Returns all cast and crew for a show/movie, depending on how much data is available.
Usage
movies_people(id, extended = "min")
shows_people(id, guest_stars = FALSE, extended = "min")
seasons_people(id, season = 1L, guest_stars = FALSE, extended = "min")
episodes_people(
id,
season = 1L,
episode = 1L,
guest_stars = FALSE,
extended = "min"
)Source
movies_people() wraps endpoint /movies/:id/people.
shows_people() wraps endpoint /shows/:id/people.
seasons_people() wraps endpoint /shows/:id/seasons/:season/people.
episodes_people() wraps endpoint /shows/:id/seasons/:season/episodes/:episode/people.
Arguments
- id
character(1): The ID of the item requested. Preferably thetraktID (e.g.1429). Other options are the trakt.tvslug(e.g."the-wire") orimdbID (e.g."tt0306414"). Can also be of length greater than 1, in which case the function is called on allidvalues separately and the result is combined. Seevignette("tRakt")for more details.- extended
character: Level of detail for the API response."min"(default): Minimal info (title, year, IDs). Omits theextendedquery param."full": Complete info including overview, ratings, runtime, etc."images": Minimal info plus image URLs (returned as a list-column)."full,images": Complete info plus images."metadata": Collection endpoints only; adds video/audio metadata.
Multiple values can be combined as a comma-separated string (e.g.
"full,images") or a character vector (e.g.c("full", "images")).- guest_stars
![[Deprecated]](figures/lifecycle-deprecated.svg)
logical(1) ["FALSE"]: Previously requested a separateguest_starstable. The trakt.tv API no longer returns that array — guest cast is now included incast. This argument is currently a no-op and will be removed in a future release.- season, episode
integer(1) [1L]: The season and episode number. If longer, e.g.1:5, the function is vectorized and the output will be combined. This may result in a lot of API calls. Use wisely.
Value
A list of one or more tibbles for cast
and/or crew. The latter tibble objects are as flat as possible.
Note
As of 2019-09-30, there are two representations of character[s] and
job[s]:
One is a regular character variable, and the other is a list-column. The former is
deprecated and only included for
compatibility reasons.
See also
people_media, for the other direction: People that have credits in shows/movies.
Other people data:
media_lists,
people_media(),
people_summary()
Other movie data:
anticipated_media,
collected_media,
media_aliases,
media_comments,
media_lists,
media_ratings(),
media_stats(),
media_translations,
media_watching,
movies_boxoffice(),
movies_related(),
movies_releases(),
movies_summary(),
people_media(),
played_media,
popular_media,
trending_media,
updated_media,
watched_media
Other show data:
collected_media,
media_aliases,
media_comments,
media_lists,
media_ratings(),
media_stats(),
media_translations,
media_watching,
people_media(),
played_media,
shows_next_episode(),
shows_related(),
shows_summary(),
updated_media
Other season data:
media_comments,
media_lists,
media_ratings(),
media_stats(),
seasons_episodes(),
seasons_season(),
seasons_summary()
Other episode data:
episodes_summary(),
media_comments,
media_lists,
media_ratings(),
media_stats(),
media_translations,
media_watching,
seasons_episodes(),
seasons_summary(),
shows_next_episode()
Examples
movies_people("deadpool-2016")
#> $cast
#> # A tibble: 50 × 18
#> character characters images$headshot name death gender height birthday
