624 episodes. 42 seasons. 1 package!
survivoR is a collection of data sets detailing events across all 41 seasons of the US Survivor, including castaway information, vote history, immunity and reward challenge winners and jury votes.
Now on CRAN (v1.0).
install.packages("survivoR")Or install from Git for the latest (v1.0.1). We are constantly improving the data sets and the github version is likely to be slightly improved.
devtools::install_github("doehm/survivoR")survivoR v1.0.1
advantage_movementadvantage_detailsboot_mappingchallenge_resultsvote_history
tribevote_eventsplit_votetieviewers
imdb_ratingversion and version_season now on all data
sets in prep for non-US seasonscastaways
swapped_tribeswapped_tribe_2merged_tribetotal_vote_receivedimmunity_idols_wonFor episode by episode updates follow me on .
Dev version v1.0.1 includes episodes 1 to 13.
Confessional counts from myself, Carly Levitz and juststrategic
A table containing summary details of each season of Survivor, including the winner, runner ups and location.
Confessional
counts
Infographic
Confessional
countsseason_summary
#> # A tibble: 42 × 22
#> version version_season season_name season location country tribe_setup
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 US US01 Survivor: Borneo 1 Pulau T… Malays… Two tribes…
#> 2 US US02 Survivor: The Aus… 2 Herbert… Austra… Two tribes…
#> 3 US US03 Survivor: Africa 3 Shaba N… Kenya Two tribes…
#> 4 US US04 Survivor: Marques… 4 Nuku Hi… Polyne… Two tribes…
#> 5 US US05 Survivor: Thailand 5 Ko Taru… Thaila… Two tribes…
#> 6 US US06 Survivor: The Ama… 6 Rio Neg… Brazil Two tribes…
#> 7 US US07 Survivor: Pearl I… 7 Pearl I… Panama Two tribes…
#> 8 US US08 Survivor: All-Sta… 8 Pearl I… Panama Three trib…
#> 9 US US09 Survivor: Vanuatu 9 Efate, … Vanuatu Two tribes…
#> 10 US US10 Survivor: Palau 10 Koror, … Palau A schoolya…
#> # … with 32 more rows, and 15 more variables: full_name <chr>, winner_id <chr>,
#> # winner <chr>, runner_ups <chr>, final_vote <chr>, timeslot <chr>,
#> # premiered <date>, ended <date>, filming_started <date>,
#> # filming_ended <date>, viewers_premier <dbl>, viewers_finale <dbl>,
#> # viewers_reunion <dbl>, viewers_mean <dbl>, rank <dbl>This data set contains season and demographic information about each castaway. It is structured to view their results for each season. Castaways that have played in multiple seasons will feature more than once with the age and location representing that point in time. Castaways that re-entered the game will feature more than once in the same season as they technically have more than one boot order e.g. Natalie Anderson - Winners at War.
Each castaway has a unique castaway_id which links the
individual across all data sets and seasons. It also links to the
following ID’s found on the vote_history,
jury_votes and challenges data sets.
