AE 01: Bechdel + data visualization

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("data/bechdel.csv")

Note

This a modified version of the bechdel dataset from the previous application exercise. It’s been modified to include some new variables derived from existing variables as well as to limit the scope of the data to movies released between 1990 and 2013.

Get to know the data

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

glimpse(bechdel)

Rows: 1,615
Columns: 17
$ title         <chr> "21 & Over", "Dredd 3D", "12 Years a Slave", "2 Guns", "…
$ year          <dbl> 2013, 2012, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 20…
$ gross_2013    <dbl> 67878146, 55078343, 211714070, 208105475, 190040426, 184…
$ budget_2013   <dbl> 13000000, 45658735, 20000000, 61000000, 40000000, 225000…
$ roi           <dbl> 5.221396, 1.206305, 10.585703, 3.411565, 4.751011, 0.818…
$ binary        <chr> "FAIL", "PASS", "FAIL", "FAIL", "FAIL", "FAIL", "FAIL", …
$ clean_test    <chr> "notalk", "ok", "notalk", "notalk", "men", "men", "notal…
$ imdb          <chr> "tt1711425", "tt1343727", "tt2024544", "tt1272878", "tt0…
$ test          <chr> "notalk", "ok-disagree", "notalk-disagree", "notalk", "m…
$ budget        <dbl> 1.30e+07, 4.50e+07, 2.00e+07, 6.10e+07, 4.00e+07, 2.25e+…
$ domgross      <dbl> 25682380, 13414714, 53107035, 75612460, 95020213, 383624…
$ intgross      <dbl> 42195766, 40868994, 158607035, 132493015, 95020213, 1458…
$ code          <chr> "2013FAIL", "2012PASS", "2013FAIL", "2013FAIL", "2013FAI…
$ domgross_2013 <dbl> 25682380, 13611086, 53107035, 75612460, 95020213, 383624…
$ intgross_2013 <dbl> 42195766, 41467257, 158607035, 132493015, 95020213, 1458…
$ period_code   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ decade_code   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…

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

Each observation represents a different movie.

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 17 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?

Budget vs. gross revenue

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 = "blueviolet")

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 = "blueviolet") + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue"
    )

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, color = binary)) +
  geom_point() + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test"
    )

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.

ggplot(bechdel, 
       aes(x = budget_2013, y = gross_2013, color = binary, size = roi)) +
  geom_point() + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test"
    )

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

Step 5 - Your turn

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

ggplot(bechdel, 
       aes(x = budget_2013, y = gross_2013, color = binary, size = roi)) +
  geom_point(alpha = 0.5) + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test"
    )

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

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.

ggplot(bechdel, 
       aes(x = budget_2013, y = gross_2013, color = binary, size = roi)) +
  geom_point(alpha = 0.5) + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test"
    ) +
  facet_wrap(facets = vars(clean_test))

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

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.

ggplot(bechdel, 
       aes(x = budget_2013, y = gross_2013, color = binary, size = roi)) +
  geom_point(alpha = 0.5) + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test"
    ) +
  facet_wrap(facets = vars(clean_test)) +
  scale_x_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +
  scale_y_continuous(labels = label_dollar(scale_cut = cut_short_scale()))

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

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.

ggplot(bechdel, 
       aes(x = budget_2013, y = gross_2013, color = binary, size = roi)) +
  geom_point(alpha = 0.5) + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test"
    ) +
  facet_grid(rows = vars(clean_test), cols = vars(binary)) +
  scale_x_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +
  scale_y_continuous(labels = label_dollar(scale_cut = cut_short_scale()))

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

It doesn’t seem particularly useful since binary is a subset of clean_test and maps on to exactly one value.

Step 9 - Demo

What other improvements could we make to this plot?

# Answers may vary
# Personally, I would label the legend titles and move the legends to the bottom
ggplot(bechdel, 
       aes(x = budget_2013, y = gross_2013, color = binary, size = roi)) +
  geom_point(alpha = 0.5) + 
  labs(
    x = "Budget (in 2013 dollars)", 
    y = "Gross revenue (in 2013 dollars)", 
    title = "Budget vs. gross revenue, by Bechdel test",
    color = "Bechdel test",
    size = "Return on investment"
    ) +
  facet_wrap(facets = vars(clean_test)) +
  scale_x_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +
  scale_y_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +
  theme(legend.position = "bottom")

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

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.

ggplot(bechdel, 
       aes(x = clean_test, y = roi, color = binary)) +
  geom_boxplot() +
  labs(
    title = "Return on investment vs. Bechdel test result",
    x = "Detailed Bechdel result",
    y = "Return-on-investment (gross / budget)",
    color = "Bechdel\nresult"
    )

Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).

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 < 400 to get a better view of how the medians across the categories compare.

