---
title: "Bechdel analysis using the `tidyverse`"
author: "Chester Ismay"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    df_print: kable
vignette: |
  %\VignetteIndexEntry{Bechdel tidyverse analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

This vignette is based on tidyverse-ifying the R code [here](https://raw.githubusercontent.com/fivethirtyeight/data/master/bechdel/analyze-bechdel.R) 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](http://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/).

Load required packages to reproduce analysis.  Also load the `bechdel` dataset for analysis.

```{r, message=FALSE, warning=FALSE}
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)
```


## Filter to only 1990 - 2013

Focus only on films from 1990 to 2013

```{r bechdel90_13}
bechdel90_13 <- bechdel %>% filter(between(year, 1990, 2013))
```

## Calculate variables

Create international gross only and return on investment (ROI) columns and add to `bechdel_90_13` data frame

```{r mutate}
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)
```

## Create `generous` variable

```{r generous}
bechdel90_13 %<>%
  mutate(generous = ifelse(test = clean_test %in% c("ok", "dubious"),
                           yes = TRUE,
                           no = FALSE))
```

## Determine median ROI and budget based on categories

```{r summary_ROI}
ROI_by_binary <- bechdel90_13 %>% 
  group_by(binary) %>% 
  summarize(median_ROI = median(roi_total, na.rm = TRUE))
ROI_by_binary
bechdel90_13 %>% 
  summarize(
    `Median Overall Return on Investment` = median(roi_total, na.rm = TRUE))
```

```{r summary_budget}
budget_by_binary <- bechdel90_13 %>% 
  group_by(binary) %>% 
  summarize(median_budget = median(budget_2013, na.rm = TRUE))
budget_by_binary
bechdel90_13 %>% 
  summarize(`Median Overall Budget` = median(budget_2013, na.rm = TRUE))
```

## View Distributions

Look at the distributions of budget, international gross, ROI, and their logarithms

```{r budget-plot, fig.width = 5, warning = FALSE}
ggplot(data = bechdel90_13, mapping = aes(x = budget)) +
  geom_histogram(color = "white", bins = 20) +
  labs(title = "Histogram of budget")
```

```{r log-budget-plot, fig.width = 5, warning = FALSE}
ggplot(data = bechdel90_13, mapping = aes(x = log(budget))) +
  geom_histogram(color = "white", bins = 20) +
  labs(title = "Histogram of Logarithm of Budget")
```

```{r intgross-plot, fig.width = 5, warning = FALSE}
ggplot(data = bechdel90_13, mapping = aes(x = intgross_2013)) +
  geom_histogram(color = "white", bins = 20) +
  labs(title = "Histogram of International Gross")
```

```{r log-intgross-plot, fig.width = 5, warning = FALSE}
ggplot(data = bechdel90_13, mapping = aes(x = log(intgross_2013))) +
  geom_histogram(color = "white", bins = 20) +
  labs(title = "Histogram of Logarithm of International Gross")
```

```{r roi-plot, fig.width = 5, warning = FALSE}
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](https://github.com/mustafaascha).)

```{r roi-plot2, fig.width = 5, warning = FALSE}
ggplot(data = bechdel90_13, mapping = aes(x = roi_total)) +
  geom_histogram(color = "white", bins = 20) +
  labs(title = "Histogram of ROI") +
  xlim(0, 25)
```

```{r log-roi-plot, fig.width = 5, warning = FALSE}
ggplot(data = bechdel90_13, mapping = aes(x = log(roi_total))) +
  geom_histogram(color = "white", bins = 20) +
  labs(title = "Histogram of Logarithm of ROI")
```

## Linear Regression Models

### Movies with higher budgets make more international gross revenues using logarithms on both variables

```{r scatplot1, fig.width = 5, warning=FALSE}
ggplot(data = bechdel90_13, 
       mapping = aes(x = log(budget_2013), y = log(intgross_2013))) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)
```

```{r reg1}
gross_vs_budget <- lm(log(intgross_2013) ~ log(budget_2013), 
                      data = bechdel90_13)
tidy(gross_vs_budget)
```

### Bechdel dummy is not a significant predictor of `log(intgross_2013)` assuming `log(budget_2013)` is in the model

Note that the regression lines nearly completely overlap.

```{r scatplot2, fig.width = 5, warning=FALSE}
ggplot(data = bechdel90_13, 
       mapping = aes(x = log(budget_2013), y = log(intgross_2013), 
                     color = binary)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)
```

```{r reg2}
gross_vs_budget_binary <- lm(log(intgross_2013) ~ log(budget_2013) + factor(binary), 
                      data = bechdel90_13)
tidy(gross_vs_budget_binary)
```

Note the $p$-value on `factor(binary)PASS` here that is around 0.40.

### Movies with higher budgets have lower ROI

```{r scatplot3, warning=FALSE}
ggplot(data = bechdel90_13, 
       mapping = aes(x = log(budget_2013), y = log(roi_total))) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)
```

```{r reg3}
roi_vs_budget <- lm(log(roi_total) ~ log(budget_2013), 
                      data = bechdel90_13)
tidy(roi_vs_budget)
```

Note the negative coefficient here on `log(budget_2013)` and its corresponding small $p$-value.

### Bechdel dummy is not a significant predictor of `log(roi_total)` assuming `log(budget_2013)` is in the model

Note that the regression lines nearly completely overlap.

```{r scatplot4, warning=FALSE}
ggplot(data = bechdel90_13, 
       mapping = aes(x = log(budget_2013), y = log(roi_total), 
                     color = binary)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)
```

```{r reg4}
roi_vs_budget_binary <- lm(log(roi_total) ~ log(budget_2013) + factor(binary), 
                      data = bechdel90_13)
tidy(roi_vs_budget_binary)
```

Note the $p$-value on `factor(binary)PASS` here that is around 0.40.

## Dollars Earned for Every Dollar Spent graphic 

Calculating the values and creating a tidy data frame

```{r roi-graphic}
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")
```

```{r roi-graphic2, fig.width=5}
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")
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

```{r basic-538, fig.width=8}
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`

```{r roi-plot-538, fig.width=8}
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)
```

