AE 02: Bechdel + data visualization

In this mini analysis we work with the data used in the FiveThirtyEight story titled “The Dollar-And-Cents Case Against Hollywood’s Exclusion of Women”.

This analysis is about the Bechdel test, a measure of the representation of women in fiction.

Getting started

Packages

We start with loading the packages we’ll use: tidyverse for majority of the analysis and scales for pretty plot labels later on.

library(tidyverse)
library(scales)

Data

The data are stored as a CSV (comma separated values) file in the data folder of your repository. Let’s read it from there and save it as an object called bechdel.

bechdel <- read_csv("https://sta199-s24.github.io/data/bechdel.csv")

Get to know the data

We can use the glimpse function to get an overview (or “glimpse”) of the data.

# add code here

What does each observation (row) in the data set represent?

Each observation represents a ___.

How many observations (rows) are in the data set?

There are 1615 movies in the dataset.

How many variables (columns) are in the data set?

There are ___ columns in the dataset.

Variables of interest

The variables we’ll focus on are the following:

budget_2013: Budget in 2013 inflation adjusted dollars.
gross_2013: Gross (US and international combined) in 2013 inflation adjusted dollars.
roi: Return on investment, calculated as the ratio of the gross to budget.
clean_test: Bechdel test result:
- ok = passes test
- dubious
- men = women only talk about men
- notalk = women don’t talk to each other
- nowomen = fewer than two women
binary: Bechdel Test PASS vs FAIL binary

We will also use the year of release in data prep and title of movie to take a deeper look at some outliers.

There are a few other variables in the dataset, but we won’t be using them in this analysis.

Visualizing data with `ggplot2`

ggplot2 is the package and ggplot() is the function in this package that is used to create a plot.

ggplot() creates the initial base coordinate system, and we will add layers to that base. We first specify the data set we will use with data = bechdel.

ggplot(data = bechdel)

The mapping argument is paired with an aesthetic (aes()), which tells us how the variables in our data set should be mapped to the visual properties of the graph.

ggplot(
  data = bechdel, 
  mapping = aes(x = budget_2013, y = gross_2013)
)

As we previously mentioned, we often omit the names of the first two arguments in R functions. So you’ll often see this written as:

ggplot(bechdel, aes(x = budget_2013, y = gross_2013))

Note that the result is exactly the same.

The geom_xx function specifies the type of plot we want to use to represent the data. In the code below, we use geom_point which creates a plot where each observation is represented by a point.

ggplot(bechdel, aes(x = budget_2013, y = gross_2013)) +
  geom_point()

Warning: Removed 15 rows containing missing values (`geom_point()`).

Note that this results in a warning as well. What does the warning mean?

Gross revenue vs. budget

Step 1 - Your turn

Modify the following plot to change the color of all points to a different color.

Tip

See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for many color options you can use by name in R or use the hex code for a color of your choice.

ggplot(bechdel, aes(x = budget_2013, y = gross_2013)) +
  geom_point(color = "deepskyblue3")

Warning: Removed 15 rows containing missing values (`geom_point()`).

Step 2 - Your turn

Add labels for the title and x and y axes.

ggplot(bechdel, aes(x = budget_2013, y = gross_2013))+
  geom_point(color = "deepskyblue3") + 
  labs(
    x = "___", 
    y = "___", 
    title = "___"
  )

Warning: Removed 15 rows containing missing values (`geom_point()`).

Step 3 - Your turn

An aesthetic is a visual property of one of the objects in your plot. Commonly used aesthetic options are:

color
fill
shape
size
alpha (transparency)

Modify the plot below, so the color of the points is based on the variable binary.

ggplot(bechdel, aes(x = budget_2013, y = gross_2013)) +
  geom_point() + 
  labs(
    x = "Budget (in 2013 $)", 
    y = "Gross revenue (in 2013 $)", 
    title = "Gross revenue vs. budget, by ___"
  )

Warning: Removed 15 rows containing missing values (`geom_point()`).

Step 4 - Your turn

Expand on your plot from the previous step to make the size of your points based on roi.

# add code here

Step 5 - Your turn

Expand on your plot from the previous step to make the transparency (alpha) of the points 0.5.

# add code here

Step 6 - Your turn

Expand on your plot from the previous step by using facet_wrap to display the association between budget and gross for different values of clean_test.

# add code here

Step 7 - Demo

Improve your plot from the previous step by making the x and y scales more legible.

Tip

Make use of the scales package, specifically the scale_x_continuous() and scale_y_continuous() functions.

# add code here

Step 8 - Your turn

Expand on your plot from the previous step by using facet_grid to display the association between budget and gross for different combinations of clean_test and binary. Comment on whether this was a useful update.

# add code here

Add comment here…

Step 9 - Demo

What other improvements could we make to this plot?

# add code here

Render, commit, and push

If you made any changes since the last render, render again to get the final version of the AE.
Check the box next to each document in the Git tab (this is called “staging” the changes). Commit the changes you made using a simple and informative message.
Use the green arrow to push your changes to your repo on GitHub.
Check your repo on GitHub and see the updated files. Once your updated files are in your repo on GitHub, you’re good to go!

Return-on-investment

Finally, let’s take a look at return-on-investment (ROI).

Step 1 - Your turn

Create side-by-side box plots of roi by clean_test where the boxes are colored by binary.

# add code here

What are those movies with very high returns on investment?

bechdel |>
  filter(roi > 400) |>
  select(title, roi, budget_2013, gross_2013, year, clean_test)

# A tibble: 3 × 6
  title                     roi budget_2013 gross_2013  year clean_test
  <chr>                   <dbl>       <dbl>      <dbl> <dbl> <chr>     
1 Paranormal Activity      671.      505595  339424558  2007 dubious   
2 The Blair Witch Project  648.      839077  543776715  1999 ok        
3 El Mariachi              583.       11622    6778946  1992 nowomen

Step 2 - Demo

Expand on your plot from the previous step to zoom in on movies with roi < ___ to get a better view of how the medians across the categories compare.

# add code here

What does this plot say about return-on-investment on movies that pass the Bechdel test?

Render, commit, and push

If you made any changes since the last render, render again to get the final version of the AE.
Check the box next to each document in the Git tab (this is called “staging” the changes). Commit the changes you made using a simple and informative message.
Use the green arrow to push your changes to your repo on GitHub.
Check your repo on GitHub and see the updated files. Once your updated files are in your repo on GitHub, you’re good to go!

Getting started

Packages

Data

Get to know the data

Variables of interest

Visualizing data with ggplot2

Gross revenue vs. budget

Step 1 - Your turn

Step 2 - Your turn

Step 3 - Your turn

Step 4 - Your turn

Step 5 - Your turn

Step 6 - Your turn

Step 7 - Demo

Step 8 - Your turn

Step 9 - Demo

Render, commit, and push

Return-on-investment

Step 1 - Your turn

Step 2 - Demo

Render, commit, and push

Visualizing data with `ggplot2`