75 seasons. 1417 people. 1 package!
survivoR is a collection of data sets detailing events across 75seasons of Survivor US, Australia, South Africa, New Zealand and UK. Itincludes castaway information, vote history, immunity and rewardchallenge winners, jury votes, advantage details and a lot more.
For analysis and updates you can follow me on Bluesky
For those that aren’t R users you can get the data onGoogleSheets as wel, ordownloadas an xlsx.
You can also access the data inJSONformat to feed directly into applications
Install from CRAN (v2.3.7) or Git(v2.3.8).
If Git > CRAN I’d suggest install from Git. We are constantlyimproving the data sets so the github version is likely to be slightlyimproved.
install.packages("survivoR")devtools::install_github("doehm/survivoR")The next release is planned for the 9th Oct for CRAN. There are a fewkey data updates so definitely reccommend installing from Git untilthen.
Survivor Stats Db is thesurvivoR package’s companion. It holds interactive tables and chartsdetailing the castaways, challenges, vote history, confessionals,ratings, and more.
Included in the package is a confessional timing app to record thelength of confessionals while watching the episode.
To launch the app, first install the package and run,
library(survivoR)launch_confessional_app()
To try it out online 👉Confessional timingapp
More infohere.
There are 19 data sets included in the package:
advantage_movementadvantage_detailsboot_mappingcastaway_detailscastaway_scorescastawayschallenge_resultschallenge_descriptionchallenge_summaryconfessionalsjury_votesseason_summarytribe_colourstribe_mappingepisodesvote_historysurvivor_auctionauction_detailsscreen_timeseason_palettesjourneysSee the sections below for more details on the key data sets.
A table containing summary details of each season of Survivor,including the winner, runner ups and location.
season_summary#> # A tibble: 75 × 26#> version version_season season_name season location country tribe_setup n_cast#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <int>#> 1 US US50 Survivor: … 50 <NA> <NA> <NA> 24#> 2 US US49 Survivor: … 49 <NA> <NA> <NA> 18#> 3 US US48 Survivor: … 48 Mamanuc… Fiji "Three tri… 18#> 4 US US47 Survivor: … 47 Mamanuc… Fiji "Three tri… 18#> 5 US US46 Survivor: … 46 Mamanuc… Fiji "Three tri… 18#> 6 US US45 Survivor: … 45 Mamanuc… Fiji "Three tri… 18#> 7 US US44 Survivor: … 44 Mamanuc… Fiji "Three tri… 18#> 8 US US43 Survivor: … 43 Mamanuc… Fiji "Three tri… 18#> 9 US US42 Survivor: … 42 Mamanuc… Fiji "Three tri… 18#> 10 US US41 Survivor: … 41 Mamanuc… Fiji "Three tri… 18#> 11 US US40 Survivor: … 40 Mamanuc… Fiji "Two tribe… 20#> 12 US US39 Survivor: … 39 Mamanuc… Fiji "Two tribe… 20#> 13 US US38 Survivor: … 38 Mamanuc… Fiji "Two tribe… 18#> 14 US US37 Survivor: … 37 Mamanuc… Fiji "Two tribe… 20#> 15 US US36 Survivor: … 36 Mamanuc… Fiji "Two tribe… 20#> 16 US US35 Survivor: … 35 Mamanuc… Fiji "Three tri… 18#> 17 US US34 Survivor: … 34 Mamanuc… Fiji "Two tribe… 20#> 18 US US33 Survivor: … 33 Mamanuc… Fiji "Two tribe… 20#> 19 US US32 Survivor: … 32 Koh Ron… Cambod… "Three tri… 18#> 20 US US31 Survivor: … 31 Koh Ron… Cambod… "Two tribe… 20#> 21 US US30 Survivor: … 30 San Jua… Nicara… "Three tri… 18#> 22 US US29 Survivor: … 29 San Jua… Nicara… "Nine pair… 18#> 23 US US28 Survivor: … 28 Palaui … Philip… "Three tri… 18#> 24 US US27 Survivor: … 27 Palaui … Philip… "Two tribe… 20#> 25 US US26 Survivor: … 26 Caramoa… Philip… "Two tribe… 20#> 26 US US25 Survivor: … 25 Caramoa… Philip… "Three tri… 18#> 27 US US24 Survivor: … 24 San Jua… Nicara… "Two tribe… 18#> 28 US US23 Survivor: … 23 San Jua… Nicara… "Upolu, Sa… 18#> 29 US US22 Survivor: … 22 San Jua… Nicara… "Two tribe… 18#> 30 US US21 Survivor: … 21 San Jua… Nicara… "Two tribe… 20#> # ℹ 45 more rows#> # ℹ 18 more variables: n_tribes <int>, n_finalists <int>, n_jury <int>,#> # 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_reunion <dbl>,#> # viewers_premiere <dbl>, viewers_finale <dbl>, viewers_mean <dbl>,#> # rank <dbl>This data set contains season and demographic information about eachcastaway. It is structured to view their results for each season.Castaways that have played in multiple seasons will feature more thanonce with the age and location representing that point in time.Castaways that re-entered the game will feature more than once in thesame season as they technically have more than one boot ordere.g. Natalie Anderson - Winners at War.