#> <chr> <list> <list> <chr> <chr> <chr> <dbl> <date>
#> 1 Wade Wilson … <chr [2]> <chr [1]> Ryan… NA male 188. 1976-10-23
#> 2 Vanessa <chr [1]> <chr [1]> More… NA female 171. 1979-06-02
#> 3 Ajax <chr [1]> <chr [1]> Ed S… NA male 185 1983-03-29
#> 4 Weasel <chr [1]> <chr [1]> T.J.… NA male 188 1981-06-04
#> 5 Angel Dust <chr [1]> <chr [1]> Gina… NA female 173 1982-04-16
#> 6 Blind Al <chr [1]> <chr [1]> Lesl… NA female 168. 1943-05-25
#> 7 Negasonic Te… <chr [1]> <chr [1]> Bria… NA female 160 1996-08-14
#> 8 Colossus (vo… <chr [1]> <chr [1]> Stef… NA male 193. 1978-12-01
#> 9 Dopinder <chr [1]> <chr [1]> Kara… NA male 174 1989-01-08
#> 10 Buck <chr [1]> <chr [1]> Rand… NA male 193. 1971-11-27
#> # ℹ 40 more rows
#> # ℹ 10 more variables: homepage <chr>, biography <chr>, birthplace <chr>,
#> # social_ids <df[,4]>, updated_at <dttm>, known_for_department <chr>,
#> # imdb <chr>, slug <chr>, tmdb <chr>, trakt <chr>
#>
#> $crew
#> # A tibble: 186 × 19
#> job jobs images$headshot name death gender height birthday homepage
#> <chr> <lis> <list> <chr> <chr> <chr> <dbl> <date> <chr>
#> 1 Casting <chr> <chr [1]> Ronn… NA female NA 1959-12-29 NA
#> 2 Producer <chr> <chr [1]> Laur… NA female 168. 1949-06-23 NA
#> 3 Executiv… <chr> <chr [1]> Stan… 2018… male 185. 1922-12-28 https:/…
#> 4 Executiv… <chr> <chr [0]> John… NA male NA 1967-06-10 NA
#> 5 Executiv… <chr> <chr [1]> Rhet… NA male NA 1975-01-01 NA
#> 6 Executiv… <chr> <chr [1]> Jona… NA male NA NA NA
#> 7 Executiv… <chr> <chr [1]> Paul… NA male NA 1972-08-02 NA
#> 8 Casting <chr> <chr [1]> Cori… NA female NA 1970-09-25 NA
#> 9 Casting <chr> <chr [0]> Jenn… NA female NA 1972-04-07 NA
#> 10 Producti… <chr> <chr [1]> Juli… NA female NA NA NA
#> # ℹ 176 more rows
#> # ℹ 10 more variables: biography <chr>, birthplace <chr>, social_ids <df[,4]>,
#> # updated_at <dttm>, known_for_department <chr>, imdb <chr>, slug <chr>,
#> # tmdb <chr>, trakt <chr>, crew_type <chr>
#>
shows_people("breaking-bad")
#> $cast
#> # A tibble: 229 × 20
#> character characters images$headshot episode_count order name death gender
#> <chr> <list> <list> <int> <int> <chr> <chr> <chr>
#> 1 Walter Whi… <chr [1]> <chr [1]> 62 0 Brya… NA male
#> 2 Jesse Pink… <chr [1]> <chr [1]> 62 1 Aaro… NA male
#> 3 Skyler Whi… <chr [1]> <chr [1]> 62 2 Anna… NA female
#> 4 Walter Whi… <chr [1]> <chr [1]> 62 3 RJ M… NA male
#> 5 Hank Schra… <chr [1]> <chr [1]> 62 4 Dean… NA male
#> 6 Marie Schr… <chr [1]> <chr [1]> 62 5 Bets… NA female
#> 7 Gus Fring <chr [1]> <chr [1]> 28 6 Gian… NA male
#> 8 Saul Goodm… <chr [1]> <chr [1]> 43 7 Bob … NA male
#> 9 Steven Gom… <chr [1]> <chr [1]> 33 8 Stev… NA male
#> 10 Mike Ehrma… <chr [1]> <chr [1]> 36 9 Jona… NA male
#> # ℹ 219 more rows
#> # ℹ 12 more variables: height <dbl>, birthday <date>, homepage <chr>,
#> # biography <chr>, birthplace <chr>, social_ids <df[,4]>, updated_at <dttm>,
#> # known_for_department <chr>, imdb <chr>, slug <chr>, tmdb <chr>, trakt <chr>
#>
#> $crew
#> # A tibble: 27 × 20
#> job jobs images$headshot episode_count name death gender height
#> <chr> <lis> <list> <int> <chr> <chr> <chr> <dbl>
#> 1 Casting <chr> <chr [1]> 62 Shar… NA female NA
#> 2 Executive Prod… <chr> <chr [1]> 62 Mich… NA female NA
#> 3 Co-Executive P… <chr> <chr [1]> 62 Pete… NA male 180
#> 4 Co-Executive P… <chr> <chr [1]> 62 Thom… NA male NA
#> 5 Casting <chr> <chr [1]> 62 Sher… NA female NA
#> 6 Producer <chr> <chr [1]> 62 Brya… NA male 179.
