Package 'survivoR' reference manual

Title:	Data from all Seasons of Survivor (US) TV Series in Tidy Format
Description:	Several datasets which detail the results and events of each season of Survivor. This includes details on the cast, voting history, immunity and reward challenges, jury votes and viewers. This data is useful for practicing data wrangling, graph analytics and analysing how each season of Survivor played out. Includes 'ggplot2' scales and colour palettes for visualisation.
Authors:	Daniel Oehm [aut, cre], Carly Levitz [ctb], Dario Mavec [ctb]
Maintainer:	Daniel Oehm <[email protected]>
License:	MIT + file LICENSE
Version:	2.3.4
Built:	2024-09-28 07:18:12 UTC
Source:	CRAN

Advantage Details

Description

A dataset containing the details and characteristics of each idol and advantage. This maps to 'advantage_movement'

Usage

advantage_details
advantage_details

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
advantage_id: The ID / primary key of the advantage
advantage_type: Advantage type e.g. hidden immunity idol, extra vote, steal a vote, etc
clue_details: Details if a clue existed for the advantage and if so where was the clue found
location_found: The location the idol or advantage was found
conditions: Extra details about the unique conditions of the idol or advantage

Details

There are split idols which need to be combined to be played. In these case the first one found is given an ID. The second or subsequent parts are given the same ID with a trailing letter. For example in season 40 Denise found an idol that was split (USHI4002). Later she found the other half (USHI4002b). When played the second half is considered to have 'absorbed' into the first idol. The first idol found is always considered the primary idol.

Advantage Movement

Description

A dataset containing the movement details of each advantage or hidden immunity idol. Each row is considered an event e.g. the idol was found, played, etc. If the advantage changed hands it records who received it. The logical flow is identified by the 'sequence_id'.

Usage

advantage_movement
advantage_movement

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
castaway: Name of the castaway involved in the event e.g. found, played, received, etc.
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU.
advantage_id: The ID / primary key of the advantage
sequence_id: The sequence of events. For example 'sequence_id == 1' usually means the advantage was found. Each subsequent event follows the 'sequence_id'
day: The day the event occurred
episode: The episode the event occurred
event: The event e.g. the advantage was found, played, received, etc
played_for: If the advantage or idol was played this records who it was played for
played_for_id: the ID for who the advantage or idol was played for
success: If the play was successful or not. Only relevant for advantages since playing a hidden immunity idol is always successful in terms of saving who it was played for.
votes_nullified: In the case of hidden immunity idols this is the count of how many votes were nullified when played

Survivor Auction Details

Description

The details of the items purchased at the Survivor Auction. survivor_auction is at the castaway level and includes all castaways whether or not they purchased an item and auction_details is at the item level.

Usage

auction_details
auction_details

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
item: Item number
item_description: Item description
category: The item category. See details for more.
castaway: Castaway
castaway_id: Castaway ID
covered: If the item was covered or not
cost: The amount paid for the item
money_remaining: How much money the castaway has remaining
auction_num: If the same item is auctioned for a second time it has a value of 2
participated: The names of castaways that could participate in the purchased item e.g. sharing a tub of peanut butter with the tribe
notes: Additional notes
alternative_offered: If and alternative was offered to the player after purchase
alternative_accepted: If they accepted the alternative offer
other_item: Description of the refused item
other_item_category: Category of the refused item

Details

Each item has been categorised into 5 main categories: 1. Food and drink: The most common item. It may be simply food or drink, not necessarily both. 2. Comfort: Things like a shower, toothpaste, etc 3. Letters from home 4. Advantage: Could be a clue to a hidden immunity idol, advantage in the next challenge, or in the current auction 5. Bad item: The not good item, typically one of the covered items. Whether or not it's actually bad is subjective, but where someone is hoping for pizza and gets bat soup I consider it a bad item.

Source

https://survivor.fandom.com/wiki/Main_Page

Boot mapping

Description

A mapping table for easily filtering to the set of castaways that are still in the game after a specified number of boots.

Usage

boot_mapping
boot_mapping

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
order: The number of boots that there have been in the game e.g. if 'order == 2' there have been 2 boots in the game so far and there are N-2 castaways left in the game
final_n: The final number of castaways e.g. you can filter to the final 4 by 'filter(boot_mapping, final_n == 4)'. There are missing values where players have returned to the game. This means there are multiple stages of the game where there is a different make up of the final 8, for example. This field just takes the last set so that you can filter for 'final_n' and it will return a single set of castaways.
n_boots: Similar to 'final_n' but the number of boots in the game. This is different to 'order' where order counts if someone has been booted twice. 'n_boots' is simply the number of people in the season minus the 'final_n'.
sog_id: Stage of game ID for joining to vote_history and challenge_results
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU.
castaway: Name of the castaway
tribe: Name of the tribe the castaway was on
tribe_status: The status of the tribe e.g. original, swapped, merged, etc. See details for more
game_status: Logical flag to identify if the castaway is currently in the game. If 'FALSE' the castaway is on Redemption Island or Edge of Extinction.

