What’s the Most American of American Films? An Analysis with {gt} and {gtExtras}

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I love movies. I enjoy watching them, I enjoy reading about the industry (sometimes), and as a bit of a data-nerd (exhibit a: my blog), I enjoy learning about the outliers in the industry. One of my favorite trends to follow is the shifting dynamics of Hollywood being driven more by International Box Office and the impact this has on the types of movies being made. One of my favorite examples is the movie Warcraft. From a critical perspective the movie is not good sporting a Rotten Tomatoes score of 28% (although the audience score is 76%). However, there is a massive disparity in the box office gross with only $47M of its $439M coming from the United States. Ultimately, this movie was a failure in the US but incredibly popular internationally.

With the announcement of the RStudio 2021 Table Contest, I wanted to look into identifying what are the movies that were the successful abroad but a failure in the US. But after playing with the data a bit I decided to flip the question to ask what is the most “American” movie. That is what were the most successful movies in the US that did not perform well abroad.

Part 1: Gathering the Data

Box Office Mojo has a table with the Top 1000 grossing movies with their split between Domestic and International grosses. This table should form the best backbone of finding successful US movies. However, since the most “American” movie could be anywhere in the Top 1000, I’ll need to gather all 1000.

Loading Libraries

Aside from tidyverse the main package needed to extract this table will be rvest which is used for tidy web scraping. The glue package will be used to make string construction a bit easier and httr will be used to access the Open Movie Database API to augment the initial Box Office gross data.

library(rvest) # Scrape Table From BoxOfficeMofo
library(tidyverse) # Data Manipulations
library(glue) # String Interpolation
library(httr) # Accessing the OMDB API

Since the Box Office Mojo table is paginated, I’ll need a loop to get through all 1000. The starting point for the table is controlled by the offset parameter in the URL. The map_dfr function from purrr will make it very easy to loop through the different offset parameters and combine each run into a single data set.

I’ll be feeding map_dfr parameter values of 0, 200, 400, 600, and 800 iteratively and passing it into the Box Office Mojo URL. The glue() function allows me to insert the offset value directly into the string through the {}. In this code block each iteration:

  1. Grabs an offset parameter (0 to 800, by 200)
  2. Passes that into an anonymous function that as the parameter x
  3. Runs read_html() on the URL with the offset and extracts the table element with html_elements()
  4. Extract the information from the table with html_table() into a tibble
  5. When I get to the OMDB API piece rather than searching by title I can search directly by IMDB ID and since Box Office Mojo is owned by IMDB, I’m going to extract the ID from the links in the table:
# Iterate through 0 to 800 by 200 and pass as X into the function
tbl <- map_dfr(seq(0, 800, 200),
               function(x){
                 #Read URL
                 base <- glue("https://www.boxofficemojo.com/chart/ww_top_lifetime_gross/?offset={x}") %>%
                   read_html() %>% 
                   # Extract Table Structure
                   html_element('table')
                 
                   bind_cols(
                     #Get Actual Table Data
                     base %>% html_table(convert = F),
                     
                     #Get IMDB IDs From Links
                     imdb_id = base %>% 
                       html_elements('a') %>% 
                       html_attr('href') %>%
                       keep(~str_detect(.x, 'tt')) %>%
                       str_extract('tt\\d+')
                     
                   )
               })

In order to get the html_table() piece to work correctly, I needed to set convert=F which tells the function not to try to turn numeric-looking values into numbers. Since everything was read in as a character, I need to do some light data cleaning using the parse_number() function from readr to turn characters that look like numbers into numbers.

I’ll also need to define what I mean when I say a movie is the “Most American”. What I want is to find movies that did well in the US and didn’t do well abroad. But…

  • If I look at the highest percentage of Domestic Gross I’ll get movies that might not have had an International release or did not have a large US gross (and therefore might not have been successful in the US)
  • If I look at the highest differences between US and International Gross I might find things that made a lot of money both Domestically and Internationally but just more domestically.

