Mapping data to graphics

Getting started

You’ll be doing all your R work in R Markdown this time (and from now on). You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.

You’ll need to download three CSV files and put them somewhere on your computer or upload them to RStudio.cloud—preferably in a folder named data in your project folder:

• The_Fellowship_Of_The_Ring.csv
• The_Two_Towers.csv
• The_Return_Of_The_King.csv

To help you, I’ve created a skeleton R Markdown file with a template for this exercise. Download that here and include it in your project:

• 03-exercise.Rmd

In the end, the structure of your project directory should look something like this:

your-project-name\
  03-exercise.Rmd
  your-project-name.Rproj
  data\
    The_Fellowship_Of_The_Ring.csv
    The_Two_Towers.csv
    The_Return_Of_The_King.csv

To check that you put everything in the right places, you can download and unzip this file, which contains everything in the correct structure:

• 03-exercise.zip

Task 1: Reflection

Write your reflection for the day’s readings.

Task 2: Lord of the Rings

Answer the following questions:

• Use group_by() and summarize() on the lotr data to find the total number of words spoken by race. Don’t worry about plotting it. How many words did male hobbits say in the movies?

• Use group_by() and summarize() to answer these questions with bar plots (geom_col())

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

  • 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.

  • 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.

• 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.

You’ll need to insert your own code chunks where needed. Rather than typing them by hand (that’s tedious and you might miscount the number of backticks!), use the “Insert” button at the top of the editing window, or type ctrl + alt + i on Windows, or ⌘ + ⌥ + i on macOS.

Turning everything in

When you’re all done, click on the “Knit” button at the top of the editing window and create an HTML or Word version (or PDF if you’ve installed tinytex) of your document. Upload that file to iCollege.