Nothing
R/espn_wnba_data.R
In wehoop: Access Women's Basketball Play by Play Data
Defines functions
espn_wnba_scoreboard
espn_wnba_teams
espn_wnba_game_rosters
espn_wnba_player_box
espn_wnba_team_box
espn_wnba_pbp
espn_wnba_game_all
Documented in
espn_wnba_game_all
espn_wnba_game_rosters
espn_wnba_pbp
espn_wnba_player_box
espn_wnba_scoreboard
espn_wnba_team_box
espn_wnba_teams
#' Get ESPN's WNBA game data (play-by-play, team and player box)
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return A named list of dataframes: Plays, Team, Player
#'
#' **Plays**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_plays_schema] table.
#'
#' **Team**
#'
#'
#' Columns as documented in the shared [espn_basketball_team_box_schema] table.
#'
#' **Player**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_player_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Game
#' @family ESPN WNBA Functions
#' @examples
#' \donttest{
#' try(espn_wnba_game_all(game_id = 401244185))
#' }
espn_wnba_game_all <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
pbp <- list(Plays = NULL, Team = NULL, Player = NULL)
resp <- NULL
plays_df <- NULL
team_box_score <- NULL
player_box_score <- NULL
#---- Fetch the summary endpoint (single outer tryCatch) -------------------
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
},
error = function(e) .report_api_error(
e,
hint = "Could not fetch game summary for game_id = {game_id}",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {}
)
if (is.null(resp)) {
return(pbp)
}
#---- Play-by-Play ------
tryCatch(
expr = {
plays_df <- helper_espn_wnba_pbp(resp)
if (is.null(plays_df)) {
cli::cli_alert_danger("{Sys.time()}: No play-by-play data for {game_id} available!")
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no play-by-play data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
#---- Team Box ------
tryCatch(
expr = {
team_box_score <- helper_espn_wnba_team_box(resp)
if (is.null(team_box_score)) {
cli::cli_alert_danger("{Sys.time()}: No team box score data for {game_id} available!")
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no team box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
#---- Player Box ------
tryCatch(
expr = {
player_box_score <- helper_espn_wnba_player_box(resp)
if (is.null(player_box_score)) {
cli::cli_alert_danger("{Sys.time()}: No player box score data for {game_id} available!")
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no player box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
pbp <- c(list(plays_df), list(team_box_score), list(player_box_score))
names(pbp) <- c("Plays", "Team", "Player")
return(pbp)
}
#' Get ESPN's WNBA play by play data
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return Returns a play-by-play data frame
#'
#' **Plays**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_plays_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA PBP
#' @family ESPN WNBA Functions
#' @examples
#'
#' \donttest{
#' try(espn_wnba_pbp(game_id = 401455681))
#' }
espn_wnba_pbp <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
#---- Play-by-Play ------
plays_df <- NULL
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
plays_df <- helper_espn_wnba_pbp(resp)
if (is.null(plays_df)) {
return(cli::cli_alert_danger("{Sys.time()}: No play-by-play data for {game_id} available!"))
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no play-by-play data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(plays_df)
}
#' Get ESPN's WNBA team box data
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return Returns a team boxscore data frame
#'
#' **Team**
#'
#'
#' Columns as documented in the shared [espn_basketball_team_box_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Team Box
#' @family ESPN WNBA Functions
#' @examples
#'
#' \donttest{
#' try(espn_wnba_team_box(game_id = 401244185))
#' }
espn_wnba_team_box <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
#---- Team Box ------
team_box_score <- NULL
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
team_box_score <- helper_espn_wnba_team_box(resp)
if (is.null(team_box_score)) {
return(cli::cli_alert_danger("{Sys.time()}: No team box score data for {game_id} available!"))
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no team box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(team_box_score)
}
#' Get ESPN's WNBA player box data
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return Returns a player boxscore data frame
#'
#' **Player**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_player_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Player Box
#' @family ESPN WNBA Functions
#' @examples
#' \donttest{
#' try(espn_wnba_player_box(game_id = 401244185))
#' }
#'
espn_wnba_player_box <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
#---- Player Box ------
player_box_score <- NULL
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
player_box_score <- helper_espn_wnba_player_box(resp)
if (is.null(player_box_score)) {
return(cli::cli_alert_danger("{Sys.time()}: No player box score data for {game_id} available!"))
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no player box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(player_box_score)
}
#' **Get ESPN WNBA game rosters**
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return A game rosters data frame
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' athlete_id \tab integer \tab Unique athlete identifier (ESPN). \cr
#' athlete_uid \tab character \tab ESPN athlete UID (universal identifier). \cr
#' athlete_guid \tab character \tab ESPN athlete GUID. \cr
#' athlete_type \tab character \tab Athlete type / class. \cr
#' sdr \tab integer \tab Sdr. \cr
#' first_name \tab character \tab Player's first name. \cr
#' last_name \tab character \tab Player's last name. \cr
#' full_name \tab character \tab Player's full name. \cr
#' athlete_display_name \tab character \tab Athlete display name (full). \cr
#' short_name \tab character \tab Short display name. \cr
#' weight \tab numeric \tab Player weight in pounds. \cr
#' display_weight \tab character \tab Player weight in display format (e.g. '180 lbs'). \cr
#' height \tab numeric \tab Player height (string e.g. '6-2' or inches). \cr
#' display_height \tab character \tab Player height in display format (e.g. '6-2'). \cr
#' age \tab integer \tab Player age (in years). \cr
#' date_of_birth \tab character \tab Date of birth (YYYY-MM-DD). \cr
#' slug \tab character \tab URL-safe identifier. \cr
#' headshot_href \tab character \tab Headshot image URL. \cr
#' headshot_alt \tab character \tab Alternative-text label for the headshot. \cr
#' jersey \tab character \tab Jersey number worn by the player. \cr
#' position_id \tab integer \tab Unique position identifier. \cr
#' position_name \tab character \tab Listed roster position ('Guard', 'Forward', 'Center'). \cr
#' position_display_name \tab character \tab Position display name. \cr
#' position_abbreviation \tab character \tab Position abbreviation ('G' / 'F' / 'C'). \cr
#' position_leaf \tab logical \tab Position leaf. \cr
#' linked \tab logical \tab TRUE if the record is linked to a related entity. \cr
#' years \tab integer \tab Years. \cr
#' active \tab logical \tab TRUE if the row represents an active record (player / team / season). \cr
#' status_id \tab integer \tab Status identifier. \cr
#' status_name \tab character \tab Status label. \cr
#' status_type \tab character \tab Status type. \cr
#' status_abbreviation \tab character \tab Status abbreviation. \cr
#' birth_place_city \tab character \tab Birth place city. \cr
#' birth_place_state \tab character \tab Birth place state. \cr
#' birth_place_country \tab character \tab Birth place country. \cr
#' starter \tab logical \tab TRUE if the player was in the starting lineup; FALSE otherwise. \cr
#' valid \tab logical \tab Valid. \cr
#' did_not_play \tab logical \tab TRUE if the player did not appear in the game. \cr
#' display_name \tab character \tab Display name. \cr
#' reason \tab character \tab Reason. \cr
#' ejected \tab logical \tab TRUE if the player was ejected from the game. \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team_guid \tab character \tab ESPN team GUID. \cr
#' team_uid \tab character \tab ESPN universal team identifier (UID format 's:40~l:...~t:...'). \cr
#' team_sdr \tab integer \tab ESPN team SDR identifier. \cr
#' team_slug \tab character \tab URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces'). \cr
#' team_location \tab character \tab Team city or location string. \cr
#' team_name \tab character \tab Full team display name (e.g. 'Las Vegas Aces'). \cr
#' team_abbreviation \tab character \tab Short team abbreviation (e.g. 'LAS'). \cr
#' team_display_name \tab character \tab Full team display name. \cr
#' team_short_display_name \tab character \tab Short team display name (e.g. 'Aces'). \cr
#' team_color \tab character \tab Team primary color (hex without leading '#'). \cr
#' team_alternate_color \tab character \tab Team alternate color (hex without leading '#'). \cr
#' team_is_active \tab logical \tab TRUE if the team is currently active. \cr
#' is_all_star \tab logical \tab Is all star. \cr
#' logo_href \tab character \tab Team or league logo URL. \cr
#' logo_dark_href \tab character \tab Logo URL for dark backgrounds. \cr
#' logos_href_2 \tab character \tab Logos href 2. \cr
#' logos_href_3 \tab character \tab Logos href 3. \cr
#' game_id \tab integer \tab Unique game identifier. \cr
#' order \tab integer \tab Display order within the result set. \cr
#' home_away \tab character \tab Game venue label ('home' or 'away'). \cr
#' winner \tab logical \tab Winner. \cr
#' draft_display_text \tab character \tab Draft display text. \cr
#' draft_round \tab integer \tab Round of the draft selection. \cr
#' draft_year \tab integer \tab Draft year (4-digit). \cr
#' draft_selection \tab integer \tab Draft selection. \cr
#' hand_type \tab character \tab Hand type. \cr
#' hand_abbreviation \tab character \tab Hand abbreviation. \cr
#' hand_display_value \tab character \tab Hand display value. \cr
#' citizenship \tab character \tab Citizenship. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Game Roster
#' @family ESPN WNBA Functions
#'
#' @examples
#' \donttest{
#' try(espn_wnba_game_rosters(game_id = 401244185))
#' }
espn_wnba_game_rosters <- function(game_id) {
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
athlete_roster_df <- data.frame()
tryCatch(
expr = {
play_base_url <- paste0(
"https://sports.core.api.espn.com/v2/sports/basketball/leagues/wnba/events/",
game_id, "/competitions/",
game_id,"/competitors/")
game_res <- .retry_request(play_base_url)
# Check the result
check_status(game_res)
game_resp <- game_res %>%
.resp_text()
game_df <- jsonlite::fromJSON(game_resp)[["items"]] %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE) %>%
dplyr::rename("team_statistics_href" = "statistics.$ref")
colnames(game_df) <- gsub(".\\$ref","_href", colnames(game_df))
game_df <- game_df %>%
dplyr::rename(
"team_id" = "id",
"team_uid" = "uid")
game_df$game_id <- game_id
teams_df <- purrr::map_dfr(game_df$team_href, function(x){
res <- .retry_request(x)
# Check the result
check_status(res)
team_df <- res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_df[["links"]] <- NULL
team_df[["injuries"]] <- NULL
team_df[["record"]] <- NULL
team_df[["athletes"]] <- NULL
team_df[["venue"]] <- NULL
team_df[["groups"]] <- NULL
team_df[["ranks"]] <- NULL
team_df[["statistics"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["links"]] <- NULL
team_df[["notes"]] <- NULL
team_df[["franchise"]] <- NULL
team_df[["againstTheSpreadRecords"]] <- NULL
team_df[["oddsRecords"]] <- NULL
team_df[["college"]] <- NULL
team_df[["transactions"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["depthCharts"]] <- NULL
team_df[["awards"]] <- NULL
team_df[["events"]] <- NULL
team_df <- team_df %>%
purrr::map_if(is.list, as.data.frame) %>%
as.data.frame() %>%
dplyr::select(
-dplyr::any_of(
c("logos.width",
"logos.height",
"logos.alt",
"logos.rel..full.",
"logos.rel..default.",
"logos.rel..scoreboard.",
"logos.rel..scoreboard..1",
"logos.rel..scoreboard.2",
"logos.lastUpdated",
"logos.width.1",
"logos.height.1",
"logos.alt.1",
"logos.rel..full..1",
"logos.rel..dark.",
"logos.rel..dark..1",
"logos.lastUpdated.1",
"logos.width.2",
"logos.height.2",
"logos.alt.2",
"logos.rel..full..2",
"logos.rel..scoreboard.",
"logos.lastUpdated.2",
"logos.width.3",
"logos.height.3",
"logos.alt.3",
"logos.rel..full..3",
"logos.lastUpdated.3",
"X.ref",
"X.ref.1",
"X.ref.2"))) %>%
janitor::clean_names()
colnames(team_df)[1:13] <- paste0("team_", colnames(team_df)[1:13])
team_df <- team_df %>%
dplyr::rename(
"logo_href" = "logos_href",
"logo_dark_href" = "logos_href_1") %>%
dplyr::left_join(
game_df %>%
dplyr::select(
"game_id",
"team_id",
"team_uid",
"order",
"homeAway",
"winner",
"roster_href"),
by = c("team_id" = "team_id",
"team_uid" = "team_uid")
)
})
## Inputs
## game_id
team_roster_df <- purrr::map_dfr(teams_df$team_id, function(x){
res <- .retry_request(paste0(play_base_url, x, "/roster"))
# Check the result
check_status(res)
resp <- res %>%
.resp_text()
raw_play_df <- jsonlite::fromJSON(resp)[["entries"]]
raw_play_df <- raw_play_df %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE) %>%
dplyr::mutate(team_id = x) %>%
dplyr::select(-"period", -"forPlayerId", -"active")
raw_play_df <- raw_play_df %>%
dplyr::left_join(teams_df, by = c("team_id" = "team_id"))
})
colnames(team_roster_df) <- gsub(".\\$ref","_href", colnames(team_roster_df))
athlete_roster_df <- purrr::map_dfr(team_roster_df$athlete_href, function(x){
res <- .retry_request(x)
# Check the result
check_status(res)
resp <- res %>%
.resp_text()
raw_play_df <- jsonlite::fromJSON(resp, flatten = TRUE)
raw_play_df[["links"]] <- NULL
raw_play_df[["injuries"]] <- NULL
raw_play_df[["teams"]] <- NULL
raw_play_df[["team"]] <- NULL
raw_play_df[["college"]] <- NULL
raw_play_df[["proAthlete"]] <- NULL
raw_play_df[["statistics"]] <- NULL
raw_play_df[["notes"]] <- NULL
raw_play_df[["eventLog"]] <- NULL
raw_play_df[["$ref"]] <- NULL
raw_play_df[["position"]][["$ref"]] <- NULL
birth_place <- raw_play_df %>%
purrr::pluck("birthPlace") %>%
as.data.frame()
raw_play_df[["birthPlace"]] <- NULL
raw_play_df2 <- raw_play_df %>%
as.data.frame() %>%
dplyr::bind_cols(birth_place) %>%
dplyr::mutate(id = as.integer(.data$id)) %>%
dplyr::rename(
"athlete_id" = "id",
"athlete_uid" = "uid",
"athlete_guid" = "guid",
"athlete_type" = "type",
"athlete_display_name" = "displayName"
)
raw_play_df2 <- raw_play_df2 %>%
dplyr::left_join(team_roster_df, by = c("athlete_id" = "playerId"))
})
colnames(athlete_roster_df) <- gsub(".\\$ref","_href", colnames(athlete_roster_df))
athlete_roster_df <- athlete_roster_df %>%
janitor::clean_names() %>%
dplyr::rename(
"birth_place_city" = "city",
"birth_place_state" = "state",
"birth_place_country" = "country") %>%
dplyr::select(-dplyr::any_of(c(
"x_ref",
"x_ref_1",
"contract_ref",
"contract_ref_1",
"contract_ref_2",
"draft_ref",
"draft_ref_1",
"athlete_href",
"position_ref",
"position_href",
"roster_href",
"statistics_href"))) %>%
dplyr::mutate_at(c(
"game_id",
"athlete_id",
"team_id",
"position_id",
"status_id",
"sdr",
"team_sdr"), as.integer) %>%
make_wehoop_data("ESPN WNBA Game Roster Information from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no game roster data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(athlete_roster_df)
}
#' Get ESPN's WNBA team names and ids
#' @author Saiem Gilani
#' @return Returns a tibble
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team \tab character \tab Team-side label or team identifier. \cr
#' mascot \tab character \tab Team mascot. \cr
#' display_name \tab character \tab Display name. \cr
#' short_name \tab character \tab Short display name. \cr
#' abbreviation \tab character \tab Short abbreviation. \cr
#' color \tab character \tab Primary color (hex without leading '#'). \cr
#' alternate_color \tab character \tab Alternate color (hex without leading '#'). \cr
#' logo \tab character \tab Team or league logo URL. \cr
#' logo_dark \tab character \tab Logo dark. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows row_number group_by mutate as_tibble ungroup
#' @importFrom tidyr unnest unnest_wider everything pivot_wider
#' @import rvest
#' @export
#' @keywords WNBA Teams
#' @family ESPN WNBA Functions
#' @examples
#' \donttest{
#' try(espn_wnba_teams())
#' }
espn_wnba_teams <- function(){
.args <- .capture_args()
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
play_base_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/teams?limit=1000"
wnba_teams <- data.frame()
tryCatch(
expr = {
res <- .retry_request(play_base_url)
check_status(res)
resp <- res %>%
.resp_text()
leagues <- jsonlite::fromJSON(resp)[["sports"]][["leagues"]][[1]][['teams']][[1]][['team']] %>%
dplyr::group_by(.data$id) %>%
tidyr::unnest_wider("logos", names_sep = "_") %>%
tidyr::unnest_wider("logos_href", names_sep = "_") %>%
dplyr::select(
-"logos_width",
-"logos_height",
-"logos_alt",
-"logos_rel") %>%
dplyr::ungroup()
wnba_teams <- leagues %>%
dplyr::select(
"id",
"location",
"name",
"displayName",
"shortDisplayName",
"abbreviation",
"color",
"alternateColor",
"logos_href_1",
"logos_href_2") %>%
dplyr::rename(
"logo" = "logos_href_1",
"logo_dark" = "logos_href_2",
"mascot" = "name",
"team" = "location",
"team_id" = "id",
"alternate_color" = "alternateColor",
"short_name" = "shortDisplayName",
"display_name" = "displayName") %>%
dplyr::mutate(team_id = as.integer(.data$team_id)) %>%
make_wehoop_data("ESPN WNBA Teams Information from ESPN.com", Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Could not fetch ESPN WNBA teams",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {}
)
return(wnba_teams)
}
#' **Get WNBA schedule for a specific year/date from ESPN's API**
#'
#' @param season Either numeric or character
#' @author Saiem Gilani.