In order to find a balance between the two, I create the “domestic score” which is ratio of the percent of Worldwide Gross that was Domestic to the percent of Worldwide Gross that was International (in order to maximum “US-centric movies) but also to weight this ratio by the log2 of the Domestic Lifetime Gross in order to make sure that we’re finding successful movies and not just small movies that were only released in the US.

Then since I want my results to be in a table I don’t need all 1,000 movies, so I’ll use arrange() and head() to grab the Top 5 by the domestic score.

tbl_clean <- tbl %>% 
  janitor::clean_names() %>% 
  mutate(
    rank = parse_number(rank),
    worldwide_lifetime_gross = parse_number(worldwide_lifetime_gross),
    domestic_lifetime_gross = parse_number(domestic_lifetime_gross),
    domestic_percent = parse_number(domestic_percent)/100,
    foreign_lifetime_gross = parse_number(foreign_lifetime_gross),
    foreign_percent = parse_number(foreign_percent)/100,
    year = parse_number(year),
    # Developing a way to get the highest domestic percentages that also did well domestically
    domestic_score = (domestic_percent / foreign_percent)*log2(domestic_lifetime_gross)
  ) %>%
  arrange(-domestic_score) %>%
  # Keep The Top 10 As Candidates for the API
  head(5)

To make this table a little more fun there’s a couple elements that I’d like to bring in from the Open Movie Database such as the Rotten Tomatoes score, release dates, awards, and URL for the movie’s poster. In order to use the API you first need to register for an API key. I’ve stored that in my .Renviron file so I can place it into glue.

To use the API I can search for movies using the IMDB Id that I had gotten from above which gets used as part of the i= parameter to the URL which gets passed to the GET() function from the httr package. The information for the 5 movies from above get passed in using the map_dfr() function. The anonymous function takes in the IMDB id and returns a tibble that contains the extra information that I wanted for the table.

###Use OMDB Data for the Country Filters and Poster Data
omdb_data <- map_dfr(tbl_clean$imdb_id,
                      function(id){
                        omdb_resp <- GET(URLencode(glue("https://www.omdbapi.com/?apikey={Sys.getenv('OMDB_API_KEY')}&i={id}&type=movie&r=json")))
                        if(content(omdb_resp)$Response == "True"){
                          return(
                            content(omdb_resp, as = 'parsed') %>% 
                              tibble(
                                imdb_id = id,
                                api_title = .$Title,
                                release_date = .$Released,
                                runtime = .$Runtime,
                                language = .$Language,
                                country = .$Country,
                                awards = .$Awards,
                                poster_url = .$Poster,
                                ratings_source = ifelse(length(.$Ratings) > 0,
                                                        .$Ratings[[2]]$Source,
                                                        "missing"),
                                rating = ifelse(length(.$Ratings) > 0,
                                                .$Ratings[[2]]$Value,
                                                "-99")
                              ) %>% select(-.) %>% distinct() 
                          )
                        }
                      })

The raw JSON returned from the API looks like:

and output of the OMDB data table looks like:

field value
imdb_id tt0878804
api_title The Blind Side
release_date 20 Nov 2009
runtime 129 min
language English
country United States
awards Won 1 Oscar. 9 wins & 30 nominations total
poster_url https://m.media-amazon.com/images/M/[email protected]@._V1_SX300.jpg
ratings_source Rotten Tomatoes
rating 66%

With the Box Office Data and the OMDB Data in separate data sets, I can combine them together through the common IMDB id. Finally, I’ll keep only movies listed as the United States (can’t be American if not at least partially made in the good ol USA) and I’ll extract the number of Oscars won our of the awards string to be used later.

#Combine All Data
combine_dt <- tbl_clean %>% 
  inner_join(omdb_data, by = "imdb_id") %>%
  #Keep US Movies
  filter(str_detect(country, "United States")) %>%
  extract(awards, "num_oscars", "Won (\\d+) Oscar", remove = F, convert = T) %>%
  replace_na(list(num_oscars = 0))

With the data set constructed, now onto the table.

