---
title: "Introduction to theamazingrace"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to theamazingrace}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Overview

The `theamazingrace` package provides tidy datasets for the multi-Emmy award-winning television series **The Amazing Race** (US Franchise, Seasons 1–36).

The data is structured into seven relational tables:
1. `seasons`: Season summaries, dates, distance traveled, and winners.
2. `episodes`: Episode titles, premiere air dates, and Nielsen television ratings.
3. `contestants`: Individual contestant demographics, ages, hometowns, and relationships.
4. `teams`: Team configurations, relationships, final finish placements, and legs won.
5. `legs`: Leg-level routing, countries visited, itinerary stops, and detailed narratives.
6. `leg_results`: Team finishing order on each leg, non-eliminations, Fast Forwards, and U-Turns.
7. `tasks`: Challenge descriptions across Roadblocks, Detours, Route Info, and Speed Bumps.

## Setup and Data Inspection

```{r setup, eval = FALSE}
library(theamazingrace)
library(dplyr)
library(ggplot2)

data(seasons)
data(episodes)
data(leg_results)
data(tasks)
```

## Analysis 1: Season Distance Trends

How has total race distance changed across the 36 seasons?

```{r distance-trend, eval = FALSE}
ggplot(seasons, aes(x = season, y = distance_miles)) +
  geom_line(color = "#1f77b4", size = 1) +
  geom_point(color = "#d62728", size = 2) +
  theme_minimal() +
  labs(
    title = "Total Race Distance per Season",
    x = "Season",
    y = "Distance (Miles)"
  )
```

## Analysis 2: Task Types Breakdown

What types of challenges appear most frequently across all legs?

```{r task-types, eval = FALSE}
tasks |>
  count(task_type, sort = TRUE)
```

## Analysis 3: Team Placement Trajectories

Trace the path of the winning team through the race:

```{r winner-trajectory, eval = FALSE}
s1_winners <- leg_results |>
  filter(season == 1, team_name == "Rob & Brennan")

ggplot(s1_winners, aes(x = leg_number, y = placement)) +
  geom_line() +
  geom_point(size = 3) +
  scale_y_reverse(breaks = 1:11) +
  theme_minimal() +
  labs(
    title = "Rob & Brennan's Leg Placements (Season 1)",
    x = "Leg Number",
    y = "Placement (1st = Top)"
  )
```