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series) https://survivor.fandom.com/wiki/Main_Page

Castaway details

Description

A dataset containing details on the castaways for each season

Usage

castaway_details
castaway_details

Format

This data frame contains the following columns:

castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU (TBA).
full_name: Full name of the castaway
full_name_detailed: A detailed version of full_name for plotting e.g. 'Boston' Rob Mariano
castaway: Short name of the castaway. Name typically used during the season. Sometimes there are multiple people with the same name e.g. Rob C and Rob M in Survivor All-Stars. This field takes the most verbose name used
date_of_birth: Date of birth
date_of_death: Date of death
gender: Gender of castaway
african: TRUE if African-American or African-Canadian as per https://survivor.fandom.com/wiki/Main_Page
asian: TRUE if Asian-American or Asian-Canadian as per https://survivor.fandom.com/wiki/Main_Page
latin_american: TRUE if Latin-American as per https://survivor.fandom.com/wiki/Main_Page
native_american: TRUE if Native-American as per https://survivor.fandom.com/wiki/Main_Page
bipoc: Black, Indigenous, or Person of Colour
lgbt: LGBTQIA+ status as listed on the survivor wiki.
personality_type: The Myer-Briggs personality type of the castaway
occupation: Occupation
three_words: Answer to the question "three words to describe you?"
hobbies: Answer to the question "what are you favourite hobbies?"
pet_peeves: Answer to the question "what are your pet peeves?"
race: Race (if known)
ethnicity: Ethnicity (if known)

Details

Race and ethnicity data is included if known and can point to a source, rather than making an assumption about an individual.

poc has been deprecated and replaced with bipoc which is now logical and only for the US. bipoc is TRUE if any of african, asian, latin_american, or native_american is TRUE.

Source

https://survivor.fandom.com/wiki/Main_Page, https://www.personality-database.com/

Examples

library(dplyr)
castaway_details |>
  count(gender)
library(dplyr)
castaway_details |>
  count(gender)

Castaways

Description

A dataset containing details on the results for every castaway and season

Usage

castaways
castaways

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season: Season number
season_name: Season name
full_name: Full name of the castaway
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU (TBA).
castaway: Name of castaway. Generally this is the name they were most commonly referred to or nickname e.g. no one called Coach, Benjamin. He was simply Coach
age: Age of the castaway during the season they played
city: City of residence during the season they played
state: State of residence during the season they played
episode: Episode number
day: Number of days the castaway survived. A missing value indicates they later returned to the game that season
order: Boot order. Order in which castaway was voted out e.g. 5 is the 5th person voted of the island
result: Final result
result_number: Result number i.e. the final place. NA for castaways that were voted out but later returned e.g. Redemption Island
jury_status: Jury status
original_tribe: Original tribe name
finalist: Logical. TRUE if the castaway was a finalists
jury: Logical. TRUE if the castaway was a jury member
winner: Logical. TRUE if the castaway was the winner

Details

Note that in the seasons where castaways returned to the game e.g. Redemption Island, a castaway may appear twice.

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series) https://survivor.fandom.com/wiki/Main_Page

Examples

library(dplyr)
castaways %>%
  filter(season == 40)
library(dplyr)
castaways %>%
  filter(season == 40)

Challenge Description

Description

A dataset detailing the challenges played and the elements they include over all seasons of Survivor