Each castaway has a uniquecastaway_id which links theindividual across all data sets and seasons. It also links to thefollowing ID’s found on thevote_history,jury_votes andchallenges data sets.
vote_idvoted_out_idfinalist_idcastaways|>filter(season==45)#> # A tibble: 18 × 26#> version version_season season full_name castaway_id castaway age city#> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr>#> 1 US US45 45 Hannah Rose US0669 Hannah 33 Balt…#> 2 US US45 45 Brandon Donlon US0665 Brandon 25 Sick…#> 3 US US45 45 Sabiyah Brode… US0677 Sabiyah 27 Jack…#> 4 US US45 45 Sean Edwards US0678 Sean 34 Prov…#> 5 US US45 45 Brando Meyer US0664 Brando 23 Seat…#> 6 US US45 45 J. Maya US0670 J. Maya 24 Los …#> 7 US US45 45 Sifu Alsup US0679 Sifu 30 O'Fa…#> 8 US US45 45 Kaleb Gebrewo… US0673 Kaleb 29 Vanc…#> 9 US US45 45 Kellie Nalban… US0675 Kellie 30 New …#> 10 US US45 45 Kendra McQuar… US0676 Kendra 30 Stea…#> 11 US US45 45 Bruce Perreau… US0657 Bruce 46 Warw…#> 12 US US45 45 Emily Flippen US0668 Emily 28 Laur…#> 13 US US45 45 Drew Basile US0667 Drew 23 Phil…#> 14 US US45 45 Julie Alley US0672 Julie 49 Bren…#> 15 US US45 45 Katurah Topps US0674 Katurah 34 Broo…#> 16 US US45 45 Jake O'Kane US0671 Jake 26 Bost…#> 17 US US45 45 Austin Li Coon US0663 Austin 26 Chic…#> 18 US US45 45 Dee Valladares US0666 Dee 26 Miami#> # ℹ 18 more variables: state <chr>, episode <dbl>, day <dbl>, order <dbl>,#> # result <chr>, jury_status <chr>, place <dbl>, original_tribe <chr>,#> # jury <lgl>, finalist <lgl>, winner <lgl>, acknowledge <lgl>,#> # ack_look <lgl>, ack_speak <lgl>, ack_gesture <lgl>, ack_smile <lgl>,#> # ack_quote <chr>, ack_score <dbl>A few castaways have changed their name from season to season or havebeen referred to by a different name during the season e.g. AmberMariano; in season 8 Survivor All-Stars there was Rob C and Rob M. Thatinformation has been retained here in thecastaways dataset.
castaway_details contains unique information for eachcastaway. It takes the full name from their most current season andtheir most verbose short name which is handy for labelling.
It also includes gender, date of birth, occupation, race, ethnicityand other data. If no source was found to determine a castaways race andethnicity, the data is kept as missing rather than making anassumption.
african_american,asian_american,latin_american,native_american,race,ethnicity, andbipoc datais complete only for the US.bipoc isTRUEwhen any of the*_american fields areTRUE.These fields have been recorded as per the (Survivor wiki)[https://survivor.fandom.com/wiki/Main_Page]. Otherversions have been left blank as the data is not complete and the term‘people of colour’ is typically only used in the US.
I have deprecated the old fieldpoc in order to be moreinclusive and to make using the race/ethnicity fields simpler.
I have included acollar field is experimental andderived from a language model. I suggest caution with it’s use as manyoccupations may not fit neatly into a classification.