#' @return Returns a tibble
#'
#' Columns as documented in the shared [espn_basketball_scoreboard_schema] table.
#'
#' @import utils
#' @import rvest
#' @importFrom dplyr select rename any_of mutate
#' @importFrom jsonlite fromJSON
#' @importFrom tidyr unnest_wider unchop hoist
#' @importFrom lubridate with_tz ymd_hm
#' @import rvest
#' @export
#' @keywords WNBA Scoreboard
#' @family ESPN WNBA Functions
#' @examples
#' # Get schedule from date 2022-08-31
#' \donttest{
#' try(espn_wnba_scoreboard (season = "20220831"))
#' }
espn_wnba_scoreboard <- function(season){
.args <- mget(setdiff(names(formals()), "..."))
# cli::cli_alert_danger("Returning data for {season}!"))
max_year <- substr(Sys.Date(), 1,4)
if (!(as.integer(substr(season, 1, 4)) %in% c(2001:max_year))) {
message(paste("Error: Season must be between 2001 and", max_year))
}
# year > 2000
season <- as.character(season)
season_dates <- season
schedule_api <- paste0("http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/scoreboard?limit=1000&dates=",
season_dates)
tryCatch(
expr = {
res <- .retry_request(schedule_api)
check_status(res)
raw_sched <- res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
wnba_data <- raw_sched[["events"]] %>%
tibble::tibble(data = .data$.) %>%
tidyr::unnest_wider("data") %>%
tidyr::unchop("competitions") %>%
dplyr::select(
-"id",
-"uid",
-"date",
-"status") %>%
tidyr::unnest_wider("competitions") %>%
dplyr::rename(
"matchup" = "name",
"matchup_short" = "shortName",
"game_id" = "id",
"game_uid" = "uid",
"game_date" = "date"
) %>%
tidyr::hoist("status",
status_name = list("type", "name")) %>%
dplyr::select(!dplyr::any_of(
c(
"timeValid",
"neutralSite",
"conferenceCompetition",
"recent",
"venue",
"type"
)
)) %>%
tidyr::unnest_wider("season", names_sep = "_") %>%
dplyr::rename("season" = "season_year") %>%
dplyr::select(-dplyr::any_of("status"))
wnba_data <- wnba_data %>%
dplyr::mutate(
game_date_time = lubridate::ymd_hm(substr(.data$game_date, 1, nchar(.data$game_date) - 1)) %>%
lubridate::with_tz(tzone = "America/New_York"),
game_date = as.Date(substr(.data$game_date_time, 1, 10)))
wnba_data <- wnba_data %>%
tidyr::hoist(
"competitors",
homeAway = list(1,"homeAway")
)
wnba_data <- wnba_data %>%
tidyr::hoist(
"competitors",
team1_team_name = list(1, "team", "name"),
team1_team_logo = list(1, "team", "logo"),
team1_team_abb = list(1, "team", "abbreviation"),
team1_team_id = list(1, "team", "id"),
team1_team_location = list(1, "team", "location"),
team1_team_full = list(1, "team", "displayName"),
team1_team_color = list(1, "team", "color"),
team1_score = list(1, "score"),
team1_win = list(1, "winner"),
team1_record = list(1, "records", 1, "summary"),
# away team
team2_team_name = list(2, "team", "name"),
team2_team_logo = list(2, "team", "logo"),
team2_team_abb = list(2, "team", "abbreviation"),
team2_team_id = list(2, "team", "id"),
team2_team_location = list(2, "team", "location"),
team2_team_full = list(2, "team", "displayName"),
team2_team_color = list(2, "team", "color"),
team2_score = list(2, "score"),
team2_win = list(2, "winner"),
team2_record = list(2, "records", 1, "summary"))
wnba_data <- wnba_data %>%
dplyr::mutate(
home_team_name = ifelse(.data$homeAway == "home",.data$team1_team_name, .data$team2_team_name),
home_team_logo = ifelse(.data$homeAway == "home",.data$team1_team_logo, .data$team2_team_logo),
home_team_abb = ifelse(.data$homeAway == "home",.data$team1_team_abb, .data$team2_team_abb),
home_team_id = ifelse(.data$homeAway == "home",.data$team1_team_id, .data$team2_team_id),
home_team_location = ifelse(.data$homeAway == "home",.data$team1_team_location, .data$team2_team_location),
home_team_full_name = ifelse(.data$homeAway == "home",.data$team1_team_full, .data$team2_team_full),
home_team_color = ifelse(.data$homeAway == "home",.data$team1_team_color, .data$team2_team_color),
home_score = ifelse(.data$homeAway == "home",.data$team1_score, .data$team2_score),
home_win = ifelse(.data$homeAway == "home",.data$team1_win, .data$team2_win),
home_record = ifelse(.data$homeAway == "home",.data$team1_record, .data$team2_record),
away_team_name = ifelse(.data$homeAway == "away",.data$team1_team_name, .data$team2_team_name),
away_team_logo = ifelse(.data$homeAway == "away",.data$team1_team_logo, .data$team2_team_logo),
away_team_abb = ifelse(.data$homeAway == "away",.data$team1_team_abb, .data$team2_team_abb),
away_team_id = ifelse(.data$homeAway == "away",.data$team1_team_id, .data$team2_team_id),
away_team_location = ifelse(.data$homeAway == "away",.data$team1_team_location, .data$team2_team_location),
away_team_full_name = ifelse(.data$homeAway == "away",.data$team1_team_full, .data$team2_team_full),
away_team_color = ifelse(.data$homeAway == "away",.data$team1_team_color, .data$team2_team_color),
away_score = ifelse(.data$homeAway == "away",.data$team1_score, .data$team2_score),
away_win = ifelse(.data$homeAway == "away",.data$team1_win, .data$team2_win),
away_record = ifelse(.data$homeAway == "away",.data$team1_record, .data$team2_record)
)
wnba_data <- wnba_data %>%
dplyr::mutate_at(c(
"game_id",
"home_team_id",
"home_win",
"away_team_id",
"away_win",
"home_score",
"away_score"), as.integer)
wnba_data <- wnba_data %>%
dplyr::select(-dplyr::any_of(dplyr::starts_with("team1")),
-dplyr::any_of(dplyr::starts_with("team2")),
-dplyr::any_of(c("homeAway")))
if ("broadcasts" %in% names(wnba_data) && !any(is.na(wnba_data[['broadcasts']]))) {
wnba_data %>%
tidyr::hoist(
"broadcasts",
broadcast_market = list(1, "market"),
broadcast_name = list(1, "names", 1)) %>%
dplyr::select(!where(is.list)) %>%
janitor::clean_names() %>%
make_wehoop_data("ESPN WNBA Scoreboard Information from ESPN.com",Sys.time())
} else {
wnba_data %>%
dplyr::select(!where(is.list)) %>%
janitor::clean_names() %>%
make_wehoop_data("ESPN WNBA Scoreboard Information from ESPN.com",Sys.time())
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no scoreboard data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
}
#' **Get ESPN WNBA Standings**
#'
#' @author Geoff Hutchinson
#' @param year Either numeric or character (YYYY)
#' @return Returns a tibble
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team \tab character \tab Team-side label or team identifier. \cr
#' avgpointsagainst \tab numeric \tab Avgpointsagainst. \cr
#' avgpointsfor \tab numeric \tab Avgpointsfor. \cr
#' clincher \tab numeric \tab Clincher. \cr
#' differential \tab numeric \tab Differential. \cr
#' divisionwinpercent \tab numeric \tab Divisionwinpercent. \cr
#' gamesbehind \tab numeric \tab Gamesbehind. \cr
#' leaguewinpercent \tab numeric \tab Leaguewinpercent. \cr
#' losses \tab numeric \tab Total losses. \cr
#' playoffseed \tab numeric \tab Playoffseed. \cr
#' streak \tab numeric \tab Current streak (e.g. 'W3' for three-game win streak). \cr
#' winpercent \tab numeric \tab Winpercent. \cr
#' wins \tab numeric \tab Total wins. \cr
#' leaguestandings \tab character \tab Leaguestandings. \cr
#' home \tab character \tab Home. \cr
#' road \tab character \tab Road. \cr
#' vsdiv \tab character \tab Vsdiv. \cr
#' vsconf \tab character \tab Vsconf. \cr
#' lasttengames \tab character \tab Lasttengames. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr select rename
#' @importFrom tidyr pivot_wider
#' @importFrom data.table rbindlist
#' @keywords WNBA Standings
#' @family ESPN WNBA Functions
#' @export
#' @examples
#' \donttest{
#' try(espn_wnba_standings(year = 2021))
#' }
espn_wnba_standings <- function(year){
.args <- mget(setdiff(names(formals()), "..."))
standings_url <- "https://site.web.api.espn.com/apis/v2/sports/basketball/wnba/standings?region=us&lang=en&contentorigin=espn&type=0&level=1&sort=winpercent%3Adesc%2Cwins%3Adesc%2Cgamesbehind%3Aasc&"
## Inputs
## year
full_url <- paste0(standings_url,
"season=", year)
standings <- data.frame()
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
raw_standings <- jsonlite::fromJSON(resp)[["standings"]]
#Create a dataframe of all NBA teams by extracting from the raw_standings file
teams <- raw_standings[["entries"]][["team"]]
teams <- teams %>%
dplyr::select(
"id",
"displayName") %>%
dplyr::rename(
"team_id" = "id",
"team" = "displayName")
#creating a dataframe of the WNBA raw standings table from ESPN
standings_df <- raw_standings[["entries"]][["stats"]]
standings_data <- data.table::rbindlist(standings_df, fill = TRUE, idcol = T)
#Use the following code to replace NA's in the dataframe with the correct corresponding values and removing all unnecessary columns
standings_data$value <- ifelse(is.na(standings_data$value) & !is.na(standings_data$summary), standings_data$summary, standings_data$value)
standings_data <- standings_data %>%
dplyr::select(
".id",
"type",
"value")
#Use pivot_wider to transpose the dataframe so that we now have a standings row for each team
standings_data <- standings_data %>%
tidyr::pivot_wider(names_from = "type", values_from = "value")
standings_data <- standings_data %>%
dplyr::select(-".id")
#joining the 2 dataframes together to create a standings table
standings <- cbind(teams, standings_data) %>%
dplyr::mutate(team_id = as.integer(.data$team_id)) %>%
dplyr::mutate_at(c(
"avgpointsagainst",
"avgpointsfor",
"clincher",
"differential",
"divisionwinpercent",
"gamesbehind",
"leaguewinpercent",
"losses",
"playoffseed",
"streak",
"winpercent",
"wins"
), as.numeric) %>%
make_wehoop_data("ESPN WNBA Standings Information from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no standings data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(standings)
}
#' @import utils
utils::globalVariables(c("where"))
#' @title
#' **Get ESPN WNBA team stats data**
#' @author Saiem Gilani
#' @param team_id Team ID
#' @param year Year
#' @param season_type (character, default: regular): Season type - regular or postseason
#' @param total (boolean, default: FALSE): Totals
#' @return Returns a tibble with the team stats data
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team_guid \tab character \tab ESPN team GUID. \cr
#' team_uid \tab character \tab ESPN universal team identifier (UID format 's:40~l:...~t:...'). \cr
#' team_sdr \tab integer \tab ESPN team SDR identifier. \cr
#' team_slug \tab character \tab URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces'). \cr
#' team_location \tab character \tab Team city or location string. \cr
#' team_name \tab character \tab Full team display name (e.g. 'Las Vegas Aces'). \cr
#' team_abbreviation \tab character \tab Short team abbreviation (e.g. 'LAS'). \cr
#' team_display_name \tab character \tab Full team display name. \cr
#' team_short_display_name \tab character \tab Short team display name (e.g. 'Aces'). \cr
#' team_color \tab character \tab Team primary color (hex without leading '#'). \cr
#' team_alternate_color \tab character \tab Team alternate color (hex without leading '#'). \cr
#' team_is_active \tab logical \tab TRUE if the team is currently active. \cr
#' team_is_all_star \tab logical \tab TRUE if the row represents an All-Star team. \cr
#' logo_href \tab character \tab Team or league logo URL. \cr
#' logo_dark_href \tab character \tab Logo URL for dark backgrounds. \cr
#' defensive_blocks \tab numeric \tab Short for blocked shot, number of times when a defensive player legally deflects a field goal attempt from an offensive player. \cr
#' defensive_defensive_rebounds \tab numeric \tab The number of times when the defense obtains the possession of the ball after a missed shot by the offense. \cr
#' defensive_steals \tab numeric \tab The number of times a defensive player forced a turnover by intercepting or deflecting a pass or a dribble of an offensive player. \cr
#' defensive_avg_defensive_rebounds \tab numeric \tab The average defensive rebounds per game. \cr
#' defensive_avg_blocks \tab numeric \tab The average blocks per game. \cr
#' defensive_avg_steals \tab numeric \tab The average steals per game. \cr
#' defensive_avg48defensive_rebounds \tab numeric \tab The average number of defensive rebounds per 48 minutes. \cr
#' defensive_avg48blocks \tab numeric \tab The average number of blocks per 48 minutes. \cr
#' defensive_avg48steals \tab numeric \tab The average number of steals per 48 minutes. \cr
#' general_disqualifications \tab numeric \tab The number of times a player reached the foul limit. \cr
#' general_flagrant_fouls \tab numeric \tab The number of fouls that the officials thought were unnecessary or excessive. \cr
#' general_fouls \tab numeric \tab The number of times a player had illegal contact with the opponent. \cr
#' general_ejections \tab numeric \tab The number of times a player or coach is removed from the game as a result of a serious offense. \cr
#' general_technical_fouls \tab numeric \tab The number of times an player or coach was called for a technical foul (unsportsmanlike conduct or violations). \cr
#' general_rebounds \tab numeric \tab The total number of rebounds (offensive and defensive). \cr
#' general_avg_minutes \tab numeric \tab The average number of minutes per game. \cr
#' general_nba_rating \tab numeric \tab General nba rating. \cr
#' general_plus_minus \tab numeric \tab A player's estimated on-court impact on team performance measured in point differential per 100 possessions. \cr
#' general_game_day_of_year \tab numeric \tab The day of the season on which this game falls; if it's the 15th day of the regular season in Nov, the game day of year is 15 (ie, game day of year != day of year). \cr
#' general_avg_rebounds \tab numeric \tab The average rebounds per game. \cr
#' general_avg_fouls \tab numeric \tab The average fouls committed per game. \cr
#' general_avg_flagrant_fouls \tab numeric \tab The average number of flagrant fouls per game. \cr
#' general_avg_technical_fouls \tab numeric \tab The average number of technical fouls per game. \cr
#' general_avg_ejections \tab numeric \tab The average ejections per game. \cr
#' general_avg_disqualifications \tab numeric \tab The average number of disqualifications per game. \cr
#' general_assist_turnover_ratio \tab numeric \tab The average number of assists a player or team records per turnover. \cr
#' general_steal_foul_ratio \tab numeric \tab The average number of steals a player or team records per foul committed. \cr
#' general_block_foul_ratio \tab numeric \tab The average number of blocks a player or record per foul committed. \cr
#' general_avg_team_rebounds \tab numeric \tab The average number of rebounds for a team per game. \cr
#' general_total_rebounds \tab numeric \tab The total number of rebounds for a team or player. \cr
#' general_total_technical_fouls \tab numeric \tab The total number of technical fouls for a team or player. \cr
#' general_team_assist_turnover_ratio \tab numeric \tab The number of assists per turnover for a team. \cr
#' general_team_rebounds \tab numeric \tab The total number of rebounds for a team. \cr
#' general_steal_turnover_ratio \tab numeric \tab The number of steals per turnover. \cr
#' general_avg48rebounds \tab numeric \tab The average number of rebounds per 48 minutes. \cr
#' general_avg48fouls \tab numeric \tab The average number of fouls committed per 48 minutes. \cr
#' general_avg48flagrant_fouls \tab numeric \tab The average number of flagrant fouls committed per 48 minutes. \cr
#' general_avg48technical_fouls \tab numeric \tab The average number of technical fouls committed per 48 minutes. \cr
#' general_avg48ejections \tab numeric \tab The average number of ejections per 48 minutes. \cr
#' general_avg48disqualifications \tab numeric \tab The average number of disqualifications per 48 minutes. \cr
#' general_games_played \tab numeric \tab Games Played. \cr
#' general_games_started \tab numeric \tab The number of games started by an athlete. \cr
#' general_double_double \tab numeric \tab The number of times double digit values were accumulated in 2 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' general_triple_double \tab numeric \tab The number of times double digit values were accumulated in 3 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' offensive_assists \tab numeric \tab The number of times a player who passes the ball to a teammate in a way that leads to a score by field goal, meaning that he or she was "assisting" in the basket. There is some judgment involved in deciding whether a passer should be credited with an assist. \cr
#' offensive_field_goals \tab numeric \tab Field Goal makes and attempts. \cr
#' offensive_field_goals_attempted \tab numeric \tab The number of times a 2pt field goal was attempted. \cr
#' offensive_field_goals_made \tab numeric \tab The number of times a 2pt field goal was made. \cr