Usage

challenge_description
challenge_description

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
challenge_id: Primary key
challenge_number
challenge_type
name: The name of the challenge
recurring_name: Challenges can go by different names but are often associated with a particular challenge or element of a challenge. Some challenges use combinations of other challenges so it's not perfect but consistent with the wiki page. Use recurring_name to analyse how often a challenge has been run.
description: Description of the challenge
reward: Description of the reward
additional_stipulation: Some challenges come with various rules or success criteria. This states those conditions.
race: If the challenge is a race between tribes, teams or individuals
endurance: If the challenge is an endurance event e.g. last tribe, team, individual standing
turn_based: If the challenge is turn bases i.e. conducted in rounds
puzzle: If the challenge contains a puzzle element
puzzle_slide: If the challenge contained a slide puzzle
puzzle_word: If the challenge contained a word puzzle
precision: If the challenge contains a precision element e.g. shooting an arrow, hitting a target, etc
precision_catch: If the challenge featured catching a ball or similar
precision_roll_ball: If the challenge featured rolling a ball
precision_slingshot: If the challenge featured a slingshot, either the large version or handheld version
precision_throw_balls: If the challenge featured throwing balls
precision_throw_coconuts: If the challenge featured throwing coconuts
precision_throw_rings: if the challenge featured throwing rings
precision_throw_sandbags: if the challenge featured throwing sandbags
strength: If the challenge has a strength based
balance: If the challenge contains a balancing element. My refer to the player balancing on something or the player balancing an object on something e.g. The Ball Drop
balance_beam: If the challenge featured a balance beam of similar they were required to balance on
balance_ball: If the challenge featured balancing a ball on something
food: If the challenge contains a food element e.g. the food challenge, biting off chunks of meat
knowledge: If the challenge contains a knowledge component e.g. Q and A about the location
memory: If the challenge contains a memory element e.g. memorising a sequence of items
fire: If the challenge contains an element of fire making / maintaining
water: If the challenge is held, in part, in the water
water_swim: If castaways had to swim in the challenge
water_paddling: If castwways were required to paddle a boat or similar
obstacle_blindfolded: If the challenge required castaways to be blindfolded
obstacle_cargo_net: If the challenge featured a cargo net
obstacle_chopping: If castaways were required to chop a rope or similar
obstacle_combination_lock: If the challenge feature a combination lock
obstacle_digging: If the challenge involved digging
obstacle_knots: If the challenge involved untying knots
obstacle_padlocks: If the challenge featured opening padlocks
mud: If the challenge required castaways to get covered in mud

Details

This data set contains the name, description, and descriptive features for each challenge where it is known. Challenges can go by different names so have included the unique name and the recurring challenge name. These are taken directly from the [Survivor Wiki](https://survivor.fandom.com/wiki/Category:Recurring_Challenges). Sometimes there can be variations made on the challenge but go but the same name, or the challenge is integrated with a longer obstacle. In these cases the challenge may share the same recurring challenge name but have a different challenge name. Even if they share the same names the description could be different.

The features of each challenge have been determined largely through string searches of key words that describe the challenge. It may not be 100 different and inconsistent descriptions but in most part they will provide a good basis for analysis.

If any descriptive features need altering please let me know in the [issues](https://github.com/doehm/survivoR/issues).

For updated data please see the git version.

Source

https://survivor.fandom.com/wiki/Category:Challenges https://survivor.fandom.com/wiki/Main_Page

Examples

library(dplyr)
library(tidyr)
challenge_description
library(dplyr)
library(tidyr)
challenge_description

Challenge Results

Description

A dataset detailing the challenges played including reward and immunity challenges.

Usage

challenge_results
challenge_results

Format

This data frame contains the following columns

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
n_boots: The number of boots that there have been in the game e.g. if 'n_boots == 2' there have been 2 boots in the game so far and there are N-2 castaways left in the game
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU (TBA).
castaway: Name of castaway. Generally this is the name they were most commonly referred to or nickname e.g. no one called Coach, Benjamin. He was simply Coach
outcome_type: Whether the challenge is individual or tribal. Some individual reward challenges may involve multiple castaways as the winner gets to choose who they bring along
tribe: Current tribe the castaway is on
tribe_status: The status of the tribe e.g. original, swapped, merged, etc. See details for more
challenge_type: The challenge type e.g. immunity, reward, etc
challenge_id: Primary key to the challenge_description data set which contains features of the challenge
result: Result of challenge
result_notes: Additional notes about the result of the challenge
order_of_finish: Order of finish for tribal challenges. Useful when there are 3 or more tribes to see who actually came first, second and who lost the challenge.
chosen_for_reward: If after the reward challenge the castaway was chosen to participate in the reward
sit_out: TRUE if they sat out of the challenge or FALSE if they participate
team: Team allocation when they are split into teams
sog_id: Stage of game ID for joining to boot_mapping and vote_history

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series) https://survivor.fandom.com/wiki/Main_Page

Examples

library(dplyr)
library(tidyr)
challenge_results %>%
  filter(season == 40)
library(dplyr)
library(tidyr)
challenge_results %>%
  filter(season == 40)