#> 7 Producer <chr> <chr [1]> 62 Stew… NA male NA
#> 8 Co-Executive P… <chr> <chr [1]> 62 Meli… NA female NA
#> 9 Co-Executive P… <chr> <chr [1]> 62 Geor… NA male NA
#> 10 Producer <chr> <chr [1]> 62 Dian… NA female NA
#> # ℹ 17 more rows
#> # ℹ 12 more variables: birthday <date>, homepage <chr>, biography <chr>,
#> # birthplace <chr>, social_ids <df[,4]>, updated_at <dttm>,
#> # known_for_department <chr>, imdb <chr>, slug <chr>, tmdb <chr>,
#> # trakt <chr>, crew_type <chr>
#>
seasons_people("breaking-bad", season = 1)
#> $cast
#> # A tibble: 47 × 20
#> character characters images$headshot episode_count order name death gender
#> <chr> <list> <list> <int> <int> <chr> <chr> <chr>
#> 1 Walter Whi… <chr [1]> <chr [1]> 7 0 Brya… NA male
#> 2 Jesse Pink… <chr [1]> <chr [1]> 7 1 Aaro… NA male
#> 3 Skyler Whi… <chr [1]> <chr [1]> 7 2 Anna… NA female
#> 4 Walter Whi… <chr [1]> <chr [1]> 7 3 RJ M… NA male
#> 5 Hank Schra… <chr [1]> <chr [1]> 7 4 Dean… NA male
#> 6 Marie Schr… <chr [1]> <chr [1]> 7 5 Bets… NA female
#> 7 Steven Gom… <chr [1]> <chr [1]> 4 8 Stev… NA male
#> 8 Carmen Mol… <chr [1]> <chr [1]> 4 643 Carm… NA female
#> 9 Krazy-8 <chr [1]> <chr [1]> 3 504 Max … NA male
#> 10 No-Doze <chr [1]> <chr [1]> 2 515 Cesa… NA male
#> # ℹ 37 more rows
#> # ℹ 12 more variables: height <dbl>, birthday <date>, homepage <chr>,
#> # biography <chr>, birthplace <chr>, social_ids <df[,4]>, updated_at <dttm>,
#> # known_for_department <chr>, imdb <chr>, slug <chr>, tmdb <chr>, trakt <chr>
#>
#> $crew
#> # A tibble: 49 × 20
#> job jobs images$headshot episode_count name death gender height
#> <chr> <lis> <list> <int> <chr> <chr> <chr> <dbl>
#> 1 Associate Prod… <chr> <chr [0]> 2 Gina… NA female NA
#> 2 Co-Producer <chr> <chr [1]> 68 Stew… NA male NA
#> 3 Producer <chr> <chr [0]> 6 Patt… NA female NA
#> 4 Casting <chr> <chr [1]> 69 Shar… NA female NA
#> 5 Casting <chr> <chr [1]> 69 Sher… NA female NA
#> 6 Producer <chr> <chr [0]> 7 Kare… NA female NA
#> 7 Co-Producer <chr> <chr [1]> 69 Meli… NA female NA
#> 8 Executive Prod… <chr> <chr [1]> 69 Mark… NA male NA
#> 9 Executive Prod… <chr> <chr [1]> 69 Vinc… NA male 183
#> 10 Executive Prod… <chr> <chr [1]> 62 Mich… NA female NA
#> # ℹ 39 more rows
#> # ℹ 12 more variables: birthday <date>, homepage <chr>, biography <chr>,
#> # birthplace <chr>, social_ids <df[,4]>, updated_at <dttm>,
