---
title: "Palmer Penguins — classification (species)"
author: "Aparna Pandey and Stephan Peischl"
format:
  html:
    toc: true
    code-tools: true
engine: knitr
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(palmerpenguins)
library(dplyr)
library(ggplot2)
library(GGally)
library(rpart)
library(rpart.plot)
library(tidymodels)
library(tidyr)
library(rlang)
theme_set(theme_classic())
```

# Overview

This notebook uses **[Palmer Penguins](../data/cards/palmer-penguins.qmd)** for **classification**. We use a **binary** task — **Adelie vs Gentoo** — after dropping Chinstrap (cleaner boundaries in 2D plots; for **three species** see [`penguins-species-multiclass.Rmd`](penguins-species-multiclass.Rmd)).

**Models:** `glm` logistic regression and an `rpart` tree. **Splits and metrics:** `tidymodels` / `yardstick` (`conf_mat`).

**Same scientific task with `tidymodels`:** preprocessing + workflow + resampling live on the website in [Module 04](../modules/module-04-pipeline.qmd#train-test-last-fit) (starts with train/test + `glm`, then the [Tuesday tuned-tree pipeline](../modules/module-04-pipeline.qmd#canonical-pipeline-tuesday)), with follow-ups [Module 07](../modules/module-07-penguins-choose-metrics.qmd) (pick a metric) and [Module 08](../modules/module-08-penguins-compare-models.qmd) (compare RF / XGBoost / MLP).

The **synthetic gene / disease** notebook (`logistic-regression-gene-disease.Rmd`) stays the place for **known-truth** logistic stories.

## Prepare data

```{r}
data("penguins", package = "palmerpenguins")
pg <- penguins |>
  filter(species %in% c("Adelie", "Gentoo")) |>
  mutate(species = droplevels(species)) |>
  tidyr::drop_na()
table(pg$species)
```

## Pair plot (first five columns)

```{r fig.width=8, fig.height=5}
GGally::ggpairs(
  pg,
  columns = c("bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", "sex"),
  aes(color = species)
) +
  theme_minimal()
```

## Logistic regression

```{r}
log_fit <- glm(
  species ~ bill_length_mm + bill_depth_mm + flipper_length_mm + body_mass_g + island + sex,
  data = pg,
  family = binomial()
)
summary(log_fit)
```

## Classification tree

```{r fig.width=8, fig.height=5}
tree_fit <- rpart(
  species ~ bill_length_mm + bill_depth_mm + flipper_length_mm + body_mass_g + island + sex,
  data = pg,
  method = "class"
)
rpart.plot(tree_fit, type = 4, extra = 104, main = "Adelie vs Gentoo (rpart)")
```

## Confusion matrix (tree, training data)

```{r}
pred_class <- predict(tree_fit, type = "class")
tibble(truth = pg$species, .pred_class = pred_class) |>
  conf_mat(truth = truth, estimate = .pred_class)
```

## Decision-region sketch (logistic)

Other predictors held at their training means / modes.

```{r fig.width=7, fig.height=4.5}
plot_boundary <- function(model, data, f1, f2) {
  r1 <- range(data[[f1]])
  r2 <- range(data[[f2]])
  grid <- expand.grid(
    seq(r1[1], r1[2], length.out = 120),
    seq(r2[1], r2[2], length.out = 120)
  )
  names(grid) <- c(f1, f2)
  for (nm in setdiff(names(data), c(f1, f2, "species"))) {
    v <- data[[nm]]
    grid[[nm]] <- if (is.numeric(v)) {
      mean(v, na.rm = TRUE)
    } else {
      tab <- table(v)
      grid[[nm]] <- names(tab)[which.max(tab)]
    }
  }
  grid$p_Gentoo <- predict(model, newdata = grid, type = "response")
  grid$cls <- factor(ifelse(grid$p_Gentoo > 0.5, "Gentoo", "Adelie"), levels = c("Adelie", "Gentoo"))
  ggplot(data, aes(!!sym(f1), !!sym(f2))) +
    geom_raster(data = grid, aes(!!sym(f1), !!sym(f2), fill = cls), alpha = 0.25, inherit.aes = FALSE) +
    geom_point(aes(shape = species, fill = species), size = 2.5, color = "gray20") +
    scale_fill_brewer(palette = "Set2") +
    theme_minimal() +
    labs(
      title = "Logistic decision regions (bill length vs flipper length)",
      subtitle = "Other predictors fixed at typical values",
      fill = "Region", shape = "Truth"
    )
}
print(plot_boundary(log_fit, pg, "bill_length_mm", "flipper_length_mm"))
```

## Takeaways

- Adelie and Gentoo are fairly separable in measurement space — discuss **overlap**, **costs of errors**, and why **test-set** evaluation matters.
- The Tuesday slide deck reuses the same **Adelie vs Gentoo** idea inside the shared `tidymodels` pipeline include.