Summer Movies

README.md

@@ -60,6 +60,7 @@ If you are using TidyTuesday to teach data-related skills, [please let us know](
 | 28 | `2024-07-09` | [David Robinson's TidyTuesday Functions](data/2024/2024-07-09/readme.md) | [funspotr examples](https://raw.githubusercontent.com/brshallo/funspotr-examples/main/data/funs/drob-tidy-tuesdays-funs-20220114.csv) | [Network Visualizations of Code Collections](https://www.bryanshalloway.com/2022/03/17/network-plots-of-code-collections-funspotr-part-3/) | 
 | 29 | `2024-07-16` | [English Women's Football](data/2024/2024-07-16/readme.md) | [The English Women's Football (EWF) Database, May 2024](https://github.com/probjects/ewf-database) | [The English Women's Football (EWF) Database, May 2024](https://github.com/probjects/ewf-database) | 
 | 30 | `2024-07-23` | [American Idol data](data/2024/2024-07-23/readme.md) | [American Idol data](https://github.com/kkakey/American_Idol) | [American Idol data](https://github.com/kkakey/American_Idol) | 
+| 31 | `2024-07-30` | [Summer Movies](data/2024/2024-07-30/readme.md) | [IMDb non-commercial datasets](https://developer.imdb.com/non-commercial-datasets/) | [IMDb's 2023 Summer Movie Guide](https://www.imdb.com/list/ls569932833) | 
 
 ***  
 

data/2024/2024-07-30/meta.yaml

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+title: Summer Movies
+article:
+  title: IMDb's 2023 Summer Movie Guide
+  url: https://www.imdb.com/list/ls569932833
+data_source:
+  title: IMDb non-commercial datasets
+  url: https://developer.imdb.com/non-commercial-datasets/
+images:
+# Please include at least one image, and up to three images
+- file: summer_school.png
+  alt: A movie poster for Summer School, a 1987 comedy film starting Mark Harmon. The poster shows Mark Harmon in a Hawaiian shirt and a lei, holding sunglasses. It also shows a dog, also with a lei, and wearing sunglasses. The text says At Ocean Front High, what do they call a guy who cuts classes, hates homework, and lives for summer vacations? Teacher.

data/2024/2024-07-30/readme.md

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+# Summer Movies
+
+This week we're exploring "summer" movies: movies with "summer" in their title!
+
+The data this week comes from the [Internet Movie Database](https://developer.imdb.com/non-commercial-datasets/).
+We don't have an article using exactly this dataset, but you might get inspiration from [IMDb's 2023 Summer Movie Guide](https://www.imdb.com/list/ls569932833/).
+
+## The Data
+
+```r
+# Option 1: tidytuesdayR package 
+## install.packages("tidytuesdayR")
+
+tuesdata <- tidytuesdayR::tt_load('2024-07-30')
+## OR
+tuesdata <- tidytuesdayR::tt_load(2024, week = 31)
+
+summer_movie_genres <- tuesdata$summer_movie_genres
+summer_movies <- tuesdata$summer_movies
+
+# Option 2: Read directly from GitHub
+
+summer_movie_genres <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-07-30/summer_movie_genres.csv')
+summer_movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-07-30/summer_movies.csv')
+```
+
+## How to Participate
+
+- [Explore the data](https://r4ds.hadley.nz/), 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](https://shiny.posit.co/), 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](../../../sharing.md) on social media with the #TidyTuesday hashtag.
+
+### Data Dictionary
+
+# `summer_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 |
+
+# `summer_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            |integer   |the release year of a title |
+|runtime_minutes |integer   |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       |integer   |number of votes the title has received on IMDb (titles with fewer than 10 votes were not included in this dataset) |
+
+### Cleaning Script
+
+```r
+library(tidyverse)
+library(janitor)
+
+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")
+
+summer_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, "summer")) |> 
+  dplyr::mutate(
+    dplyr::across(
+      c("year", "runtime_minutes", "num_votes"),
+      as.integer
+    )
+  )
+
+summer_movie_genres <- summer_movies |> 
+  dplyr::select(tconst, genres) |> 
+  tidyr::separate_longer_delim(genres, ",")
+```