CODING LANGUAGE: R

The Behdel Test
The Bechdel test asks whether a work of fiction features at least two women who talk to each other
about something other than a man, and there must be two women named characters.
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"
https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-
women/
Start with loading the packages: fivethirtyeight, tidyverse
1. What information does this dataset contain? What commands did you use to see this?
For our purposes of analysis we will focus our analysis on movies released between 1990 and 2013.
bechdel90_13 <- bechdel %>%
filter(between(year, 1990, 2013))
2. How many movies are in our filtered data set?
The financial variables we'll focus on are the following:
- `budget_2013`: Budget in 2013 inflation adjusted dollars
- `domgross_2013`: Domestic gross (US) in 2013 inflation adjusted dollars
- `intgross_2013`: Total International (i.e., worldwide) gross in 2013 inflation adjusted dollars
And we'll also use the `binary` and `clean_test` variables for **grouping**.
Let's take a look at how median budget and gross vary by whether the movie passed the Bechdel test,
which is stored in the `binary` variable.
bechdel90_13 %>%
group_by(binary) %>%
summarise(med_budget = median(budget_2013),
med_domgross = median(domgross_2013, na.rm = TRUE),
med_intgross = median(intgross_2013, na.rm = TRUE))

Next, let us take a look at how median budget and gross vary by a more detailed indicator of the
Bechdel test result.
This information is stored in the `clean_test` variable, which takes on the following values:
- `ok` = passes test
- `dubious`
- `men` = women only talk about men
- `notalk` = women don't talk to each other
- `nowomen` = fewer than two women
bechdel90_13 %>%
#group_by(___) %>%
summarise(med_budget = median(budget_2013),
med_domgross = median(domgross_2013, na.rm = TRUE),
med_intgross = median(intgross_2013, na.rm = TRUE))
In order to evaluate how return on investment varies among movies that pass and fail the Bechdel test,
we'll first create a new variable called `roi` as the ratio of the gross to budget.
bechdel90_13 <- bechdel90_13 %>%
mutate(roi = (intgross_2013 + domgross_2013) / budget_2013)
Let's see which movies have the highest return on investment.
bechdel90_13 %>%
arrange(desc(roi)) %>%
select(title, roi, year)

Below is a visualization of the return on investment by test result, however it's difficult to see the
distributions due to a few extreme observations.
ggplot(data = bechdel90_13,
mapping = aes(x = clean_test, y = roi, color = binary)) +
geom_boxplot() +
labs(title = "Return on investment vs. Bechdel test result",
x = "Detailed Bechdel result",
y = "___",
color = "Binary Bechdel result")
3. What are those movies with *very* high returns on investment?
bechdel90_13 %>%
filter(roi > 400) %>%
select(title, budget_2013, domgross_2013, year)
Zooming in on the movies with `roi < ___` provides a better view of how the medians across the
categories compare:
ggplot(data = bechdel90_13, mapping = aes(x = clean_test, y = roi, color = binary)) +
geom_boxplot() +
labs(title = "Return on investment vs. Bechdel test result",
subtitle = "___", # Something about zooming in to a certain level
x = "Detailed Bechdel result",
y = "Return on investment",
color = "Binary Bechdel result") +
coord_cartesian(ylim = c(0, 15))