DC vs Marvel Comic Universe: which movies are doing better?

The latest Spider-man movie, Spider-man: Homecoming, has high hopes for the Marvel Comic Universe just as Wonder woman from the DC universe proved a huge success. The Marvel and DC comic universes both have some great superheros, but only a few of them are succeeding at the box office. I wanted to compare these super-hero movies and their success at the box office using R.

Marvel vs DC? It’s on. As close as we can get seeing Spider-man against Batman.

Let’s get started.

Load favorite libraries

# To get data from Wikipedia
library(rvest)
# To manipulate data
library(dplyr)
# To plot
library(ggplot2)
# Make plots look good
library(ggthemes)
# Apply lables
library(directlabels)
# Better labeling
library(scales)

Get the Marvel and DC comic movies data

Thankfully, Wikipedia already lists the movie data for us. The challenge was to get it scraped. The package rvest makes it very easy. You just need to find the right element. In Chrome, you right click on the table and select Inspect. Once you get to the element you want to scrape, you right-click on the HTML markup and select Copy then Copy XPath.

marvel_movies_data <- read_html('https://en.wikipedia.org/wiki/List_of_films_based_on_Marvel_Comics') %>% 
  html_node(xpath = '//*[@id="mw-content-text"]/div/table[10]') %>%
  html_table(fill = TRUE, trim = TRUE) %>%
  setNames(., nm = paste0("X", 1:8)) %>%
  slice(4:n()-2) %>%
  mutate_at(.cols = 4:8, .funs = funs(as.numeric(gsub(pattern = '[$,]', replacement = '', x = .)))) %>%
  mutate(release_date = as.Date(X3, format = '%B %d, %Y'),
         movie = X1, universe = 'marvel',
         budget = X4*10^6, opening_wkend = X5, worldwide_gross = X8)

Here are the details line by line:

  1. Read the html page
  2. Select the table using the copied XPath
  3. Set some parameters to take care of merged cells
  4. Rename all columns to X1 to X8
  5. Filter out the first three rows and the last two rows
  6. Remove the dollar sign and commas from the numbers
  7. Change the format of the date and rename a few columns

Do the same thing for the DC movies data.

dc_movies_data <- read_html('https://en.wikipedia.org/wiki/List_of_films_based_on_DC_Comics') %>% 
  html_node(xpath = '//*[@id="mw-content-text"]/div/table[12]') %>%
  html_table(fill = TRUE, trim = TRUE) %>%
  setNames(., nm = paste0("X", 1:7)) %>%
  slice(3:n()-1) %>%
  mutate_at(.cols = 4:7, .funs = funs(as.numeric(gsub(pattern = '[$,]', replacement = '', x = .)))) %>%
  mutate(release_date = as.Date(X3, format = '%B %d, %Y'),
         movie = X1, universe = 'dc',
         budget = X4*10^6, opening_wkend = X5, worldwide_gross = X7)

Manipulate the data further

Combine the Marvel and DC comic universe data, but select only a few columns. Also calculate the profits and the profit ratio.

marvel_dc <- bind_rows(select(marvel_movies_data, universe, movie, release_date, budget, opening_wkend, worldwide_gross), select(dc_movies_data, universe, movie, release_date, budget, opening_wkend, worldwide_gross)) %>% 
  mutate(profit = worldwide_gross - budget, profit_ratio = worldwide_gross/budget)

Find out the top movies by profit, total box office, and expense. We will leave Spider-man in for comparison.

highest_profit_ratio <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = profit_ratio) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
highest_grossing <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = worldwide_gross) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
most_expensive <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = budget) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
most_profitable <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = profit) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))

Let’s start plotting

Worldwide Gross at the Box Office

First, let’s look at the box office result of these movies.

g <- ggplot(data = marvel_dc, aes(x = release_date, y = worldwide_gross, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + theme_fivethirtyeight()
g <- g + ylab("Worldwide Gross") + xlab(label = "Release Date") + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = highest_grossing, aes(x = release_date, y = worldwide_gross, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Worldwide Gross at the Box Office", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g

Worldwide Gross Box Office marvel vs dc

From the Marvel universe, The Avengers scored huge, collecting over $1.5B worldwide. The Dark Knight Rises, the everybody’s favorite Batman movie, collected slightly over $1B. After 2006 or so, it also looks like that the Marvel comic-heroes started bringing more money in the box office.

