Marvel vs. DC

This script contains racing data for the 2023 Racing season. It contains all racing drivers as well as their total overall points.

[1] "Collecting page 1 of 2"
[1] "Being polite..."
[1] "75 Total franchises scraped!"
[1] "Collecting page 2 of 2"
[1] "Being polite..."
[1] "139 Total franchises scraped!"

Marvel & DC Gross Revenue

Marvel is a well known movie franchise beloved by teenage boys and all nerds alike. Many fans have flocked to movie theaters to witness grandeur that is marvel movies. But even when movies slam dunk opening sales, do they continue to accrue more movie sales than other movies? My question that I am solving is do movies that perform well on opening night, become the highest lifetime grossing movies.

I will answer these question by creating visualizations for opening and lifetime sales for marvel & DC movies. I will use a data set pulled from box office mojo containing a list of movies and their revenue.

# A tibble: 139 × 9
    Rank Release               `Lifetime Gross` `Max Theaters` Opening `Open Th`
   <int> <chr>                 <chr>            <chr>          <chr>   <chr>    
 1     1 The Dark Knight       $533345358       4,366          $158,4… 4,366    
 2     2 The Dark Knight Rises $448139099       4,404          $160,8… 4,404    
 3     3 Wonder Woman          $412563408       4,165          $103,2… 4,165    
 4     4 The Batman            $369345583       4,417          $134,0… 4,417    
 5     5 Joker                 $335451311       4,374          $96,20… 4,374    
 6     6 Aquaman               $335061807       4,184          $67,87… 4,125    
 7     7 Batman v Superman: D… $330360194       4,256          $166,0… 4,242    
 8     8 Suicide Squad         $325100054       4,255          $133,6… 4,255    
 9     9 Man of Steel          $291045518       4,207          $116,6… 4,207    
10    10 Batman                $251188924       2,201          $40,48… 2,194    
# ℹ 129 more rows
# ℹ 3 more variables: `Release Date` <chr>, Distributor <chr>, Estimated <chr>

# A tibble: 139 × 9
    Rank Release               `Lifetime Gross` `Max Theaters` Opening `Open Th`
   <int> <chr>                 <chr>            <chr>          <chr>   <chr>    
 1     1 The Dark Knight       $533345358       4,366          $158,4… 4,366    
 2     2 The Dark Knight Rises $448139099       4,404          $160,8… 4,404    
 3     3 Wonder Woman          $412563408       4,165          $103,2… 4,165    
 4     4 The Batman            $369345583       4,417          $134,0… 4,417    
 5     5 Joker                 $335451311       4,374          $96,20… 4,374    
 6     6 Aquaman               $335061807       4,184          $67,87… 4,125    
 7     7 Batman v Superman: D… $330360194       4,256          $166,0… 4,242    
 8     8 Suicide Squad         $325100054       4,255          $133,6… 4,255    
 9     9 Man of Steel          $291045518       4,207          $116,6… 4,207    
10    10 Batman                $251188924       2,201          $40,48… 2,194    
# ℹ 129 more rows
# ℹ 3 more variables: `Release Date` <chr>, Distributor <chr>, Estimated <chr>

# A tibble: 139 × 3
   Release                            Opening      `Lifetime Gross`
   <chr>                              <chr>        <chr>           
 1 The Dark Knight                    $158,411,483 $533,345,358    
 2 The Dark Knight Rises              $160,887,295 $448,139,099    
 3 Wonder Woman                       $103,251,471 $412,563,408    
 4 The Batman                         $134,008,624 $369,345,583    
 5 Joker                              $96,202,337  $335,451,311    
 6 Aquaman                            $67,873,522  $335,061,807    
 7 Batman v Superman: Dawn of Justice $166,007,347 $330,360,194    
 8 Suicide Squad                      $133,682,248 $325,100,054    
 9 Man of Steel                       $116,619,362 $291,045,518    
10 Batman                             $40,489,746  $251,188,924    
# ℹ 129 more rows

# A tibble: 2 × 4
  Distributor                         Release           `Lifetime Gross` Opening
  <chr>                               <chr>             <chr>            <chr>  
1 Warner Bros.                        The Dark Knight   $533,345,358     $158,4…
2 Walt Disney Studios Motion Pictures Avengers: Endgame $858,373,000     $357,1…

Distribution Gross Revenue

One hurdle within this experiment is that all movie sales increased from opening sales to lifetime grossing sales, which was predicted. But by looking at the rations of growth is where can truly see the different. After reviewing the data, we can see that opening gross sales to do not predict the lifetime gross revenue. Two examples of this is The Joker and Aquaman. Both of these movies have low opening revenue but grew to have the same revenue as other movie within their franchise. But one thing is clear, Marvel movies on average gross higher revenues with their movies then DC. Therefore the age old question of what franchise is more popular, Marvel vs. DC, came to a clear winner today. Marvel is the higher revenue grossing franchise.

