survivoR

67 seasons. 1082 people. 1 package!

survivoR is a collection of data sets detailing events across 67 seasons of Survivor US, Australia, South Africa, New Zealand and UK. It includes castaway information, vote history, immunity and reward challenge winners, jury votes, advantage details and a lot more.

Installation

Now on CRAN (v2.3.0) or Git (v2.3.1).

If Git > CRAN I’d suggest install from Git. We are constantly improving the data sets so the github version is likely to be slightly improved.

install.packages("survivoR")
devtools::install_github("doehm/survivoR")

News: survivoR 2.3.0

Additional notes: * The non-US auction versions are missing alternative item data but will be there in the next release. * Additional challenge descriptions and characteristics will be included in the next release.

Any corrections needed, please let me know.

Confessionals

Confessionals repo

The following link takes you to a repository of complete confessional tables, inlcuding counts and confessional timing for a few seasons.

Confessional counts from myself, Carly Levitz, Sam, Grace.

Confessional timing

Included in the package is a confessional timing app to record the length 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 timing app

More info here.

Dataset overview

There are 17 data sets included in the package:

  1. advantage_details
  2. advantage_details
  3. boot_mapping
  4. castaway_details
  5. castaways
  6. challenge_results
  7. challenge_description
  8. confessionals
  9. jury_votes
  10. screen_time
  11. season_palettes
  12. season_summary
  13. survivor_auction
  14. tribe_colours
  15. tribe_mapping
  16. episodes
  17. vote_history
  18. auction_details

See the sections below for more details on the key data sets.

Season summary

Season summary

A table containing summary details of each season of Survivor, including the winner, runner ups and location.

season_summary
#> # A tibble: 67 × 24
#>    version version_season season_name season location country tribe_setup n_cast
#>    <chr>   <chr>          <chr>        <dbl> <chr>    <chr>   <chr>        <int>
#>  1 US      US01           Survivor: …      1 Pulau T… Malays… Two tribes…     16
#>  2 US      US02           Survivor: …      2 Herbert… Austra… Two tribes…     16
#>  3 US      US03           Survivor: …      3 Shaba N… Kenya   Two tribes…     16
#>  4 US      US04           Survivor: …      4 Nuku Hi… Polyne… Two tribes…     16
#>  5 US      US05           Survivor: …      5 Ko Taru… Thaila… Two tribes…     16
#>  6 US      US06           Survivor: …      6 Rio Neg… Brazil  Two tribes…     16
#>  7 US      US07           Survivor: …      7 Pearl I… Panama  Two tribes…     16
#>  8 US      US08           Survivor: …      8 Pearl I… Panama  Three trib…     18
#>  9 US      US09           Survivor: …      9 Efate, … Vanuatu Two tribes…     18
#> 10 US      US10           Survivor: …     10 Koror, … Palau   A schoolya…     20
#> # ℹ 57 more rows
#> # ℹ 16 more variables: n_tribes <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_premiere <dbl>, viewers_finale <dbl>,
#> #   viewers_reunion <dbl>, viewers_mean <dbl>, rank <dbl>

Castaways

Castaways

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.

castaways |> 
  filter(season == 45)
#> # A tibble: 18 × 20
#>    version version_season season_name  season full_name     castaway_id castaway
#>    <chr>   <chr>          <chr>         <dbl> <chr>         <chr>       <chr>   
#>  1 US      US45           Survivor: 45     45 Hannah Rose   US0669      Hannah  
#>  2 US      US45           Survivor: 45     45 Brandon Donl… US0665      Brandon 
#>  3 US      US45           Survivor: 45     45 Sabiyah Brod… US0677      Sabiyah 
#>  4 US      US45           Survivor: 45     45 Sean Edwards  US0678      Sean    
#>  5 US      US45           Survivor: 45     45 Brando Meyer  US0664      Brando  
#>  6 US      US45           Survivor: 45     45 J. Maya       US0670      J. Maya 
#>  7 US      US45           Survivor: 45     45 Sifu Alsup    US0679      Sifu    
#>  8 US      US45           Survivor: 45     45 Kaleb Gebrew… US0673      Kaleb   
#>  9 US      US45           Survivor: 45     45 Kellie Nalba… US0675      Kellie  
#> 10 US      US45           Survivor: 45     45 Kendra McQua… US0676      Kendra  
#> 11 US      US45           Survivor: 45     45 Bruce Perrea… US0657      Bruce   
#> 12 US      US45           Survivor: 45     45 Emily Flippen US0668      Emily   
#> 13 US      US45           Survivor: 45     45 Drew Basile   US0667      Drew    
#> 14 US      US45           Survivor: 45     45 Julie Alley   US0672      Julie   
#> 15 US      US45           Survivor: 45     45 Katurah Topps US0674      Katurah 
#> 16 US      US45           Survivor: 45     45 Jake O'Kane   US0671      Jake    
#> 17 US      US45           Survivor: 45     45 Austin Li Co… US0663      Austin  
#> 18 US      US45           Survivor: 45     45 Dee Valladar… US0666      Dee     
#> # ℹ 13 more variables: age <dbl>, city <chr>, state <chr>, episode <dbl>,
#> #   day <dbl>, order <dbl>, result <chr>, jury_status <chr>,
#> #   original_tribe <chr>, result_number <dbl>, jury <lgl>, finalist <lgl>,
#> #   winner <lgl>