Budget of the Movies

Next, let’s see the budget of these movies.

g <- ggplot(data = marvel_dc, aes(x = release_date, y = budget, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = most_expensive, aes(x = release_date, y = budget, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Budget of the Movies", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g

budget movies marvel vs dc

Majority of these movies are over $100M and again you see an upward trend after the early 2000s.

Budget vs Worldwide Gross

So, if there was an uptick in the budget and we saw more money coming in, is there a correlation between budget and worldwide gross? Let’s see:

g <- ggplot(data = marvel_dc, aes(x = budget, y = worldwide_gross, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + scale_x_continuous(labels = dollar)  + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
#g <- g + geom_text(data = most_expensive, aes(x = release_date, y = budget, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Budget and Worldwide Gross", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none") 
g

budget worldwide gross movies marvel vs dc

You can see definitely see a positive trend.

Profits

Now let’s look at the profits of these movies.

g <- ggplot(data = marvel_dc, aes(x = release_date, y = profit, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = most_profitable, aes(x = release_date, y = profit, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Profit from the Movies", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g

profit movies marvel vs dc

The Avengers and the Dark Knight are still the most profitable for the both the universes. Whereas, the Spider-man lags behind.

Profit Ratio

Pure profit doesn’t show the ratio of revenue over expenses. Let’s take a look at the profit ratio instead.

g <- ggplot(data = marvel_dc, aes(x = release_date, y = profit_ratio, group = universe, color = universe)) + geom_point() + scale_y_continuous() + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = highest_profit_ratio, aes(x = release_date, y = profit_ratio, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Profit Ratio of the Movies", subtitle = "Profit/Budget. Using all current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g <- g + geom_hline(yintercept = 1, color = "grey80", linetype = 2) + geom_text(x = 1980, y = 1, label = "Breakeven", hjust = -0.45, vjust = -0.5, color = "grey40", size = rel(3))
g

profit ratio budget profit movies marvel vs dc

Surprise, surprise. Deadpool had the budget of close to $60M, yet it collected more than $783M from the box office. That’s a profit to budget ratio of an impressive 13.5. For the DC universe, the Batman with Michael Keaton as Batman and Jack Nicholson as the Joker, released in 1989, grossed more than $411M with the budget of $35M. That’s impressive! And, ironically enough, Michael Keaton is the main villain of the new Spider-man movie, which is very close the break-even line.

Do you see any other trends that I missed?

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Complete Script

knitr::opts_chunk$set(echo = TRUE)
# To get data from Wikipedia
library(rvest)
# To manipulate data
library(dplyr)
# To plot
library(ggplot2)
# Make plots look good
library(ggthemes)
# Apply lables
library(directlabels)
# Better labeling
library(scales)
marvel_movies_data <- read_html('https://en.wikipedia.org/wiki/List_of_films_based_on_Marvel_Comics') %>% 
  html_node(xpath = '//*[@id="mw-content-text"]/div/table[10]') %>%
  html_table(fill = TRUE, trim = TRUE) %>%
  setNames(., nm = paste0("X", 1:8)) %>%
  slice(4:n()-2) %>%
  mutate_at(.cols = 4:8, .funs = funs(as.numeric(gsub(pattern = '[$,]', replacement = '', x = .)))) %>%
  mutate(release_date = as.Date(X3, format = '%B %d, %Y'),
         movie = X1, universe = 'marvel',
         budget = X4*10^6, opening_wkend = X5, worldwide_gross = X8)
dc_movies_data <- read_html('https://en.wikipedia.org/wiki/List_of_films_based_on_DC_Comics') %>% 
  html_node(xpath = '//*[@id="mw-content-text"]/div/table[12]') %>%
  html_table(fill = TRUE, trim = TRUE) %>%
  setNames(., nm = paste0("X", 1:7)) %>%
  slice(3:n()-1) %>%
  mutate_at(.cols = 4:7, .funs = funs(as.numeric(gsub(pattern = '[$,]', replacement = '', x = .)))) %>%
  mutate(release_date = as.Date(X3, format = '%B %d, %Y'),
         movie = X1, universe = 'dc',
         budget = X4*10^6, opening_wkend = X5, worldwide_gross = X7) 
marvel_dc <- bind_rows(select(marvel_movies_data, universe, movie, release_date, budget, opening_wkend, worldwide_gross), select(dc_movies_data, universe, movie, release_date, budget, opening_wkend, worldwide_gross)) %>% 
  mutate(profit = worldwide_gross - budget, profit_ratio = worldwide_gross/budget)
 