Part 2: Constructing the Table

The libraries used to construct the table are gt and gtExtras

library(gt)
library(gtExtras)

I plan to use images for the number of Oscars won, the Rotten Tomatoes score (fresh or rotten), and flags to show the Domestic Box Office and International Box Office so rather than have long URLs in the table construction itself, I’ll create constant variables and refer to those in the code:

ROTTEN_URL = 'https://www.rottentomatoes.com/assets/pizza-pie/images/icons/tomatometer/tomatometer-rotten.f1ef4f02ce3.svg'
FRESH_URL = 'https://www.rottentomatoes.com/assets/pizza-pie/images/icons/tomatometer/tomatometer-fresh.149b5e8adc3.svg'
OSCAR_URL = 'https://upload.wikimedia.org/wikipedia/en/7/7f/Academy_Award_trophy.png'
US_FLAG_URL = 'https://upload.wikimedia.org/wikipedia/en/thumb/a/a4/Flag_of_the_United_States.svg/188px-Flag_of_the_United_States.svg.png'
WORLD_FLAG_URL = 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3b/EarthFlag1.svg/525px-EarthFlag1.svg.png'

Since gt has a lot of syntax, I’ll combine a bunch of steps together rather than showing each individual change. But the start of the table is just the gt() function.

p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() 

At first this is pretty ugly as a table but fortunately gt and gtExtras have a lot of very convenient features to make the table very pretty very quickly. The first set of steps will be:

  • Turn the URL to the movie poster into the action poster with gt_img_rows() from gtExtras
  • Turn the domestic percentage field to a percent format with fmt_percent() from gt
  • Turn the Domestic and Foreign Box Office Gross Values to dollar in millions with fmt_currency from gt
  • Turn the Worldwide Lifetime Gross into a bar plot with gt_plt_bar() from gtExtas
p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
  
  #New Code
  gt_img_rows(poster_url, height = 75) %>%
  fmt_percent(domestic_percent, decimals = 1) %>%
  fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
               suffixing = T, decimals = 1) %>%
  gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50)

The next steps will use the text_transform functions from gt to turn the number of Oscars won into the literal Oscar image for each Oscar won, and for the Rotten Tomatoes score, I’ll use with the “Fresh” image if the score is above 60% or the “Rotten” image if below 60%.

In general the text_tranform() function takes two parameters. The first is where is the function will be applied. In the first example, locations = cells_body(rating) means that I will apply the function defined in fn to the rating column. Then for the fn I’m using glue() to choose the FRESH_URL or ROTTEN_URL based on the numeric value of the rating itself and using web_image() to display the image.

For the number of Oscars…. I’m not 100% sure why I needed to use the lapply() and html() rendering to get the number Oscar statues to repeat. I suppose its has to do with the way that data is being passed around in the text_transform() function. However, “working” is better than perfect in this case. The function takes the num_oscars field and replicates the Oscar image as many times as necessary.

p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
    gt_img_rows(poster_url, height = 75) %>%
    fmt_percent(domestic_percent, decimals = 1) %>%
    fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
                 suffixing = T, decimals = 1) %>%
    gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50) %>% 
  
  
    #### NEW CODE
    text_transform(
        locations = cells_body(rating),
        fn = function(rating){
          glue('{web_image(img)}<br />{rating}', 
               img = if_else(parse_number(rating) < 60, ROTTEN_URL, FRESH_URL)
          )
        }
      ) %>%
      text_transform(
        locations = cells_body(num_oscars),
        fn = function(x){
          int_x <- as.integer(x)
          lapply(int_x, function(y){
            rep(web_image(OSCAR_URL, height=60), y) %>%
              gt::html()
          })
          }
      )

The gtExtras package has an awesome function called gt_merge_stack() that will take one column and stack it on top of a second column. This is a really cool way to condense information in an easy way. Using this I will merge the title and release date columns and place the release date under the title.

p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
    gt_img_rows(poster_url, height = 75) %>%
    fmt_percent(domestic_percent, decimals = 1) %>%
    fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
                 suffixing = T, decimals = 1) %>%
    gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50) %>% 
    text_transform(
        locations = cells_body(rating),
        fn = function(rating){
          glue('{web_image(img)}<br />{rating}', 
               img = if_else(parse_number(rating) < 60, ROTTEN_URL, FRESH_URL)
          )
        }
      ) %>%
      text_transform(
        locations = cells_body(num_oscars),
        fn = function(x){
          int_x <- as.integer(x)
          lapply(int_x, function(y){
            rep(web_image(OSCAR_URL, height=60), y) %>%
              gt::html()
          })
          }
      ) %>%
  
