The data this week comes from FiveThirtyEight and the corresponding article from FiveThirtyEight.
Audiences and creators know that on one level or another, there’s an inherent gender bias in the movie business — whether it’s the disproportionately low number of films with female leads, the process of pigeonholing actresses into predefined roles (action chick, romantic interest, middle-aged mother, etc.), or the lack of serious character development for women on screen compared to their male counterparts. What’s challenging is quantifying this dysfunction, putting numbers to a trend that is — at least anecdotally — a pretty clear reality.
One of the most enduring tools to measure Hollywood’s gender bias is a test originally promoted by cartoonist Alison Bechdel in a 1985 strip from her “Dykes To Watch Out For” series. Bechdel said that if a movie can satisfy three criteria — there are at least two named women in the picture, they have a conversation with each other at some point, and that conversation isn’t about a male character — then it passes “The Rule,” whereby female characters are allocated a bare minimum of depth. You can see a copy of that strip here.
- Bechdel Test data sourced via Bechdeltest.com API.
raw_bechdel.csv includes data from 1970 - 2020, for ONLY bechdel testing, while the movies.csv includes IMDB scores, budget/gross revenue, and ratings but only from 1970 - 2013.
# Get the Data
# Read in with tidytuesdayR package
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest
# Either ISO-8601 date or year/week works!
tuesdata <- tidytuesdayR::tt_load('2021-03-09')
tuesdata <- tidytuesdayR::tt_load(2021, week = 11)
bechdel <- tuesdata$bechdel
# Or read in the data manually
raw_bechdel <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-09/raw_bechdel.csv')
movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-09/movies.csv')
| variable | class | description |
|---|---|---|
| year | integer | Year of release |
| id | integer | ID of film |
| imdb_id | character | IMDB ID |
| title | character | Title of film |
| rating | integer | Rating (0-3), 0 = unscored, 1. It has to have at least two [named] women in it, 2. Who talk to each other, 3. About something besides a man |
| variable | class | description |
|---|---|---|
| year | double | Year |
| imdb | character | IMDB |
| title | character | Title of movie |
| test | character | Bechdel Test outcome |
| clean_test | character | Bechdel Test cleaned |
| binary | character | Binary pass/fail of bechdel |
| budget | double | Budget as of release year |
| domgross | character | Domestic gross in release year |
| intgross | character | International gross in release year |
| code | character | Code |
| budget_2013 | double | Budget normalized to 2013 |
| domgross_2013 | character | Domestic gross normalized to 2013 |
| intgross_2013 | character | International gross normalized to 2013 |
| period_code | double | Period code |
| decade_code | double | Decade Code |
| imdb_id | character | IMDB ID |
| plot | character | Plot of movie |
| rated | character | Rating of movie |
| response | character | Response? |
| language | character | Language of film |
| country | character | Country produced in |
| writer | character | Writer of film |
| metascore | double | Metascore rating (0-100) |
| imdb_rating | double | IMDB Rating 0-10 |
| director | character | Director of movie |
| released | character | Released date |
| actors | character | Actors |
| genre | character | Genre |
| awards | character | Awards |
| runtime | character | Runtime |
| type | character | Type of film |
| poster | character | Poster image |
| imdb_votes | character | IMDB Votes |
| error | character | Error? |
library(tidyverse)
library(jsonlite)
raw_json <- jsonlite::parse_json(url("http://bechdeltest.com/api/v1/getAllMovies"))
all_movies <- raw_json %>%
map_dfr(~as.data.frame(.x, stringsAsFactors = FALSE)) %>%
rename(imdb_id = imdbid) %>%
tibble()
all_movies %>%
filter(year >= 1970)
cleaned_bechdel <- all_movies %>%
mutate(title = case_when(
str_detect(title, ", The") ~ str_remove(title, ", The") %>% paste("The", .),
TRUE ~ str_replace(title, "'", "’")
))
cleaned_bechdel %>%
write_csv("2021/2021-03-09/raw_bechdel.csv")
# IMDB data ---------------------------------------------------------------
imdb_json <- jsonlite::parse_json(url("https://raw.githubusercontent.com/brianckeegan/Bechdel/master/imdb_data.json"))
all_imdb <- imdb_json %>%
map_dfr(~as.data.frame(.x, stringsAsFactors = FALSE))
cleaned_imdb <- all_imdb %>%
janitor::clean_names() %>%
mutate(metascore = parse_number(metascore),
imdb_rating = parse_number(imdb_rating),
year = as.integer(year)) %>%
mutate(imdb_id = str_remove(imdb_id, "tt")) %>%
tibble()
all_imdb
# 538 Data ----------------------------------------------------------------
movies <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/bechdel/movies.csv")
cleaned_movies <- movies %>%
mutate(imdb_id = str_remove(imdb, "tt"))
combo_movies <- cleaned_movies %>%
left_join(cleaned_imdb) %>%
janitor::clean_names()
combo_movies
combo_movies %>%
write_csv("2021/2021-03-09/movies.csv")