Castaway details

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, ethnicity and other data. If no source was found to determine a castaways race and ethnicity, the data is kept as missing rather than making an assumption.

african_american, asian_american, latin_american, native_american, race, ethnicity, and bipoc data is complete only for the US. bipoc is TRUE when any of the *_american fields are TRUE. These fields have been recorded as per the (Survivor wiki)[https://survivor.fandom.com/wiki/Main_Page]. Other versions 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 field poc in order to be more inclusive and to make using the race/ethnicity fields simpler.

castaway_details
#> # A tibble: 1,082 × 20
#>    castaway_id full_name full_name_detailed castaway date_of_birth date_of_death
#>    <chr>       <chr>     <chr>              <chr>    <date>        <date>       
#>  1 US0001      Sonja Ch… Sonja Christopher  Sonja    1937-01-28    NA           
#>  2 US0002      B.B. And… B.B. Andersen      B.B.     1936-01-18    2013-10-29   
#>  3 US0003      Stacey S… Stacey Stillman    Stacey   1972-08-11    NA           
#>  4 US0004      Ramona G… Ramona Gray        Ramona   1971-01-20    NA           
#>  5 US0005      Dirk Been Dirk Been          Dirk     1976-06-15    NA           
#>  6 US0006      Joel Klug Joel Klug          Joel     1972-04-13    NA           
#>  7 US0007      Gretchen… Gretchen Cordy     Gretchen 1962-02-07    NA           
#>  8 US0008      Greg Buis Greg Buis          Greg     1975-12-31    NA           
#>  9 US0009      Jenna Le… Jenna Lewis        Jenna L. 1977-07-16    NA           
#> 10 US0010      Gervase … Gervase Peterson   Gervase  1969-11-02    NA           
#> # ℹ 1,072 more rows
#> # ℹ 14 more variables: gender <chr>, african <lgl>, asian <lgl>,
#> #   latin_american <lgl>, native_american <lgl>, bipoc <lgl>, lgbt <lgl>,
#> #   personality_type <chr>, occupation <chr>, three_words <chr>, hobbies <chr>,
#> #   pet_peeves <chr>, race <chr>, ethnicity <chr>

Vote history

Vote history

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.

There is some information on split votes to help calculate if a player engaged in a split vote but ultimately hit their target. There are events which influence the vote e.g. Extra votes, safety without power, etc. These are recorded here as well.

vh <- vote_history |> 
  filter(
    season == 45,
    episode == 9
  ) 
vh
#> # A tibble: 9 × 22
#>   version version_season season_name  season episode   day tribe_status tribe   
#>   <chr>   <chr>          <chr>         <dbl>   <dbl> <dbl> <chr>        <chr>   
#> 1 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 2 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 3 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 4 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 5 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 6 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 7 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 8 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> 9 US      US45           Survivor: 45     45       9    17 Merged       Dakuwaqa
#> # ℹ 14 more variables: castaway <chr>, 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>
vh |> 
  count(vote)
#> # A tibble: 3 × 2
#>   vote       n
#>   <chr>  <int>
#> 1 Jake       1
#> 2 Kendra     6
#> 3 <NA>       2

Challenges

Challenge results

Note: From v1.1 the challenge_results dataset has been improved but could break existing code. The old table is maintained at challenge_results_dep

There are two tables challenge_results and challenge_description.