  
  ###NEW CODE
      gt_merge_stack(title, release_date)

To make a valuable info-graphic I’ll need to add in titles, subtitle, and to have appropriate attribution to myself, I’ll add in source notes as well. To this do, I’ll use the tab_header() to define the title and subtitle, and the tab_source_note() option to add the source line. Within this blocks the html() and md() functions allow for the use of HTML and Markdown respectively to render text.

p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
    gt_img_rows(poster_url, height = 75) %>%
    fmt_percent(domestic_percent, decimals = 1) %>%
    fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
                 suffixing = T, decimals = 1) %>%
    gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50) %>% 
    text_transform(
        locations = cells_body(rating),
        fn = function(rating){
          glue('{web_image(img)}<br />{rating}', 
               img = if_else(parse_number(rating) < 60, ROTTEN_URL, FRESH_URL)
          )
        }
      ) %>%
    text_transform(
        locations = cells_body(num_oscars),
        fn = function(x){
          int_x <- as.integer(x)
          lapply(int_x, function(y){
            rep(web_image(OSCAR_URL, height=60), y) %>%
              gt::html()
          })
          }
        ) %>%
    gt_merge_stack(title, release_date) %>%
  
  ###NEW CODE
    tab_header(
      title = html("What are the most <b><span style='color:#002868'>American</span></b> of American Films?"),
      subtitle = html("As measured by the share of Box Office Gross coming from the United States versus the rest of the world, movies with or about <b>Adam Sandler</b>, <b>Football</b>, and <b>Christmas</b> tend to be Box Office successes in the United States but not the rest of the world.  Although, it is unclear whether it is Football or Adam Sandler that makes the movie most appealing to American tastes.")
      ) %>%
      tab_source_note(
      md("***Author:*** JLaw | ***Sources:*** [BoxOfficeMojo,com](https://www.boxofficemojo.com/chart/ww_top_lifetime_gross/?offset=0) and [Open Movie Database](https://www.omdbapi.com/)")
    ) 

Since the table can get pretty wide, it would be helpful to alternate the background colors of the rows so that its easy to follow the information. This can be done with opt_row_striping() which will add the striping with defaults and the row.striping.background_color option within tab_options().

p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
    gt_img_rows(poster_url, height = 75) %>%
    fmt_percent(domestic_percent, decimals = 1) %>%
    fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
                 suffixing = T, decimals = 1) %>%
    gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50) %>% 
    text_transform(
        locations = cells_body(rating),
        fn = function(rating){
          glue('{web_image(img)}<br />{rating}', 
               img = if_else(parse_number(rating) < 60, ROTTEN_URL, FRESH_URL)
          )
        }
      ) %>%
    text_transform(
        locations = cells_body(num_oscars),
        fn = function(x){
          int_x <- as.integer(x)
          lapply(int_x, function(y){
            rep(web_image(OSCAR_URL, height=60), y) %>%
              gt::html()
          })
          }
        ) %>%
    gt_merge_stack(title, release_date) %>%
    tab_header(
      title = html("What are the most <b><span style='color:#002868'>American</span></b> of American Films?"),
      subtitle = html("As measured by the share of Box Office Gross coming from the United States versus the rest of the world, movies with or about <b>Adam Sandler</b>, <b>Football</b>, and <b>Christmas</b> tend to be Box Office successes in the United States but not the rest of the world.  Although, it is unclear whether it is Football or Adam Sandler that makes the movie most appealing to American tastes.")
      ) %>%
      tab_source_note(
      md("***Author:*** JLaw | ***Sources:*** [BoxOfficeMojo,com](https://www.boxofficemojo.com/chart/ww_top_lifetime_gross/?offset=0) and [Open Movie Database](https://www.omdbapi.com/)")
    ) %>%
  
  ###NEW CODE
  opt_row_striping() %>%
  tab_options(row.striping.background_color = "#ececec") 

Now every other row was has a light shade of grey.