castaway_details#> # A tibble: 1,180 × 22#> castaway_id full_name full_name_detailed castaway last_name date_of_birth#> <chr> <chr> <chr> <chr> <chr> <date>#> 1 US0001 Sonja Christ… Sonja Christopher Sonja Christop… 1937-01-28#> 2 US0002 B.B. Andersen B.B. Andersen B.B. Andersen 1936-01-18#> 3 US0003 Stacey Still… Stacey Stillman Stacey Stillman 1972-08-11#> 4 US0004 Ramona Gray Ramona Gray Ramona Gray 1971-01-20#> 5 US0005 Dirk Been Dirk Been Dirk Been 1976-06-15#> 6 US0006 Joel Klug Joel Klug Joel Klug 1972-04-13#> 7 US0007 Gretchen Cor… Gretchen Cordy Gretchen Cordy 1962-02-07#> 8 US0008 Greg Buis Greg Buis Greg Buis 1975-12-31#> 9 US0009 Jenna Lewis Jenna Lewis Jenna L. Lewis 1977-07-16#> 10 US0010 Gervase Pete… Gervase Peterson Gervase Peterson 1969-11-02#> 11 US0011 Colleen Hask… Colleen Haskell Colleen Haskell 1976-12-06#> 12 US0012 Sean Kenniff Sean Kenniff Sean Kenniff 1969-11-27#> 13 US0013 Susan Hawk Susan Hawk Sue Hawk 1961-08-17#> 14 US0014 Rudy Boesch Rudy Boesch Rudy Boesch 1928-01-20#> 15 US0015 Kelly Wigles… Kelly Wiglesworth Kelly Wigleswo… 1977-06-24#> 16 US0016 Richard Hatch Richard Hatch Richard Hatch 1961-04-08#> 17 US0017 Debb Eaton Debb Eaton Debb Eaton 1955-06-11#> 18 US0018 Kel Gleason Kel Gleason Kel Gleason 1968-01-05#> 19 US0019 Maralyn Hers… Maralyn Hershey Maralyn Hershey 1949-01-24#> 20 US0020 Mitchell Ols… Mitchell Olson Mitchell Olson 1977-03-17#> 21 US0021 Kimmi Kappen… Kimmi Kappenberg Kimmi Kappenbe… 1972-11-11#> 22 US0022 Michael Skup… Michael Skupin Michael Skupin 1962-01-29#> 23 US0023 Jeff Varner Jeff Varner Jeff Varner 1966-04-16#> 24 US0024 Alicia Calaw… Alicia Calaway Alicia Calaway 1968-05-01#> 25 US0025 Jerri Manthey Jerri Manthey Jerri Manthey 1970-09-05#> 26 US0026 Nick Brown Nick Brown Nick Brown 1977-04-02#> 27 US0027 Amber Mariano Amber Mariano Amber Mariano 1978-08-11#> 28 US0028 Rodger Bingh… Rodger Bingham Rodger Bingham 1947-07-05#> 29 US0029 Elisabeth Fi… Elisabeth Filarski Elisabe… Filarski 1977-05-28#> 30 US0030 Keith Famie Keith Famie Keith Famie 1960-02-11#> # ℹ 1,150 more rows#> # ℹ 16 more variables: date_of_death <date>, gender <chr>, african <lgl>,#> # asian <lgl>, latin_american <lgl>, native_american <lgl>, bipoc <lgl>,#> # lgbt <lgl>, personality_type <chr>, occupation <chr>, collar <chr>,#> # three_words <chr>, hobbies <chr>, pet_peeves <chr>, race <chr>,#> # ethnicity <chr>I have created a measure for challenge success, vote history ortribal council success and advantage success. For more details pleasesee follow the links:
castaway_scores#> # A tibble: 1,129 × 55#> version version_season season castaway castaway_id score_overall score_outwit#> <fct> <chr> <dbl> <chr> <chr> <dbl> <dbl>#> 1 US US01 1 Sonja US0001 0.0266 0.000000975#> 2 US US01 1 B.B. US0002 0.0612 0.0120#> 3 US US01 1 Stacey US0003 0.124 0.137#> 4 US US01 1 Ramona US0004 0.233 0.355#> 5 US US01 1 Dirk US0005 0.269 0.391#> 6 US US01 1 Joel US0006 0.348 0.515#> 7 US US01 1 Gretchen US0007 0.555 0.688#> 8 US US01 1 Greg US0008 0.556 0.423#> 9 US US01 1 Jenna US0009 0.521 0.561#> 10 US US01 1 Gervase US0010 0.590 0.454#> 11 US US01 1 Colleen US0011 0.612 0.516#> 12 US US01 1 Sean US0012 0.554 0.529#> 13 US US01 1 Sue US0013 0.574 0.653#> 14 US US01 1 Rudy US0014 0.559 0.503#> 15 US US01 1 Kelly US0015 0.852 0.748#> 16 US US01 1 Richard US0016 0.662 0.706#> 17 US US02 2 Debb US0017 0.0266 0.00000527#> 18 US US02 2 Kel US0018 0.0577 0.00331#> 19 US US02 2 Maralyn US0019 0.205 0.318#> 20 US US02 2 Mitchell US0020 0.271 0.450#> 21 US US02 2 Kimmi US0021 0.297 0.442#> 22 US US02 2 Michael US0022 0.432 0.714#> 23 US US02 2 Jeff US0023 0.516 0.582#> 24 US US02 2 Alicia US0024 0.507 0.536#> 25 US US02 2 Jerri US0025 0.584 0.597#> 26 US US02 2 Nick US0026 0.529 0.382#> 27 US US02 2 Amber US0027 0.475 0.416#> 28 US US02 2 Rodger US0028 0.491 0.405#> 29 US US02 2 Elisabe… US0029 0.546 0.537#> 30 US US02 2 Keith US0030 0.624 0.526#> # ℹ 1,099 more rows#> # ℹ 48 more variables: score_outplay <dbl>, score_outlast <dbl>,#> # score_result <dbl>, score_jury <dbl>, score_vote <dbl>, score_adv <dbl>,#> # score_inf <dbl>, r_score_chal_all <dbl>, r_score_chal_immunity <dbl>,#> # r_score_chal_reward <dbl>, r_score_chal_tribal <dbl>,#> # r_score_chal_tribal_immunity <dbl>, r_score_chal_tribal_reward <dbl>,#> # r_score_chal_individual <dbl>, r_score_chal_individual_immunity <dbl>, …This data frame contains a complete history of votes cast across allseasons of Survivor. This allows you to see who who voted for who atwhich Tribal Council. It also includes details on who had individualimmunity as well as who had their votes nullified by a hidden immunityidol. This details the key events for the season.