Challenge results

A tidy data frame of immunity and reward challenge results. The winners 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       5     2                7                 0
#>  3 Brandon      0     3                3                 0
#>  4 Bruce        9     4               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         6    11               18                 2
#> 11 Julie        7     8               17                 1
#> 12 Kaleb        3     5                9                 0
#> 13 Katurah      7    10               18                 2
#> 14 Kellie       6     3               10                 0
#> 15 Kendra       6     4               11                 0
#> 16 Sabiyah      1     4                5                 0
#> 17 Sean         1     5                6                 0
#> 18 Sifu         7     2                9                 0

The challenge_id is the primary key for the challenge_description data set. The challange_id will change as the data or descriptions change.

Challenge description

Note: This data frame is going through a massive revamp. Stay tuned.

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. 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% accurate due to the 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.

challenge_description
#> # A tibble: 1,731 × 46
#>    version version_season season_name      season episode challenge_id
#>    <fct>   <chr>          <chr>             <dbl>   <dbl>        <dbl>
#>  1 US      US01           Survivor: Borneo      1       1            1
#>  2 US      US01           Survivor: Borneo      1       2            2
#>  3 US      US01           Survivor: Borneo      1       2            3
#>  4 US      US01           Survivor: Borneo      1       3            4
#>  5 US      US01           Survivor: Borneo      1       3            5
#>  6 US      US01           Survivor: Borneo      1       4            6
#>  7 US      US01           Survivor: Borneo      1       4            7
#>  8 US      US01           Survivor: Borneo      1       5            8
#>  9 US      US01           Survivor: Borneo      1       5            9
#> 10 US      US01           Survivor: Borneo      1       6           10
#> # ℹ 1,721 more rows
#> # ℹ 40 more variables: challenge_number <dbl>, 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>, …

challenge_description |> 
  summarise_if(is_logical, ~sum(.x, na.rm = TRUE)) |> 
  glimpse()
#> Rows: 1
#> Columns: 33
#> $ balance                   <int> 322
#> $ balance_ball              <int> 34
#> $ balance_beam              <int> 139
#> $ endurance                 <int> 405
#> $ fire                      <int> 65
#> $ food                      <int> 24
#> $ knowledge                 <int> 76
#> $ memory                    <int> 28
#> $ mud                       <int> 45
#> $ obstacle_blindfolded      <int> 51
#> $ obstacle_cargo_net        <int> 137
#> $ obstacle_chopping         <int> 30
#> $ obstacle_combination_lock <int> 21
#> $ obstacle_digging          <int> 87
#> $ obstacle_knots            <int> 40
#> $ obstacle_padlocks         <int> 72
#> $ precision                 <int> 272
#> $ precision_catch           <int> 60
#> $ precision_roll_ball       <int> 12
#> $ precision_slingshot       <int> 50
#> $ precision_throw_balls     <int> 64
#> $ precision_throw_coconuts  <int> 20
#> $ precision_throw_rings     <int> 19
#> $ precision_throw_sandbags  <int> 49
#> $ puzzle                    <int> 383
#> $ puzzle_slide              <int> 16
#> $ puzzle_word               <int> 27
#> $ race                      <int> 1277
#> $ strength                  <int> 100
#> $ turn_based                <int> 219
#> $ water                     <int> 339
#> $ water_paddling            <int> 145
#> $ water_swim                <int> 246

See the help manual for more detailed descriptions of the features.

Jury votes

Jury votes

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 == 45)
#> # 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      US45           Survivor: …     45 Bruce    Austin       1 US0657     
#>  2 US      US45           Survivor: …     45 Drew     Austin       1 US0667     
#>  3 US      US45           Survivor: …     45 Emily    Austin       0 US0668     
#>  4 US      US45           Survivor: …     45 Julie    Austin       0 US0672     
#>  5 US      US45           Survivor: …     45 Kaleb    Austin       0 US0673     
#>  6 US      US45           Survivor: …     45 Katurah  Austin       0 US0674     
#>  7 US      US45           Survivor: …     45 Kellie   Austin       0 US0675     
#>  8 US      US45           Survivor: …     45 Kendra   Austin       1 US0676     
#>  9 US      US45           Survivor: …     45 Bruce    Dee          0 US0657     
#> 10 US      US45           Survivor: …     45 Drew     Dee          0 US0667     
#> # ℹ 14 more rows
#> # ℹ 1 more variable: finalist_id <chr>
jury_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         0