The next thing to do is to fix up the column labels. This is done with the col_labels() function which allows me to change how the variable names used for each column will be displayed. Using the use of glue(), html(), web_image(), and emo::ji() and I can insert images into the column titles. Also, since so many columns are related to Box Office Grosses, I’ll create a column spanner with tab_spanner() that goes from the domestic_gross column to the worldwide_lifetime_gross. Finally, since removing the label of poster_url will shrink the column width, I’ll increase the width with cols_width() and the px() function.

p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
    gt_img_rows(poster_url, height = 75) %>%
    fmt_percent(domestic_percent, decimals = 1) %>%
    fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
                 suffixing = T, decimals = 1) %>%
    gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50) %>% 
    text_transform(
        locations = cells_body(rating),
        fn = function(rating){
          glue('{web_image(img)}<br />{rating}', 
               img = if_else(parse_number(rating) < 60, ROTTEN_URL, FRESH_URL)
          )
        }
      ) %>%
    text_transform(
        locations = cells_body(num_oscars),
        fn = function(x){
          int_x <- as.integer(x)
          lapply(int_x, function(y){
            rep(web_image(OSCAR_URL, height=60), y) %>%
              gt::html()
          })
          }
        ) %>%
    gt_merge_stack(title, release_date) %>%
    tab_header(
      title = html("What are the most <b><span style='color:#002868'>American</span></b> of American Films?"),
      subtitle = html("As measured by the share of Box Office Gross coming from the United States versus the rest of the world, movies with or about <b>Adam Sandler</b>, <b>Football</b>, and <b>Christmas</b> tend to be Box Office successes in the United States but not the rest of the world.  Although, it is unclear whether it is Football or Adam Sandler that makes the movie most appealing to American tastes.")
      ) %>%
      tab_source_note(
      md("***Author:*** JLaw | ***Sources:*** [BoxOfficeMojo,com](https://www.boxofficemojo.com/chart/ww_top_lifetime_gross/?offset=0) and [Open Movie Database](https://www.omdbapi.com/)")
    ) %>%
  opt_row_striping() %>%
  tab_options(row.striping.background_color = "#ececec") %>%
  
  ### New Code
  cols_label(
      poster_url = "",
      title = "Title",
      domestic_lifetime_gross = html(glue("{web_image(US_FLAG_URL)}United States")),
      foreign_lifetime_gross = html(glue("{web_image(WORLD_FLAG_URL)}Rest of World")),
      domestic_percent = "US % of Total",
      worldwide_lifetime_gross = glue("{emo::ji('dollar')}Total{emo::ji('dollar')}"),
      num_oscars = "# Oscars won",
      rating = "Rotten Tomatoes Score"
    ) %>%
  tab_spanner(label = "Box Office Gross", columns = domestic_lifetime_gross:worldwide_lifetime_gross) %>%
  cols_width(
      poster_url ~ px(75)
    )

Now this has come a lot a long way from the first image but there’s a lot of cleaning up that needs to be done with the various tab_style() functions. The tab_style() function takes two arguments. The style which is how things will look and the location which is where the styling will be applied. For the style I’ll be using the cell_text() helper to alter the size, weight (bolding), transform (to turn to all uppercase), alignment and font (using the google_font() helper).

For the locations, there are helpers for each part of the table. There is cells_body() for the cell text, cells_column_labels() for the column headers, cells_title(), which can take a “title” or “subtitle” option for those elements and cells_column_spanners() for the column spanners I created in the prior step. Within locations, you can further specify which columns the formatting will apply to. While it defaults to everything(), the columns can be entered as if they’re part of a select statement for dplyr. Finally, if wanting to include multiple locations (or multiple styles) in the same code block, the various helpers can be wrapped in a list().