There is some information on split votes to help calculate if aplayer engaged in a split vote but ultimately hit their target. Thereare events which influence the vote e.g. Extra votes, safety withoutpower, etc. These are recorded here as well.
vh<- vote_history|>filter( season==45, episode==9 )vh#> # A tibble: 9 × 23#> version version_season season episode day tribe_status tribe castaway#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>#> 1 US US45 45 9 17 Merged Dakuwaqa Bruce#> 2 US US45 45 9 17 Merged Dakuwaqa Jake#> 3 US US45 45 9 17 Merged Dakuwaqa Katurah#> 4 US US45 45 9 17 Merged Dakuwaqa Dee#> 5 US US45 45 9 17 Merged Dakuwaqa Julie#> 6 US US45 45 9 17 Merged Dakuwaqa Kendra#> 7 US US45 45 9 17 Merged Dakuwaqa Emily#> 8 US US45 45 9 17 Merged Dakuwaqa Austin#> 9 US US45 45 9 17 Merged Dakuwaqa Drew#> # ℹ 15 more variables: immunity <chr>, vote <chr>, vote_event <chr>,#> # vote_event_outcome <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>, sog_id <dbl>, challenge_id <dbl>vh|>count(vote)#> # A tibble: 3 × 2#> vote n#> <chr> <int>#> 1 Jake 1#> 2 Kendra 6#> 3 <NA> 2Note: From v1.1 thechallenge_results dataset has beenimproved but could break existing code. The old table is maintained atchallenge_results_dep
There are 3 tableschallenge_results,challenge_description, andchallenge_summary.
A tidy data frame of immunity and reward challenge results. Thewinners and losers of the challenges are found recorded here.
challenge_results|>filter(season==45)|>group_by(castaway)|>summarise(won =sum(result=="Won"),lost =sum(result=="Lost"),total_challenges =n(),chosen_for_reward =sum(chosen_for_reward) )#> # A tibble: 18 × 5#> castaway won lost total_challenges chosen_for_reward#> <chr> <int> <int> <int> <int>#> 1 Austin 10 7 18 1#> 2 Brando 4 3 7 0#> 3 Brandon 0 3 3 0#> 4 Bruce 8 5 13 0#> 5 Dee 9 9 18 2#> 6 Drew 8 8 16 0#> 7 Emily 3 11 14 0#> 8 Hannah 0 2 2 0#> 9 J. Maya 6 2 8 0#> 10 Jake 5 12 18 2#> 11 Julie 7 8 17 1#> 12 Kaleb 3 5 9 0#> 13 Katurah 6 11 18 2#> 14 Kellie 5 4 10 0#> 15 Kendra 5 5 11 0#> 16 Sabiyah 1 4 5 0#> 17 Sean 1 5 6 0#> 18 Sifu 7 2 9 0Thechallenge_id is the primary key for thechallenge_description data set. Thechallange_id will change as the data or descriptionschange.
Note: This data frame is going through a massive revamp. Staytuned.
This data set contains the name, description, and descriptivefeatures for each challenge where it is known. Challenges can go bydifferent names so have included the unique name and the recurringchallenge name. These are taken directly from theSurvivorWiki. Sometimes there can be variations made on the challenge but gobut the same name, or the challenge is integrated with a longerobstacle. In these cases the challenge may share the same recurringchallenge name but have a different challenge name. Even if they sharethe same names the description could be different.
The features of each challenge have been determined largely throughstring searches of key words that describe the challenge. It may not be100% accurate due to the different and inconsistent descriptions but inmost part they will provide a good basis for analysis.