Challenge Summary

Description

A dataset summarising challenge_results

Usage

challenge_summary
challenge_summary

Format

This data frame contains the following columns

category: The category of the challenge e.g. tribal, individual, individual immunity, duel, etc. This makes it easy to split out the difference types of challenges and avoid complications such as 'Team / Individual' challenges where there is a dependent outcome structure. Join to challenge_results using challenge_id, version_season and castaway_id
version_season: Version season key
challenge_id: Primary key to the challenge_description data set which contains features of the challenge
challenge_type: The challenge type e.g. immunity, reward, etc
outcome_type: Whether the challenge is individual or tribal. Some individual reward challenges may involve multiple castaways as the winner gets to choose who they bring along
tribe: Current tribe the castaway is on
castaway: Name of castaway. Generally this is the name they were most commonly referred to or nickname e.g. no one called Coach, Benjamin. He was simply Coach
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU (TBA).
n_entities: Number of entities competing for the win e.g. the number of tribes, teams, or people.
n_winners: Number of winners (or winning entities) e.g. if there are two tribes there is only one winning tribe, if there are three tribes like the new era there are two winning tribes and one that goes to tribal council.
n_in_team: The number of people in the tribe or team
won: If the castaway won

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series) https://survivor.fandom.com/wiki/Main_Page

Examples

library(dplyr)
library(tidyr)
challenge_summary %>%
  filter(version_season == 46)
library(dplyr)
library(tidyr)
challenge_summary %>%
  filter(version_season == 46)

Confessionals

Description

A dataset containing the count of confessionals per castaway per episode. A confessional is when the castaway is speaking directly to the camera about their game.

Usage

confessionals
confessionals

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
castaway: Name of the castaway
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU.
confessional_count: The count of confessionals for the castaway during the episode
confessional_time: The total time for all confessionals for the episode for each castaway
index_count: The index based on the confessional counts. See details.
index_time: The index based on the confessional time. See details.

Details

Confessional data has been counted by contributors of the survivoR R package and consolidated with external sources. The aim is to establish consistency in confessional counts in the absence of official sources. Given the subjective nature of the counts and the potential for clerical error no single source is more valid than another. Therefore, it is reasonable to average across all sources.

In the case of double or extended episodes, if the episode only has one title it is considered a single episode. This means the average number of confessionals per person is likely to be higher for this episode given it's length. If there are two episode titles the confessionals are counted for the appropriate episode. This is to ensure consistency across all other datasets.

In the case of recap episodes, this episode is left blank.

The indexes are a measure of how many more confessional counts or time the castaway has received given the point in the game. For example a 'index_count' of 1 implies the castaway has received the expected number of confessionals given equal share within tribe. An index of 1.5 implies have have received 50 typically receives more confessionals for the episode. Makes sense. 'index_time' is the same but using time instead of counts.

If you also count confessionals, please get in touch and I'll add them into the package.

Episode summary

Description

A dataset containing a summary of all US episodes seasons of Survivor

Usage

episode_summary
episode_summary

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
episode: Episode number
episode_summary: summary of the episode

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series)

Episodes

Description

A dataset containing details for each episode

Usage

episodes
episodes

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: Season number
episode_number_overall: The cumulative episode number
episode: Episode number for the season
episode_title: Episode title
episode_label: A standardised episode label
episode_date: Date the episode aired
episode_length: Episode length in minutes
viewers: Number of viewers (millions) who tuned in
imdb_rating: IMDb rating for the episode on a scale of 0-10
n_ratings: The number of ratings submitted to IMDb
episode_summary: Description of the episode from wikipedia

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series)

Castaway images

Description

Returns the URL for the image of the specified castaways by their 'castaway_id' and season / version they were in

Usage

get_castaway_image(castaway_ids, version_season)
get_castaway_image(castaway_ids, version_season)

Arguments

`castaway_ids`	Castaway ID
`version_season`	Version season key for the season they played

Value

Character vector of URLs

Examples

library(dplyr)

survivoR::castaways %>%
  filter(version_season == "US42") %>%
  mutate(castaway_image = get_castaway_image(castaway_id, version_season))
library(dplyr)

survivoR::castaways %>%
  filter(version_season == "US42") %>%
  mutate(castaway_image = get_castaway_image(castaway_id, version_season))

Confessional time

Description

Takes the output of the times recorded from the Shiny app and aggregates to the final confessional times and confessional counts. confessional_time is the total duration in seconds for the episode. confessional_count is the number of confessionals recorded to be at least 10 seconds apart.

Usage

get_confessional_timing(x, .vs, .episode, .mda = 3)
get_confessional_timing(x, .vs, .episode, .mda = 3)

Arguments

`x`	Either a data frame or path(s) to the csv file containing all the time stamps from the Shiny app
`.vs`	Version season
`.episode`	Episode
`.mda`	Missing duration adjustment (MDA) in seconds. If either start or stop is missing from the records, the missing value is imputed with a 3 second adjustment by default.

