Doctor Who Episodes

Doctor Who is an extremely long-running British television program. The show was revived in 2005, and has proven very popular since then. To celebrate this year’s 60th anniversary of Doctor Who, we have three datasets.

The data this week comes from Wikipedia’s [List of Doctor Who episodes](https://en.wikipedia.org/wiki/List_of_Doctor_Who_episodes_(2005%E2%80%93present) via the {datardis} package by Jonathan Kitt. Thank you to Jonathan for compiling and sharing this data!

As of 2023-11-24, the data only includes episodes from the “revived” era. For an added challenge, consider submitting a pull request to the {datardis} package to update the data-extraction scripts to also fetch the “classic” era data!

The Data

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

tuesdata <- tidytuesdayR::tt_load('2023-11-28')
## OR
tuesdata <- tidytuesdayR::tt_load(2023, week = 48)

drwho_episodes <- tuesdata$drwho_episodes
drwho_directors <- tuesdata$drwho_directors
drwho_writers <- tuesdata$drwho_writers

# Option 2: Read directly from GitHub

drwho_episodes <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-11-28/drwho_episodes.csv')
drwho_directors <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-11-28/drwho_directors.csv')
drwho_writers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-11-28/drwho_writers.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

`drwho_episodes.csv`

variable	class	description
era	character	Whether the episode is in the “classic” or “revived” era. All data in this dataset is within the “revived” era.
season_number	double	The season number within the era. Note that some episodes are outside of a season.
serial_title	character	Serial title if available
story_number	character	Story number
episode_number	double	Episode number in season
episode_title	character	Episode title
type	character	“episode” or “special”
first_aired	double	Date the episode first aired in the U.K.
production_code	character	Episode’s production code if available
uk_viewers	double	Number of U.K. viewers (millions)
rating	double	Episode’s rating
duration	double	Episode’s duration in minutes

`drwho_directors.csv`

variable	class	description
story_number	character	Story number
director	character	Episode’s director

`drwho_writers.csv`

variable	class	description
story_number	character	Story number
writer	character	Episode’s writer

Cleaning Script

Clean data from the {datardis} package.

library(tidyverse)
library(here)
library(fs)
# pak::pak("datardis")
library(datardis)

working_dir <- here::here("data", "2023", "2023-11-28")

readr::write_csv(
  datardis::drwho_episodes,
  fs::path(working_dir, "drwho_episodes.csv")
)
readr::write_csv(
  datardis::drwho_directors,
  fs::path(working_dir, "drwho_directors.csv")
)
readr::write_csv(
  datardis::drwho_writers,
  fs::path(working_dir, "drwho_writers.csv")
)