If any descriptive features need altering please let me know in theissues.
challenge_description#> # A tibble: 1,876 × 45#> version version_season season episode challenge_id challenge_number#> <fct> <chr> <dbl> <dbl> <dbl> <dbl>#> 1 US US01 1 1 1 1#> 2 US US01 1 2 2 1#> 3 US US01 1 2 3 2#> 4 US US01 1 3 4 1#> 5 US US01 1 3 5 2#> 6 US US01 1 4 6 1#> 7 US US01 1 4 7 2#> 8 US US01 1 5 8 1#> 9 US US01 1 5 9 2#> 10 US US01 1 6 10 1#> 11 US US01 1 6 11 2#> 12 US US01 1 7 12 1#> 13 US US01 1 8 13 1#> 14 US US01 1 8 14 2#> 15 US US01 1 9 15 1#> 16 US US01 1 9 16 2#> 17 US US01 1 10 17 1#> 18 US US01 1 10 18 2#> 19 US US01 1 11 19 1#> 20 US US01 1 11 20 2#> 21 US US01 1 11 21 3#> 22 US US01 1 12 22 1#> 23 US US01 1 12 23 2#> 24 US US01 1 13 24 1#> 25 US US01 1 13 25 2#> 26 US US02 2 1 1 1#> 27 US US02 2 2 2 1#> 28 US US02 2 2 3 2#> 29 US US02 2 3 4 1#> 30 US US02 2 3 5 2#> # ℹ 1,846 more rows#> # ℹ 39 more variables: challenge_type <chr>, name <chr>, recurring_name <chr>,#> # description <chr>, reward <chr>, additional_stipulation <chr>,#> # balance <lgl>, balance_ball <lgl>, balance_beam <lgl>, endurance <lgl>,#> # fire <lgl>, food <lgl>, knowledge <lgl>, memory <lgl>, mud <lgl>,#> # obstacle_blindfolded <lgl>, obstacle_cargo_net <lgl>,#> # obstacle_chopping <lgl>, obstacle_combination_lock <lgl>, …challenge_description|>summarise_if(is_logical,~sum(.x,na.rm =TRUE))|>glimpse()#> Rows: 1#> Columns: 33#> $ balance <int> 361#> $ balance_ball <int> 46#> $ balance_beam <int> 156#> $ endurance <int> 455#> $ fire <int> 68#> $ food <int> 24#> $ knowledge <int> 77#> $ memory <int> 29#> $ mud <int> 49#> $ obstacle_blindfolded <int> 52#> $ obstacle_cargo_net <int> 150#> $ obstacle_chopping <int> 32#> $ obstacle_combination_lock <int> 22#> $ obstacle_digging <int> 96#> $ obstacle_knots <int> 40#> $ obstacle_padlocks <int> 74#> $ precision <int> 304#> $ precision_catch <int> 65#> $ precision_roll_ball <int> 13#> $ precision_slingshot <int> 54#> $ precision_throw_balls <int> 79#> $ precision_throw_coconuts <int> 23#> $ precision_throw_rings <int> 20#> $ precision_throw_sandbags <int> 65#> $ puzzle <int> 409#> $ puzzle_slide <int> 17#> $ puzzle_word <int> 29#> $ race <int> 1338#> $ strength <int> 131#> $ turn_based <int> 237#> $ water <int> 358#> $ water_paddling <int> 149#> $ water_swim <int> 263See the help manual for more detailed descriptions of thefeatures.
Thechallenge_summary table is solving an annoyingproblem withchallenge_results and the way some challengesare constructed. You may want to count how many individual challengessomeone has won, or tribal immunities, etc. To do so you’ll have to usethechallenge_type,outcome_type, andresults fields. There are some challenges which arecombined e.g. Team / Individual challenges which makes thisnot a straight process to summarise the table.
Hence whychallenge_summary exisits. Thecategory column consists of the following categories:
There is obviously overlap with the categories but this structuremakes it simple to summarise the table how you desire e.g.
challenge_summary|>group_by(category, version_season, castaway)|>summarise(n_challenges =n(),n_won =sum(won) )#> `summarise()` has grouped output by 'category', 'version_season'. You can#> override using the `.groups` argument.#> # A tibble: 11,677 × 5#> # Groups: category, version_season [761]#> category version_season castaway n_challenges n_won#> <chr> <chr> <chr> <int> <dbl>#> 1 All AU01 Andrew 17 7#> 2 All AU01 Barry 9 5#> 3 All AU01 Bianca 3 2#> 4 All AU01 Brooke 29 20#> 5 All AU01 Conner 22 8#> 6 All AU01 Craig 18 7#> 7 All AU01 Des 2 0#> 8 All AU01 El 35 16#> 9 All AU01 Evan 5 1#> 10 All AU01 Flick 34 18#> 11 All AU01 Jennah-Louise 27 18#> 12 All AU01 Kat 15 5#> 13 All AU01 Kate 23 7#> 14 All AU01 Kristie 35 6#> 15 All AU01 Kylie 25 19#> 16 All AU01 Lee 35 17#> 17 All AU01 Matt 33 18#> 18 All AU01 Nick 24 17#> 19 All AU01 Peter 6 5#> 20 All AU01 Phoebe 21 5#> 21 All AU01 Rohan 14 5#> 22 All AU01 Sam 32 18#> 23 All AU01 Sue 26 7#> 24 All AU01 Tegan 11 7#> 25 All AU02 AK 21 12#> 26 All AU02 Adam 5 3#> 27 All AU02 Aimee 10 5#> 28 All AU02 Anneliese 28 13#> 29 All AU02 Ben 22 11#> 30 All AU02 Henry 29 15#> # ℹ 11,647 more rowsHow to add the challenge scores to challenge summary.