vote_idvoted_out_idfinalist_idwinner_idcastaways |>
filter(season == 41)
#> # A tibble: 18 × 17
#> version version_season season_name season full_name castaway_id castaway
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 US US41 Survivor: 41 41 Erika Casupa… US0594 Erika
#> 2 US US41 Survivor: 41 41 Deshawn Radd… US0601 Deshawn
#> 3 US US41 Survivor: 41 41 Xander Hasti… US0597 Xander
#> 4 US US41 Survivor: 41 41 Heather Aldr… US0593 Heather
#> 5 US US41 Survivor: 41 41 Ricard Foye US0596 Ricard
#> 6 US US41 Survivor: 41 41 Danny McCray US0599 Danny
#> 7 US US41 Survivor: 41 41 Liana Wallace US0608 Liana
#> 8 US US41 Survivor: 41 41 Shantel Smith US0606 Shan
#> 9 US US41 Survivor: 41 41 Evvie Jagoda US0598 Evvie
#> 10 US US41 Survivor: 41 41 Naseer Mutta… US0600 Naseer
#> 11 US US41 Survivor: 41 41 Tiffany Seely US0604 Tiffany
#> 12 US US41 Survivor: 41 41 Sydney Segal US0605 Sydney
#> 13 US US41 Survivor: 41 41 Genie Chen US0595 Genie
#> 14 US US41 Survivor: 41 41 Jairus Robin… US0603 JD
#> 15 US US41 Survivor: 41 41 Brad Reese US0602 Brad
#> 16 US US41 Survivor: 41 41 David Voce US0607 Voce
#> 17 US US41 Survivor: 41 41 Sara Wilson US0592 Sara
#> 18 US US41 Survivor: 41 41 Eric Abraham US0591 Abraham
#> # … with 10 more variables: age <dbl>, city <chr>, state <chr>,
#> # personality_type <chr>, episode <dbl>, day <dbl>, order <dbl>,
#> # result <chr>, jury_status <chr>, original_tribe <chr>A few castaways have changed their name from season to season or have
been referred to by a different name during the season e.g. Amber
Mariano; in season 8 Survivor All-Stars there was Rob C and Rob M. That
information has been retained here in the castaways data
set.
castaway_details contains unique information for each
castaway. It takes the full name from their most current season and
their most verbose short name which is handy for labelling.
It also includes gender, date of birth, occupation, race and ethnicity data. If no source was found to determine a castaways race and ethnicity, the data is kept as missing rather than making an assumption.
castaway_details
#> # A tibble: 626 × 11
#> castaway_id full_name short_name date_of_birth date_of_death gender race
#> <chr> <chr> <chr> <date> <date> <chr> <chr>
#> 1 US0001 Sonja Christ… Sonja 1937-01-28 NA Female <NA>
#> 2 US0002 B.B. Andersen B.B. 1936-01-18 2013-10-29 Male <NA>
#> 3 US0003 Stacey Still… Stacey 1972-08-11 NA Female <NA>
#> 4 US0004 Ramona Gray Ramona 1971-01-20 NA Female Black
#> 5 US0005 Dirk Been Dirk 1976-06-15 NA Male <NA>
#> 6 US0006 Joel Klug Joel 1972-04-13 NA Male <NA>
#> 7 US0007 Gretchen Cor… Gretchen 1962-02-07 NA Female <NA>
#> 8 US0008 Greg Buis Greg 1975-12-31 NA Male <NA>
#> 9 US0009 Jenna Lewis Jenna L. 1977-07-16 NA Female <NA>
#> 10 US0010 Gervase Pete… Gervase 1969-11-02 NA Male Black
#> # … with 616 more rows, and 4 more variables: ethnicity <chr>, poc <chr>,
#> # occupation <chr>, personality_type <chr>This data frame contains a complete history of votes cast across all seasons of Survivor. This allows you to see who who voted for who at which Tribal Council. It also includes details on who had individual immunity as well as who had their votes nullified by a hidden immunity idol. This details the key events for the season.
vh <- vote_history |>
filter(
season == 41,
episode == 9
)
vh
#> # A tibble: 17 × 21
#> version version_season season_name season episode day tribe_status tribe
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 2 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 3 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 4 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 5 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 6 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 7 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 8 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 9 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 10 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 11 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 12 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 13 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 14 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 15 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 16 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> 17 US US41 Survivor: 41 41 9 17 Merged Via Ka…
#> # … with 13 more variables: castaway <chr>, immunity <chr>, vote <chr>,
#> # vote_event <chr>, split_vote <chr>, nullified <lgl>, tie <lgl>,
#> # voted_out <chr>, order <dbl>, vote_order <dbl>, castaway_id <chr>,
#> # vote_id <chr>, voted_out_id <chr>vh |>
count(vote)
#> # A tibble: 5 × 2
#> vote n
#> <chr> <int>
#> 1 Evvie 3
#> 2 Heather 2
#> 3 Liana 2
#> 4 Naseer 8
#> 5 <NA> 2Events in the game such as fire challenges, rock draws, steal-a-vote
advantages or countbacks in the early days often mean a vote wasn’t
placed for an individual. Rather a challenge may be won, lost, no vote
cast but attended Tribal Council, etc. These events are recorded in the
vote field. I have included a function
clean_votes for when only need the votes cast for
individuals. If the input data frame has the vote column it
can simply be piped.
vh |>
clean_votes() |>
count(vote)
#> # A tibble: 4 × 2
#> vote n
#> <chr> <int>
#> 1 Evvie 3
#> 2 Heather 2
#> 3 Liana 2
#> 4 Naseer 8The challenge_results and
challenge_description data sets supersede the
challenges data set.
