Links between common Words in Monster Movies’ titles

‘Monster’ takes the spotlight as expected, but an eerie ensemble of connections—Loch Ness, Frankenstein, islands, madness, and party—reveals the intriguing, varied themes lurking within monster movie titles.

This week’s dataset dives into the world of “monster” movies, focusing on films with the word “monster” in their title. The dataset, curated from the Internet Movie Database (IMDb), provides an exciting opportunity to analyze the genre combinations, titles, and trends surrounding monster-themed films. Questions arise: what are the most frequent genre pairings among these “monster” movies? And how do they compare with summer movies or holiday movies? For those interested in the psychology behind the appeal of horror, “Why Do People Like Horror Films? A Statistical Analysis” offers insights into why audiences are drawn to these spine-chilling experiences. This dataset was prepared by Jon Harmon and is accessible through the tidytuesdayR package or directly via GitHub.

This network graph highlights the 40 most common words in “monster” movie titles, with “monster” at the center. Notable links include “Loch Ness,” “Frankenstein,” and “madness,” capturing the diverse and imaginative themes that define this genre.

How I made this graphic?

Loading required libraries, data import & creating custom functions.

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(janitor)              # Cleaning names etc.
library(here)                 # Root Directory Management

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours

library(tidygraph)            # Using tidy functions with igraph 
library(ggraph)               # igraph in R

# Option 1: Loading data directly from GitHub
monster_movie_genres <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-10-29/monster_movie_genres.csv')
monster_movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-10-29/monster_movies.csv')

Visualization Parameters

# Font for titles
font_add_google("Flavors",
  family = "title_font"
) 

# Font for the caption
font_add_google("Saira Extra Condensed",
  family = "caption_font"
) 

# Font for plot text
font_add_google("Oswald",
  family = "body_font"
) 

showtext_auto()

# A base Colour
base_col <- "#6c25cf"

# Colour for the text
text_col <- colorspace::darken(base_col, 0.7) 

# Colour for highlighted text
text_hil <- colorspace::darken(base_col, 0.4) 

link_col <- colorspace::lighten(base_col, 0.6)

bg_col <- colorspace::lighten(base_col, 0.9)


# Define Base Text Size
bts <- 90 

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")

# Add text to plot--------------------------------------------------------------
plot_title <- "Monster Movies"

plot_subtitle <- "Commonly occuring words in the titles of IMDb movies with the word \"monster\". Width of linkages reflect the times words occur together. Size of the word represents its frequency of occurence."

plot_caption <- paste0(
  "**Data:** IMDb Non-Commercial Datasets", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, 
   social_caption_2)

Exploratory Data Analysis and Wrangling

# A tibble of words grouped by the movie title (i.e. a group for each movie)
dfnet <- monster_movies |>  
  select(tconst, original_title, simple_title, genres) |> 
  group_by(original_title) |> 
  separate_longer_delim(cols = simple_title,
                        delim = " ") |> 
  rename(word = simple_title) |> 
  anti_join(stop_words) |> 
  mutate(word = if_else(word == "monsters", "monster", word))
  
# Network graph for commonly occurring words, faceted by genre

# A tibble of nodes
dfnodes <- dfnet |> 
  ungroup() |> 
  count(word, sort = TRUE) |> 
  rename(importance = n) |> 
  filter(word != "")


# Titles with 2 or more words  
filter_title <- dfnet |>
  group_by(tconst) |> 
  count(sort = TRUE) |> 
  filter(n > 1)

# Computing the combinations of words within the titles
# And, making a tibble of edges
dfedges <- dfnet |> 
  right_join(filter_title) |> 
  group_by(original_title) |>
  do(data.frame(t(combn(.$word, 2)))) |> 
  count(X1, X2, sort = TRUE) |> 
  rename(from = X1,
         to = X2,
         weightage = n,
         group = original_title) |> 
  ungroup() |> 
  group_by(from, to) |> 
  summarise(
    weightage = sum(weightage)
    ) |> 
  filter(from != "" & to != "") |> 
  arrange(desc(weightage)) |> 
  filter(from != to) |> 
  ungroup()
  
# Creating a tidygraph object

movgraph <- tbl_graph(nodes = dfnodes,
          edges = dfedges,
          directed = FALSE,
          node_key = "word")

sel_nodes <- dfedges |> 
  filter(weightage > 2) |> 
  ungroup() |> 
  select(from, to) |> 
  pivot_longer(cols = everything()) |> 
  pull(value) |> 
  unique()

The Base Plot

set.seed(3)

g <- movgraph |>
  activate(nodes) |>
  slice_max(order_by = importance, n = 40) |> 
  filter(!(word %in% c("2", "monsterland", "der"))) |> 
  ggraph(layout = "kk") +
  geom_edge_bend(
    mapping = aes(
      width = weightage, 
      alpha = weightage
    ),
    color = link_col,
    lineend = "round",
    linejoin = "bevel",
    strength = 0.2,
    n = 100
  ) +
  geom_node_text(
    aes(
      label = word,
      size = importance
    ),
    family = "title_font",
    fontface = "bold",
    alpha = 0.8,
    colour = text_col
  ) +

  scale_size_continuous(range = c(40, 80)) +
  scale_alpha_continuous(range = c(0.1, 0.4)) +
  scale_edge_width(range = c(5, 25)) +

  theme_void() +
  labs(
    title = plot_title,
    caption = plot_caption,
    subtitle = str_wrap(plot_subtitle, 100)
  ) +
  theme(
    plot.caption = element_textbox(
      family = "caption_font",
      hjust = 0.5,
      colour = text_col,
      size = bts / 1.5,
      margin = margin(0,0,10,0, "mm")
    ),
    plot.title = element_text(
      hjust = 0.5,
      size = 4 * bts,
      family = "title_font",
      face = "bold",
      colour = text_hil,
      margin = margin(0,0,5,0, "mm")
    ),
    plot.subtitle = element_text(
      hjust = 0.5,
      size = 1.2 * bts,
      family = "caption_font",
      colour = text_col,
      lineheight = 0.3,
      margin = margin(5,0,0,0, "mm")
    ),
    plot.background = element_rect(
      fill = bg_col,
      colour = bg_col,
      linewidth = 1
    ),
    legend.position = "none",
    plot.margin = margin(10,0,0,0, "mm"),
    
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_monster_movies.png"
  ),
  plot = g,
  width = 400,
  height = 500,
  units = "mm",
  bg = bg_col
)

Savings the graphics

# Saving a thumbnail

library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", "tidy_monster_movies.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(
    here::here(
      "data_vizs", 
      "thumbnails", 
      "tidy_monster_movies.png"
    )
  )

Session Info

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(janitor)              # Cleaning names etc.
library(here)                 # Root Directory Management

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours

library(tidygraph)            # Using tidy functions with igraph 
library(ggraph)               # igraph in R


sessioninfo::session_info()$packages |> 
  as_tibble() |> 
  select(package, 
         version = loadedversion, 
         date, source) |> 
  arrange(package) |> 
  janitor::clean_names(
    case = "title"
  ) |> 
  gt::gt() |> 
  gt::opt_interactive(
    use_search = TRUE
  ) |> 
  gtExtras::gt_theme_espn()

Table 1: R Packages and their versions used in the creation of this page and graphics