Value

data frame

Examples

# After running app and recording confessionals, run...
# Example from a saved timing file

library(readr)

path <- system.file(package = "survivoR", "extdata/US4412.csv")
df_us4412 <- read_csv(path)
get_confessional_timing(df_us4412, .vs = "US44", .episode = 12)
# After running app and recording confessionals, run...
# Example from a saved timing file

library(readr)

path <- system.file(package = "survivoR", "extdata/US4412.csv")
df_us4412 <- read_csv(path)
get_confessional_timing(df_us4412, .vs = "US44", .episode = 12)

Jury votes

Description

A dataset containing details on the final jury votes to determine the winner for each season

Usage

jury_votes
jury_votes

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
castaway: Name of the castaway
finalist: The finalists for which a vote can be placed
vote: Vote. 0-1 variable for easy summation
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU.
finalist_id: The ID of the finalist for which a vote can be placed. Consistent with castaway ID

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series)

Examples

library(dplyr)
jury_votes %>%
  filter(season == 40) %>%
  group_by(finalist) %>%
  summarise(votes = sum(vote))
library(dplyr)
jury_votes %>%
  filter(season == 40) %>%
  group_by(finalist) %>%
  summarise(votes = sum(vote))

Launch Confessional App

Description

Launches the confessional timing app in either a browser or viewer. Default is set to browser. The user is required to provide a path for which the time stamps are recorded.

Usage

launch_confessional_app(browser = TRUE, path = NULL, write = TRUE)
launch_confessional_app(browser = TRUE, path = NULL, write = TRUE)

Arguments

`browser`	Open in browser instead of viewer. Default `TRUE`
`path`	Parent directory for output files. Default is a sub-folder `'confessional-timing'` in the current working directory.
`write`	Write to disc. Default `TRUE`.

Value

An active R shiny application

Examples

## Only run this example in interactive R sessions

if(interactive()) {

  # launch app
  # launch_confessional_app()

}

## Only run this example in interactive R sessions

if(interactive()) {

  # launch app
  # launch_confessional_app()

}

Screen Time

Description

A dataset summarising the screen time of contestants on the TV show Survivor. Currently only contains Season 1-4 and 42.

Usage

screen_time
screen_time

Format

This data frame contains the following columns:

version_season: Version season key
episode: Episode number
castaway_id: ID of the castaway (primary key). Also includes two special IDs of host (i.e. Jeff Probst) or unknown (the image detection couldn't identify the face with sufficient accuracy)
screen_time: Estimated screen time for the individual in seconds.

Details

Individuals' screen time is calculated, at a high-level, via the following process:

Frames are sampled from episodes on a 1 second time interval
MTCNN detects the human faces within each frame
VGGFace2 converts each detected face into a 512d vector space
A training set of labelled images (1 for each contestant + 3 for Jeff Probst) is processed in the same way to determine where they sit in the vector space. TODO: This could be made more accurate by increasing the number of training images per contestant.
The Euclidean distance is calculated for the faces detected in the frame to each of the contestants in the season (+Jeff). If the minimum distance is greater than 1.2 the face is labelled as "unknown". TODO: Review how robust this distance cutoff truly is - currently based on manual review of Season 42.
A multi-class SVM is trained on the training set to label faces. For any face not identified as "unknown", the vector embedding is run into this model and a label is generated.
All labelled faces are aggregated together, with an assumption of 1 full second of screen time each time a face is seen.

Season palettes

Description

A dataset containing palettes generated from the season logos

Usage

season_palettes
season_palettes

Format

This nested data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
palette: The season palette

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series)

Season summary

Description

A dataset containing a summary of all seasons of Survivor

Usage

season_summary
season_summary

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: Season name
season: Season number
n_cast: Number of cast in the season
n_tribes: Number of starting tribes
n_finalists: Number of finalists
n_jury: Number of jury members
location: Location of the season
country: Country the season was held
tribe_setup: Initial setup of the tribe e.g. heroes vs Healers vs Hustlers
full_name: Full name of the winner
winner_id: ID for the winner of the season (primary key)
winner: Winner of the season
runner_ups: Runner ups for the season. Either one or two runner ups as a string
final_vote: Final vote allocation. See the jury_votes data set for better aggregation of this data
timeslot: Timeslot of the show in the US
premiered: Date the first episode aired
ended: Date the season ended
filming_started: Date the filming of the season started
filming_ended: Date the filming ended (39 or 42 days after the start)
viewers_premiere: Number of viewers (millions) who tuned in for the premier
viewers_finale: Number of viewers (millions) who tuned in for the finale
viewers_reunion: Number of viewers (millions) who tuned in for the reunion
viewers_mean: Average number of viewers (millions) who tuned in over the season
rank: Season rank

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series) https://survivor.fandom.com/wiki/Main_Page

Survivor Auction

Description

A dataset showing who attended the Survivor Auction during the seasons they were held. survivor_auction is at the castaway level and includes all castaways whether or not they purchased an item and auction_details is at the item level.