challenge_summary|>group_by(category, version_season, castaway_id, castaway)|>summarise(n_challenges =n_distinct(challenge_id),n_won =sum(won),.groups ="drop" )|>left_join( castaway_scores|>select(version_season, castaway_id,starts_with("score_chal"))|>pivot_longer(c(-version_season,-castaway_id),names_to ="category",values_to ="score")|>mutate(category =str_remove(category,"score_chal_"),category =str_replace_all(category,"_"," "),category =str_to_title(category) )|>select(category, version_season, castaway_id, score),join_by(category, version_season, castaway_id) )#> Error in `pivot_longer()`:#> ! `cols` must select at least one column.See the R docs for more details on the fields. Join tochallenge_results withversion_season andchallenge_id.
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 makesit easier to summarise castaways that received no votes.
jury_votes|>filter(season==45)#> # A tibble: 24 × 8#> version version_season season castaway finalist vote castaway_id finalist_id#> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr>#> 1 US US45 45 Bruce Austin 1 US0657 US0663#> 2 US US45 45 Drew Austin 1 US0667 US0663#> 3 US US45 45 Emily Austin 0 US0668 US0663#> 4 US US45 45 Julie Austin 0 US0672 US0663#> 5 US US45 45 Kaleb Austin 0 US0673 US0663#> 6 US US45 45 Katurah Austin 0 US0674 US0663#> 7 US US45 45 Kellie Austin 0 US0675 US0663#> 8 US US45 45 Kendra Austin 1 US0676 US0663#> 9 US US45 45 Bruce Dee 0 US0657 US0666#> 10 US US45 45 Drew Dee 0 US0667 US0666#> 11 US US45 45 Emily Dee 1 US0668 US0666#> 12 US US45 45 Julie Dee 1 US0672 US0666#> 13 US US45 45 Kaleb Dee 1 US0673 US0666#> 14 US US45 45 Katurah Dee 1 US0674 US0666#> 15 US US45 45 Kellie Dee 1 US0675 US0666#> 16 US US45 45 Kendra Dee 0 US0676 US0666#> 17 US US45 45 Bruce Jake 0 US0657 US0671#> 18 US US45 45 Drew Jake 0 US0667 US0671#> 19 US US45 45 Emily Jake 0 US0668 US0671#> 20 US US45 45 Julie Jake 0 US0672 US0671#> 21 US US45 45 Kaleb Jake 0 US0673 US0671#> 22 US US45 45 Katurah Jake 0 US0674 US0671#> 23 US US45 45 Kellie Jake 0 US0675 US0671#> 24 US US45 45 Kendra Jake 0 US0676 US0671jury_votes|>filter(season==45)|>group_by(finalist)|>summarise(votes =sum(vote))#> # A tibble: 3 × 2#> finalist votes#> <chr> <dbl>#> 1 Austin 3#> 2 Dee 5#> 3 Jake 0This dataset lists the hidden idols and advantages in the game forall seasons. It details where it was found, if there was a clue to theadvantage, location and other advantage conditions. This maps to theadvantage_movement table.
advantage_details|>filter(season==45)#> # A tibble: 10 × 8#> version version_season season advantage_id advantage_type clue_details#> <chr> <chr> <dbl> <dbl> <chr> <chr>#> 1 US US45 45 1 Hidden Immunity Idol No clue#> 2 US US45 45 2 Hidden Immunity Idol No clue#> 3 US US45 45 3 Safety without Power No clue#> 4 US US45 45 4 Goodwill Advantage No clue#> 5 US US45 45 5 Amulet No clue#> 6 US US45 45 6 Amulet No clue#> 7 US US45 45 7 Amulet No clue#> 8 US US45 45 8 Hidden Immunity Idol No clue#> 9 US US45 45 9 Hidden Immunity Idol Found around…#> 10 US US45 45 10 Challenge Advantage No clue#> # ℹ 2 more variables: location_found <chr>, conditions <chr>Theadvantage_movement table tracks who found theadvantage, who they may have handed it to and who the played it for.Each step is called an event. Thesequence_id tracks thelogical step of the advantage. For example in season 41, JD found anExtra Vote advantage. JD gave it to Shan in good faith who then votedhim out keeping the Extra Vote. Shan gave it to Ricard in good faith whoeventually gave it back before Shan played it for Naseer. That movementis recorded in this table.
