VK: Split 05-recruits into four Cardinal Virtue tutorials =

NEWS.md

@@ -1,5 +1,9 @@
 # primer.tutorials (development version)
 
+* Fixed render failures in `dgm-logistic` caused by `plot_predictions()` on a parsnip `logistic_reg()` fit under `marginaleffects` 0.32.0 (`condition = <continuous>` exhausts memory; `by =` crashes; the same calls on an `lm` fit or via `extract_fit_engine()` are fine). Exercises 10 and 11 now build an explicit grid of hypothetical employees (`tibble()` / `expand_grid()`) and pass it to `predictions()`; Exercise 12 uses `avg_predictions(by = "OverTime")` piped into `ggplot()`. All are equivalent, version-robust, and keep the columns named as the `ggplot()` code expects. The section now builds every plot directly from `predictions()` / `avg_predictions()` output rather than from `plot_predictions()`.
+
+* **Split `05-recruits` into four per-virtue tutorials** --- `05-recruits-wisdom`, `05-recruits-justice`, `05-recruits-courage`, and `05-recruits-temperance` --- each with its own Introduction and Summary and its own YAML `id:`. Students carry one `analysis.qmd` in one `recruits` repo across the sequence. The `05-recruits-courage` tutorial absorbs the tidymodels content (Structure → Method → Fitting) from the retired `dgm-linear` tutorial, and `05-recruits-temperance` absorbs its marginaleffects content (Question → Units → Answer, the marginal/conditional distinction, `avg_predictions()`); `dgm-linear` is removed (its siblings `dgm-logistic`, `dgm-multinomial`, and `dgm-ordered` each re-teach the same framing and are unaffected). Knowledge drops in the Courage and Temperance tutorials are sourced from the DGM essays in `PPBDS/primer` (`guide/dgm.md`) or from the data. **Breaking change for student progress records:** completion data keyed on `05-recruits` or `dgm-linear` does not carry forward.
+
 * Renamed `08-sps` to `08-seguro-popular` so the tutorial slug matches the program name students are studying (the dataset in `primer.data` is still named `sps`, but that internal tibble name was not a useful identifier for the tutorial). Tutorial directory, chapter file, YAML `id:`, and student repo name all change from `sps` to `seguro-popular`; the fitted-model variable changes from `fit_sps` to `fit_sp` (matching the program's "SP" abbreviation). **Breaking change for student progress records:** completion data keyed on `08-sps` does not carry forward.
 
 * **Renumbered all tutorials so that each tutorial's number matches its matching book chapter.** Specifically: discarded the `04-mechanics` tutorial (no longer in the curriculum); renamed `05-cardinal-virtues` → `04-cardinal-virtues`; and renumbered every example tutorial down by one (`06-recruits` → `05-recruits`, `07-trains` → `06-trains`, `08-colleges` → `07-colleges`, `09-sps` → `08-sps`, `10-smokes` → `09-smokes`, `11-shaming` → `10-shaming`). The matching book chapters in `book/` were renamed correspondingly. YAML `id:` fields and the `description:` "Tutorial #NN" lines were updated to match the new numbers. **Breaking change for student progress records:** completion data keyed on any of the old IDs (`04-mechanics`, `05-cardinal-virtues`, `06-recruits`, `07-trains`, `08-colleges`, `09-sps`, `10-smokes`, `11-shaming`) does not carry forward. The total curriculum is now 4 misc + 12 example = 16 tutorials, with the EMH split at `05–08` Easy, `09–12` Medium, `13–16` Hard.