# add code here
ggplot(bechdel, 
       aes(x = clean_test, y = roi, color = binary)) +
  geom_boxplot() +
  labs(
    title = "Return on investment vs. Bechdel test result",
    x = "Detailed Bechdel result",
    y = "Return-on-investment (gross / budget)",
    color = "Bechdel\nresult"
    ) +
  coord_cartesian(ylim = c(0, 18))

Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).

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

Session information

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.2 (2023-10-31)
 os       macOS Ventura 13.5.2
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2024-02-01
 pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version date (UTC) lib source
 bit           4.0.5   2022-11-15 [1] CRAN (R 4.3.0)
 bit64         4.0.5   2020-08-30 [1] CRAN (R 4.3.0)
 cli           3.6.2   2023-12-11 [1] CRAN (R 4.3.1)
 colorspace    2.1-0   2023-01-23 [1] CRAN (R 4.3.0)
 crayon        1.5.2   2022-09-29 [1] CRAN (R 4.3.0)
 digest        0.6.34  2024-01-11 [1] CRAN (R 4.3.1)
 dplyr       * 1.1.4   2023-11-17 [1] CRAN (R 4.3.1)
 evaluate      0.23    2023-11-01 [1] CRAN (R 4.3.1)
 fansi         1.0.6   2023-12-08 [1] CRAN (R 4.3.1)
 farver        2.1.1   2022-07-06 [1] CRAN (R 4.3.0)
 fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
 forcats     * 1.0.0   2023-01-29 [1] CRAN (R 4.3.0)
 generics      0.1.3   2022-07-05 [1] CRAN (R 4.3.0)
 ggplot2     * 3.4.4   2023-10-12 [1] CRAN (R 4.3.1)
 glue          1.7.0   2024-01-09 [1] CRAN (R 4.3.1)
 gtable        0.3.4   2023-08-21 [1] CRAN (R 4.3.0)
 here          1.0.1   2020-12-13 [1] CRAN (R 4.3.0)
 hms           1.1.3   2023-03-21 [1] CRAN (R 4.3.0)
 htmltools     0.5.7   2023-11-03 [1] CRAN (R 4.3.1)
 htmlwidgets   1.6.4   2023-12-06 [1] CRAN (R 4.3.1)
 jsonlite      1.8.8   2023-12-04 [1] CRAN (R 4.3.1)
 knitr         1.45    2023-10-30 [1] CRAN (R 4.3.1)
 labeling      0.4.3   2023-08-29 [1] CRAN (R 4.3.0)
 lifecycle     1.0.4   2023-11-07 [1] CRAN (R 4.3.1)
 lubridate   * 1.9.3   2023-09-27 [1] CRAN (R 4.3.1)
 magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
 munsell       0.5.0   2018-06-12 [1] CRAN (R 4.3.0)
 pillar        1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
 pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
 purrr       * 1.0.2   2023-08-10 [1] CRAN (R 4.3.0)
 R6            2.5.1   2021-08-19 [1] CRAN (R 4.3.0)
 readr       * 2.1.5   2024-01-10 [1] CRAN (R 4.3.1)
 rlang         1.1.3   2024-01-10 [1] CRAN (R 4.3.1)
 rmarkdown     2.25    2023-09-18 [1] CRAN (R 4.3.1)
 rprojroot     2.0.4   2023-11-05 [1] CRAN (R 4.3.1)
 rstudioapi    0.15.0  2023-07-07 [1] CRAN (R 4.3.0)
 scales      * 1.2.1   2024-01-18 [1] Github (r-lib/scales@c8eb772)
 sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
 stringi       1.8.3   2023-12-11 [1] CRAN (R 4.3.1)
 stringr     * 1.5.1   2023-11-14 [1] CRAN (R 4.3.1)
 tibble      * 3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
 tidyr       * 1.3.0   2023-01-24 [1] CRAN (R 4.3.0)
 tidyselect    1.2.0   2022-10-10 [1] CRAN (R 4.3.0)
 tidyverse   * 2.0.0   2023-02-22 [1] CRAN (R 4.3.0)
 timechange    0.2.0   2023-01-11 [1] CRAN (R 4.3.0)
 tzdb          0.4.0   2023-05-12 [1] CRAN (R 4.3.0)
 utf8          1.2.4   2023-10-22 [1] CRAN (R 4.3.1)
 vctrs         0.6.5   2023-12-01 [1] CRAN (R 4.3.1)
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 yaml          2.3.8   2023-12-11 [1] CRAN (R 4.3.1)

 [1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library

──────────────────────────────────────────────────────────────────────────────

Getting started

Packages

Data

Get to know the data

Variables of interest

Visualizing data with ggplot2

Budget vs. gross revenue

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

Visualizing data with `ggplot2`