A nested tidy data frame of immunity and reward challenge results.
The winners and winning tribe of the challenge are found by expanding
the winners column. For individual immunity challenges the
winning tribe is simply NA.
challenge_results |>
filter(season == 41)
#> # A tibble: 21 × 13
#> version version_season season_name season episode day order episode_title
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 US US41 Survivor: 41 41 1 3 0 A New Era
#> 2 US US41 Survivor: 41 41 1 3 0 A New Era
#> 3 US US41 Survivor: 41 41 2 5 2 Juggling Chai…
#> 4 US US41 Survivor: 41 41 3 7 3 My Million Do…
#> 5 US US41 Survivor: 41 41 4 9 4 They Hate Me …
#> 6 US US41 Survivor: 41 41 4 9 4 They Hate Me …
#> 7 US US41 Survivor: 41 41 5 11 5 The Strategis…
#> 8 US US41 Survivor: 41 41 6 13 5 Ready to Play…
#> 9 US US41 Survivor: 41 41 7 14 6 There's Gonna…
#> 10 US US41 Survivor: 41 41 7 14 6 There's Gonna…
#> # … with 11 more rows, and 5 more variables: challenge_name <chr>,
#> # challenge_type <chr>, outcome_type <chr>, challenge_id <chr>,
#> # winners <list>Typically in the merge if a single person win a reward they are
allowed to bring others along with them. This is identified by
outcome_status column. If it states
Chosen to particpate it means they were chosen by the
winner to particpate in the reward.
The day field on this data set represents the day of the
tribal council rather than the day of the challenge. This is to more
easily associate the reward challenge with the immunity challenge and
result of the tribal council. It also helps for joining tables.
The challenge_id is the primary key for the
challenge_description data set. The
challange_id will change as the data or descriptions
change.
This data set contains descriptive binary fields for each challenge. Challenges can go by different names but where possible recurring challenges are kept consistent. While there are tweaks to the challenges, where the main components of the challenge is consistent, they share the same name.
The features of each challenge have been determined largely through string searches of key words that describe the challenge. It may not capture the full essence of the challenge but on the whole will provide a good basis for analysis. Since the description is simply a short paragraph or sentence it may not flag all appropriate features. If any descriptive features need altering please let me know in the issues.
Features:
puzzle: If the challenge contains a puzzle
element.race: If the challenge is a race between tribes, teams
or individuals.precision: If the challenge contains a precision
element e.g. shooting an arrow, hitting a target, etc.endurance: If the challenge is an endurance event
e.g. last tribe, team, individual standing.strength: If the challenge is largerly strength based
e.g. Shoulder the Load.turn_based: If the challenge is conducted in a series
of rounds until a certain amount of points are scored or there is one
player remaining.balance: If the challenge contains a balancing
element.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.challenge_description
#> # A tibble: 886 × 14
#> challenge_id challenge_name puzzle race precision endurance strength
#> <chr> <chr> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 CC0053 Barrel of Monkeys FALSE TRUE TRUE FALSE FALSE
#> 2 CC0079 Blue Lagoon Bustle TRUE TRUE TRUE FALSE FALSE
#> 3 CC0114 By the Numbers FALSE TRUE FALSE FALSE FALSE
#> 4 CC0138 Choose Your Weapon FALSE TRUE TRUE FALSE FALSE
#> 5 CC0232 Flashback FALSE FALSE FALSE TRUE FALSE
#> 6 CC0305 Home Stretch TRUE TRUE TRUE FALSE FALSE
#> 7 CC0334 Kenny Log-Ins TRUE TRUE TRUE FALSE FALSE
#> 8 CC0358 Log Jam FALSE TRUE FALSE TRUE FALSE
#> 9 CC0371 Marooning FALSE TRUE FALSE FALSE FALSE
#> 10 CC0408 O-Black Water FALSE TRUE TRUE FALSE FALSE
#> # … with 876 more rows, and 7 more variables: turn_based <lgl>, balance <lgl>,
#> # food <lgl>, knowledge <lgl>, memory <lgl>, fire <lgl>, water <lgl>
challenge_description |>
summarise_if(is_logical, sum)
#> # A tibble: 1 × 12
#> puzzle race precision endurance strength turn_based balance food knowledge
#> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 238 721 184 115 50 132 143 23 55
#> # … with 3 more variables: memory <int>, fire <int>, water <int>History of jury votes. It is more verbose than it needs to be, however having a 0-1 column indicating if a vote was placed or not makes it easier to summarise castaways that received no votes.