#' offensive_field_goal_pct \tab numeric \tab The ratio of field goals made to field goals attempted: FGM / FGA. \cr
#' offensive_free_throws \tab numeric \tab Free Throw makes and attempts. \cr
#' offensive_free_throw_pct \tab numeric \tab The ratio of free throws made to free throws attempted: FTM / FTA. \cr
#' offensive_free_throws_attempted \tab numeric \tab The number of times a free throw was attempted. \cr
#' offensive_free_throws_made \tab numeric \tab The number of times a free throw was made. \cr
#' offensive_offensive_rebounds \tab numeric \tab The number of times when the offense obtains the possession of the ball after a missed shot. \cr
#' offensive_points \tab numeric \tab The number of points scored. \cr
#' offensive_turnovers \tab numeric \tab The number of times a player loses possession to the other team. \cr
#' offensive_three_point_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_three_point_field_goals_attempted \tab numeric \tab The number of times a 3pt field goal was attempted. \cr
#' offensive_three_point_field_goals_made \tab numeric \tab The number of times a 3pt field goal was made. \cr
#' offensive_team_turnovers \tab numeric \tab The number of turnovers for the team. \cr
#' offensive_total_turnovers \tab numeric \tab The number of turnovers plus team turnovers for the team. \cr
#' offensive_points_in_paint \tab numeric \tab The amount of points scored in the area known as "the Paint"(the rectangle between the foul line and the baseline). \cr
#' offensive_brick_index \tab numeric \tab How many points a player costs his team with his shooting compared with the league average on a per-40-minute basis. ((52.8 - TS\%) x (FGA + (FTA x 0.44))) / (Min/40) . \cr
#' offensive_avg_field_goals_made \tab numeric \tab The average field goals made per game. \cr
#' offensive_avg_field_goals_attempted \tab numeric \tab The average field goals attempted per game. \cr
#' offensive_avg_three_point_field_goals_made \tab numeric \tab The average three point field goals made per game. \cr
#' offensive_avg_three_point_field_goals_attempted \tab numeric \tab The average three point field goals attempted per game. \cr
#' offensive_avg_free_throws_made \tab numeric \tab The average free throw shots made per game. \cr
#' offensive_avg_free_throws_attempted \tab numeric \tab The average free throw shots attempted per game. \cr
#' offensive_avg_points \tab numeric \tab The average number of points scored per game. \cr
#' offensive_avg_points_allowed \tab numeric \tab The average number of points allowed per game. \cr
#' offensive_avg_offensive_rebounds \tab numeric \tab The average offensive rebounds per game. \cr
#' offensive_avg_assists \tab numeric \tab The average assists per game. \cr
#' offensive_avg_turnovers \tab numeric \tab The average turnovers committed per game. \cr
#' offensive_offensive_rebound_pct \tab numeric \tab The percentage of the number of times they obtain the possession of the ball after a missed shot. \cr
#' offensive_estimated_possessions \tab numeric \tab An estimation of the number of possessions for a team or player. \cr
#' offensive_avg_estimated_possessions \tab numeric \tab The average number of estimated possessions per game for a team or player. \cr
#' offensive_points_per_estimated_possessions \tab numeric \tab The number of points per estimated possession for a team or player. \cr
#' offensive_avg_team_turnovers \tab numeric \tab The average number of turnovers for a team per game. \cr
#' offensive_avg_total_turnovers \tab numeric \tab The average number of total turnovers for a team per game. \cr
#' offensive_three_point_field_goal_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made for a team or player. \cr
#' offensive_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted for a team or player. \cr
#' offensive_avg_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made per game for a team or player. \cr
#' offensive_avg_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted per game for a team or player. \cr
#' offensive_two_point_field_goal_pct \tab numeric \tab The percentage of 2-points fields goals made by a team or player. \cr
#' offensive_shooting_efficiency \tab numeric \tab The efficiency with which a team or player shoots the basketball. \cr
#' offensive_scoring_efficiency \tab numeric \tab The efficiency with which a team or player scores the basketball. \cr
#' offensive_avg48field_goals_made \tab numeric \tab The average number of fieldgoals made per 48 minutes. \cr
#' offensive_avg48field_goals_attempted \tab numeric \tab The average number of fieldgoals attempted per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_made \tab numeric \tab The average per number of 3-Pointers made per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_attempted \tab numeric \tab The average number of 3-pointers attempted per 48 minutes. \cr
#' offensive_avg48free_throws_made \tab numeric \tab The average number of Free Throws made per 48 minutes. \cr
#' offensive_avg48free_throws_attempted \tab numeric \tab The average number of free throws attempted per 48 minutes. \cr
#' offensive_avg48points \tab numeric \tab The average number of points scored per 48 minutes. \cr
#' offensive_avg48offensive_rebounds \tab numeric \tab The average number of offenseive rebounds per 48 minutes. \cr
#' offensive_avg48assists \tab numeric \tab The average number of assists per 48 minutes. \cr
#' offensive_avg48turnovers \tab numeric \tab The average number of turnovers per 48 minutes. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @export
#' @keywords WNBA Team Stats
#' @family ESPN WNBA Functions
#'
#' @examples
#' \donttest{
#' try(espn_wnba_team_stats(team_id = 17, year = 2020))
#' }
espn_wnba_team_stats <- function(
team_id,
year,
season_type = 'regular',
total = FALSE
){
.args <- mget(setdiff(names(formals()), "..."))
if (!(tolower(season_type) %in% c("regular","postseason"))) {
# Check if season_type is appropriate, if not regular
cli::cli_abort("Enter valid season_type: regular or postseason")
}
s_type <- ifelse(season_type == "postseason", 3, 2)
base_url <- "https://sports.core.api.espn.com/v2/sports/basketball/leagues/wnba/seasons/"
totals <- ifelse(total == TRUE, 0, "")
full_url <- paste0(
base_url,
year,
'/types/',s_type,
'/teams/',team_id,
'/statistics/', totals
)
df <- data.frame()
tryCatch(
expr = {
# Create the GET request and set response as res
res <- .retry_request(full_url)
# Check the result
check_status(res)
# Get the content and return result as data.frame
df <- res %>%
.resp_text() %>%
jsonlite::fromJSON()
team_url <- df[["team"]][["$ref"]]
# Create the GET request and set response as res
team_res <- .retry_request(team_url)
# Check the result
check_status(team_res)
# Get the content and return result as data.frame
team_df <- team_res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_df[["links"]] <- NULL
team_df[["injuries"]] <- NULL
team_df[["record"]] <- NULL
team_df[["athletes"]] <- NULL
team_df[["venue"]] <- NULL
team_df[["groups"]] <- NULL
team_df[["ranks"]] <- NULL
team_df[["statistics"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["links"]] <- NULL
team_df[["notes"]] <- NULL
team_df[["franchise"]] <- NULL
team_df[["record"]] <- NULL
team_df[["college"]] <- NULL
team_df <- team_df %>%
purrr::map_if(is.list,as.data.frame) %>%
as.data.frame() %>%
dplyr::select(
-dplyr::any_of(
c("logos.width",
"logos.height",
"logos.alt",
"logos.rel..full.",
"logos.rel..default.",
"logos.lastUpdated",
"logos.width.1",
"logos.height.1",
"logos.alt.1",
"logos.rel..full..1",
"logos.rel..dark.",
"logos.lastUpdated.1",
"X.ref",
"X.ref.1",
"X.ref.2",
"X.ref.3"))) %>%
janitor::clean_names()
colnames(team_df)[1:14] <- paste0("team_",colnames(team_df)[1:14])
team_df <- team_df %>%
dplyr::rename(
"logo_href" = "logos_href",
"logo_dark_href" = "logos_href_1")
df <- df %>%
purrr::pluck("splits") %>%
purrr::pluck("categories") %>%
tidyr::unnest("stats", names_sep = "_")
df <- df %>%
dplyr::mutate(
stats_category_name = paste0(.data$name, "_", .data$stats_name)) %>%
dplyr::select(
"stats_category_name",
"stats_value") %>%
tidyr::pivot_wider(
names_from = "stats_category_name",
values_from = "stats_value",
values_fn = dplyr::first) %>%
janitor::clean_names()
df <- team_df %>%
dplyr::bind_cols(df)
df <- df %>%
dplyr::mutate_at(c(
"team_id",
"team_sdr"), as.integer) %>%
make_wehoop_data("ESPN WNBA Team Season Stats from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no team season stats data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(df)
}
#' @title
#' **Get ESPN WNBA player stats data**
#' @author Saiem Gilani
#' @param athlete_id Athlete ID
#' @param year Year
#' @param season_type (character, default: regular): Season type - regular or postseason
#' @param total (boolean, default: FALSE): Totals
#' @return Returns a tibble with the player stats data
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' athlete_id \tab integer \tab Unique athlete identifier (ESPN). \cr
#' athlete_uid \tab character \tab ESPN athlete UID (universal identifier). \cr
#' athlete_guid \tab character \tab ESPN athlete GUID. \cr
#' athlete_type \tab character \tab Athlete type / class. \cr
#' sdr \tab integer \tab Sdr. \cr
#' first_name \tab character \tab Player's first name. \cr
#' last_name \tab character \tab Player's last name. \cr
#' full_name \tab character \tab Player's full name. \cr
#' display_name \tab character \tab Display name. \cr
#' short_name \tab character \tab Short display name. \cr
#' weight \tab numeric \tab Player weight in pounds. \cr
#' display_weight \tab character \tab Player weight in display format (e.g. '180 lbs'). \cr
#' height \tab numeric \tab Player height (string e.g. '6-2' or inches). \cr
#' display_height \tab character \tab Player height in display format (e.g. '6-2'). \cr
#' age \tab integer \tab Player age (in years). \cr
#' date_of_birth \tab character \tab Date of birth (YYYY-MM-DD). \cr
#' slug \tab character \tab URL-safe identifier. \cr
#' headshot_href \tab character \tab Headshot image URL. \cr
#' headshot_alt \tab character \tab Alternative-text label for the headshot. \cr
#' position_id \tab integer \tab Unique position identifier. \cr
#' position_name \tab character \tab Listed roster position ('Guard', 'Forward', 'Center'). \cr
#' position_display_name \tab character \tab Position display name. \cr
#' position_abbreviation \tab character \tab Position abbreviation ('G' / 'F' / 'C'). \cr
#' position_leaf \tab logical \tab Position leaf. \cr
#' linked \tab logical \tab TRUE if the record is linked to a related entity. \cr
#' years \tab integer \tab Years. \cr
#' active \tab logical \tab TRUE if the row represents an active record (player / team / season). \cr
#' status_id \tab integer \tab Status identifier. \cr
#' status_name \tab character \tab Status label. \cr
#' status_type \tab character \tab Status type. \cr
#' status_abbreviation \tab character \tab Status abbreviation. \cr
#' defensive_blocks \tab numeric \tab Short for blocked shot, number of times when a defensive player legally deflects a field goal attempt from an offensive player. \cr
#' defensive_defensive_rebounds \tab numeric \tab The number of times when the defense obtains the possession of the ball after a missed shot by the offense. \cr
#' defensive_steals \tab numeric \tab The number of times a defensive player forced a turnover by intercepting or deflecting a pass or a dribble of an offensive player. \cr
#' defensive_avg_defensive_rebounds \tab numeric \tab The average defensive rebounds per game. \cr
#' defensive_avg_blocks \tab numeric \tab The average blocks per game. \cr
#' defensive_avg_steals \tab numeric \tab The average steals per game. \cr
#' defensive_avg48defensive_rebounds \tab numeric \tab The average number of defensive rebounds per 48 minutes. \cr
#' defensive_avg48blocks \tab numeric \tab The average number of blocks per 48 minutes. \cr
#' defensive_avg48steals \tab numeric \tab The average number of steals per 48 minutes. \cr
#' general_disqualifications \tab numeric \tab The number of times a player reached the foul limit. \cr
#' general_flagrant_fouls \tab numeric \tab The number of fouls that the officials thought were unnecessary or excessive. \cr
#' general_fouls \tab numeric \tab The number of times a player had illegal contact with the opponent. \cr
#' general_ejections \tab numeric \tab The number of times a player or coach is removed from the game as a result of a serious offense. \cr
#' general_technical_fouls \tab numeric \tab The number of times an player or coach was called for a technical foul (unsportsmanlike conduct or violations). \cr
#' general_rebounds \tab numeric \tab The total number of rebounds (offensive and defensive). \cr
#' general_vorp \tab numeric \tab Value Over Replacement Player. \cr
#' general_minutes \tab numeric \tab The total number of minutes played. \cr
#' general_avg_minutes \tab numeric \tab The average number of minutes per game. \cr
#' general_fantasy_rating \tab numeric \tab The Fantasy Rating of a player. \cr
#' general_nba_rating \tab numeric \tab General nba rating. \cr
#' general_plus_minus \tab numeric \tab A player's estimated on-court impact on team performance measured in point differential per 100 possessions. \cr
#' general_avg_rebounds \tab numeric \tab The average rebounds per game. \cr
#' general_avg_fouls \tab numeric \tab The average fouls committed per game. \cr
#' general_avg_flagrant_fouls \tab numeric \tab The average number of flagrant fouls per game. \cr
#' general_avg_technical_fouls \tab numeric \tab The average number of technical fouls per game. \cr
#' general_avg_ejections \tab numeric \tab The average ejections per game. \cr
#' general_avg_disqualifications \tab numeric \tab The average number of disqualifications per game. \cr
#' general_assist_turnover_ratio \tab numeric \tab The average number of assists a player or team records per turnover. \cr
#' general_steal_foul_ratio \tab numeric \tab The average number of steals a player or team records per foul committed. \cr
#' general_block_foul_ratio \tab numeric \tab The average number of blocks a player or record per foul committed. \cr
#' general_avg_team_rebounds \tab numeric \tab The average number of rebounds for a team per game. \cr
#' general_total_rebounds \tab numeric \tab The total number of rebounds for a team or player. \cr
#' general_total_technical_fouls \tab numeric \tab The total number of technical fouls for a team or player. \cr
#' general_team_assist_turnover_ratio \tab numeric \tab The number of assists per turnover for a team. \cr
#' general_steal_turnover_ratio \tab numeric \tab The number of steals per turnover. \cr
#' general_avg48rebounds \tab numeric \tab The average number of rebounds per 48 minutes. \cr
#' general_avg48fouls \tab numeric \tab The average number of fouls committed per 48 minutes. \cr
#' general_avg48flagrant_fouls \tab numeric \tab The average number of flagrant fouls committed per 48 minutes. \cr
#' general_avg48technical_fouls \tab numeric \tab The average number of technical fouls committed per 48 minutes. \cr
#' general_avg48ejections \tab numeric \tab The average number of ejections per 48 minutes. \cr
#' general_avg48disqualifications \tab numeric \tab The average number of disqualifications per 48 minutes. \cr
#' general_games_played \tab numeric \tab Games Played. \cr
#' general_games_started \tab numeric \tab The number of games started by an athlete. \cr
#' general_double_double \tab numeric \tab The number of times double digit values were accumulated in 2 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' general_triple_double \tab numeric \tab The number of times double digit values were accumulated in 3 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' offensive_assists \tab numeric \tab The number of times a player who passes the ball to a teammate in a way that leads to a score by field goal, meaning that he or she was "assisting" in the basket. There is some judgment involved in deciding whether a passer should be credited with an assist. \cr
#' offensive_field_goals \tab numeric \tab Field Goal makes and attempts. \cr
#' offensive_field_goals_attempted \tab numeric \tab The number of times a 2pt field goal was attempted. \cr
#' offensive_field_goals_made \tab numeric \tab The number of times a 2pt field goal was made. \cr
#' offensive_field_goal_pct \tab numeric \tab The ratio of field goals made to field goals attempted: FGM / FGA. \cr
#' offensive_free_throws \tab numeric \tab Free Throw makes and attempts. \cr
#' offensive_free_throw_pct \tab numeric \tab The ratio of free throws made to free throws attempted: FTM / FTA. \cr