advantage_movement|>filter(advantage_id=="USEV4102")#> # A tibble: 0 × 15#> # ℹ 15 variables: version <chr>, version_season <chr>, season <dbl>,#> # castaway <chr>, castaway_id <chr>, advantage_id <dbl>, sequence_id <dbl>,#> # day <dbl>, episode <dbl>, event <chr>, played_for <chr>,#> # played_for_id <chr>, success <chr>, votes_nullified <dbl>, sog_id <dbl>A dataset containing the number of confessionals for each castaway byseason and episode. There are multiple contributors to this data. Wherethere are multiple sets of counts for a season the average is taken andadded to the package. The aim is to establish consistency inconfessional counts in the absence of official sources. Given thesubjective nature of the counts and the potential for clerical error nosingle source is more valid than another. So it is reasonable to averageacross all sources.
Confessional time exists for a few seasons. This is the totalcumulative time for each castaway in seconds. This is a much moreaccurate indicator of the ‘edit’.
confessionals|>filter(season==45)|>group_by(castaway)|>summarise(count =sum(confessional_count),time =sum(confessional_time) )#> # A tibble: 18 × 3#> castaway count time#> <chr> <dbl> <dbl>#> 1 Austin 72 1436#> 2 Brando 10 147#> 3 Brandon 12 214#> 4 Bruce 38 735#> 5 Dee 67 1102#> 6 Drew 64 1171#> 7 Emily 62 1332#> 8 Hannah 4 44#> 9 J. Maya 11 210#> 10 Jake 60 1290#> 11 Julie 46 814#> 12 Kaleb 45 692#> 13 Katurah 66 1169#> 14 Kellie 29 515#> 15 Kendra 37 506#> 16 Sabiyah 22 342#> 17 Sean 16 325#> 18 Sifu 11 236The confessional index is available on this data set. The index is astandardised measure of the number of confessionals the player hasreceived compared to the others. It is stratified by tribe so itmeasures how many confessionals each player gets proportional to evenshare within tribe e.g. an index of 1.5 means that player as received50% more than others in their tribe.
The tribe grouping is important since the tribe that attends tribalcouncil typical get more screen time, which is fair enough. I don’tthink we should expect even share across everyone in the pre-merge stageof the game.
The index is cumulative with episode, so the players final index isthe index in their final episode.
confessionals|>filter(season==45)|>group_by(castaway)|>slice_max(episode)|>arrange(desc(index_time))|>select(castaway, episode, confessional_count, confessional_time, index_count, index_time)#> Error in `arrange()`:#> ℹ In argument: `..1 = index_time`.#> Caused by error:#> ! object 'index_time' not foundThis dataset contains the estimated screen time for each castawayduring an episode. Please note that this is still in the early days ofdevelopment. There is likely to be misclassification and other sourcesof error. The model will be refined over time.
An individuals’ screen time is calculated, at a high-level, via thefollowing process:
Frames are sampled from episodes on a 1 second timeinterval
MTCNN detects the human faces within each frame
VGGFace2 converts each detected face into a 512d vectorspace
A training set of labelled images (1 for each contestant + 3 forJeff Probst) is processed in the same way to determine where they sit inthe vector space. TODO: This could be made more accurate by increasingthe number of training images per contestant.
The Euclidean distance is calculated for the faces detected inthe frame to each of the contestants in the season (+Jeff). If theminimum distance is greater than 1.2 the face is labelled as “unknown”.TODO: Review how robust this distance cutoff truly is - currently basedon 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 runinto this model and a label is generated.
All labelled faces are aggregated together, with an assumption of1-5 full second of screen time each time a face is seen and factoring intime between detection capping at a max of 5 seconds.
screen_time|>filter(version_season=="US45")|>group_by(castaway_id)|>summarise(total_mins =sum(screen_time)/60)|>left_join( castaway_details|>select(castaway_id,castaway = short_name),by ="castaway_id" )|>arrange(desc(total_mins))#> Error in `select()`:#> ! Can't select columns that don't exist.#> ✖ Column `short_name` doesn't exist.Currently it only includes data for season 42. More seasons will beadded as they are completed.
A mapping table to detail who is still alive at each stage of thegame. It is useful for easy filtering to say the final players.