jury_votes |>
filter(season == 41)
#> # A tibble: 24 × 9
#> version version_season season_name season castaway finalist vote castaway_id
#> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr>
#> 1 US US41 Survivor: … 41 Heather Deshawn 0 US0593
#> 2 US US41 Survivor: … 41 Ricard Deshawn 0 US0596
#> 3 US US41 Survivor: … 41 Danny Deshawn 1 US0599
#> 4 US US41 Survivor: … 41 Liana Deshawn 0 US0608
#> 5 US US41 Survivor: … 41 Shan Deshawn 0 US0606
#> 6 US US41 Survivor: … 41 Evvie Deshawn 0 US0598
#> 7 US US41 Survivor: … 41 Naseer Deshawn 0 US0600
#> 8 US US41 Survivor: … 41 Tiffany Deshawn 0 US0604
#> 9 US US41 Survivor: … 41 Heather Erika 1 US0593
#> 10 US US41 Survivor: … 41 Ricard Erika 1 US0596
#> # … with 14 more rows, and 1 more variable: finalist_id <chr>jury_votes |>
filter(season == 41) |>
group_by(finalist) |>
summarise(votes = sum(vote))
#> # A tibble: 3 × 2
#> finalist votes
#> <chr> <dbl>
#> 1 Deshawn 1
#> 2 Erika 7
#> 3 Xander 0This dataset lists the hidden idols and advantages in the game for
all seasons. It details where it was found, if there was a clue to the
advantage, location and other advantage conditions. This maps to the
advantage_movement table.
advantage_details |>
filter(season == 41)
#> # A tibble: 9 × 9
#> version version_season season_name season advantage_id advantage_type
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 US US41 Survivor: 41 41 USEV4101 Extra vote
#> 2 US US41 Survivor: 41 41 USEV4102 Extra vote
#> 3 US US41 Survivor: 41 41 USEV4103 Extra vote
#> 4 US US41 Survivor: 41 41 USHI4101 Hidden immunity idol
#> 5 US US41 Survivor: 41 41 USHI4102 Hidden immunity idol
#> 6 US US41 Survivor: 41 41 USHI4103 Hidden immunity idol
#> 7 US US41 Survivor: 41 41 USHI4104 Hidden immunity idol
#> 8 US US41 Survivor: 41 41 USKP4101 Knowledge is power
#> 9 US US41 Survivor: 41 41 USVS4101 Steal a vote
#> # … with 3 more variables: clue_details <chr>, location_found <chr>,
#> # conditions <chr>The advantage_movement table tracks who found the
advantage, who they may have handed it to and who the played it for.