#' offensive_free_throws_attempted \tab numeric \tab The number of times a free throw was attempted. \cr
#' offensive_free_throws_made \tab numeric \tab The number of times a free throw was made. \cr
#' offensive_offensive_rebounds \tab numeric \tab The number of times when the offense obtains the possession of the ball after a missed shot. \cr
#' offensive_points \tab numeric \tab The number of points scored. \cr
#' offensive_turnovers \tab numeric \tab The number of times a player loses possession to the other team. \cr
#' offensive_three_point_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_three_point_field_goals_attempted \tab numeric \tab The number of times a 3pt field goal was attempted. \cr
#' offensive_three_point_field_goals_made \tab numeric \tab The number of times a 3pt field goal was made. \cr
#' offensive_total_turnovers \tab numeric \tab The number of turnovers plus team turnovers for the team. \cr
#' offensive_points_in_paint \tab numeric \tab The amount of points scored in the area known as "the Paint"(the rectangle between the foul line and the baseline). \cr
#' offensive_brick_index \tab numeric \tab How many points a player costs his team with his shooting compared with the league average on a per-40-minute basis. ((52.8 - TS\%) x (FGA + (FTA x 0.44))) / (Min/40) . \cr
#' offensive_avg_field_goals_made \tab numeric \tab The average field goals made per game. \cr
#' offensive_avg_field_goals_attempted \tab numeric \tab The average field goals attempted per game. \cr
#' offensive_avg_three_point_field_goals_made \tab numeric \tab The average three point field goals made per game. \cr
#' offensive_avg_three_point_field_goals_attempted \tab numeric \tab The average three point field goals attempted per game. \cr
#' offensive_avg_free_throws_made \tab numeric \tab The average free throw shots made per game. \cr
#' offensive_avg_free_throws_attempted \tab numeric \tab The average free throw shots attempted per game. \cr
#' offensive_avg_points \tab numeric \tab The average number of points scored per game. \cr
#' offensive_avg_offensive_rebounds \tab numeric \tab The average offensive rebounds per game. \cr
#' offensive_avg_assists \tab numeric \tab The average assists per game. \cr
#' offensive_avg_turnovers \tab numeric \tab The average turnovers committed per game. \cr
#' offensive_offensive_rebound_pct \tab numeric \tab The percentage of the number of times they obtain the possession of the ball after a missed shot. \cr
#' offensive_estimated_possessions \tab numeric \tab An estimation of the number of possessions for a team or player. \cr
#' offensive_avg_estimated_possessions \tab numeric \tab The average number of estimated possessions per game for a team or player. \cr
#' offensive_points_per_estimated_possessions \tab numeric \tab The number of points per estimated possession for a team or player. \cr
#' offensive_avg_team_turnovers \tab numeric \tab The average number of turnovers for a team per game. \cr
#' offensive_avg_total_turnovers \tab numeric \tab The average number of total turnovers for a team per game. \cr
#' offensive_three_point_field_goal_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made for a team or player. \cr
#' offensive_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted for a team or player. \cr
#' offensive_avg_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made per game for a team or player. \cr
#' offensive_avg_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted per game for a team or player. \cr
#' offensive_two_point_field_goal_pct \tab numeric \tab The percentage of 2-points fields goals made by a team or player. \cr
#' offensive_shooting_efficiency \tab numeric \tab The efficiency with which a team or player shoots the basketball. \cr
#' offensive_scoring_efficiency \tab numeric \tab The efficiency with which a team or player scores the basketball. \cr
#' offensive_avg48field_goals_made \tab numeric \tab The average number of fieldgoals made per 48 minutes. \cr
#' offensive_avg48field_goals_attempted \tab numeric \tab The average number of fieldgoals attempted per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_made \tab numeric \tab The average per number of 3-Pointers made per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_attempted \tab numeric \tab The average number of 3-pointers attempted per 48 minutes. \cr
#' offensive_avg48free_throws_made \tab numeric \tab The average number of Free Throws made per 48 minutes. \cr
#' offensive_avg48free_throws_attempted \tab numeric \tab The average number of free throws attempted per 48 minutes. \cr
#' offensive_avg48points \tab numeric \tab The average number of points scored per 48 minutes. \cr
#' offensive_avg48offensive_rebounds \tab numeric \tab The average number of offenseive rebounds per 48 minutes. \cr
#' offensive_avg48assists \tab numeric \tab The average number of assists per 48 minutes. \cr
#' offensive_avg48turnovers \tab numeric \tab The average number of turnovers per 48 minutes. \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team_guid \tab character \tab ESPN team GUID. \cr
#' team_uid \tab character \tab ESPN universal team identifier (UID format 's:40~l:...~t:...'). \cr
#' team_sdr \tab integer \tab ESPN team SDR identifier. \cr
#' team_slug \tab character \tab URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces'). \cr
#' team_location \tab character \tab Team city or location string. \cr
#' team_name \tab character \tab Full team display name (e.g. 'Las Vegas Aces'). \cr
#' team_abbreviation \tab character \tab Short team abbreviation (e.g. 'LAS'). \cr
#' team_display_name \tab character \tab Full team display name. \cr
#' team_short_display_name \tab character \tab Short team display name (e.g. 'Aces'). \cr
#' team_color \tab character \tab Team primary color (hex without leading '#'). \cr
#' team_alternate_color \tab character \tab Team alternate color (hex without leading '#'). \cr
#' team_is_active \tab logical \tab TRUE if the team is currently active. \cr
#' team_is_all_star \tab logical \tab TRUE if the row represents an All-Star team. \cr
#' logo_href \tab character \tab Team or league logo URL. \cr
#' logo_dark_href \tab character \tab Logo URL for dark backgrounds. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @export
#' @keywords WNBA Player Stats
#' @family ESPN WNBA Functions
#'
#' @examples
#' \donttest{
#' try(espn_wnba_player_stats(athlete_id = 2529130, year = 2022))
#' }
espn_wnba_player_stats <- function(
athlete_id,
year,
season_type = 'regular',
total = FALSE
){
.args <- mget(setdiff(names(formals()), "..."))
if (!(tolower(season_type) %in% c("regular","postseason"))) {
# Check if season_type is appropriate, if not regular
cli::cli_abort("Enter valid season_type: regular or postseason")
}
s_type <- ifelse(season_type == "postseason", 3, 2)
base_url <- "https://sports.core.api.espn.com/v2/sports/basketball/leagues/wnba/seasons/"
totals <- ifelse(total == TRUE, 0, "")
full_url <- paste0(
base_url,
year,
'/types/',s_type,
'/athletes/', athlete_id,
'/statistics/', totals
)
athlete_url <- paste0(
base_url,
year,
'/athletes/', athlete_id
)
df <- data.frame()
tryCatch(
expr = {
# Create the GET request and set response as res
res <- .retry_request(full_url)
# Check the result
check_status(res)
# Create the GET request and set response as res
athlete_res <- .retry_request(athlete_url)
# Check the result
check_status(athlete_res)
athlete_df <- athlete_res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_url <- athlete_df[["team"]][["$ref"]]
# Create the GET request and set response as res
team_res <- .retry_request(team_url)
# Check the result
check_status(team_res)
team_df <- team_res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_df[["links"]] <- NULL
team_df[["injuries"]] <- NULL
team_df[["record"]] <- NULL
team_df[["athletes"]] <- NULL
team_df[["venue"]] <- NULL
team_df[["groups"]] <- NULL
team_df[["ranks"]] <- NULL
team_df[["statistics"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["links"]] <- NULL
team_df[["notes"]] <- NULL
team_df[["franchise"]] <- NULL
team_df[["record"]] <- NULL
team_df[["college"]] <- NULL
team_df <- team_df %>%
purrr::map_if(is.list,as.data.frame) %>%
as.data.frame() %>%
dplyr::select(
-dplyr::any_of(
c("logos.width",
"logos.height",
"logos.alt",
"logos.rel..full.",
"logos.rel..default.",
"logos.lastUpdated",
"logos.width.1",
"logos.height.1",
"logos.alt.1",
"logos.rel..full..1",
"logos.rel..dark.",
"logos.lastUpdated.1",
"X.ref",
"X.ref.1",
"X.ref.2",
"X.ref.3"))) %>%
janitor::clean_names()
colnames(team_df)[1:14] <- paste0("team_",colnames(team_df)[1:14])
team_df <- team_df %>%
dplyr::rename(
"logo_href" = "logos_href",
"logo_dark_href" = "logos_href_1")
athlete_df[["links"]] <- NULL
athlete_df[["injuries"]] <- NULL
athlete_df[["birthPlace"]] <- NULL
athlete_df <- athlete_df %>%
purrr::map_if(is.list, as.data.frame) %>%
tibble::tibble(data = .data$.)
athlete_df <- athlete_df$data %>%
as.data.frame() %>%
dplyr::select(-dplyr::any_of(c("X.ref","X.ref.1","X.ref.2","X.ref.3","X.ref.4","X.ref.5","X.ref.6","X.ref.7","position.X.ref"))) %>%
janitor::clean_names() %>%
dplyr::rename(
"athlete_id" = "id",
"athlete_uid" = "uid",
"athlete_guid" = "guid",
"athlete_type" = "type")
# Get the content and return result as data.frame
df <- res %>%
.resp_text() %>%
jsonlite::fromJSON() %>%
purrr::pluck("splits") %>%
purrr::pluck("categories") %>%
tidyr::unnest("stats", names_sep = "_")
df <- df %>%
dplyr::mutate(
stats_category_name = paste0(.data$name, "_", .data$stats_name)) %>%
dplyr::select(
"stats_category_name",
"stats_value") %>%
tidyr::pivot_wider(
names_from = "stats_category_name",
values_from = "stats_value",
values_fn = dplyr::first) %>%
janitor::clean_names()
df <- athlete_df %>%
dplyr::bind_cols(df) %>%
dplyr::bind_cols(team_df)
df <- df %>%
dplyr::mutate_at(c(
"athlete_id",
"team_id",
"position_id",
"status_id",
"sdr",
"team_sdr"), as.integer) %>%
make_wehoop_data("ESPN WNBA Player Season Stats from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no player season stats data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(df)
}
#' **Parse ESPN WNBA PBP, helper function**
#' @param resp Response object from the ESPN WNBA game summary endpoint
#' @return Returns a tibble
#' @importFrom lubridate with_tz ymd_hm
#' @export
helper_espn_wnba_pbp <- function(resp){
game_json <- resp %>%
jsonlite::fromJSON()
pbp_source <- game_json[["header"]][["competitions"]][["playByPlaySource"]]
plays <- game_json %>%
purrr::pluck("plays") %>%
dplyr::as_tibble()
if (pbp_source != "none" && nrow(plays) > 10) {
homeAway1 <- jsonlite::fromJSON(resp)[['header']][['competitions']][['competitors']][[1]][['homeAway']][1]
gameId <- as.integer(game_json[["header"]][["id"]])
season <- game_json[['header']][['season']][['year']]
season_type <- game_json[['header']][['season']][['type']]
game_date_time <- substr(game_json[['header']][['competitions']][['date']], 1,
nchar(game_json[['header']][['competitions']][['date']]) - 1) %>%
lubridate::ymd_hm() %>%
lubridate::with_tz(tzone = "America/New_York")
game_date <- as.Date(substr(game_date_time, 0, 10))
id_vars <- data.frame()
if (homeAway1 == "home") {
homeTeamId = as.integer(game_json[["header"]][["competitions"]][["competitors"]][[1]][['team']][['id']] %>%
purrr::pluck(1, .default = NA_integer_))
homeTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(1, .default = NA_character_))
homeTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamId = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['team']][['id']] %>%
purrr::pluck(2, .default = NA_integer_))
awayTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(2, .default = NA_integer_))
awayTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(2, .default = NA_character_)
id_vars <- data.frame(
homeTeamId,
homeTeamMascot,
homeTeamName,
homeTeamAbbrev,
homeTeamLogo,
homeTeamLogoDark,
homeTeamFullName,
homeTeamColor,
homeTeamAlternateColor,
homeTeamScore,
homeTeamWinner,
homeTeamRecord,
awayTeamId,
awayTeamMascot,
awayTeamName,
awayTeamAbbrev,
awayTeamLogo,
awayTeamLogoDark,
awayTeamFullName,
awayTeamColor,
awayTeamAlternateColor,
awayTeamScore,
awayTeamWinner,
awayTeamRecord
)
} else {
awayTeamId = as.integer(game_json[["header"]][["competitions"]][["competitors"]][[1]][['team']][['id']] %>%
purrr::pluck(1, .default = NA_integer_))
awayTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(1, .default = NA_integer_))
awayTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamId = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['team']][['id']] %>%
purrr::pluck(2, .default = NA_integer_))
homeTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(2, .default = NA_integer_))
homeTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(2, .default = NA_character_)
id_vars <- data.frame(
homeTeamId,
homeTeamMascot,
homeTeamName,
homeTeamAbbrev,
homeTeamLogo,
homeTeamLogoDark,
homeTeamFullName,
homeTeamColor,
homeTeamAlternateColor,
homeTeamScore,
homeTeamWinner,
homeTeamRecord,
awayTeamId,
awayTeamMascot,
awayTeamName,
awayTeamAbbrev,
awayTeamLogo,
awayTeamLogoDark,
awayTeamFullName,
awayTeamColor,
awayTeamAlternateColor,
awayTeamScore,
awayTeamWinner,
awayTeamRecord
)
}
game_json <- game_json %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE)
plays <- game_json %>%
purrr::pluck("plays")
if (("coordinate.x" %in% names(plays)) && ("coordinate.y" %in% names(plays))) {
plays <- plays %>%
dplyr::mutate(
# convert types
coordinate.x = as.double(.data$coordinate.x),
coordinate.y = as.double(.data$coordinate.y),
# Free throws are adjusted automatically to 19' from baseline, which
# corresponds to 13.75' from the center of the basket (originally
# the center of the basket is (25, 0))
coordinate.y = dplyr::case_when(
stringr::str_detect(.data$type.text, "Free Throw") ~ 13.75,
TRUE ~ .data$coordinate.y
),
coordinate.x = dplyr::case_when(
stringr::str_detect(.data$type.text, "Free Throw") ~ 25,
TRUE ~ .data$coordinate.x
),
coordinate_x_transformed = dplyr::case_when(
.data$team.id == homeTeamId ~ -1 * (.data$coordinate.y - 41.75),
TRUE ~ .data$coordinate.y - 41.75
),
coordinate_y_transformed = dplyr::case_when(
.data$team.id == homeTeamId ~ -1 * (.data$coordinate.x - 25),
TRUE ~ .data$coordinate.x - 25
)
) %>%
dplyr::rename(
"coordinate.x.raw" = "coordinate.x",
"coordinate.y.raw" = "coordinate.y",
"coordinate.x" = "coordinate_x_transformed",
"coordinate.y" = "coordinate_y_transformed"
)
}
## Written this way for compliance with data repository processing
if ("participants" %in% names(plays)) {
plays <- plays %>%
tidyr::unnest_wider("participants")
suppressWarnings(
aths <- plays %>%
dplyr::group_by(.data$id) %>%
dplyr::select(
"id",
"athlete.id") %>%
tidyr::unnest_wider("athlete.id", names_sep = "_")
)
names(aths) <- c("play.id", paste0("athlete.id.", seq_len(ncol(aths) - 1)))
for (nm in paste0("athlete.id.", 1:3)) {
if (!nm %in% names(aths)) aths[[nm]] <- NA_character_
}
aths <- aths[, c("play.id", paste0("athlete.id.", 1:3))]
plays <- plays %>%
dplyr::bind_cols(aths) %>%
janitor::clean_names() %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"athlete_id_1",
"athlete_id_2",
"athlete_id_3"
)), ~as.integer(.x)))
}
## Written this way for compliance with data repository processing
if (!("homeTeamName" %in% names(plays))) {
plays <- plays %>%
dplyr::bind_cols(id_vars)
}
plays <- plays %>%
dplyr::select(-dplyr::any_of(c("athlete.id", "athlete_id"))) %>%
janitor::clean_names() %>%
dplyr::mutate(
game_id = gameId,
season = season,
season_type = season_type,
game_date = game_date,
game_date_time = game_date_time) %>%
dplyr::rename(dplyr::any_of(c(
"athlete_id_1" = "participants_0_athlete_id",
"athlete_id_2" = "participants_1_athlete_id",
"athlete_id_3" = "participants_2_athlete_id")))
plays <- plays %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"athlete_id_1",
"athlete_id_2",
"athlete_id_3",
"type_id",
"team_id"
)), ~as.integer(.x)))
plays_df <- plays %>%
make_wehoop_data("ESPN WNBA Play-by-Play Information from ESPN.com", Sys.time())
return(plays_df)
}
}
#' **Parse ESPN WNBA Team Box, helper function**
#' @rdname helper_espn_wnba_pbp
#' @param resp Response object from the ESPN WNBA game summary endpoint
#' @return Returns a tibble
#' @importFrom lubridate with_tz ymd_hm
#' @export
helper_espn_wnba_team_box <- function(resp){
game_json <- resp %>%
jsonlite::fromJSON()
gameId <- as.integer(game_json[["header"]][["id"]])
game_date_time <- substr(game_json[['header']][['competitions']][['date']], 1,
nchar(game_json[['header']][['competitions']][['date']]) - 1) %>%
lubridate::ymd_hm() %>%
lubridate::with_tz(tzone = "America/New_York")
game_date <- as.Date(substr(game_date_time, 0, 10))
