Tidy Tuesday - Monster Movies

29th October 2024

Author

Arun Koundinya Parasa

Published

October 31, 2024

0. Loading Libraries

This particular visualization is done on R to visualize the data with custom back ground.

Loading Libraries 
library(tidyverse)
library(grid)
library(jpeg)
library(patchwork)

1. Data Loading

Loading Data 
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')

2. Plots

2.1. Production of Horror Films Increased Since 2000

Plot1 Code 
bg_image <- readJPEG("horror.jpg")
bg_image <- rasterGrob(bg_image, interpolate=TRUE)

coeff <- 4
monster_movies |>
  group_by(year) |>
  summarise(AvgRunTime = mean(runtime_minutes), AvgRating = mean(average_rating), AvgVotes = mean(num_votes),
            NoOfMovies = n(), .groups = "drop") |>
  mutate(
    Year = as.Date(paste("01/01/", year, sep=""), "%d/%m/%Y")
  ) |>
  ggplot(aes(x = Year)) + 
  annotation_custom(
    bg_image, xmin = as.Date("1919-01-01"), xmax = as.Date("2024-01-31"), ymin = -3.3, 
    ymax = 14
  ) +
  geom_bar(aes(y=AvgRating), stat="identity", linewidth=.1, fill = "cornsilk3") +
  geom_line(aes(y=NoOfMovies / coeff), size=1.4, color = "brown4") +
  scale_x_date(breaks = seq(from = as.Date("1918-01-01"),
                            to = as.Date("2025-01-01"),
                            by = "15 years"),
               date_labels = "%Y") +
  scale_y_continuous(
    name = "Average Movie Ratings by Year",
    sec.axis = sec_axis(~.*coeff, name = "Number of Movies Released")
  ) + 
  theme_minimal() +
  labs(
    title = "Yearly Trends in Movie Ratings and Release Volumes of Horror Movies",
    subtitle = "Analysis Period: 1919 to 2024",
    x = "Release Year of Horror Movies",
    caption = "Line Graph Represents: Number of Movies Released YonY               Bar Graph Represens: Average Rating of Movies YonY"
  ) +
  theme(
    plot.background = element_rect(fill = "black", color = "black"),
    panel.background = element_rect(fill = "black"),
    axis.text = element_text(color = "white"),
    axis.title = element_text(color = "white"),
    axis.title.y = element_text(size=12),
    axis.title.y.right = element_text(size=12),
    axis.title.x = element_text(size=12),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold", color = "white"),
    plot.subtitle = element_text(hjust = 0.5, size = 14, color = "white"),
    plot.caption = element_text(hjust = 0.5, size = 8, color = "white")
  )

However, the average rating of the genres remained same

3.2. Number of Good Horror Movies increased Since 2000

Plot2 Code 
monster_movies |> 
  mutate(
    RatingBucket = case_when(
      average_rating > 6 ~ "Good Rating",
      average_rating <= 6 ~ "Poor Rating"
    )
  ) |>
  group_by(year,RatingBucket) |>
  summarise(NoOfMovies = n(), .groups = "drop") |>
  mutate(
    Year = as.Date(paste("01/01/", year, sep=""), "%d/%m/%Y")
  ) |>
  ggplot(aes(x = Year, y = NoOfMovies, fill = RatingBucket)) +
  geom_bar(stat = "identity", linewidth = .1) +
  scale_fill_manual(values = c("Good Rating" = "brown3", "Poor Rating" = "cornsilk3")) +
  scale_x_date(breaks = seq(from = as.Date("1919-01-01"),
                            to = as.Date("2024-01-01"),
                            by = "15 years"),
               date_labels = "%Y") +
  
  theme_minimal() +
  labs(
    title = "Yearly Trends in Release Volumes of Horror Movies by Rating",
    subtitle = "Analysis Period: 1919 to 2024",
    x = "Release Year of Horror Movies",
    y = "Number of Movies Released",
    caption = "Good Rating: Movie Rating > 6"
  ) +
  theme(
    plot.background = element_rect(fill = "black", color = "black"),
    panel.background = element_rect(fill = "black"),
    axis.text = element_text(color = "white"),
    axis.title = element_text(color = "white"),
    axis.title.y = element_text(size=12),
    axis.title.y.right = element_text(size=12),
    axis.title.x = element_text(size=12),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold", color = "white"),
    plot.subtitle = element_text(hjust = 0.5, size = 14, color = "white"),
    legend.position = c(0.1,0.3),
    legend.text = element_text(color = "white"),
    plot.caption = element_text(hjust = 0.5, size = 8, color = "white")
  )