Each step is called an event. The sequence_id tracks the
logical step of the advantage. For example in season 41, JD found an
Extra Vote advantage. JD gave it to Shan in good faith who then voted
him out keeping the Extra Vote. Shan gave it to Ricard in good faith who
eventually gave it back before Shan played it for Naseer. That movement
is recorded in this table.
advantage_movement |>
filter(advantage_id == "USEV4102")
#> # A tibble: 5 × 15
#> version version_season season_name season castaway castaway_id advantage_id
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 US US41 Survivor: 41 41 JD US0603 USEV4102
#> 2 US US41 Survivor: 41 41 Shan US0606 USEV4102
#> 3 US US41 Survivor: 41 41 Ricard US0596 USEV4102
#> 4 US US41 Survivor: 41 41 Shan US0606 USEV4102
#> 5 US US41 Survivor: 41 41 Shan US0606 USEV4102
#> # … with 8 more variables: sequence_id <dbl>, day <dbl>, episode <dbl>,
#> # event <chr>, played_for <chr>, played_for_id <chr>, success <chr>,
#> # votes_nullified <dbl>A dataset containing the number of confessionals for each castaway by season and episode. The 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.
confessionals |>
filter(season == 41) |>
group_by(castaway) |>
summarise(n_confessionals = sum(confessional_count))
#> # A tibble: 18 × 2
#> castaway n_confessionals
#> <chr> <dbl>
#> 1 Abraham 2
#> 2 Brad 14
#> 3 Danny 29
#> 4 Deshawn 56
#> 5 Erika 39
#> 6 Evvie 35
#> 7 Genie 11
#> 8 Heather 13
#> 9 JD 22
#> 10 Liana 34
#> 11 Naseer 11
#> 12 Ricard 36
#> 13 Sara 4
#> 14 Shan 51
#> 15 Sydney 15
#> 16 Tiffany 25
#> 17 Voce 6
#> 18 Xander 56A 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. Each season by 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 (usual day 2) 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.
tribe_mapping |>
filter(season == 41)
#> # A tibble: 154 × 10
#> version version_season season_name season episode day castaway_id castaway
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 US US41 Survivor: 41 41 1 3 US0591 Abraham
#> 2 US US41 Survivor: 41 41 1 3 US0592 Sara
#> 3 US US41 Survivor: 41 41 1 3 US0593 Heather
#> 4 US US41 Survivor: 41 41 1 3 US0594 Erika
#> 5 US US41 Survivor: 41 41 1 3 US0595 Genie
#> 6 US US41 Survivor: 41 41 1 3 US0596 Ricard
#> 7 US US41 Survivor: 41 41 1 3 US0597 Xander
#> 8 US US41 Survivor: 41 41 1 3 US0598 Evvie
#> 9 US US41 Survivor: 41 41 1 3 US0599 Danny
#> 10 US US41 Survivor: 41 41 1 3 US0600 Naseer
#> # … with 144 more rows, and 2 more variables: tribe <chr>, tribe_status <chr>A mapping table for easily filtering to the set of castaways that are
still in the game after a specified number of boots. How this differs
from the tribe mapping is that rather than being focused on an episode,
it is focused on the boot which is often more useful. This table can be
used to calculate how many people participated in certain challenges
once mapped to challenge_results.
When someone quits the game or is medically evacuated it is considered a boot. This table tracks multiple boots per episode.
# filter to season 41 and when there are 6 people left
# 18 people in the season, therefore 12 boots
boot_mapping |>
filter(
season == 41,
order == 12
)
#> # A tibble: 6 × 11
#> version version_season season_name season episode order castaway castaway_id
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 US US41 Survivor: 41 41 12 12 Heather US0593
#> 2 US US41 Survivor: 41 41 12 12 Erika US0594
#> 3 US US41 Survivor: 41 41 12 12 Ricard US0596
#> 4 US US41 Survivor: 41 41 12 12 Xander US0597
#> 5 US US41 Survivor: 41 41 12 12 Danny US0599
#> 6 US US41 Survivor: 41 41 12 12 Deshawn US0601
#> # … with 3 more variables: tribe <chr>, tribe_status <chr>, in_the_game <lgl>A data frame containing the viewer information for every episode across all seasons. It also includes the rating and viewer share information for viewers aged 18 to 49 years of age.