# ESPN's header `boxscoreAvailable` flag is unreliable for archival games
# (the WBB twin drops pre-2014 boxscores over it), so availability is
# derived from the payload itself; the statistics-length check below
# remains the real gate.
box_score_available <- length(game_json[["boxscore"]][["teams"]]) > 0
if (box_score_available == TRUE) {
teams_box_score_df <- game_json[["boxscore"]][["teams"]] %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE)
if (length(teams_box_score_df[["statistics"]][[1]]) > 0) {
# Teams info columns and values
teams_df <- game_json[["header"]][["competitions"]][["competitors"]][[1]]
homeAway1 <- teams_df[["homeAway"]][1]
homeAway1_team.id <- as.integer(teams_df[["id"]][1])
homeAway1_team.score <- as.integer(teams_df[["score"]][1])
homeAway1_team.winner <- teams_df[["winner"]][1]
homeAway2 <- teams_df[["homeAway"]][2]
homeAway2_team.id <- as.integer(teams_df[["id"]][2])
homeAway2_team.score <- as.integer(teams_df[["score"]][2])
homeAway2_team.winner <- teams_df[["winner"]][2]
# Pivoting the table values for each team from long to wide
statistics_df_1 <- teams_box_score_df[["statistics"]][[1]] %>%
tibble::tibble() %>%
dplyr::select("name", "displayValue") %>%
tidyr::spread("name", "displayValue")
statistics_df_2 <- teams_box_score_df[["statistics"]][[2]] %>%
tibble::tibble() %>%
dplyr::select("name", "displayValue") %>%
tidyr::spread("name", "displayValue")
# Assigning values to the correct data frame rows - 1
statistics_df_1$team.homeAway <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
homeAway1,
homeAway2
)
statistics_df_1$team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
as.integer(homeAway1_team.score),
as.integer(homeAway2_team.score)
)
statistics_df_1$team.winner <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
homeAway1_team.winner,
homeAway2_team.winner
)
statistics_df_1$team.id <- as.integer(teams_box_score_df[["team.id"]][[1]])
statistics_df_1$team.uid <- teams_box_score_df[["team.uid"]][[1]]
statistics_df_1$team.slug <- teams_box_score_df[["team.slug"]][[1]]
statistics_df_1$team.location <- teams_box_score_df[["team.location"]][[1]]
statistics_df_1$team.name <- teams_box_score_df[["team.name"]][[1]]
statistics_df_1$team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[1]]
statistics_df_1$team.displayName <- teams_box_score_df[["team.displayName"]][[1]]
statistics_df_1$team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[1]]
statistics_df_1$team.color <- teams_box_score_df[["team.color"]][[1]]
statistics_df_1$team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[1]]
statistics_df_1$team.logo <- teams_box_score_df[["team.logo"]][[1]]
statistics_df_1$opponent.team.id <- as.integer(teams_box_score_df[["team.id"]][[2]])
statistics_df_1$opponent.team.uid <- teams_box_score_df[["team.uid"]][[2]]
statistics_df_1$opponent.team.slug <- teams_box_score_df[["team.slug"]][[2]]
statistics_df_1$opponent.team.location <- teams_box_score_df[["team.location"]][[2]]
statistics_df_1$opponent.team.name <- teams_box_score_df[["team.name"]][[2]]
statistics_df_1$opponent.team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[2]]
statistics_df_1$opponent.team.displayName <- teams_box_score_df[["team.displayName"]][[2]]
statistics_df_1$opponent.team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[2]]
statistics_df_1$opponent.team.color <- teams_box_score_df[["team.color"]][[2]]
statistics_df_1$opponent.team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[2]]
statistics_df_1$opponent.team.logo <- teams_box_score_df[["team.logo"]][[2]]
statistics_df_1$opponent.team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
as.integer(homeAway2_team.score),
as.integer(homeAway1_team.score)
)
# Assigning values to the correct data frame rows - 2
statistics_df_2$team.homeAway <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
homeAway2,
homeAway1
)
statistics_df_2$team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
as.integer(homeAway2_team.score),
as.integer(homeAway1_team.score)
)
statistics_df_2$team.winner <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
homeAway2_team.winner,
homeAway1_team.winner
)
statistics_df_2$team.id <- as.integer(teams_box_score_df[["team.id"]][[2]])
statistics_df_2$team.uid <- teams_box_score_df[["team.uid"]][[2]]
statistics_df_2$team.slug <- teams_box_score_df[["team.slug"]][[2]]
statistics_df_2$team.location <- teams_box_score_df[["team.location"]][[2]]
statistics_df_2$team.name <- teams_box_score_df[["team.name"]][[2]]
statistics_df_2$team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[2]]
statistics_df_2$team.displayName <- teams_box_score_df[["team.displayName"]][[2]]
statistics_df_2$team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[2]]
statistics_df_2$team.color <- teams_box_score_df[["team.color"]][[2]]
statistics_df_2$team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[2]]
statistics_df_2$team.logo <- teams_box_score_df[["team.logo"]][[2]]
statistics_df_2$opponent.team.id <- as.integer(teams_box_score_df[["team.id"]][[1]])
statistics_df_2$opponent.team.uid <- teams_box_score_df[["team.uid"]][[1]]
statistics_df_2$opponent.team.slug <- teams_box_score_df[["team.slug"]][[1]]
statistics_df_2$opponent.team.location <- teams_box_score_df[["team.location"]][[1]]
statistics_df_2$opponent.team.name <- teams_box_score_df[["team.name"]][[1]]
statistics_df_2$opponent.team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[1]]
statistics_df_2$opponent.team.displayName <- teams_box_score_df[["team.displayName"]][[1]]
statistics_df_2$opponent.team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[1]]
statistics_df_2$opponent.team.color <- teams_box_score_df[["team.color"]][[1]]
statistics_df_2$opponent.team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[1]]
statistics_df_2$opponent.team.logo <- teams_box_score_df[["team.logo"]][[1]]
statistics_df_2$opponent.team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
as.integer(homeAway1_team.score),
as.integer(homeAway2_team.score)
)
complete_statistics_df <- statistics_df_1 %>%
dplyr::bind_rows(statistics_df_2)
# Assigning game/season level data to team box score and converting types
complete_statistics_df$season <- game_json[["header"]][["season"]][["year"]]
complete_statistics_df$season_type <- game_json[["header"]][["season"]][["type"]]
complete_statistics_df$game_date <- as.Date(substr(game_json[["header"]][["competitions"]][["date"]], 0, 10))
complete_statistics_df$game_id <- as.integer(gameId)
complete_statistics_df$game_date_time <- game_date_time
complete_statistics_df$game_date <- game_date
suppressWarnings(
complete_statistics_df <- complete_statistics_df %>%
tidyr::separate("fieldGoalsMade-fieldGoalsAttempted",
into = c("fieldGoalsMade", "fieldGoalsAttempted"),
sep = "-") %>%
tidyr::separate("freeThrowsMade-freeThrowsAttempted",
into = c("freeThrowsMade", "freeThrowsAttempted"),
sep = "-") %>%
tidyr::separate("threePointFieldGoalsMade-threePointFieldGoalsAttempted",
into = c("threePointFieldGoalsMade", "threePointFieldGoalsAttempted"),
sep = "-") %>%
dplyr::mutate(dplyr::across(c(
"fieldGoalPct",
"freeThrowPct",
"threePointFieldGoalPct"
), ~as.numeric(.x))) %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"assists",
"blocks",
"defensiveRebounds",
"fieldGoalsMade",
"fieldGoalsAttempted",
"flagrantFouls",
"fouls",
"freeThrowsMade",
"freeThrowsAttempted",
"offensiveRebounds",
"steals",
"teamTurnovers",
"technicalFouls",
"threePointFieldGoalsMade",
"threePointFieldGoalsAttempted",
"totalRebounds",
"totalTechnicalFouls",
"totalTurnovers",
"turnovers"
)), ~as.integer(.x)))
)
team_box_score <- complete_statistics_df %>%
janitor::clean_names() %>%
dplyr::select(dplyr::any_of(c(
"game_id",
"season",
"season_type",
"game_date",
"game_date_time",
"team_id",
"team_uid",
"team_slug",
"team_location",
"team_name",
"team_abbreviation",
"team_display_name",
"team_short_display_name",
"team_color",
"team_alternate_color",
"team_logo",
"team_home_away",
"team_score",
"team_winner")),
tidyr::everything()) %>%
make_wehoop_data("ESPN WNBA Team Box Information from ESPN.com", Sys.time())
return(team_box_score)
}
}
}
#' **Parse ESPN WNBA Player Box, helper function**
#' @rdname helper_espn_wnba_pbp
#' @param resp Response object from the ESPN WNBA game summary endpoint
#' @return Returns a tibble
#' @importFrom lubridate with_tz ymd_hm
#' @export
helper_espn_wnba_player_box <- function(resp){
game_json <- resp %>%
jsonlite::fromJSON(flatten = TRUE)
players_box_score_df <- game_json[["boxscore"]][["players"]] %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE) %>%
as.data.frame()
gameId <- as.integer(game_json[["header"]][["id"]])
season <- game_json[["header"]][["season"]][["year"]]
season_type <- game_json[["header"]][["season"]][["type"]]
game_date_time <- substr(game_json[['header']][['competitions']][['date']], 1,
nchar(game_json[['header']][['competitions']][['date']]) - 1) %>%
lubridate::ymd_hm() %>%
lubridate::with_tz(tzone = "America/New_York")
game_date <- as.Date(substr(game_date_time, 0, 10))
boxScoreAvailable <- game_json[["header"]][["competitions"]][["boxscoreAvailable"]]
boxScoreSource <- game_json[["header"]][["competitions"]][["boxscoreSource"]]
# This is checking if [[athletes]][[1]]'s stat rebounds is able to be converted to a numeric value
# without introducing NA's
suppressWarnings(
valid_stats <- players_box_score_df[["statistics"]][[1]][["athletes"]][[1]][["stats"]][[1]] %>%
purrr::pluck(7) %>%
as.numeric()
)
# Payload presence replaces ESPN's unreliable header `boxscoreAvailable`
# flag (archival games carry stats while the flag says FALSE); the athlete
# and stat validity conjuncts below remain the real gate.
if (length(game_json[["boxscore"]][["players"]]) > 0 &&
length(players_box_score_df[["statistics"]][[1]][["athletes"]][[1]]) > 1 &&
!is.na(valid_stats)) {
players_df <- players_box_score_df %>%
tidyr::unnest("statistics") %>%
tidyr::unnest("athletes")
if (length(players_box_score_df[["statistics"]]) > 1 &&
length(players_df$stats[[1]]) > 0) {
players_df <- jsonlite::fromJSON(jsonlite::toJSON(game_json[["boxscore"]][["players"]]), flatten = TRUE) %>%
tidyr::unnest("statistics") %>%
tidyr::unnest("athletes")
stat_cols <- players_df$keys[[1]]
stats <- players_df$stats
stats_df <- as.data.frame(do.call(rbind,stats))
colnames(stats_df) <- stat_cols
suppressWarnings(
stats_df <- stats_df %>%
tidyr::separate("fieldGoalsMade-fieldGoalsAttempted",
into = c("fieldGoalsMade", "fieldGoalsAttempted"),
sep = "-") %>%
tidyr::separate("freeThrowsMade-freeThrowsAttempted",
into = c("freeThrowsMade", "freeThrowsAttempted"),
sep = "-") %>%
tidyr::separate("threePointFieldGoalsMade-threePointFieldGoalsAttempted",
into = c("threePointFieldGoalsMade", "threePointFieldGoalsAttempted"),
sep = "-") %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"minutes",
"fieldGoalPct",
"freeThrowPct",
"threePointFieldGoalPct"
)), ~as.numeric(.x))) %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"assists",
"blocks",
"defensiveRebounds",
"fieldGoalsMade",
"fieldGoalsAttempted",
"flagrantFouls",
"fouls",
"freeThrowsMade",
"freeThrowsAttempted",
"offensiveRebounds",
"steals",
"teamTurnovers",
"technicalFouls",
"threePointFieldGoalsMade",
"threePointFieldGoalsAttempted",
"rebounds",
"totalTechnicalFouls",
"totalTurnovers",
"turnovers",
"points"
)), ~as.integer(.x)))
)
players_df_did_not_play <- players_df %>%
dplyr::filter(.data$didNotPlay) %>%
dplyr::select(dplyr::any_of(c(
"starter",
"ejected",
"didNotPlay",
"reason",
"active",
"athlete.displayName",
"athlete.jersey",
"athlete.id",
"athlete.shortName",
"athlete.headshot.href",
"athlete.position.name",
"athlete.position.abbreviation",
"team.displayName",
"team.shortDisplayName",
"team.location",
"team.name",
"team.logo",
"team.id",
"team.uid",
"team.slug",
"team.abbreviation",
"team.color",
"team.alternateColor"
)))
players_df <- players_df %>%
dplyr::filter(!.data$didNotPlay) %>%
dplyr::select(dplyr::any_of(c(
"starter",
"ejected",
"didNotPlay",
"reason",
"active",
"athlete.displayName",
"athlete.jersey",
"athlete.id",
"athlete.shortName",
"athlete.headshot.href",
"athlete.position.name",
"athlete.position.abbreviation",
"team.displayName",
"team.shortDisplayName",
"team.location",
"team.name",
"team.logo",
"team.id",
"team.uid",
"team.slug",
"team.abbreviation",
"team.color",
"team.alternateColor"
)))
players_df <- stats_df %>%
dplyr::bind_cols(players_df) %>%
dplyr::bind_rows(players_df_did_not_play)
players_df <- players_df %>%
dplyr::select(dplyr::any_of(c(
"athlete.displayName",
"team.shortDisplayName")),
tidyr::everything()) %>%
janitor::clean_names() %>%
dplyr::mutate(
game_id = gameId,
season = season,
season_type = season_type,
game_date = game_date,
game_date_time = game_date_time)
teams_df <- game_json[["header"]][["competitions"]][["competitors"]][[1]]
homeAway1 <- teams_df[["homeAway"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.id <- as.integer(teams_df[["id"]] %>%
purrr::pluck(1, .default = NA_integer_))
homeAway1_team.location <- teams_df[["team.location"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.name <- teams_df[["team.name"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.abbreviation <- teams_df[["team.abbreviation"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.displayName <- teams_df[["team.displayName"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.logos <- teams_df[["team.logos"]][[1]][["href"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.color <- teams_df[["team.color"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.alternateColor <- teams_df[["team.alternateColor"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.winner <- teams_df[["winner"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.score <- as.integer(teams_df[["score"]] %>%
purrr::pluck(1, .default = NA_integer_))
homeAway2 <- teams_df[["homeAway"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.id <- as.integer(teams_df[["id"]] %>%
purrr::pluck(2, .default = NA_integer_))
homeAway2_team.location <- teams_df[["team.location"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.name <- teams_df[["team.name"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.abbreviation <- teams_df[["team.abbreviation"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.displayName <- teams_df[["team.displayName"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.logos <- teams_df[["team.logos"]][[2]][["href"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway2_team.color <- teams_df[["team.color"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.alternateColor <- teams_df[["team.alternateColor"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.winner <- teams_df[["winner"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.score <- as.integer(teams_df[["score"]] %>%
purrr::pluck(2, .default = NA_integer_))
players_df <- players_df %>%
dplyr::mutate(
home_away = ifelse(.data$team_id == homeAway1_team.id, homeAway1, homeAway2),
team_winner = ifelse(.data$team_id == homeAway1_team.id, homeAway1_team.winner, homeAway2_team.winner),
team_score = ifelse(.data$team_id == homeAway1_team.id, homeAway1_team.score, homeAway2_team.score),
opponent_team_id = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.id, homeAway1_team.id),
opponent_team_name = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.name, homeAway1_team.name),
opponent_team_location = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.location, homeAway1_team.location),
opponent_team_display_name = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.displayName, homeAway1_team.displayName),
opponent_team_abbreviation = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.abbreviation, homeAway1_team.abbreviation),
opponent_team_logo = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.logos, homeAway1_team.logos),
opponent_team_color = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.color, homeAway1_team.color),
opponent_team_alternate_color = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.alternateColor, homeAway1_team.alternateColor),
opponent_team_score = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.score, homeAway1_team.score),
) %>%
dplyr::arrange(.data$home_away)
player_box_score <- players_df %>%
dplyr::select(dplyr::any_of(c(
"game_id",
"season",
"season_type",
"game_date",
"game_date_time",
"athlete_id",
"athlete_display_name",
"team_id",
"team_name",
"team_location",
"team_short_display_name",
"minutes",
"field_goals_made",
"field_goals_attempted",
"three_point_field_goals_made",
"three_point_field_goals_attempted",
"free_throws_made",
"free_throws_attempted",
"offensive_rebounds",
"defensive_rebounds",
"rebounds",
"assists",
"steals",
"blocks",
"turnovers",
"fouls",
"plus_minus",
"points",
"starter",
"ejected",
"did_not_play",
"reason",
"active",
"athlete_jersey",
"athlete_short_name",
"athlete_headshot_href",
"athlete_position_name",
"athlete_position_abbreviation",
"team_display_name",
"team_uid",
"team_slug",
"team_logo",
"team_abbreviation",
"team_color",
"team_alternate_color",
"home_away",
"team_winner",
"team_score",
"opponent_team_id",
"opponent_team_name",
"opponent_team_location",
"opponent_team_display_name",
"opponent_team_abbreviation",
"opponent_team_logo",
"opponent_team_color",
"opponent_team_alternate_color",
"opponent_team_score"
))) %>%
dplyr::mutate_at(c(
"athlete_id",
"team_id",
"team_score",
"opponent_team_score"
), as.integer) %>%
make_wehoop_data("ESPN WNBA Player Box Information from ESPN.com", Sys.time())
return(player_box_score)
}
}
}
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Defines functions espn_wnba_scoreboard espn_wnba_teams espn_wnba_game_rosters espn_wnba_player_box espn_wnba_team_box espn_wnba_pbp espn_wnba_game_all
Documented in espn_wnba_game_all espn_wnba_game_rosters espn_wnba_pbp espn_wnba_player_box espn_wnba_scoreboard espn_wnba_team_box espn_wnba_teams
#' Get ESPN's WNBA game data (play-by-play, team and player box)
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return A named list of dataframes: Plays, Team, Player
#'
#' **Plays**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_plays_schema] table.
