+  p  <- plot(gg)
+  expect_s3_class(p, "patchwork")
+  # Force every sub-plot to actually build.
+  for (sub in p$patches$plots) {
+    expect_no_error(ggplot2::ggplot_build(sub))
+  }
+})
+
+test_that("plot.gg_variable default xvar matches column names exactly, not substring", {
+  # Regression: the pre-existing default-xvar exclusion used grep("time", ...)
+  # and grep("event", ...), which silently dropped any predictor whose name
+  # *contained* those substrings -- e.g. the documented veteran-data survival
+  # predictor `diagtime`. Switch to exact matching for event/time/yvar/outcome
+  # and a prefix match for yhat (to catch yhat.<class> columns).
+  data(veteran, package = "randomForestSRC")
+  set.seed(42L)
+  Surv <- survival::Surv # nolint: object_name_linter
+  rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran,
+                                          ntree = 25, nsplit = 3)
+  gg <- gg_variable(rfsrc_veteran, time = 90)
+  expect_true("diagtime" %in% names(gg))
+  # The default xvar list must include diagtime (not dropped by substring match).
+  p <- plot(gg)
+  # The plot should be a patchwork (multiple predictors) -- and diagtime should
+  # appear as one of the panels. Force every sub-plot to build cleanly.
+  expect_s3_class(p, "patchwork")
+  for (sub in p$patches$plots) {
+    expect_no_error(ggplot2::ggplot_build(sub))
+  }
+  # Spot-check: at least one panel's data has `var` sourced from diagtime by
+  # confirming diagtime is NOT in the residual data columns of every panel
+  # (it was pivoted into `var` for exactly one panel).
+  var_panel_count <- sum(vapply(p$patches$plots, function(sub) {
+    !("diagtime" %in% names(sub$data))
+  }, logical(1L)))
+  expect_gte(var_panel_count, 1L)
+})
+
 test_that("gg_variable.randomForest classification: norm.votes=FALSE still gives [0,1] fractions", {
   skip_if_not_installed("randomForest")
   set.seed(42L)