viewers |>
filter(season == 41)
#> # A tibble: 14 × 12
#> version version_season season_name season episode_number_overall episode
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 US US41 Survivor: 41 41 597 1
#> 2 US US41 Survivor: 41 41 598 2
#> 3 US US41 Survivor: 41 41 599 3
#> 4 US US41 Survivor: 41 41 600 4
#> 5 US US41 Survivor: 41 41 601 5
#> 6 US US41 Survivor: 41 41 602 6
#> 7 US US41 Survivor: 41 41 603 7
#> 8 US US41 Survivor: 41 41 604 8
#> 9 US US41 Survivor: 41 41 605 9
#> 10 US US41 Survivor: 41 41 606 10
#> 11 US US41 Survivor: 41 41 607 11
#> 12 US US41 Survivor: 41 41 608 12
#> 13 US US41 Survivor: 41 41 609 13
#> 14 US US41 Survivor: 41 41 610 14
#> # … with 6 more variables: episode_title <chr>, episode_date <date>,
#> # viewers <dbl>, rating_18_49 <dbl>, share_18_49 <dbl>, imdb_rating <dbl>This data frame contains the tribe names and colours for each season, including the RGB values. These colours can be joined with the other data frames to customise colours for plots. Another option is to add tribal colours to ggplots with the scale functions.
tribe_colours
#> # A tibble: 150 × 7
#> version version_season season_name season tribe tribe_colour tribe_status
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 US US01 Survivor: Born… 1 Pago… #FFFF05 Original
#> 2 US US01 Survivor: Born… 1 Ratt… #7CFC00 Merged
#> 3 US US01 Survivor: Born… 1 Tagi #FF9900 Original
#> 4 US US02 Survivor: The … 2 Barr… #FF6600 Merged
#> 5 US US02 Survivor: The … 2 Kucha #32CCFF Original
#> 6 US US02 Survivor: The … 2 Ogak… #A7FC00 Original
#> 7 US US03 Survivor: Afri… 3 Boran #FFD700 Original
#> 8 US US03 Survivor: Afri… 3 Moto… #00A693 Merged
#> 9 US US03 Survivor: Afri… 3 Samb… #E41A2A Original
#> 10 US US04 Survivor: Marq… 4 Mara… #DFFF00 Original
#> # … with 140 more rows
Included are ggplot2 scale functions of the form
scale_fill_survivor() and scale_fill_tribes()
to add season and tribe colours to ggplot. The
scale_fill_survivor() scales uses a colour palette
extracted from the season logo and scale_fill_tribes()
scales uses the tribal colours of the specified season as a colour
palette.
All that is required for the ‘survivor’ palettes is the desired season as input. If not season is provided it will default to season 40.
castaways |>
count(season, personality_type) |>
ggplot(aes(x = season, y = n, fill = personality_type)) +
geom_bar(stat = "identity") +
scale_fill_survivor(40) +
theme_minimal()
Below are the palettes for all seasons.
To use the tribe scales, simply input the season number desired to
use those tribe colours. If the fill or colour aesthetic is the tribe
name, this needs to be passed to the scale function as
scale_fill_tribes(season, tribe = tribe) (for now) where
tribe is on the input data frame. If the fill or colour
aesthetic is independent from the actual tribe names, like gender for
example, tribe does not need to be specified and will
simply use the tribe colours as a colour palette, such as the viewers
line graph above.
ssn <- 35
labels <- castaways |>
filter(
season == ssn,
str_detect(result, "Sole|unner")
) |>
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"
)
}
Given the variable nature of the game of Survivor and changing of the rules, there are bound to be edges cases where the data is not quite right. Before logging an issue please install the git version to see if it has already been corrected. If not, please log an issue and I will correct the datasets.
New features will be added, such as details on exiled castaways across the seasons. If you have a request for specific data let me know in the issues and I’ll see what I can do. Also, if you’d like to contribute by adding to existing datasets or contribute a new dataset, please contact me directly.
Carly Levitz has developed a fantastic dashboard showcasing the data and allowing you to drill down into seasons, castaways, voting history and challenges.
This looks at the number of immunity idols won and votes received for each winner.
A big thank you to:
Data was almost entirely sourced from Wikipedia. Other data, such as the tribe colours, was manually recorded and entered by myself and contributors.
Torch graphic in hex: Fire Torch Vectors by Vecteezy