Advantages

Advantage Details

This 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 == 45)
#> # A tibble: 10 × 9
#>    version version_season season_name  season advantage_id advantage_type      
#>    <chr>   <chr>          <chr>         <dbl>        <dbl> <chr>               
#>  1 US      US45           Survivor: 45     45           NA Amulet              
#>  2 US      US45           Survivor: 45     45           NA Amulet              
#>  3 US      US45           Survivor: 45     45           NA Amulet              
#>  4 US      US45           Survivor: 45     45           NA Challenge Advantage 
#>  5 US      US45           Survivor: 45     45           NA Goodwill Advantage  
#>  6 US      US45           Survivor: 45     45           NA Hidden Immunity Idol
#>  7 US      US45           Survivor: 45     45           NA Hidden Immunity Idol
#>  8 US      US45           Survivor: 45     45           NA Hidden Immunity Idol
#>  9 US      US45           Survivor: 45     45           NA Hidden Immunity Idol
#> 10 US      US45           Survivor: 45     45           NA Safety Without Power
#> # ℹ 3 more variables: clue_details <chr>, location_found <chr>,
#> #   conditions <chr>

Advantage Movement

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: 0 × 15
#> # ℹ 15 variables: version <chr>, version_season <chr>, season_name <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>

Confessionals

Confessionals

A dataset containing the number of confessionals for each castaway by season and episode. There are multiple contributors to this data. Where there are multiple sets of counts for a season the average is taken and added to the package. 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. So it is reasonable to average across all sources.

Confessional time exists for a few seasons. This is the total cumulative time for each castaway in seconds. This is a much more accurate 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   236

The confessional index is available on this data set. The index is a standardised measure of the number of confessionals the player has received compared to the others. It is stratified by tribe so it measures how many confessionals each player gets proportional to even share within tribe e.g. an index of 1.5 means that player as received 50% more than others in their tribe.

The tribe grouping is important since the tribe that attends tribal council typical get more screen time, which is fair enough. I don’t think we should expect even share across everyone in the pre-merge stage of the game.

The index is cumulative with episode, so the players final index is the 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)
#> # A tibble: 18 × 6
#> # Groups:   castaway [18]
#>    castaway episode confessional_count confessional_time index_count index_time
#>    <chr>      <dbl>              <dbl>             <dbl>       <dbl>      <dbl>
#>  1 Emily         11                  8               203       1.09       1.31 
#>  2 Kaleb          7                  7                96       1.43       1.22 
#>  3 Sabiyah        3                  6               112       1.32       1.20 
#>  4 Brandon        2                  6               115       1.13       1.20 
#>  5 Austin        13                 14               214       1.09       1.17 
#>  6 Kellie         8                  6                81       1.11       1.16 
#>  7 Bruce         10                  5               104       1.01       1.12 
#>  8 Drew          12                  9               158       1.15       1.12 
#>  9 Jake          13                 14               250       0.946      1.10 
#> 10 Katurah       13                  8               203       1.04       1.00 
#> 11 Dee           13                 11               173       1.04       0.896
#> 12 Kendra         9                  6                83       1.11       0.895
#> 13 Sean           4                  9               211       0.783      0.884
#> 14 Julie         13                  5                64       0.714      0.665
#> 15 Hannah         1                  4                44       0.828      0.597
#> 16 Brando         5                  5                71       0.648      0.579
#> 17 J. Maya        6                  2                47       0.593      0.574
#> 18 Sifu           7                  1                33       0.486      0.535

Screen time

Screen time [EXPERIMENTAL]

This dataset contains the estimated screen time for each castaway during an episode. Please note that this is still in the early days of development. There is likely to be misclassifcation and other sources of error. The model will be refined over time.

An individuals’ screen time is calculated, at a high-level, via the following process:

  1. Frames are sampled from episodes on a 1 second time interval

  2. MTCNN detects the human faces within each frame

  3. VGGFace2 converts each detected face into a 512d vector space

  4. 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.

  5. 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.

  6. 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.

  7. All labelled faces are aggregated together, with an assumption of 1-5 full second of screen time each time a face is seen and factoring in time 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 subset columns that don't exist.
#> ✖ Column `short_name` doesn't exist.

Currently it only includes data for season 42. More seasons will be added as they are completed.