3.3. Sub Genre Mix of Horror Movies

Plot3 Code 
monster_movies |> 
  mutate(
    RatingBucket = case_when(
      average_rating > 6 ~ "Good Rating",
      average_rating <= 6 ~ "Poor Rating"
    )
  ) |>
  filter(
    RatingBucket == "Good Rating"
  ) -> GoodRating

monster_movies |> 
  mutate(
    RatingBucket = case_when(
      average_rating > 6 ~ "Good Rating",
      average_rating <= 6 ~ "Poor Rating"
    )
  ) |>
  filter(
    RatingBucket == "Poor Rating"
  ) -> PoorRating


GoodRatings <- subset(monster_movie_genres,monster_movie_genres$tconst %in% GoodRating$tconst)
PoorRatings <- subset(monster_movie_genres,monster_movie_genres$tconst %in% PoorRating$tconst)

rm(GoodRating,PoorRating)

GoodRatings <- subset(GoodRatings, GoodRatings$genres != "Horror")
PoorRatings <- subset(PoorRatings, PoorRatings$genres != "Horror")


# Plot for Good Ratings
good_ratings_plot <- GoodRatings |>
  group_by(genres) |>
  summarise(NoOfGenres = n()) |>
  arrange(desc(NoOfGenres)) |>
  ggplot(aes(x = reorder(genres, -NoOfGenres), y = NoOfGenres)) +
  geom_bar(stat = "identity", fill = "skyblue", color = "black") +
  labs(
    title = "Distribution of Good Ratings",
    y = "Number of Movies",
    x = ""
  ) +
  theme_minimal() +
  theme(
    plot.background = element_rect(fill = "black", color = "black"),
    panel.background = element_rect(fill = "black"),
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold", color = "white"),
    axis.text = element_text(color = "white"),
    axis.text.x = element_text(angle = 45, hjust = 1, color = "white"),
    axis.title = element_text(color = "white"),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
  )

# Plot for Poor Ratings
poor_ratings_plot <- PoorRatings |>
  group_by(genres) |>
  summarise(NoOfGenres = n()) |>
  arrange(desc(NoOfGenres)) |>
  ggplot(aes(x = reorder(genres, -NoOfGenres), y = NoOfGenres)) +
  geom_bar(stat = "identity", fill = "salmon", color = "black") +
  labs(
    title = "Distribution of Poor Ratings",
    x = "Genre",
    y = "Number of Movies"
  ) +
  theme_minimal() +
  theme(
    plot.background = element_rect(fill = "black", color = "black"),
    panel.background = element_rect(fill = "black"),
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold", color = "white"),
    axis.text = element_text(color = "white"),
    axis.text.x = element_text(angle = 45, hjust = 1, color = "white"),
    axis.title = element_text(color = "white"),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
  )

# Combine plots using patchwork
combined_plot <- good_ratings_plot + poor_ratings_plot + 
  plot_layout(ncol = 1) + 
  plot_annotation(
    title = "Distribution of Ratings by Genre",
    theme = theme(
      plot.background = element_rect(fill = "black"),
      plot.title = element_text(color = "white", size = 20, face = "bold",hjust = 0.5)
    )
  )

# Display combined plot
combined_plot

  • A blend of Documentary and Comedy is becoming a preferred genre combination for horror movies.
  • A pure mix of Comedy and Sci-Fi is generally seen as a miss with audiences.

4. References

  • https://r-graph-gallery.com/line-chart-dual-Y-axis-ggplot2.html
  • https://pixabay.com/photos/silhouette-ghost-horror-halloween-3777403/
  • https://www.statsignificant.com/p/why-do-people-like-horror-films-a