R/recruits.R

@@ -1,11 +1,12 @@
 #' Teaching cut of NHANES young-adult heights
 #'
 #' A 50-row sample drawn from the National Health and Nutrition Examination
-#' Survey (NHANES, via [primer.data::nhanes]) for the `05-recruits`
-#' tutorial. Restricted to ages 18--27 and to the columns relevant for the
-#' tutorial's question. The split between sexes (40 male, 10 female) is
+#' Survey (NHANES, via [primer.data::nhanes]) for the `05-recruits-*`
+#' tutorials (`05-recruits-wisdom`, `-justice`, `-courage`, `-temperance`).
+#' Restricted to ages 18--27 and to the columns relevant for the
+#' tutorials' question. The split between sexes (40 male, 10 female) is
 #' deliberately uneven so the two group means have visibly different
-#' standard errors --- a feature the tutorial's Temperance section asks
+#' standard errors --- a feature the `05-recruits-temperance` tutorial asks
 #' students to notice and explain.
 #'
 #' @format A tibble with 50 rows and 3 variables:

data-raw/recruits.R

@@ -1,9 +1,10 @@
 # Build the `recruits` dataset shipped with the primer.tutorials package.
 #
-# The 05-recruits tutorial uses this 50-row sample as a teaching cut of the
-# NHANES height data. The deliberate mix --- 40 men and 10 women --- gives
-# the two group means visibly different standard errors, which is one of the
-# things the tutorial is meant to make students notice and explain.
+# The 05-recruits-* tutorials (wisdom / justice / courage / temperance) use
+# this 50-row sample as a teaching cut of the NHANES height data. The
+# deliberate mix --- 40 men and 10 women --- gives the two group means
+# visibly different standard errors, which is one of the things the
+# 05-recruits-temperance tutorial is meant to make students notice and explain.
 #
 # Re-run this script (Rscript data-raw/recruits.R from the package root)
 # whenever you want to regenerate data/recruits.rda. usethis::use_data()

inst/tutorials/03-rubin-causal-model/tutorial.Rmd

@@ -880,7 +880,7 @@ This tutorial walked through the Rubin Causal Model framework by applying the fo
 * **The fundamental problem of causal inference is that we only ever observe one potential outcome.** The other is the counterfactual, and we can never see it.
 * **The predictive/causal distinction is an analyst's commitment**, not a property of the data or the model. The same fit serves both questions; what changes is the language and the assumptions you are willing to defend.
 
-The example tutorials that follow (`05-recruits`, `06-trains`, `07-colleges`, ...) walk through the same arc using real data and `tidymodels` code. The conceptual moves rehearsed here --- writing down the Preceptor Table, distinguishing potential outcomes from observable outcomes, defending unconfoundedness or acknowledging where it fails --- are exactly the moves you will use there.
+The example tutorials that follow (`05-recruits-wisdom`, `06-trains`, `07-colleges`, ...) walk through the same arc using real data and `tidymodels` code. The conceptual moves rehearsed here --- writing down the Preceptor Table, distinguishing potential outcomes from observable outcomes, defending unconfoundedness or acknowledging where it fails --- are exactly the moves you will use there.
 
 The world is always more uncertain than our models would have us believe.
 

inst/tutorials/04-cardinal-virtues/tutorial.Rmd

@@ -849,7 +849,7 @@ question_text(NULL,
 
 This tutorial walked through the four Cardinal Virtues --- *Wisdom*, *Justice*, *Courage*, and *Temperance* --- by applying them to two paired questions about high-school SAT scores: a predictive question (next year's average) and a causal one (the effect of a school-wide tutoring class). The exercises were conceptual, not coded. The takeaway is the framework itself: every data science problem proceeds through the same four-step arc, and the predictive/causal distinction is something the analyst commits to in Wisdom and defends in Justice --- not something the data dictates.
 
-The example tutorials that follow (`05-recruits`, `06-trains`, `07-colleges`, ...) walk through the same arc using real data and `tidymodels` code. The hardest concepts in those tutorials --- writing down the Preceptor Table, defending the four assumptions, choosing the probability family --- are exactly the conceptual moves rehearsed here.
+The example tutorials that follow (`05-recruits-wisdom`, `06-trains`, `07-colleges`, ...) walk through the same arc using real data and `tidymodels` code. The hardest concepts in those tutorials --- writing down the Preceptor Table, defending the four assumptions, choosing the probability family --- are exactly the conceptual moves rehearsed here.
 
 The world is always more uncertain than our models would have us believe.