#'
#' **Team**
#'
#'
#' Columns as documented in the shared [espn_basketball_team_box_schema] table.
#'
#' **Player**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_player_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Game
#' @family ESPN WNBA Functions
#' @examples
#' \donttest{
#' try(espn_wnba_game_all(game_id = 401244185))
#' }
espn_wnba_game_all <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
pbp <- list(Plays = NULL, Team = NULL, Player = NULL)
resp <- NULL
plays_df <- NULL
team_box_score <- NULL
player_box_score <- NULL
#---- Fetch the summary endpoint (single outer tryCatch) -------------------
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
},
error = function(e) .report_api_error(
e,
hint = "Could not fetch game summary for game_id = {game_id}",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {}
)
if (is.null(resp)) {
return(pbp)
}
#---- Play-by-Play ------
tryCatch(
expr = {
plays_df <- helper_espn_wnba_pbp(resp)
if (is.null(plays_df)) {
cli::cli_alert_danger("{Sys.time()}: No play-by-play data for {game_id} available!")
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no play-by-play data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
#---- Team Box ------
tryCatch(
expr = {
team_box_score <- helper_espn_wnba_team_box(resp)
if (is.null(team_box_score)) {
cli::cli_alert_danger("{Sys.time()}: No team box score data for {game_id} available!")
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no team box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
#---- Player Box ------
tryCatch(
expr = {
player_box_score <- helper_espn_wnba_player_box(resp)
if (is.null(player_box_score)) {
cli::cli_alert_danger("{Sys.time()}: No player box score data for {game_id} available!")
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no player box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
pbp <- c(list(plays_df), list(team_box_score), list(player_box_score))
names(pbp) <- c("Plays", "Team", "Player")
return(pbp)
}
#' Get ESPN's WNBA play by play data
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return Returns a play-by-play data frame
#'
#' **Plays**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_plays_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA PBP
#' @family ESPN WNBA Functions
#' @examples
#'
#' \donttest{
#' try(espn_wnba_pbp(game_id = 401455681))
#' }
espn_wnba_pbp <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
#---- Play-by-Play ------
plays_df <- NULL
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
plays_df <- helper_espn_wnba_pbp(resp)
if (is.null(plays_df)) {
return(cli::cli_alert_danger("{Sys.time()}: No play-by-play data for {game_id} available!"))
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no play-by-play data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(plays_df)
}
#' Get ESPN's WNBA team box data
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return Returns a team boxscore data frame
#'
#' **Team**
#'
#'
#' Columns as documented in the shared [espn_basketball_team_box_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Team Box
#' @family ESPN WNBA Functions
#' @examples
#'
#' \donttest{
#' try(espn_wnba_team_box(game_id = 401244185))
#' }
espn_wnba_team_box <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
#---- Team Box ------
team_box_score <- NULL
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
team_box_score <- helper_espn_wnba_team_box(resp)
if (is.null(team_box_score)) {
return(cli::cli_alert_danger("{Sys.time()}: No team box score data for {game_id} available!"))
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no team box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(team_box_score)
}
#' Get ESPN's WNBA player box data
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return Returns a player boxscore data frame
#'
#' **Player**
#'
#'
#' Columns as documented in the shared [espn_basketball_game_all_player_schema] table.
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Player Box
#' @family ESPN WNBA Functions
#' @examples
#' \donttest{
#' try(espn_wnba_player_box(game_id = 401244185))
#' }
#'
espn_wnba_player_box <- function(game_id){
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
summary_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/summary?"
## Inputs
## game_id
full_url <- paste0(summary_url,
"event=", game_id)
#---- Player Box ------
player_box_score <- NULL
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
player_box_score <- helper_espn_wnba_player_box(resp)
if (is.null(player_box_score)) {
return(cli::cli_alert_danger("{Sys.time()}: No player box score data for {game_id} available!"))
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no player box score data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(player_box_score)
}
#' **Get ESPN WNBA game rosters**
#' @rdname espn_wnba_game_all
#' @author Saiem Gilani
#' @param game_id Game ID
#' @return A game rosters data frame
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' athlete_id \tab integer \tab Unique athlete identifier (ESPN). \cr
#' athlete_uid \tab character \tab ESPN athlete UID (universal identifier). \cr
#' athlete_guid \tab character \tab ESPN athlete GUID. \cr
#' athlete_type \tab character \tab Athlete type / class. \cr
#' sdr \tab integer \tab Sdr. \cr
#' first_name \tab character \tab Player's first name. \cr
#' last_name \tab character \tab Player's last name. \cr
#' full_name \tab character \tab Player's full name. \cr
#' athlete_display_name \tab character \tab Athlete display name (full). \cr
#' short_name \tab character \tab Short display name. \cr
#' weight \tab numeric \tab Player weight in pounds. \cr
#' display_weight \tab character \tab Player weight in display format (e.g. '180 lbs'). \cr
#' height \tab numeric \tab Player height (string e.g. '6-2' or inches). \cr
#' display_height \tab character \tab Player height in display format (e.g. '6-2'). \cr
#' age \tab integer \tab Player age (in years). \cr
#' date_of_birth \tab character \tab Date of birth (YYYY-MM-DD). \cr
#' slug \tab character \tab URL-safe identifier. \cr
#' headshot_href \tab character \tab Headshot image URL. \cr
#' headshot_alt \tab character \tab Alternative-text label for the headshot. \cr
#' jersey \tab character \tab Jersey number worn by the player. \cr
#' position_id \tab integer \tab Unique position identifier. \cr
#' position_name \tab character \tab Listed roster position ('Guard', 'Forward', 'Center'). \cr
#' position_display_name \tab character \tab Position display name. \cr
#' position_abbreviation \tab character \tab Position abbreviation ('G' / 'F' / 'C'). \cr
#' position_leaf \tab logical \tab Position leaf. \cr
#' linked \tab logical \tab TRUE if the record is linked to a related entity. \cr
#' years \tab integer \tab Years. \cr
#' active \tab logical \tab TRUE if the row represents an active record (player / team / season). \cr
#' status_id \tab integer \tab Status identifier. \cr
#' status_name \tab character \tab Status label. \cr
#' status_type \tab character \tab Status type. \cr
#' status_abbreviation \tab character \tab Status abbreviation. \cr
#' birth_place_city \tab character \tab Birth place city. \cr
#' birth_place_state \tab character \tab Birth place state. \cr
#' birth_place_country \tab character \tab Birth place country. \cr
#' starter \tab logical \tab TRUE if the player was in the starting lineup; FALSE otherwise. \cr
#' valid \tab logical \tab Valid. \cr
#' did_not_play \tab logical \tab TRUE if the player did not appear in the game. \cr
#' display_name \tab character \tab Display name. \cr
#' reason \tab character \tab Reason. \cr
#' ejected \tab logical \tab TRUE if the player was ejected from the game. \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team_guid \tab character \tab ESPN team GUID. \cr
#' team_uid \tab character \tab ESPN universal team identifier (UID format 's:40~l:...~t:...'). \cr
#' team_sdr \tab integer \tab ESPN team SDR identifier. \cr
#' team_slug \tab character \tab URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces'). \cr
#' team_location \tab character \tab Team city or location string. \cr
#' team_name \tab character \tab Full team display name (e.g. 'Las Vegas Aces'). \cr
#' team_abbreviation \tab character \tab Short team abbreviation (e.g. 'LAS'). \cr
#' team_display_name \tab character \tab Full team display name. \cr
#' team_short_display_name \tab character \tab Short team display name (e.g. 'Aces'). \cr
#' team_color \tab character \tab Team primary color (hex without leading '#'). \cr
#' team_alternate_color \tab character \tab Team alternate color (hex without leading '#'). \cr
#' team_is_active \tab logical \tab TRUE if the team is currently active. \cr
#' is_all_star \tab logical \tab Is all star. \cr
#' logo_href \tab character \tab Team or league logo URL. \cr
#' logo_dark_href \tab character \tab Logo URL for dark backgrounds. \cr
#' logos_href_2 \tab character \tab Logos href 2. \cr
#' logos_href_3 \tab character \tab Logos href 3. \cr
#' game_id \tab integer \tab Unique game identifier. \cr
#' order \tab integer \tab Display order within the result set. \cr
#' home_away \tab character \tab Game venue label ('home' or 'away'). \cr
#' winner \tab logical \tab Winner. \cr
#' draft_display_text \tab character \tab Draft display text. \cr
#' draft_round \tab integer \tab Round of the draft selection. \cr
#' draft_year \tab integer \tab Draft year (4-digit). \cr
#' draft_selection \tab integer \tab Draft selection. \cr
#' hand_type \tab character \tab Hand type. \cr
#' hand_abbreviation \tab character \tab Hand abbreviation. \cr
#' hand_display_value \tab character \tab Hand display value. \cr
#' citizenship \tab character \tab Citizenship. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @import rvest
#' @export
#' @keywords WNBA Game Roster
#' @family ESPN WNBA Functions
#'
#' @examples
#' \donttest{
#' try(espn_wnba_game_rosters(game_id = 401244185))
#' }
espn_wnba_game_rosters <- function(game_id) {
.args <- mget(setdiff(names(formals()), "..."))
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
athlete_roster_df <- data.frame()
tryCatch(
expr = {
play_base_url <- paste0(
"https://sports.core.api.espn.com/v2/sports/basketball/leagues/wnba/events/",
game_id, "/competitions/",
game_id,"/competitors/")
game_res <- .retry_request(play_base_url)
# Check the result
check_status(game_res)
game_resp <- game_res %>%
.resp_text()
game_df <- jsonlite::fromJSON(game_resp)[["items"]] %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE) %>%
dplyr::rename("team_statistics_href" = "statistics.$ref")
colnames(game_df) <- gsub(".\\$ref","_href", colnames(game_df))
game_df <- game_df %>%
dplyr::rename(
"team_id" = "id",
"team_uid" = "uid")
game_df$game_id <- game_id
teams_df <- purrr::map_dfr(game_df$team_href, function(x){
res <- .retry_request(x)
# Check the result
check_status(res)
team_df <- res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_df[["links"]] <- NULL
team_df[["injuries"]] <- NULL
team_df[["record"]] <- NULL
team_df[["athletes"]] <- NULL
team_df[["venue"]] <- NULL
team_df[["groups"]] <- NULL
team_df[["ranks"]] <- NULL
team_df[["statistics"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["links"]] <- NULL
team_df[["notes"]] <- NULL
team_df[["franchise"]] <- NULL
team_df[["againstTheSpreadRecords"]] <- NULL
team_df[["oddsRecords"]] <- NULL
team_df[["college"]] <- NULL
team_df[["transactions"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["depthCharts"]] <- NULL
team_df[["awards"]] <- NULL
team_df[["events"]] <- NULL
team_df <- team_df %>%
purrr::map_if(is.list, as.data.frame) %>%
as.data.frame() %>%
dplyr::select(
-dplyr::any_of(
c("logos.width",
"logos.height",
"logos.alt",
"logos.rel..full.",
"logos.rel..default.",
"logos.rel..scoreboard.",
"logos.rel..scoreboard..1",
"logos.rel..scoreboard.2",
"logos.lastUpdated",
"logos.width.1",
"logos.height.1",
"logos.alt.1",
"logos.rel..full..1",
"logos.rel..dark.",
"logos.rel..dark..1",
"logos.lastUpdated.1",
"logos.width.2",
"logos.height.2",
"logos.alt.2",
"logos.rel..full..2",
"logos.rel..scoreboard.",
"logos.lastUpdated.2",
"logos.width.3",
"logos.height.3",
"logos.alt.3",
"logos.rel..full..3",
"logos.lastUpdated.3",
"X.ref",
"X.ref.1",
"X.ref.2"))) %>%
janitor::clean_names()
colnames(team_df)[1:13] <- paste0("team_", colnames(team_df)[1:13])
team_df <- team_df %>%
dplyr::rename(
"logo_href" = "logos_href",
"logo_dark_href" = "logos_href_1") %>%
dplyr::left_join(
game_df %>%
dplyr::select(
"game_id",
"team_id",
"team_uid",
"order",
"homeAway",
"winner",
"roster_href"),
by = c("team_id" = "team_id",
"team_uid" = "team_uid")
)
})
## Inputs
## game_id
team_roster_df <- purrr::map_dfr(teams_df$team_id, function(x){
res <- .retry_request(paste0(play_base_url, x, "/roster"))
# Check the result
check_status(res)
resp <- res %>%
.resp_text()
raw_play_df <- jsonlite::fromJSON(resp)[["entries"]]
raw_play_df <- raw_play_df %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE) %>%
dplyr::mutate(team_id = x) %>%
dplyr::select(-"period", -"forPlayerId", -"active")
raw_play_df <- raw_play_df %>%
dplyr::left_join(teams_df, by = c("team_id" = "team_id"))
})
colnames(team_roster_df) <- gsub(".\\$ref","_href", colnames(team_roster_df))
athlete_roster_df <- purrr::map_dfr(team_roster_df$athlete_href, function(x){
res <- .retry_request(x)
# Check the result
check_status(res)
resp <- res %>%
.resp_text()
raw_play_df <- jsonlite::fromJSON(resp, flatten = TRUE)
raw_play_df[["links"]] <- NULL
raw_play_df[["injuries"]] <- NULL
raw_play_df[["teams"]] <- NULL
raw_play_df[["team"]] <- NULL
raw_play_df[["college"]] <- NULL
raw_play_df[["proAthlete"]] <- NULL
raw_play_df[["statistics"]] <- NULL
raw_play_df[["notes"]] <- NULL
raw_play_df[["eventLog"]] <- NULL
raw_play_df[["$ref"]] <- NULL
raw_play_df[["position"]][["$ref"]] <- NULL
birth_place <- raw_play_df %>%
purrr::pluck("birthPlace") %>%
as.data.frame()
raw_play_df[["birthPlace"]] <- NULL
raw_play_df2 <- raw_play_df %>%
as.data.frame() %>%
dplyr::bind_cols(birth_place) %>%
dplyr::mutate(id = as.integer(.data$id)) %>%
dplyr::rename(
"athlete_id" = "id",
"athlete_uid" = "uid",
"athlete_guid" = "guid",
"athlete_type" = "type",
"athlete_display_name" = "displayName"
)
raw_play_df2 <- raw_play_df2 %>%
dplyr::left_join(team_roster_df, by = c("athlete_id" = "playerId"))
})
colnames(athlete_roster_df) <- gsub(".\\$ref","_href", colnames(athlete_roster_df))
athlete_roster_df <- athlete_roster_df %>%
janitor::clean_names() %>%
dplyr::rename(
"birth_place_city" = "city",
"birth_place_state" = "state",
"birth_place_country" = "country") %>%
dplyr::select(-dplyr::any_of(c(
"x_ref",
"x_ref_1",
"contract_ref",
"contract_ref_1",
"contract_ref_2",
"draft_ref",
"draft_ref_1",
"athlete_href",
"position_ref",
"position_href",
"roster_href",
"statistics_href"))) %>%
dplyr::mutate_at(c(
"game_id",
"athlete_id",
"team_id",
"position_id",
"status_id",
"sdr",
"team_sdr"), as.integer) %>%
make_wehoop_data("ESPN WNBA Game Roster Information from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no game roster data for {game_id} available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(athlete_roster_df)
}
#' Get ESPN's WNBA team names and ids
#' @author Saiem Gilani
#' @return Returns a tibble
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team \tab character \tab Team-side label or team identifier. \cr
#' mascot \tab character \tab Team mascot. \cr
#' display_name \tab character \tab Display name. \cr
#' short_name \tab character \tab Short display name. \cr
#' abbreviation \tab character \tab Short abbreviation. \cr
#' color \tab character \tab Primary color (hex without leading '#'). \cr
#' alternate_color \tab character \tab Alternate color (hex without leading '#'). \cr
#' logo \tab character \tab Team or league logo URL. \cr
#' logo_dark \tab character \tab Logo dark. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows row_number group_by mutate as_tibble ungroup
#' @importFrom tidyr unnest unnest_wider everything pivot_wider
#' @import rvest
#' @export
#' @keywords WNBA Teams
#' @family ESPN WNBA Functions
#' @examples
#' \donttest{
#' try(espn_wnba_teams())
#' }
espn_wnba_teams <- function(){
.args <- .capture_args()
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
play_base_url <- "http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/teams?limit=1000"
wnba_teams <- data.frame()
tryCatch(
expr = {
res <- .retry_request(play_base_url)
check_status(res)
resp <- res %>%
.resp_text()
leagues <- jsonlite::fromJSON(resp)[["sports"]][["leagues"]][[1]][['teams']][[1]][['team']] %>%
dplyr::group_by(.data$id) %>%
tidyr::unnest_wider("logos", names_sep = "_") %>%
tidyr::unnest_wider("logos_href", names_sep = "_") %>%
dplyr::select(
-"logos_width",
-"logos_height",
-"logos_alt",
-"logos_rel") %>%
dplyr::ungroup()
wnba_teams <- leagues %>%
dplyr::select(
"id",
"location",
"name",
"displayName",
"shortDisplayName",
"abbreviation",
"color",
"alternateColor",
"logos_href_1",
"logos_href_2") %>%
dplyr::rename(
"logo" = "logos_href_1",
"logo_dark" = "logos_href_2",
"mascot" = "name",
"team" = "location",
"team_id" = "id",
"alternate_color" = "alternateColor",
"short_name" = "shortDisplayName",
"display_name" = "displayName") %>%
dplyr::mutate(team_id = as.integer(.data$team_id)) %>%
make_wehoop_data("ESPN WNBA Teams Information from ESPN.com", Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Could not fetch ESPN WNBA teams",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {}
)
return(wnba_teams)
}
#' **Get WNBA schedule for a specific year/date from ESPN's API**
#'
#' @param season Either numeric or character
#' @author Saiem Gilani.