Usage

survivor_auction
survivor_auction

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
n_boots: The number of boots so far in the game
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU (TBA).
castaway: Name of castaway. Generally this is the name they were most commonly referred to or nickname e.g. no one called Coach, Benjamin. He was simply Coach
tribe_status: The status of the tribe e.g. original, swapped, merged, etc. See details for more
tribe: Tribe name
currency: Currency
total: Total amount either given to or found by the castaway

Source

https://survivor.fandom.com/wiki/Main_Page

Survivor season colour palette

Description

ggplot2 scales for each season of Survivor.

Usage

survivor_pal(season = NULL, scale_type = "d", reverse = FALSE, ...)

scale_fill_survivor(season = NULL, scale_type = "d", reverse = FALSE, ...)

scale_colour_survivor(season = NULL, scale_type = "d", reverse = FALSE, ...)
survivor_pal(season = NULL, scale_type = "d", reverse = FALSE, ...)

scale_fill_survivor(season = NULL, scale_type = "d", reverse = FALSE, ...)

scale_colour_survivor(season = NULL, scale_type = "d", reverse = FALSE, ...)

Arguments

`season`	Season number
`scale_type`	Discrete or continuous. Input `d` or `c`.
`reverse`	Logical. Reverse the palette?
`...`	Other arguments passed on to methods.

Details

Palettes are created from the logo for the season.

Value

Scale functions for ggplot2

Examples

library(ggplot2)
library(dplyr)
mpg %>%
  ggplot(aes(x = displ, fill = manufacturer)) +
  geom_histogram(colour = "black") +
  scale_fill_survivor(40)
library(ggplot2)
library(dplyr)
mpg %>%
  ggplot(aes(x = displ, fill = manufacturer)) +
  geom_histogram(colour = "black") +
  scale_fill_survivor(40)

Tribe colours

Description

A dataset containing the tribe colours for each season

Usage

tribe_colours
tribe_colours

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
tribe: Tribe name
tribe_colour: Colour of the tribe
tribe_status: Tribe status e.g. original, swapped or merged. In the instance where a tribe is formed at the swap by splitting 2 tribes into 3, the 3rd tribe will be labelled 'swapped'

Source

https://survivor.fandom.com/wiki/Tribe

Examples

library(ggplot2)
library(dplyr)
library(forcats)
df <- tribe_colours %>%
  group_by(season_name) %>%
  mutate(
    xmin = 1,
    xmax = 2,
    ymin = 1:n(),
    ymax = ymin + 1
  ) %>%
  ungroup() %>%
  mutate(
    season_name = fct_reorder(season_name, season),
    font_colour = ifelse(tribe_colour == "#000000", "white", "black")
  )
ggplot() +
  geom_rect(data = df,
    mapping = aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    fill = df$tribe_colour) +
  geom_text(data = df,
    mapping = aes(x = xmin+0.5, y = ymin+0.5, label = tribe),
    colour = df$font_colour) +
  theme_void() +
  facet_wrap(~season_name, scales = "free_y")
library(ggplot2)
library(dplyr)
library(forcats)
df <- tribe_colours %>%
  group_by(season_name) %>%
  mutate(
    xmin = 1,
    xmax = 2,
    ymin = 1:n(),
    ymax = ymin + 1
  ) %>%
  ungroup() %>%
  mutate(
    season_name = fct_reorder(season_name, season),
    font_colour = ifelse(tribe_colour == "#000000", "white", "black")
  )
ggplot() +
  geom_rect(data = df,
    mapping = aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    fill = df$tribe_colour) +
  geom_text(data = df,
    mapping = aes(x = xmin+0.5, y = ymin+0.5, label = tribe),
    colour = df$font_colour) +
  theme_void() +
  facet_wrap(~season_name, scales = "free_y")

Tribe mapping

Description

A mapping for castaways to tribes for each day (day being the day of the tribal council) This is useful for observing who is on what tribe throughout the game.

Usage

tribe_mapping
tribe_mapping

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
day: The day of the tribal council
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU.
castaway: Name of the castaway
tribe: Name of the tribe the castaway was on
tribe_status: The status of the tribe e.g. original, swapped, merged, etc. See details for more

Details

Each season by episode and day holds a complete list of castaways still in the game and which tribe they are on. Moving through each day you can observe the changes in the tribe. For example the first day has all castaways mapped to their original tribe. The next day has the same minus the castaway just voted out. This is useful for observing the changes in tribe make either due to castaways being voted off the island, tribe swaps, who is on Redemption Island and Edge of Extinction.