For the formatting, I’ll:

  1. Change the font, size, alignment, and make everything upper-case for the title.
  2. Change the font, size, and alignment for the subtitle.
  3. Change the font, size, and make everything upper-case and bold for the column headers.
  4. Make all of the column headers center aligned except for the title column.
  5. Change the font and center-align all of the cells except for the title column.
p <- combine_dt %>%
  select(poster_url, title, release_date, domestic_lifetime_gross, foreign_lifetime_gross,
         domestic_percent, worldwide_lifetime_gross, num_oscars, rating) %>%
  gt() %>%
    gt_img_rows(poster_url, height = 75) %>%
    fmt_percent(domestic_percent, decimals = 1) %>%
    fmt_currency(columns = c("domestic_lifetime_gross", "foreign_lifetime_gross"),
                 suffixing = T, decimals = 1) %>%
    gt_plt_bar(worldwide_lifetime_gross, color = 'darkgreen', width = 50) %>% 
    text_transform(
        locations = cells_body(rating),
        fn = function(rating){
          glue('{web_image(img)}<br />{rating}', 
               img = if_else(parse_number(rating) < 60, ROTTEN_URL, FRESH_URL)
          )
        }
      ) %>%
    text_transform(
        locations = cells_body(num_oscars),
        fn = function(x){
          int_x <- as.integer(x)
          lapply(int_x, function(y){
            rep(web_image(OSCAR_URL, height=60), y) %>%
              gt::html()
          })
          }
        ) %>%
    gt_merge_stack(title, release_date) %>%
    tab_header(
      title = html("What are the most <b><span style='color:#002868'>American</span></b> of American Films?"),
      subtitle = html("As measured by the share of Box Office Gross coming from the United States versus the rest of the world, movies with or about <b>Adam Sandler</b>, <b>Football</b>, and <b>Christmas</b> tend to be Box Office successes in the United States but not the rest of the world.  Although, it is unclear whether it is Football or Adam Sandler that makes the movie most appealing to American tastes.")
      ) %>%
      tab_source_note(
      md("***Author:*** JLaw | ***Sources:*** [BoxOfficeMojo,com](https://www.boxofficemojo.com/chart/ww_top_lifetime_gross/?offset=0) and [Open Movie Database](https://www.omdbapi.com/)")
    ) %>%
  opt_row_striping() %>%
  tab_options(row.striping.background_color = "#ececec") %>%
  cols_label(
      poster_url = "",
      title = "Title",
      domestic_lifetime_gross = html(glue("{web_image(US_FLAG_URL)}United States")),
      foreign_lifetime_gross = html(glue("{web_image(WORLD_FLAG_URL)}Rest of World")),
      domestic_percent = "US % of Total",
      worldwide_lifetime_gross = glue("{emo::ji('dollar')}Total{emo::ji('dollar')}"),
      num_oscars = "# Oscars won",
      rating = "Rotten Tomatoes Score"
    ) %>%
  tab_spanner(label = "Box Office Gross", columns = domestic_lifetime_gross:worldwide_lifetime_gross) %>%
  cols_width(
      poster_url ~ px(75)
    ) %>%
  
  ## New Code
  tab_style(
      style = cell_text(
        size = "x-large",
        font = google_font('Josefin Sans'),
        align = 'left',
        transform = 'uppercase'
      ),
      location = cells_title("title")
    ) %>%
  tab_style(
      style = cell_text(
        size = "medium",
        font = google_font('Inter'),
        align = 'left'
      ),
      location = cells_title("subtitle")
    ) %>%
  tab_style(
      style = cell_text(
        size = 'large',
        weight = 'bold',
        transform = 'uppercase',
        font = google_font('Bebas Neue')
      ),
      locations = list(cells_column_labels(), cells_column_spanners())
    ) %>%
  tab_style(
      style = cell_text(align = 'center'),
      locations = cells_column_labels(-title)
    ) %>%
  tab_style(
      style = cell_text(font = google_font('Sora'), align = 'center', size = 'small'),
      locations = cells_body(-title)
    ) 

And now our table looks pretty!!

Conclusion

In this blog post, I’ve defined a methodology for identifying the most “American” of US films and based on the results in the table it seems like the Most American things are Football, Adam Sandler, and Christmas.

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