tidyverseThis vignette is based on tidyverse-ifying the R code here and reproducing some of the plots and analysis done in the 538 story entitled “The Dollar-And-Cents Case Against Hollywood’s Exclusion of Women” by Walt Hickey available here.
Load required packages to reproduce analysis. Also load the bechdel dataset for analysis.
library(fivethirtyeight)
# tidyverse includes ggplot2, tibble, tidyr, readr, purrr, dplyr:
library(tidyverse)
library(knitr)
library(magrittr)
library(broom)
library(stringr)
library(ggthemes)
library(scales)
# Turn off scientific notation
options(scipen = 99)Focus only on films from 1990 to 2013
bechdel90_13 <- bechdel %>% filter(between(year, 1990, 2013))Create international gross only and return on investment (ROI) columns and add to bechdel_90_13 data frame
bechdel90_13 %<>%
mutate(int_only = intgross_2013 - domgross_2013,
roi_total = intgross_2013 / budget_2013,
roi_dom = domgross_2013 / budget_2013,
roi_int = int_only / budget_2013)generous variablebechdel90_13 %<>%
mutate(generous = ifelse(test = clean_test %in% c("ok", "dubious"),
yes = TRUE,
no = FALSE))ROI_by_binary <- bechdel90_13 %>%
group_by(binary) %>%
summarize(median_ROI = median(roi_total, na.rm = TRUE))
ROI_by_binary| binary | median_ROI |
|---|---|
| FAIL | 2.454209 |
| PASS | 2.696553 |
bechdel90_13 %>%
summarize(
`Median Overall Return on Investment` = median(roi_total, na.rm = TRUE))| Median Overall Return on Investment |
|---|
| 2.569013 |
budget_by_binary <- bechdel90_13 %>%
group_by(binary) %>%
summarize(median_budget = median(budget_2013, na.rm = TRUE))
budget_by_binary| binary | median_budget |
|---|---|
| FAIL | 48385984 |
| PASS | 31070724 |
bechdel90_13 %>%
summarize(`Median Overall Budget` = median(budget_2013, na.rm = TRUE))| Median Overall Budget |
|---|
| 37878971 |
Look at the distributions of budget, international gross, ROI, and their logarithms
ggplot(data = bechdel90_13, mapping = aes(x = budget)) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of budget")ggplot(data = bechdel90_13, mapping = aes(x = log(budget))) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of Logarithm of Budget")ggplot(data = bechdel90_13, mapping = aes(x = intgross_2013)) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of International Gross")ggplot(data = bechdel90_13, mapping = aes(x = log(intgross_2013))) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of Logarithm of International Gross")ggplot(data = bechdel90_13, mapping = aes(x = roi_total)) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of ROI")The previous distributions were skewed, but ROI is so skewed that purposefully limiting the x-axis may reveal a bit more information about the distribution: (Suggested by Mustafa Ascha.)
ggplot(data = bechdel90_13, mapping = aes(x = roi_total)) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of ROI") +
xlim(0, 25)ggplot(data = bechdel90_13, mapping = aes(x = log(roi_total))) +
geom_histogram(color = "white", bins = 20) +
labs(title = "Histogram of Logarithm of ROI")ggplot(data = bechdel90_13,
mapping = aes(x = log(budget_2013), y = log(intgross_2013))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)gross_vs_budget <- lm(log(intgross_2013) ~ log(budget_2013),
data = bechdel90_13)
tidy(gross_vs_budget)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.4300342 | 0.3898688 | 6.232953 | 0 |
| log(budget_2013) | 0.9073902 | 0.0225334 | 40.268661 | 0 |
log(intgross_2013) assuming log(budget_2013) is in the modelNote that the regression lines nearly completely overlap.
ggplot(data = bechdel90_13,
mapping = aes(x = log(budget_2013), y = log(intgross_2013),
color = binary)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)gross_vs_budget_binary <- lm(log(intgross_2013) ~ log(budget_2013) + factor(binary),
data = bechdel90_13)
tidy(gross_vs_budget_binary)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.3585962 | 0.3988812 | 5.9130285 | 0.0000000 |
| log(budget_2013) | 0.9100772 | 0.0227566 | 39.9918064 | 0.0000000 |
| factor(binary)PASS | 0.0539207 | 0.0635194 | 0.8488861 | 0.3960713 |
Note the \(p\)-value on factor(binary)PASS here that is around 0.40.