#' @return Returns a tibble
#'
#' Columns as documented in the shared [espn_basketball_scoreboard_schema] table.
#'
#' @import utils
#' @import rvest
#' @importFrom dplyr select rename any_of mutate
#' @importFrom jsonlite fromJSON
#' @importFrom tidyr unnest_wider unchop hoist
#' @importFrom lubridate with_tz ymd_hm
#' @import rvest
#' @export
#' @keywords WNBA Scoreboard
#' @family ESPN WNBA Functions
#' @examples
#' # Get schedule from date 2022-08-31
#' \donttest{
#' try(espn_wnba_scoreboard (season = "20220831"))
#' }
espn_wnba_scoreboard <- function(season){
.args <- mget(setdiff(names(formals()), "..."))
# cli::cli_alert_danger("Returning data for {season}!"))
max_year <- substr(Sys.Date(), 1,4)
if (!(as.integer(substr(season, 1, 4)) %in% c(2001:max_year))) {
message(paste("Error: Season must be between 2001 and", max_year))
}
# year > 2000
season <- as.character(season)
season_dates <- season
schedule_api <- paste0("http://site.api.espn.com/apis/site/v2/sports/basketball/wnba/scoreboard?limit=1000&dates=",
season_dates)
tryCatch(
expr = {
res <- .retry_request(schedule_api)
check_status(res)
raw_sched <- res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
wnba_data <- raw_sched[["events"]] %>%
tibble::tibble(data = .data$.) %>%
tidyr::unnest_wider("data") %>%
tidyr::unchop("competitions") %>%
dplyr::select(
-"id",
-"uid",
-"date",
-"status") %>%
tidyr::unnest_wider("competitions") %>%
dplyr::rename(
"matchup" = "name",
"matchup_short" = "shortName",
"game_id" = "id",
"game_uid" = "uid",
"game_date" = "date"
) %>%
tidyr::hoist("status",
status_name = list("type", "name")) %>%
dplyr::select(!dplyr::any_of(
c(
"timeValid",
"neutralSite",
"conferenceCompetition",
"recent",
"venue",
"type"
)
)) %>%
tidyr::unnest_wider("season", names_sep = "_") %>%
dplyr::rename("season" = "season_year") %>%
dplyr::select(-dplyr::any_of("status"))
wnba_data <- wnba_data %>%
dplyr::mutate(
game_date_time = lubridate::ymd_hm(substr(.data$game_date, 1, nchar(.data$game_date) - 1)) %>%
lubridate::with_tz(tzone = "America/New_York"),
game_date = as.Date(substr(.data$game_date_time, 1, 10)))
wnba_data <- wnba_data %>%
tidyr::hoist(
"competitors",
homeAway = list(1,"homeAway")
)
wnba_data <- wnba_data %>%
tidyr::hoist(
"competitors",
team1_team_name = list(1, "team", "name"),
team1_team_logo = list(1, "team", "logo"),
team1_team_abb = list(1, "team", "abbreviation"),
team1_team_id = list(1, "team", "id"),
team1_team_location = list(1, "team", "location"),
team1_team_full = list(1, "team", "displayName"),
team1_team_color = list(1, "team", "color"),
team1_score = list(1, "score"),
team1_win = list(1, "winner"),
team1_record = list(1, "records", 1, "summary"),
# away team
team2_team_name = list(2, "team", "name"),
team2_team_logo = list(2, "team", "logo"),
team2_team_abb = list(2, "team", "abbreviation"),
team2_team_id = list(2, "team", "id"),
team2_team_location = list(2, "team", "location"),
team2_team_full = list(2, "team", "displayName"),
team2_team_color = list(2, "team", "color"),
team2_score = list(2, "score"),
team2_win = list(2, "winner"),
team2_record = list(2, "records", 1, "summary"))
wnba_data <- wnba_data %>%
dplyr::mutate(
home_team_name = ifelse(.data$homeAway == "home",.data$team1_team_name, .data$team2_team_name),
home_team_logo = ifelse(.data$homeAway == "home",.data$team1_team_logo, .data$team2_team_logo),
home_team_abb = ifelse(.data$homeAway == "home",.data$team1_team_abb, .data$team2_team_abb),
home_team_id = ifelse(.data$homeAway == "home",.data$team1_team_id, .data$team2_team_id),
home_team_location = ifelse(.data$homeAway == "home",.data$team1_team_location, .data$team2_team_location),
home_team_full_name = ifelse(.data$homeAway == "home",.data$team1_team_full, .data$team2_team_full),
home_team_color = ifelse(.data$homeAway == "home",.data$team1_team_color, .data$team2_team_color),
home_score = ifelse(.data$homeAway == "home",.data$team1_score, .data$team2_score),
home_win = ifelse(.data$homeAway == "home",.data$team1_win, .data$team2_win),
home_record = ifelse(.data$homeAway == "home",.data$team1_record, .data$team2_record),
away_team_name = ifelse(.data$homeAway == "away",.data$team1_team_name, .data$team2_team_name),
away_team_logo = ifelse(.data$homeAway == "away",.data$team1_team_logo, .data$team2_team_logo),
away_team_abb = ifelse(.data$homeAway == "away",.data$team1_team_abb, .data$team2_team_abb),
away_team_id = ifelse(.data$homeAway == "away",.data$team1_team_id, .data$team2_team_id),
away_team_location = ifelse(.data$homeAway == "away",.data$team1_team_location, .data$team2_team_location),
away_team_full_name = ifelse(.data$homeAway == "away",.data$team1_team_full, .data$team2_team_full),
away_team_color = ifelse(.data$homeAway == "away",.data$team1_team_color, .data$team2_team_color),
away_score = ifelse(.data$homeAway == "away",.data$team1_score, .data$team2_score),
away_win = ifelse(.data$homeAway == "away",.data$team1_win, .data$team2_win),
away_record = ifelse(.data$homeAway == "away",.data$team1_record, .data$team2_record)
)
wnba_data <- wnba_data %>%
dplyr::mutate_at(c(
"game_id",
"home_team_id",
"home_win",
"away_team_id",
"away_win",
"home_score",
"away_score"), as.integer)
wnba_data <- wnba_data %>%
dplyr::select(-dplyr::any_of(dplyr::starts_with("team1")),
-dplyr::any_of(dplyr::starts_with("team2")),
-dplyr::any_of(c("homeAway")))
if ("broadcasts" %in% names(wnba_data) && !any(is.na(wnba_data[['broadcasts']]))) {
wnba_data %>%
tidyr::hoist(
"broadcasts",
broadcast_market = list(1, "market"),
broadcast_name = list(1, "names", 1)) %>%
dplyr::select(!where(is.list)) %>%
janitor::clean_names() %>%
make_wehoop_data("ESPN WNBA Scoreboard Information from ESPN.com",Sys.time())
} else {
wnba_data %>%
dplyr::select(!where(is.list)) %>%
janitor::clean_names() %>%
make_wehoop_data("ESPN WNBA Scoreboard Information from ESPN.com",Sys.time())
}
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no scoreboard data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
}
#' **Get ESPN WNBA Standings**
#'
#' @author Geoff Hutchinson
#' @param year Either numeric or character (YYYY)
#' @return Returns a tibble
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team \tab character \tab Team-side label or team identifier. \cr
#' avgpointsagainst \tab numeric \tab Avgpointsagainst. \cr
#' avgpointsfor \tab numeric \tab Avgpointsfor. \cr
#' clincher \tab numeric \tab Clincher. \cr
#' differential \tab numeric \tab Differential. \cr
#' divisionwinpercent \tab numeric \tab Divisionwinpercent. \cr
#' gamesbehind \tab numeric \tab Gamesbehind. \cr
#' leaguewinpercent \tab numeric \tab Leaguewinpercent. \cr
#' losses \tab numeric \tab Total losses. \cr
#' playoffseed \tab numeric \tab Playoffseed. \cr
#' streak \tab numeric \tab Current streak (e.g. 'W3' for three-game win streak). \cr
#' winpercent \tab numeric \tab Winpercent. \cr
#' wins \tab numeric \tab Total wins. \cr
#' leaguestandings \tab character \tab Leaguestandings. \cr
#' home \tab character \tab Home. \cr
#' road \tab character \tab Road. \cr
#' vsdiv \tab character \tab Vsdiv. \cr
#' vsconf \tab character \tab Vsconf. \cr
#' lasttengames \tab character \tab Lasttengames. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom rlang .data
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr select rename
#' @importFrom tidyr pivot_wider
#' @importFrom data.table rbindlist
#' @keywords WNBA Standings
#' @family ESPN WNBA Functions
#' @export
#' @examples
#' \donttest{
#' try(espn_wnba_standings(year = 2021))
#' }
espn_wnba_standings <- function(year){
.args <- mget(setdiff(names(formals()), "..."))