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series) https://survivor.fandom.com/wiki/Main_Page

Tribes colour palette

Description

To create scale functions for ggplot. Given a season of Survivor, a palette is created from the tribe colours for that season including the merged tribe.

Usage

tribes_pal(season = NULL, scale_type = "d", reverse = FALSE, tribe = NULL, ...)

scale_fill_tribes(season = NULL, scale_type = "d", reverse = FALSE, ...)

scale_colour_tribes(season = NULL, scale_type = "d", reverse = FALSE, ...)
tribes_pal(season = NULL, scale_type = "d", reverse = FALSE, tribe = NULL, ...)

scale_fill_tribes(season = NULL, scale_type = "d", reverse = FALSE, ...)

scale_colour_tribes(season = NULL, scale_type = "d", reverse = FALSE, ...)

Arguments

`season`	Season number
`scale_type`	Discrete or continuous. Input `d` or `c`.
`reverse`	Logical. Reverse the palette?
`tribe`	Tribe names. Default `NULL`
`...`	Other arguments passed on to methods.

Details

If it is intended the colours will correspond to the tribes e.g. a stacked bar chart of votes given to each finalist and the colour corresponds to their original tribe (as in the example below), the tribe vector needs to be passed to the scale function (for now). If no tribe vector is given it will simply treat the tribe colours as a colour palette.

Value

Scale functions for ggplot2

Examples

library(ggplot2)
library(stringr)
library(dplyr)
library(glue)
ssn <- 35
labels <- castaways %>%
  filter(
    season == ssn,
    str_detect(result, "Sole|unner")
  ) %>%
  select(castaway, original_tribe) %>%
  mutate(label = glue("{castaway} ({original_tribe})")) %>%
  select(label, castaway)
jury_votes %>%
  filter(season == ssn) %>%
  left_join(
    castaways %>%
      filter(season == ssn) %>%
      select(castaway, original_tribe),
    by = "castaway"
  ) %>%
  group_by(finalist, original_tribe) %>%
  summarise(votes = sum(vote)) %>%
  left_join(labels, by = c("finalist" = "castaway")) %>% {
    ggplot(., aes(x = label, y = votes, fill = original_tribe)) +
      geom_bar(stat = "identity", width = 0.5) +
      scale_fill_tribes(ssn, tribe = .$original_tribe) +
      theme_minimal() +
      labs(
        x = "Finalist (original tribe)",
        y = "Votes",
        fill = "Original\ntribe",
        title = "Votes received by each finalist"
      )
 }
library(ggplot2)
library(stringr)
library(dplyr)
library(glue)
ssn <- 35
labels <- castaways %>%
  filter(
    season == ssn,
    str_detect(result, "Sole|unner")
  ) %>%
  select(castaway, original_tribe) %>%
  mutate(label = glue("{castaway} ({original_tribe})")) %>%
  select(label, castaway)
jury_votes %>%
  filter(season == ssn) %>%
  left_join(
    castaways %>%
      filter(season == ssn) %>%
      select(castaway, original_tribe),
    by = "castaway"
  ) %>%
  group_by(finalist, original_tribe) %>%
  summarise(votes = sum(vote)) %>%
  left_join(labels, by = c("finalist" = "castaway")) %>% {
    ggplot(., aes(x = label, y = votes, fill = original_tribe)) +
      geom_bar(stat = "identity", width = 0.5) +
      scale_fill_tribes(ssn, tribe = .$original_tribe) +
      theme_minimal() +
      labs(
        x = "Finalist (original tribe)",
        y = "Votes",
        fill = "Original\ntribe",
        title = "Votes received by each finalist"
      )
 }

Viewers

Description

A dataset containing the viewer history for each season and episode

Usage

viewers
viewers

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: Season number
episode_number_overall: The cumulative episode number
episode: Episode number for the season
episode_title: Episode title
episode_label: A standardised episode label
episode_date: Date the episode aired
episode_length: Episode length in minutes
viewers: Number of viewers (millions) who tuned in
imdb_rating: IMDb rating for the episode on a scale of 0-10
n_ratings: The number of ratings submitted to IMDb

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series)