# filter to season 45 and when there are 6 people left# 18 people in the season, therefore 12 bootsstill_alive<-function(.version, .season, .n_boots) { survivoR::boot_mapping|>filter( version== .version, season== .season, final_n==6, game_status%in%c("In the game","Returned") )}still_alive("US",45,6)#> # A tibble: 6 × 13#> version version_season season episode order n_boots final_n sog_id castaway_id#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>#> 1 US US45 45 12 12 12 6 13 US0671#> 2 US US45 45 12 12 12 6 13 US0674#> 3 US US45 45 12 12 12 6 13 US0666#> 4 US US45 45 12 12 12 6 13 US0672#> 5 US US45 45 12 12 12 6 13 US0663#> 6 US US45 45 12 12 12 6 13 US0667#> # ℹ 4 more variables: castaway <chr>, tribe <chr>, tribe_status <chr>,#> # game_status <chr>Episodes is an episode level table. It contains the episodeinformation such as episode title, air date, length, IMDb rating and theviewer information for every episode across all seasons.
episodes|>filter(season==45)#> # A tibble: 13 × 13#> version version_season season episode_number_overall episode episode_title#> <chr> <chr> <dbl> <dbl> <dbl> <chr>#> 1 US US45 45 610 1 We Can Do Hard …#> 2 US US45 45 611 2 Brought a Bazoo…#> 3 US US45 45 612 3 No Man Left Beh…#> 4 US US45 45 613 4 Music to My Ears#> 5 US US45 45 614 5 I Don't Want to…#> 6 US US45 45 615 6 I'm Not Batman,…#> 7 US US45 45 616 7 The Thorn In My…#> 8 US US45 45 617 8 Following a Dea…#> 9 US US45 45 618 9 Sword of Damocl…#> 10 US US45 45 619 10 How Am I the Mo…#> 11 US US45 45 620 11 This Game Rips …#> 12 US US45 45 621 12 The Ex-Girlfrie…#> 13 US US45 45 622 13 Living the Surv…#> # ℹ 7 more variables: episode_label <chr>, episode_date <date>,#> # episode_length <dbl>, viewers <dbl>, imdb_rating <dbl>, n_ratings <dbl>,#> # episode_summary <chr>There are 2 data sets,survivor_acution andauction_details.survivor_auction simply showswho attended the auction andauction_details holds thedetails of the auction e.g. who bought what and at what price.
auction_details|>filter(season==45)#> # A tibble: 11 × 18#> version version_season season item item_description category castaway#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>#> 1 US US45 45 1 Salty Pretzels And Beer Food an… Kendra#> 2 US US45 45 2 French Fries, Ketchup,… Food an… Kellie#> 3 US US45 45 3 Cheese Platter, Deli M… Food an… Emily#> 4 US US45 45 4 Chocolate Milkshake Food an… Dee#> 5 US US45 45 5 Two Giant Fish Eyes Bad item Katurah#> 6 US US45 45 5 Two Giant Fish Eyes Bad item Austin#> 7 US US45 45 6 Bowl Of Lollies And Ch… Food an… Drew#> 8 US US45 45 7 Slice Of Pepperoni Piz… Food an… Austin#> 9 US US45 45 8 Toothbrush And Toothpa… Comfort Julie#> 10 US US45 45 9 Chocolate Cake Food an… Jake#> 11 US US45 45 10 Pbandj Sandwich, Chips… Food an… Kellie#> # ℹ 11 more variables: castaway_id <chr>, cost <dbl>, covered <lgl>,#> # money_remaining <dbl>, auction_num <dbl>, participated <chr>, notes <chr>,#> # alternative_offered <lgl>, alternative_accepted <lgl>, other_item <chr>,#> # other_item_category <chr>Details on Journeys in the New Era including the advantage they wonand if they lost their vote.
journeys|>filter(season==45)#> # A tibble: 10 × 12#> version season version_season episode sog_id castaway_id castaway reward#> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr>#> 1 US 45 US45 2 2 US0657 Bruce <NA>#> 2 US 45 US45 2 2 US0665 Brandon Lost vote#> 3 US 45 US45 2 2 US0667 Drew Safety Wit…#> 4 US 45 US45 5 5 US0663 Austin Amulet#> 5 US 45 US45 5 5 US0675 Kellie Amulet#> 6 US 45 US45 5 5 US0670 J. Maya Amulet#> 7 US 45 US45 9 10 US0663 Austin Regained v…#> 8 US 45 US45 9 10 US0668 Emily Lost vote#> 9 US 45 US45 9 10 US0674 Katurah Lost vote#> 10 US 45 US45 11 12 US0668 Emily <NA>#> # ℹ 4 more variables: lost_vote <lgl>, game_played <chr>, chose_to_play <lgl>,#> # event <chr>Given the variable nature of the game of Survivor and changing of therules, there are bound to be edges cases where the data is not quiteright. Before logging an issue please install the git version to see ifit has already been corrected. If not, please log an issue and I willcorrect the datasets.
New features will be added, such as details on exiled castawaysacross the seasons. If you have a request for specific data let me knowin the issues and I’ll see what I can do.
CarlyLevitz has developed a fantasticdashboardshowcasing 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 foreach winner.
A big thank you to:
castaways data frame.Data was sourced fromWikipediaand theSurvivorWiki. Other data, such as the tribe colours, was manually recordedand entered by myself and contributors.