highest_profit_ratio <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = profit_ratio) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
highest_grossing <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = worldwide_gross) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
most_expensive <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = budget) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
most_profitable <- group_by(marvel_dc, universe) %>% top_n( n = 1, wt = profit) %>% bind_rows(., filter(marvel_dc, movie == 'Spider Man: Homecoming'))
g <- ggplot(data = marvel_dc, aes(x = release_date, y = worldwide_gross, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + theme_fivethirtyeight()
g <- g + ylab("Worldwide Gross") + xlab(label = "Release Date") + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = highest_grossing, aes(x = release_date, y = worldwide_gross, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Worldwide Gross at the Box Office", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g
#g <- g + geom_smooth(se = FALSE, size = 0.4, span = 0.5)
#ggsave(filename = "Worldwide-Gross-at-the-Box-Office-marvel-vs-dc.png", plot = g, width = 8, height = 5)
g <- ggplot(data = marvel_dc, aes(x = release_date, y = budget, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = most_expensive, aes(x = release_date, y = budget, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Budget of the Movies", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g
#g <- g + geom_smooth(se = FALSE, size = 0.4, span = 0.5)
#ggsave(filename = "budget-movies-marvel-vs-dc.png", plot = g, width = 8, height = 5)
g <- ggplot(data = marvel_dc, aes(x = budget, y = worldwide_gross, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + scale_x_continuous(labels = dollar)  + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
#g <- g + geom_text(data = most_expensive, aes(x = release_date, y = budget, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Budget and Worldwide Gross", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none") 
g
#g <- g + geom_smooth(se = FALSE, size = 0.4, span = 0.5)
#ggsave(filename = "budget-worldwide-gross-movies-marvel-vs-dc.png", plot = g, width = 8, height = 5)
g <- ggplot(data = marvel_dc, aes(x = release_date, y = profit, group = universe, color = universe)) + geom_point() + scale_y_continuous(labels = dollar) + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = most_profitable, aes(x = release_date, y = profit, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Profit from the Movies", subtitle = "All current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g
#g <- g + geom_smooth(se = FALSE, size = 0.4, span = 0.5)
#ggsave(filename = "profit-movies-marvel-vs-dc.png", plot = g, width = 8, height = 5)
g <- ggplot(data = marvel_dc, aes(x = release_date, y = profit_ratio, group = universe, color = universe)) + geom_point() + scale_y_continuous() + theme_fivethirtyeight()
g <- g + scale_color_manual(values = c("dc" = "#0066A9", "marvel" = "red"))
g <- g + geom_text(data = highest_profit_ratio, aes(x = release_date, y = profit_ratio, label = movie, color = universe), vjust = -0.5, show.legend = FALSE)
g <- g + labs(title = "Profit Ratio of the Movies", subtitle = "Profit/Budget. Using all current dollars", caption = "Wikipedia data")
g <- g + theme(legend.position = "none")
g <- g + geom_hline(yintercept = 1, color = "grey80", linetype = 2) + geom_text(x = 1980, y = 1, label = "Breakeven", hjust = -0.45, vjust = -0.5, color = "grey40", size = rel(3))
g
#g <- g + geom_smooth(se = FALSE, size = 0.4, span = 0.5)
#ggsave(filename = "profit-ratio-budget-profit-movies-marvel-vs-dc.png", plot = g, width = 8, height = 5)

About the Author

The author of Tableau Data Visualization Cookbook and an award winning keynote speaker, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life cycle (from recruitment to giving). He enjoys speaking about the power of data, as well as ranting about data professionals who chase after “interesting” things. He earned his PhD/MS from West Virginia University and his BEng from Nagpur University, all in industrial engineering. Currently, he is leading the data science, reporting, and prospect development efforts at the University of Southern California.

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