Vote history

Description

A dataset containing details on the vote history for each season

Usage

vote_history
vote_history

Format

This data frame contains the following columns:

version: Country code for the version of the show
version_season: Version season key
season_name: The season name
season: The season number
episode: Episode number
day: Day the tribal council took place
tribe_status: The status of the tribe e.g. original, swapped, merged, etc. See details for more
tribe: Tribe name
castaway: Name of the castaway
immunity: Type of immunity held by the castaway at the time of the vote e.g. individual, hidden (see details for hidden immunity data)
vote: The castaway for which the vote was cast
vote_event: Extra details on the vote e.g. Won or lost the fire challenge, played an extra vote, etc
vote_event_outcome: The outcome of the vote event
split_vote: If there was a decision to split the vote this records who the vote was split with. Helps to identify successful boots
nullified: Was the vote nullified by a hidden immunity idol? Logical
tie: If the set of votes resulted in a tie. Logical
voted_out: The castaway who was voted out
order: Boot order. Order in which castaway was voted out e.g. 5 is the 5th person voted of the island
vote_order: In the case of ties this indicates the order the votes took place
castaway_id: ID of the castaway (primary key). Consistent across seasons and name changes e.g. Amber Brkich / Amber Mariano. The first two letters reference the country of the version played e.g. US, AU.
vote_id: ID of the castaway voted for
voted_out_id: ID of the castaway voted_out
sog_id: Stage of game ID for joining to boot_mapping and challenge_results
challenge_id: Primary key to the challenge_description data set which contains features of the challenge. The helps map the immunity challenge which result in the tribal.

Details

This data frame contains a complete history of votes cast across all seasons of Survivor. While there are consistent events across the seasons there are some unique events such as the 'mutiny' in Survivor: Cook Islands (season 13) or the 'Outcasts' in Survivor: Pearl Islands (season 7). For maintaining a standard, whenever there has been a change in tribe for the castaways it has been recorded as swapped. swapped is used as the term since 'the tribe swap' is a typical recurring milestone in each season of Survivor. Subsequent changes are recorded with a trailing digit e.g. swapped2. This includes absorbed tribes e.g. Stephanie was 'absorbed' in Survivor: Palau (season 10) and when 3 tribes are reduced to 2. These cases are still considered 'swapped' to indicate a change in tribe status.

Some events result in a castaway attending tribal but not voting. These are recorded as

Win: The castaway won the fire challenge
Lose: The castaway lost the fire challenge
None: The castaway did not cast a vote. This may be due to a vote steal or some other means
Immune: The castaway did not vote but were immune from the vote

Where a castaway has immunity == 'hidden' this means that player is protected by a hidden immunity idol. It may not necessarily mean they played the idol, the idol may have been played for them. While the nullified votes data is complete the immunity data does not include those who had immunity but did not receive a vote. This is a TODO.

In the case where the 'steal a vote' advantage was played, there is a second row for the castaway that stole the vote. The castaway who had their vote stolen are is recorded as None.

Many castaways have been medically evacuated, quit or left the game for some other reason. In these cases where no votes were cast there is a skip in the order variable. Since no votes were cast there is nothing to record on this data frame. The correct order in which castaways departed the island is recorded on castaways.

In the case of a tie, voted_out is recorded as tie to indicate no one was voted off the island in that instance. The re-vote is recorded with vote_order = 2 to indicate this is the second round of voting. In the case of a second tie voted_out is recorded as tie2. The third step is either a draw of rocks, fire challenge or countback (in the early days of survivor). In these cases vote is recorded as the colour of the rock drawn, result of the fire challenge or 'countback'.

Source

https://en.wikipedia.org/wiki/Survivor_(American_TV_series)

Examples

# The number of times Tony voted for each castaway in Survivor: Winners at War
library(dplyr)
vote_history %>%
  filter(
    season == 40,
    castaway == "Tony"
  ) %>%
  count(vote)
# The number of times Tony voted for each castaway in Survivor: Winners at War
library(dplyr)
vote_history %>%
  filter(
    season == 40,
    castaway == "Tony"
  ) %>%
  count(vote)

Package 'survivoR'

Help Index

Advantage Details

Description

Usage

Format

Details

Advantage Movement

Description

Usage

Format

Survivor Auction Details

Description

Usage

Format

Details

Source

Boot mapping

Description

Usage

Format

Source

Castaway details

Description

Usage

Format

Details

Source

Examples

Castaways

Description

Usage

Format

Details

Source

Examples

Challenge Description

Description

Usage

Format

Details

Source

Examples

Challenge Results

Description

Usage

Format

Source

Examples

Challenge Summary

Description

Usage

Format

Source

Examples

Confessionals

Description

Usage

Format

Details

Episode summary

Description

Usage

Format

Source

Episodes

Description

Usage

Format

Source

Castaway images

Description

Usage

Arguments

Value

Examples

Confessional time

Description

Usage

Arguments