--- title: "Marvel vs. DC" toc: true # Generates an automatic table of contents. format: html: html-math-method: katex # Defines the pretty math renderer. code-tools: TRUE # Allow the code tools option showing in the output. self-contained: TRUE # Embeds all components into a single HTML file. execute: warning: FALSE # Prevent all warnings from displaying in the output. message: FALSE # Prevent all messages from displaying in the output. echo: FALSE --- ## ```{r} library(tidyverse) # The tidyverse collection of packages library(httr) # Useful for web authentication library(rvest) # Useful tools for working with HTML and XML library(lubridate) # Working with dates library(magrittr) # Magrittr ``` This script contains racing data for the 2023 Racing season. It contains all racing drivers as well as their total overall points. ```{r} #setting the user agent set_config(user_agent("<grahamb5@xavier.edu>; +https://www.xavier.edu/business-analytics-program")) ``` ```{r} comics_ids <-c("bn3732077058/?ref_=bo_bns_table_1","bn3664968194/?ref_=bo_bns_table_3") urls<- "https://www.boxofficemojo.com/brand/" ``` ```{r} comics_urls <- paste( urls, comics_ids, sep = "" ) comics_nerds <- data.frame() ``` ```{r} for (i in seq_along(comics_urls)) { #status message print(paste("Collecting page",i,"of",length(comics_urls),sep = " ")) #defining the HTML document comics_html <- read_html(comics_urls[i]) #retrieving the HTML table object comics_nerds <- comics_html %>% html_table() %>% extract2(1) %>% bind_rows(comics_nerds) #telling people that the comp will shut off print("Being polite...") #off for 2-5 seconds Sys.sleep(runif(1,2,5)) print(paste(nrow(comics_nerds), "Total franchises scraped!", sep = " ")) } ``` ## Marvel & DC Gross Revenue Marvel is a well known movie franchise beloved by teenage boys and all nerds alike. Many fans have flocked to movie theaters to witness grandeur that is marvel movies. But even when movies slam dunk opening sales, do they continue to accrue more movie sales than other movies? My question that I am solving is do movies that perform well on opening night, become the highest lifetime grossing movies. I will answer these question by creating visualizations for opening and lifetime sales for marvel & DC movies. I will use a data set pulled from box office mojo containing a list of movies and their revenue. ```{r} comics_nerds %>% mutate(`Lifetime Gross` = str_replace_all(comics_nerds$`Lifetime Gross`,"\\$","")) %>% mutate(`Lifetime Gross` = str_replace_all(comics_nerds$`Lifetime Gross`,",","")) comics_nerds %>% mutate(`Lifetime Gross` = str_replace_all(comics_nerds$`Lifetime Gross`,"\\$",""), `Lifetime Gross` = str_replace_all(comics_nerds$`Lifetime Gross`,",","")) ``` ```{r} #First visulaization had be commented out in order for the website to render #the issue is with the dollar signs in the Lifetime gross cloumn #comics_nerds %>% #ggplot(aes(x = Release, y= as.numeric(`Lifetime Gross`))) + #geom_bar() + #labs(title = "Distribution of Profits") ``` ```{r} #second visulaization had be commented out in order for the website to render #the issue is with the dollar signs in the opening gross cloumn #comics_nerds %>% # ggplot(aes(x = Release, y= as.numeric(Opening))) + #geom_bar() + #abs(title = "Distribution of Profits") ``` ```{r} comic_suffering <- comics_nerds %>% select(Release,Opening, `Lifetime Gross`) print(comic_suffering) ``` ```{r} # I want to create a visualization that compares the top rank moves between the franchises comics_nerds %>% filter(Rank == 1) %>% select(Distributor, Release, `Lifetime Gross`, Opening) ``` ```{r} # I want to create a visulization that shows the distribution of revenue over with franchise #Fith visulaization had be commented out in order for the website to render #the issue is with the dollar signs in the Lifetime gross cloumn #comics_nerds %>% # group_by(Distributor) %>% #ggplot(aes(x = Distributor, y = as.numeric(`Lifetime Gross`))) + #geom_boxplot() + #labs(title = "Revenue by Distributor") ``` ## Distribution Gross Revenue One hurdle within this experiment is that all movie sales increased from opening sales to lifetime grossing sales, which was predicted. But by looking at the rations of growth is where can truly see the different. After reviewing the data, we can see that opening gross sales to do not predict the lifetime gross revenue. Two examples of this is *The Joker* and *Aquaman*. Both of these movies have low opening revenue but grew to have the same revenue as other movie within their franchise. But one thing is clear, Marvel movies on average gross higher revenues with their movies then DC. Therefore the age old question of what franchise is more popular, Marvel vs. DC, came to a clear winner today. Marvel is the higher revenue grossing franchise.