ggplot(data = bechdel90_13,
mapping = aes(x = log(budget_2013), y = log(roi_total))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)roi_vs_budget <- lm(log(roi_total) ~ log(budget_2013),
data = bechdel90_13)
tidy(roi_vs_budget)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.4300342 | 0.3898688 | 6.232953 | 0.0000000 |
| log(budget_2013) | -0.0926098 | 0.0225334 | -4.109890 | 0.0000416 |
Note the negative coefficient here on log(budget_2013) and its corresponding small \(p\)-value.
log(roi_total) assuming log(budget_2013) is in the modelNote that the regression lines nearly completely overlap.
ggplot(data = bechdel90_13,
mapping = aes(x = log(budget_2013), y = log(roi_total),
color = binary)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)roi_vs_budget_binary <- lm(log(roi_total) ~ log(budget_2013) + factor(binary),
data = bechdel90_13)
tidy(roi_vs_budget_binary)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.3585962 | 0.3988812 | 5.9130285 | 0.0000000 |
| log(budget_2013) | -0.0899228 | 0.0227566 | -3.9515046 | 0.0000810 |
| factor(binary)PASS | 0.0539207 | 0.0635194 | 0.8488861 | 0.3960713 |
Note the \(p\)-value on factor(binary)PASS here that is around 0.40.
Calculating the values and creating a tidy data frame
passes_bechtel_rom <- bechdel90_13 %>%
filter(generous == TRUE) %>%
summarize(median_roi = median(roi_dom, na.rm = TRUE))
median_groups_dom <- bechdel90_13 %>%
filter(clean_test %in% c("men", "notalk", "nowomen")) %>%
group_by(clean_test) %>%
summarize(median_roi = median(roi_dom, na.rm = TRUE))
pass_bech_rom <- data_frame(clean_test = "pass",
median_roi = passes_bechtel_rom$median_roi)
med_groups_dom_full <- bind_rows(pass_bech_rom, median_groups_dom) %>%
mutate(group = "U.S. and Canada")## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
passes_bechtel_int <- bechdel90_13 %>%
filter(generous == TRUE) %>%
summarize(median_roi = median(roi_int, na.rm = TRUE))
median_groups_int <- bechdel90_13 %>%
filter(clean_test %in% c("men", "notalk", "nowomen")) %>%
group_by(clean_test) %>%
summarize(median_roi = median(roi_int, na.rm = TRUE))
pass_bech_int <- data_frame(clean_test = "pass",
median_roi = passes_bechtel_int$median_roi)
med_groups_int_full <- bind_rows(pass_bech_int, median_groups_int) %>%
mutate(group = "International")## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
med_groups <- bind_rows(med_groups_dom_full, med_groups_int_full) %>%
mutate(clean_test = str_replace_all(clean_test,
"pass",
"Passes Bechdel Test"),
clean_test = str_replace_all(clean_test, "men",
"Women only talk about men"),
clean_test = str_replace_all(clean_test, "notalk",
"Women don't talk to each other"),
clean_test = str_replace_all(clean_test, "nowoWomen only talk about men",
"Fewer than two women"))
med_groups %<>% mutate(clean_test = factor(clean_test,
levels = c("Fewer than two women",
"Women don't talk to each other",
"Women only talk about men",
"Passes Bechdel Test"))) %>%
mutate(group = factor(group, levels = c("U.S. and Canada", "International"))) %>%
mutate(median_roi_dol = dollar(median_roi))Using only a few functions to plot
ggplot(data = med_groups, mapping = aes(x = clean_test, y = median_roi,
fill = group)) +
geom_bar(stat = "identity") +
facet_wrap(~ group) +
coord_flip() +
labs(title = "Dollars Earned for Every Dollar Spent", subtitle = "2013 dollars") +
scale_fill_fivethirtyeight() +
theme_fivethirtyeight()Attempt to fully reproduce Dollars Earned for Every Dollar Spent plot using ggplot
ggplot(data = med_groups, mapping = aes(x = clean_test, y = median_roi,
fill = group)) +
geom_bar(stat = "identity") +
geom_text(aes(label = median_roi_dol), hjust = -0.1) +
scale_y_continuous(expand = c(.25, 0)) +
coord_flip() +
facet_wrap(~ group) +
scale_fill_manual(values = c("royalblue", "goldenrod")) +
labs(title = "Dollars Earned for Every Dollar Spent", subtitle = "2013 dollars") +
theme_fivethirtyeight() +
theme(plot.title = element_text(hjust = -1.6),
plot.subtitle = element_text(hjust = -0.4),
strip.text.x = element_text(face = "bold", size = 16),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
guides(fill = FALSE)