#> # known_for_department <chr>, imdb <chr>, slug <chr>, tmdb <chr>,
#> # trakt <chr>, crew_type <chr>
#>
episodes_people("breaking-bad", season = 1, episode = 1)
#> $cast
#> # A tibble: 15 × 20
#> character characters images$headshot episode_count order name death gender
#> <chr> <list> <list> <int> <int> <chr> <lgl> <chr>
#> 1 Walter Whi… <chr [1]> <chr [1]> 7 0 Brya… NA male
#> 2 Jesse Pink… <chr [1]> <chr [1]> 7 1 Aaro… NA male
#> 3 Skyler Whi… <chr [1]> <chr [1]> 7 2 Anna… NA female
#> 4 Walter Whi… <chr [1]> <chr [1]> 7 3 RJ M… NA male
#> 5 Hank Schra… <chr [1]> <chr [1]> 7 4 Dean… NA male
#> 6 Marie Schr… <chr [1]> <chr [1]> 7 5 Bets… NA female
#> 7 Steven Gom… <chr [1]> <chr [1]> 1 8 Stev… NA male
#> 8 Jock <chr [1]> <chr [1]> 1 500 Aaro… NA male
#> 9 Dr. Belknap <chr [1]> <chr [1]> 1 502 Greg… NA male
#> 10 Krazy-8 <chr [1]> <chr [1]> 1 504 Max … NA male
#> 11 Bogdan Wol… <chr [1]> <chr [1]> 1 575 Mari… NA male
#> 12 Carmen Mol… <chr [1]> <chr [1]> 1 643 Carm… NA female
#> 13 E.M.T <chr [1]> <chr [1]> 1 676 Chri… NA male
#> 14 Emilio Koy… <chr [1]> <chr [1]> 1 703 John… NA male
#> 15 DEA Agent … <chr [1]> <chr [1]> 1 848 Ed D… NA male
#> # ℹ 12 more variables: height <dbl>, birthday <date>, homepage <lgl>,
#> # biography <chr>, birthplace <chr>, social_ids <df[,4]>, updated_at <dttm>,
#> # known_for_department <chr>, imdb <chr>, slug <chr>, tmdb <chr>, trakt <chr>
#>
#> $crew
#> # A tibble: 39 × 20
#> job jobs images$headshot episode_count name death gender height
#> <chr> <lis> <list> <int> <chr> <chr> <chr> <dbl>
#> 1 Associate Prod… <chr> <chr [0]> 1 Gina… NA female NA
#> 2 Casting <chr> <chr [1]> 69 Shar… NA female NA
#> 3 Casting <chr> <chr [1]> 69 Sher… NA female NA
#> 4 Producer <chr> <chr [0]> 7 Kare… NA female NA
#> 5 Co-Producer <chr> <chr [1]> 69 Meli… NA female NA
#> 6 Executive Prod… <chr> <chr [1]> 69 Mark… NA male NA
#> 7 Executive Prod… <chr> <chr [1]> 69 Vinc… NA male 183
#> 8 Executive Prod… <chr> <chr [1]> 62 Mich… NA female NA
#> 9 Co-Executive P… <chr> <chr [1]> 62 Pete… NA male 180
#> 10 Co-Executive P… <chr> <chr [1]> 62 Thom… NA male NA
#> # ℹ 29 more rows
#> # ℹ 12 more variables: birthday <date>, homepage <chr>, biography <chr>,
#> # birthplace <chr>, social_ids <df[,4]>, updated_at <dttm>,
#> # known_for_department <chr>, imdb <chr>, slug <chr>, tmdb <chr>,
#> # trakt <chr>, crew_type <chr>
#>