---
title: "Exercise 3"
author: "Put your name here"
date: "Put the date here"
output: html_document
---

# Task 1: Reflection

Put your reflection here


# Task 2: Lord of the Rings

## Load and clean data

First we load, restructure, and clean the data.

```{r load-clean-data, warning=FALSE, message=FALSE}
# You'll only need the tidyverse library for this exercise
library(tidyverse)

# Load the separate datasets
fellowship <- read_csv("data/The_Fellowship_Of_The_Ring.csv")
tt <- read_csv("data/The_Two_Towers.csv")
rotk <- read_csv("data/The_Return_Of_The_King.csv")

# bind_rows() stacks data frames on top of each other
lotr_wide <- bind_rows(fellowship, tt, rotk) %>% 
  # Make the Film column a categorical variable (factor), and put it in the
  # order the categories appear (so the films are in the correct order)
  mutate(Film = fct_inorder(Film))

# Make this wide data tidy
lotr <- lotr_wide %>% 
  # This is the new way to make data long
  pivot_longer(cols = c(Female, Male), 
               names_to = "Gender", values_to = "Words")
  # This is the old way that you learned in the RStudio primer on tidy data
  # gather(key = "Gender", value = "Words", Female, Male)
```


## Race

Does a certain race dominate the entire trilogy? (hint: group by `Race`)

```{r}
# Do stuff here
```


## Gender and film

Does a certain gender dominate a movie? (lolz of course it does, but still, graph it) (Hint: group by both `Gender` and `Film`.) Experiment with filling by `Gender` or `Film` and faceting by `Gender` or `Film`.

```{r}
# Do stuff here
```


## Race and film

Does the dominant race differ across the three movies? (Hint: group by both `Race` and `Film`.) Experiment with filling by `Race` or `Film` and faceting by `Race` or `Film`.

```{r}
# Do stuff here
```


## Race and gender and film

Create a plot that visualizes the number of words spoken by race, gender, and film simultaneously. Use the complete tidy `lotr` data frame. You don't need to create a new summarized dataset (with `group_by(Race, Gender, Film)`) because the original data already has a row for each of those (you could make a summarized dataset, but it would be identical to the full version).

You need to show `Race`, `Gender`, and `Film` at the same time, but you only have two possible aesthetics (`x` and `fill`), so you'll also need to facet by the third. Play around with different combinations (e.g. try `x = Race`, then `x = Film`) until you find one that tells the clearest story. For fun, add a `labs()` layer to add a title and subtitle and caption.

```{r}
# Do stuff here
```