Holiday Movies

Happy holidays! This week we’re exploring “holiday” movies: movies with “holiday”, “Christmas”, “Hanukkah”, or “Kwanzaa” (or variants thereof) in their title!

The data this week comes from the Internet Movie Database. We don’t have an article using exactly this dataset, but you might get inspiration from this Christmas Movies blog post by Milán Janosov at Central European University.

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2023-12-12')
## OR
tuesdata <- tidytuesdayR::tt_load(2023, week = 50)

holiday_movies <- tuesdata$holiday_movies
holiday_movie_genres <- tuesdata$holiday_movie_genres

# Option 2: Read directly from GitHub

holiday_movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-12-12/holiday_movies.csv')
holiday_movie_genres <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-12-12/holiday_movie_genres.csv')

How to Participate

Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Data Dictionary

`holiday_movies.csv`

variable	class	description
tconst	character	alphanumeric unique identifier of the title
title_type	character	the type/format of the title (movie, video, or tvMovie)
primary_title	character	the more popular title / the title used by the filmmakers on promotional materials at the point of release
original_title	character	original title, in the original language
year	double	the release year of a title
runtime_minutes	double	primary runtime of the title, in minutes
genres	character	includes up to three genres associated with the title (comma-delimited)
simple_title	character	the title in lowercase, with punctuation removed, for easier filtering and grouping
average_rating	double	weighted average of all the individual user ratings on IMDb
num_votes	double	number of votes the title has received on IMDb (titles with fewer than 10 votes were not included in this dataset)
christmas	logical	whether the title includes “christmas”, “xmas”, “x mas”, etc
hanukkah	logical	whether the title includes “hanukkah”, “chanukah”, etc
kwanzaa	logical	whether the title includes “kwanzaa”
holiday	logical	whether the title includes the word “holiday”

`holiday_movie_genres.csv`

variable	class	description
tconst	character	alphanumeric unique identifier of the title
genres	character	genres associated with the title, one row per genre

Cleaning Script

library(tidyverse)
library(here)
library(fs)
library(janitor)

working_dir <- here::here("data", "2023", "2023-12-12")

imdb_ratings <- readr::read_tsv(
  "https://datasets.imdbws.com/title.ratings.tsv.gz",
  na = c("", "NA", "\\N")
) |> 
  janitor::clean_names() |> 
  dplyr::filter(num_votes >= 10)
imdb_titles <- readr::read_tsv(
  "https://datasets.imdbws.com/title.basics.tsv.gz",
  na = c("", "NA", "\\N")
) |> 
  janitor::clean_names() |> 
  # A handful of titles have miscoded data, which can be detected by cases where
  # the "isAdult" field has a value other than 0 or 1. That's convenient,
  # because I want to get rid of anything other than 0.
  dplyr::filter(is_adult == 0) |>
  dplyr::select(-is_adult) |>
  dplyr::mutate(
    # Create a column for easier title searching.
    simple_title = tolower(primary_title) |> 
      stringr::str_remove_all("[[:punct:]]")
  )

rated_titles <- imdb_titles |> 
  dplyr::inner_join(imdb_ratings, by = "tconst")

holiday_movies <- rated_titles |> 
  dplyr::filter(title_type %in% c("movie", "video", "tvMovie")) |> 
  # "end_year" is only relevant for series.
  dplyr::select(-"end_year") |>
  dplyr::rename("year" = "start_year") |> 
  dplyr::filter(
    stringr::str_detect(simple_title, "holiday") |
      stringr::str_detect(simple_title, "christmas") |
      stringr::str_detect(simple_title, "\\bx mas\\b") |
      stringr::str_detect(simple_title, "xmas") |
      # Let's catch both "Hanukkah" and "Chanukah" (and some other variants
      # while we're at it)
      stringr::str_detect(simple_title, "hanuk") | 
      stringr::str_detect(simple_title, "kwanzaa")
  ) |> 
  dplyr::mutate(
    christmas = stringr::str_detect(simple_title, "christmas") | 
      stringr::str_detect(simple_title, "x *mas"),
    hanukkah = stringr::str_detect(simple_title, "hanuk"),
    kwanzaa = stringr::str_detect(simple_title, "kwanzaa"),
    holiday = stringr::str_detect(simple_title, "holiday")
  )

holiday_movie_genres <- holiday_movies |> 
  dplyr::select(tconst, genres) |> 
  tidyr::separate_longer_delim(genres, ",")

readr::write_csv(
  holiday_movies,
  fs::path(working_dir, "holiday_movies.csv")
)
readr::write_csv(
  holiday_movie_genres,
  fs::path(working_dir, "holiday_movie_genres.csv")
)