Hero image of the main characters

Avatar: The last airbender

The data this week comes from the appa R package created by Avery Robbins. H/t to Kelsey Gonzalez for recommending this data package.

The original data came from the Avatar Wiki, and the example code used to scrape this dataset is also covered on Avery’s blog along with a quick exploration of the available data in a separate blog post.

There is also a “Avatar” themed palette from the tvthemes R package courtesy of Ryo Nakagawara

For people who have not seen the show or read this series of books, Wikipedia covers the high level details.

Avatar: The Last Airbender is set in a world where human civilization consists of four nations, named after the four classical elements: the Water Tribes, the Earth Kingdom, the Fire Nation, and the Air Nomads. In each nation, certain people, known as “benders” (waterbenders, earthbenders, firebenders and airbenders), have the ability to telekinetically manipulate and control the element corresponding to their nation, using gestures based on Chinese martial arts. The Avatar is the only person with the ability to bend all four elements.

The series is centered around the journey of 12-year-old Aang, the current Avatar and last survivor of his nation, the Air Nomads, along with his friends Sokka, Katara, and later Toph Beifong, as they strive to end the Fire Nation’s war against the other nations of the world. It also follows the story of Zuko—the exiled prince of the Fire Nation, seeking to restore his lost honor by capturing Aang, accompanied by his wise uncle Iroh—and later, that of his ambitious sister Azula.

Avatar: The Last Airbender was commercially successful and was acclaimed by audiences and critics, who praised its art direction, soundtrack, cultural references, humor, characters, and themes. These include concepts rarely touched on in youth entertainment, such as war, genocide, imperialism, colonialism, totalitarianism, and free choice.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2020-08-11')
tuesdata <- tidytuesdayR::tt_load(2020, week = 33)

avatar <- tuesdata$avatar

# Or read in the data manually

avatar <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-08-11/avatar.csv')
scene_description <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-08-11/scene_description.csv')

Data Dictionary

avatar.csv

This is the core dataset (scene description text moved to alternative dataset as it was a list column).

variable	class	description
id	integer	Unique Row identifier
book	character	Book name
book_num	integer	Book number
chapter	character	Chapter name
chapter_num	integer	Chapter Name
character	character	Character speaking
full_text	character	Full text (scene description, character text)
character_words	character	Text coming from characters
writer	character	Writer of book
director	character	Director of episode
imdb_rating	double	IMDB rating for episode

scene_description.csv

variable	class	description
id	integer	Unique row identifier
scene_description	character	Scene description text

Cleaning Script

library(tidyverse)
library(appa)

avatar <- appa::appa

scene_description <- avatar %>% 
  select(id, scene_description) %>% 
  unnest_longer(scene_description) %>% 
  filter(!is.na(scene_description))

scene_description %>% 
  write_csv(here::here("2020", "2020-08-11", "scene_description.csv"))

avatar %>% 
  select(-scene_description) %>% 
  write_csv(here::here("2020", "2020-08-11", "avatar.csv"))