Boot mapping

Boot mapping

A mapping table to detail who is still alive at each stage of the game. 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 boots

still_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 × 12
#>   version version_season season_name  season episode order final_n castaway_id
#>   <chr>   <chr>          <chr>         <dbl>   <dbl> <dbl>   <dbl> <chr>      
#> 1 US      US45           Survivor: 45     45      12    12       6 US0663     
#> 2 US      US45           Survivor: 45     45      12    12       6 US0666     
#> 3 US      US45           Survivor: 45     45      12    12       6 US0667     
#> 4 US      US45           Survivor: 45     45      12    12       6 US0671     
#> 5 US      US45           Survivor: 45     45      12    12       6 US0672     
#> 6 US      US45           Survivor: 45     45      12    12       6 US0674     
#> # ℹ 4 more variables: castaway <chr>, tribe <chr>, tribe_status <chr>,
#> #   game_status <chr>

Episodes

Episodes

Episodes is an episode level table. It contains the episode information such as episode title, air date, length, IMDb rating and the viewer information for every episode across all seasons.

episodes |> 
  filter(season == 45)
#> # A tibble: 13 × 13
#>    version version_season season_name  season episode_number_overall episode
#>    <chr>   <chr>          <chr>         <dbl>                  <dbl>   <dbl>
#>  1 US      US45           Survivor: 45     45                    610       1
#>  2 US      US45           Survivor: 45     45                    611       2
#>  3 US      US45           Survivor: 45     45                    612       3
#>  4 US      US45           Survivor: 45     45                    613       4
#>  5 US      US45           Survivor: 45     45                    614       5
#>  6 US      US45           Survivor: 45     45                    615       6
#>  7 US      US45           Survivor: 45     45                    616       7
#>  8 US      US45           Survivor: 45     45                    617       8
#>  9 US      US45           Survivor: 45     45                    618       9
#> 10 US      US45           Survivor: 45     45                    619      10
#> 11 US      US45           Survivor: 45     45                    620      11
#> 12 US      US45           Survivor: 45     45                    621      12
#> 13 US      US45           Survivor: 45     45                    622      13
#> # ℹ 7 more variables: episode_title <chr>, episode_label <chr>,
#> #   episode_date <date>, episode_length <dbl>, viewers <dbl>,
#> #   imdb_rating <dbl>, n_ratings <dbl>

Survivor Auction

Survivor Auction

There are 2 data sets, survivor_acution and auction_details. survivor_auction simply shows who attended the auction and auction_details holds the details of the auction e.g. who bought what and at what price.

auction_details |> 
  filter(season == 45)
#> # A tibble: 11 × 19
#>    version version_season season_name  season  item item_description    category
#>    <chr>   <chr>          <chr>         <dbl> <dbl> <chr>               <chr>   
#>  1 US      US45           Survivor: 45     45     1 Salty pretzels and… Food an…
#>  2 US      US45           Survivor: 45     45     2 French fries, ketc… Food an…
#>  3 US      US45           Survivor: 45     45     3 Cheese platter, de… Food an…
#>  4 US      US45           Survivor: 45     45     4 Chocolate milkshake Food an…
#>  5 US      US45           Survivor: 45     45     5 Two giant fish eyes Bad item
#>  6 US      US45           Survivor: 45     45     5 Two giant fish eyes Bad item
#>  7 US      US45           Survivor: 45     45     6 Bowl of lollies an… Food an…
#>  8 US      US45           Survivor: 45     45     7 Slice of pepperoni… Food an…
#>  9 US      US45           Survivor: 45     45     8 Toothbrush and too… Comfort 
#> 10 US      US45           Survivor: 45     45     9 Chocolate cake      Food an…
#> 11 US      US45           Survivor: 45     45    10 Pbandj sandwich, c… Food an…
#> # ℹ 12 more variables: castaway <chr>, 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>

Issues

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.

Showcase

Survivor Dashboard

Carly Levitz has developed a fantastic dashboard showcasing the data and allowing you to drill down into seasons, castaways, voting history and challenges.

Data viz

This looks at the number of immunity idols won and votes received for each winner.

Contributors

A big thank you to:

Package contributor and maintainers

Data contributors

References

Data was sourced from Wikipedia and the Survivor Wiki. Other data, such as the tribe colours, was manually recorded and entered by myself and contributors.