standings_url <- "https://site.web.api.espn.com/apis/v2/sports/basketball/wnba/standings?region=us&lang=en&contentorigin=espn&type=0&level=1&sort=winpercent%3Adesc%2Cwins%3Adesc%2Cgamesbehind%3Aasc&"
## Inputs
## year
full_url <- paste0(standings_url,
"season=", year)
standings <- data.frame()
tryCatch(
expr = {
res <- .retry_request(full_url)
check_status(res)
resp <- res %>%
.resp_text()
raw_standings <- jsonlite::fromJSON(resp)[["standings"]]
#Create a dataframe of all NBA teams by extracting from the raw_standings file
teams <- raw_standings[["entries"]][["team"]]
teams <- teams %>%
dplyr::select(
"id",
"displayName") %>%
dplyr::rename(
"team_id" = "id",
"team" = "displayName")
#creating a dataframe of the WNBA raw standings table from ESPN
standings_df <- raw_standings[["entries"]][["stats"]]
standings_data <- data.table::rbindlist(standings_df, fill = TRUE, idcol = T)
#Use the following code to replace NA's in the dataframe with the correct corresponding values and removing all unnecessary columns
standings_data$value <- ifelse(is.na(standings_data$value) & !is.na(standings_data$summary), standings_data$summary, standings_data$value)
standings_data <- standings_data %>%
dplyr::select(
".id",
"type",
"value")
#Use pivot_wider to transpose the dataframe so that we now have a standings row for each team
standings_data <- standings_data %>%
tidyr::pivot_wider(names_from = "type", values_from = "value")
standings_data <- standings_data %>%
dplyr::select(-".id")
#joining the 2 dataframes together to create a standings table
standings <- cbind(teams, standings_data) %>%
dplyr::mutate(team_id = as.integer(.data$team_id)) %>%
dplyr::mutate_at(c(
"avgpointsagainst",
"avgpointsfor",
"clincher",
"differential",
"divisionwinpercent",
"gamesbehind",
"leaguewinpercent",
"losses",
"playoffseed",
"streak",
"winpercent",
"wins"
), as.numeric) %>%
make_wehoop_data("ESPN WNBA Standings Information from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no standings data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(standings)
}
#' @import utils
utils::globalVariables(c("where"))
#' @title
#' **Get ESPN WNBA team stats data**
#' @author Saiem Gilani
#' @param team_id Team ID
#' @param year Year
#' @param season_type (character, default: regular): Season type - regular or postseason
#' @param total (boolean, default: FALSE): Totals
#' @return Returns a tibble with the team stats data
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team_guid \tab character \tab ESPN team GUID. \cr
#' team_uid \tab character \tab ESPN universal team identifier (UID format 's:40~l:...~t:...'). \cr
#' team_sdr \tab integer \tab ESPN team SDR identifier. \cr
#' team_slug \tab character \tab URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces'). \cr
#' team_location \tab character \tab Team city or location string. \cr
#' team_name \tab character \tab Full team display name (e.g. 'Las Vegas Aces'). \cr
#' team_abbreviation \tab character \tab Short team abbreviation (e.g. 'LAS'). \cr
#' team_display_name \tab character \tab Full team display name. \cr
#' team_short_display_name \tab character \tab Short team display name (e.g. 'Aces'). \cr
#' team_color \tab character \tab Team primary color (hex without leading '#'). \cr
#' team_alternate_color \tab character \tab Team alternate color (hex without leading '#'). \cr
#' team_is_active \tab logical \tab TRUE if the team is currently active. \cr
#' team_is_all_star \tab logical \tab TRUE if the row represents an All-Star team. \cr
#' logo_href \tab character \tab Team or league logo URL. \cr
#' logo_dark_href \tab character \tab Logo URL for dark backgrounds. \cr
#' defensive_blocks \tab numeric \tab Short for blocked shot, number of times when a defensive player legally deflects a field goal attempt from an offensive player. \cr
#' defensive_defensive_rebounds \tab numeric \tab The number of times when the defense obtains the possession of the ball after a missed shot by the offense. \cr
#' defensive_steals \tab numeric \tab The number of times a defensive player forced a turnover by intercepting or deflecting a pass or a dribble of an offensive player. \cr
#' defensive_avg_defensive_rebounds \tab numeric \tab The average defensive rebounds per game. \cr
#' defensive_avg_blocks \tab numeric \tab The average blocks per game. \cr
#' defensive_avg_steals \tab numeric \tab The average steals per game. \cr
#' defensive_avg48defensive_rebounds \tab numeric \tab The average number of defensive rebounds per 48 minutes. \cr
#' defensive_avg48blocks \tab numeric \tab The average number of blocks per 48 minutes. \cr
#' defensive_avg48steals \tab numeric \tab The average number of steals per 48 minutes. \cr
#' general_disqualifications \tab numeric \tab The number of times a player reached the foul limit. \cr
#' general_flagrant_fouls \tab numeric \tab The number of fouls that the officials thought were unnecessary or excessive. \cr
#' general_fouls \tab numeric \tab The number of times a player had illegal contact with the opponent. \cr
#' general_ejections \tab numeric \tab The number of times a player or coach is removed from the game as a result of a serious offense. \cr
#' general_technical_fouls \tab numeric \tab The number of times an player or coach was called for a technical foul (unsportsmanlike conduct or violations). \cr
#' general_rebounds \tab numeric \tab The total number of rebounds (offensive and defensive). \cr
#' general_avg_minutes \tab numeric \tab The average number of minutes per game. \cr
#' general_nba_rating \tab numeric \tab General nba rating. \cr
#' general_plus_minus \tab numeric \tab A player's estimated on-court impact on team performance measured in point differential per 100 possessions. \cr
#' general_game_day_of_year \tab numeric \tab The day of the season on which this game falls; if it's the 15th day of the regular season in Nov, the game day of year is 15 (ie, game day of year != day of year). \cr
#' general_avg_rebounds \tab numeric \tab The average rebounds per game. \cr
#' general_avg_fouls \tab numeric \tab The average fouls committed per game. \cr
#' general_avg_flagrant_fouls \tab numeric \tab The average number of flagrant fouls per game. \cr
#' general_avg_technical_fouls \tab numeric \tab The average number of technical fouls per game. \cr
#' general_avg_ejections \tab numeric \tab The average ejections per game. \cr
#' general_avg_disqualifications \tab numeric \tab The average number of disqualifications per game. \cr
#' general_assist_turnover_ratio \tab numeric \tab The average number of assists a player or team records per turnover. \cr
#' general_steal_foul_ratio \tab numeric \tab The average number of steals a player or team records per foul committed. \cr
#' general_block_foul_ratio \tab numeric \tab The average number of blocks a player or record per foul committed. \cr
#' general_avg_team_rebounds \tab numeric \tab The average number of rebounds for a team per game. \cr
#' general_total_rebounds \tab numeric \tab The total number of rebounds for a team or player. \cr
#' general_total_technical_fouls \tab numeric \tab The total number of technical fouls for a team or player. \cr
#' general_team_assist_turnover_ratio \tab numeric \tab The number of assists per turnover for a team. \cr
#' general_team_rebounds \tab numeric \tab The total number of rebounds for a team. \cr
#' general_steal_turnover_ratio \tab numeric \tab The number of steals per turnover. \cr
#' general_avg48rebounds \tab numeric \tab The average number of rebounds per 48 minutes. \cr
#' general_avg48fouls \tab numeric \tab The average number of fouls committed per 48 minutes. \cr
#' general_avg48flagrant_fouls \tab numeric \tab The average number of flagrant fouls committed per 48 minutes. \cr
#' general_avg48technical_fouls \tab numeric \tab The average number of technical fouls committed per 48 minutes. \cr
#' general_avg48ejections \tab numeric \tab The average number of ejections per 48 minutes. \cr
#' general_avg48disqualifications \tab numeric \tab The average number of disqualifications per 48 minutes. \cr
#' general_games_played \tab numeric \tab Games Played. \cr
#' general_games_started \tab numeric \tab The number of games started by an athlete. \cr
#' general_double_double \tab numeric \tab The number of times double digit values were accumulated in 2 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' general_triple_double \tab numeric \tab The number of times double digit values were accumulated in 3 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' offensive_assists \tab numeric \tab The number of times a player who passes the ball to a teammate in a way that leads to a score by field goal, meaning that he or she was "assisting" in the basket. There is some judgment involved in deciding whether a passer should be credited with an assist. \cr
#' offensive_field_goals \tab numeric \tab Field Goal makes and attempts. \cr
#' offensive_field_goals_attempted \tab numeric \tab The number of times a 2pt field goal was attempted. \cr
#' offensive_field_goals_made \tab numeric \tab The number of times a 2pt field goal was made. \cr
#' offensive_field_goal_pct \tab numeric \tab The ratio of field goals made to field goals attempted: FGM / FGA. \cr
#' offensive_free_throws \tab numeric \tab Free Throw makes and attempts. \cr
#' offensive_free_throw_pct \tab numeric \tab The ratio of free throws made to free throws attempted: FTM / FTA. \cr
#' offensive_free_throws_attempted \tab numeric \tab The number of times a free throw was attempted. \cr
#' offensive_free_throws_made \tab numeric \tab The number of times a free throw was made. \cr
#' offensive_offensive_rebounds \tab numeric \tab The number of times when the offense obtains the possession of the ball after a missed shot. \cr
#' offensive_points \tab numeric \tab The number of points scored. \cr
#' offensive_turnovers \tab numeric \tab The number of times a player loses possession to the other team. \cr
#' offensive_three_point_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_three_point_field_goals_attempted \tab numeric \tab The number of times a 3pt field goal was attempted. \cr
#' offensive_three_point_field_goals_made \tab numeric \tab The number of times a 3pt field goal was made. \cr
#' offensive_team_turnovers \tab numeric \tab The number of turnovers for the team. \cr
#' offensive_total_turnovers \tab numeric \tab The number of turnovers plus team turnovers for the team. \cr
#' offensive_points_in_paint \tab numeric \tab The amount of points scored in the area known as "the Paint"(the rectangle between the foul line and the baseline). \cr
#' offensive_brick_index \tab numeric \tab How many points a player costs his team with his shooting compared with the league average on a per-40-minute basis. ((52.8 - TS\%) x (FGA + (FTA x 0.44))) / (Min/40) . \cr
#' offensive_avg_field_goals_made \tab numeric \tab The average field goals made per game. \cr
#' offensive_avg_field_goals_attempted \tab numeric \tab The average field goals attempted per game. \cr
#' offensive_avg_three_point_field_goals_made \tab numeric \tab The average three point field goals made per game. \cr
#' offensive_avg_three_point_field_goals_attempted \tab numeric \tab The average three point field goals attempted per game. \cr
#' offensive_avg_free_throws_made \tab numeric \tab The average free throw shots made per game. \cr
#' offensive_avg_free_throws_attempted \tab numeric \tab The average free throw shots attempted per game. \cr
#' offensive_avg_points \tab numeric \tab The average number of points scored per game. \cr
#' offensive_avg_points_allowed \tab numeric \tab The average number of points allowed per game. \cr
#' offensive_avg_offensive_rebounds \tab numeric \tab The average offensive rebounds per game. \cr
#' offensive_avg_assists \tab numeric \tab The average assists per game. \cr
#' offensive_avg_turnovers \tab numeric \tab The average turnovers committed per game. \cr
#' offensive_offensive_rebound_pct \tab numeric \tab The percentage of the number of times they obtain the possession of the ball after a missed shot. \cr
#' offensive_estimated_possessions \tab numeric \tab An estimation of the number of possessions for a team or player. \cr
#' offensive_avg_estimated_possessions \tab numeric \tab The average number of estimated possessions per game for a team or player. \cr
#' offensive_points_per_estimated_possessions \tab numeric \tab The number of points per estimated possession for a team or player. \cr
#' offensive_avg_team_turnovers \tab numeric \tab The average number of turnovers for a team per game. \cr
#' offensive_avg_total_turnovers \tab numeric \tab The average number of total turnovers for a team per game. \cr
#' offensive_three_point_field_goal_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made for a team or player. \cr
#' offensive_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted for a team or player. \cr
#' offensive_avg_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made per game for a team or player. \cr
#' offensive_avg_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted per game for a team or player. \cr
#' offensive_two_point_field_goal_pct \tab numeric \tab The percentage of 2-points fields goals made by a team or player. \cr
#' offensive_shooting_efficiency \tab numeric \tab The efficiency with which a team or player shoots the basketball. \cr
#' offensive_scoring_efficiency \tab numeric \tab The efficiency with which a team or player scores the basketball. \cr
#' offensive_avg48field_goals_made \tab numeric \tab The average number of fieldgoals made per 48 minutes. \cr
#' offensive_avg48field_goals_attempted \tab numeric \tab The average number of fieldgoals attempted per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_made \tab numeric \tab The average per number of 3-Pointers made per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_attempted \tab numeric \tab The average number of 3-pointers attempted per 48 minutes. \cr
#' offensive_avg48free_throws_made \tab numeric \tab The average number of Free Throws made per 48 minutes. \cr
#' offensive_avg48free_throws_attempted \tab numeric \tab The average number of free throws attempted per 48 minutes. \cr
#' offensive_avg48points \tab numeric \tab The average number of points scored per 48 minutes. \cr
#' offensive_avg48offensive_rebounds \tab numeric \tab The average number of offenseive rebounds per 48 minutes. \cr
#' offensive_avg48assists \tab numeric \tab The average number of assists per 48 minutes. \cr
#' offensive_avg48turnovers \tab numeric \tab The average number of turnovers per 48 minutes. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @importFrom jsonlite fromJSON toJSON
#' @importFrom dplyr filter select rename bind_cols bind_rows
#' @importFrom tidyr unnest unnest_wider everything
#' @export
#' @keywords WNBA Team Stats
#' @family ESPN WNBA Functions
#'
#' @examples
#' \donttest{
#' try(espn_wnba_team_stats(team_id = 17, year = 2020))
#' }
espn_wnba_team_stats <- function(
team_id,
year,
season_type = 'regular',
total = FALSE
){
.args <- mget(setdiff(names(formals()), "..."))
if (!(tolower(season_type) %in% c("regular","postseason"))) {
# Check if season_type is appropriate, if not regular
cli::cli_abort("Enter valid season_type: regular or postseason")
}
s_type <- ifelse(season_type == "postseason", 3, 2)
base_url <- "https://sports.core.api.espn.com/v2/sports/basketball/leagues/wnba/seasons/"
totals <- ifelse(total == TRUE, 0, "")
full_url <- paste0(
base_url,
year,
'/types/',s_type,
'/teams/',team_id,
'/statistics/', totals
)
df <- data.frame()
tryCatch(
expr = {
# Create the GET request and set response as res
res <- .retry_request(full_url)
# Check the result
check_status(res)
# Get the content and return result as data.frame
df <- res %>%
.resp_text() %>%
jsonlite::fromJSON()
team_url <- df[["team"]][["$ref"]]
# Create the GET request and set response as res
team_res <- .retry_request(team_url)
# Check the result
check_status(team_res)
# Get the content and return result as data.frame
team_df <- team_res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_df[["links"]] <- NULL
team_df[["injuries"]] <- NULL
team_df[["record"]] <- NULL
team_df[["athletes"]] <- NULL
team_df[["venue"]] <- NULL
team_df[["groups"]] <- NULL
team_df[["ranks"]] <- NULL
team_df[["statistics"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["links"]] <- NULL
team_df[["notes"]] <- NULL
team_df[["franchise"]] <- NULL
team_df[["record"]] <- NULL
team_df[["college"]] <- NULL
team_df <- team_df %>%
purrr::map_if(is.list,as.data.frame) %>%
as.data.frame() %>%
dplyr::select(
-dplyr::any_of(
c("logos.width",
"logos.height",
"logos.alt",
"logos.rel..full.",
"logos.rel..default.",
"logos.lastUpdated",
"logos.width.1",
"logos.height.1",
"logos.alt.1",
"logos.rel..full..1",
"logos.rel..dark.",
"logos.lastUpdated.1",
"X.ref",
"X.ref.1",
"X.ref.2",
"X.ref.3"))) %>%
janitor::clean_names()
colnames(team_df)[1:14] <- paste0("team_",colnames(team_df)[1:14])
team_df <- team_df %>%
dplyr::rename(
"logo_href" = "logos_href",
"logo_dark_href" = "logos_href_1")
df <- df %>%
purrr::pluck("splits") %>%
purrr::pluck("categories") %>%
tidyr::unnest("stats", names_sep = "_")
df <- df %>%
dplyr::mutate(
stats_category_name = paste0(.data$name, "_", .data$stats_name)) %>%
dplyr::select(
"stats_category_name",
"stats_value") %>%
tidyr::pivot_wider(
names_from = "stats_category_name",
values_from = "stats_value",
values_fn = dplyr::first) %>%
janitor::clean_names()
df <- team_df %>%
dplyr::bind_cols(df)
df <- df %>%
dplyr::mutate_at(c(
"team_id",
"team_sdr"), as.integer) %>%
make_wehoop_data("ESPN WNBA Team Season Stats from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no team season stats data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(df)
}
#' @title
#' **Get ESPN WNBA player stats data**
#' @author Saiem Gilani
#' @param athlete_id Athlete ID
#' @param year Year
#' @param season_type (character, default: regular): Season type - regular or postseason
#' @param total (boolean, default: FALSE): Totals
#' @return Returns a tibble with the player stats data
#'
#' \if{html}{\tabular{lll}{
#' col_name \tab types \tab description \cr
#' athlete_id \tab integer \tab Unique athlete identifier (ESPN). \cr
#' athlete_uid \tab character \tab ESPN athlete UID (universal identifier). \cr
#' athlete_guid \tab character \tab ESPN athlete GUID. \cr
#' athlete_type \tab character \tab Athlete type / class. \cr
#' sdr \tab integer \tab Sdr. \cr
#' first_name \tab character \tab Player's first name. \cr
#' last_name \tab character \tab Player's last name. \cr
#' full_name \tab character \tab Player's full name. \cr
#' display_name \tab character \tab Display name. \cr
#' short_name \tab character \tab Short display name. \cr
#' weight \tab numeric \tab Player weight in pounds. \cr
#' display_weight \tab character \tab Player weight in display format (e.g. '180 lbs'). \cr
#' height \tab numeric \tab Player height (string e.g. '6-2' or inches). \cr
#' display_height \tab character \tab Player height in display format (e.g. '6-2'). \cr
#' age \tab integer \tab Player age (in years). \cr
#' date_of_birth \tab character \tab Date of birth (YYYY-MM-DD). \cr
#' slug \tab character \tab URL-safe identifier. \cr
#' headshot_href \tab character \tab Headshot image URL. \cr
#' headshot_alt \tab character \tab Alternative-text label for the headshot. \cr
#' position_id \tab integer \tab Unique position identifier. \cr
#' position_name \tab character \tab Listed roster position ('Guard', 'Forward', 'Center'). \cr
#' position_display_name \tab character \tab Position display name. \cr
#' position_abbreviation \tab character \tab Position abbreviation ('G' / 'F' / 'C'). \cr
#' position_leaf \tab logical \tab Position leaf. \cr
#' linked \tab logical \tab TRUE if the record is linked to a related entity. \cr
#' years \tab integer \tab Years. \cr
#' active \tab logical \tab TRUE if the row represents an active record (player / team / season). \cr
#' status_id \tab integer \tab Status identifier. \cr
#' status_name \tab character \tab Status label. \cr
#' status_type \tab character \tab Status type. \cr
#' status_abbreviation \tab character \tab Status abbreviation. \cr
#' defensive_blocks \tab numeric \tab Short for blocked shot, number of times when a defensive player legally deflects a field goal attempt from an offensive player. \cr
#' defensive_defensive_rebounds \tab numeric \tab The number of times when the defense obtains the possession of the ball after a missed shot by the offense. \cr
#' defensive_steals \tab numeric \tab The number of times a defensive player forced a turnover by intercepting or deflecting a pass or a dribble of an offensive player. \cr
#' defensive_avg_defensive_rebounds \tab numeric \tab The average defensive rebounds per game. \cr
#' defensive_avg_blocks \tab numeric \tab The average blocks per game. \cr
#' defensive_avg_steals \tab numeric \tab The average steals per game. \cr
#' defensive_avg48defensive_rebounds \tab numeric \tab The average number of defensive rebounds per 48 minutes. \cr
#' defensive_avg48blocks \tab numeric \tab The average number of blocks per 48 minutes. \cr
#' defensive_avg48steals \tab numeric \tab The average number of steals per 48 minutes. \cr
#' general_disqualifications \tab numeric \tab The number of times a player reached the foul limit. \cr
#' general_flagrant_fouls \tab numeric \tab The number of fouls that the officials thought were unnecessary or excessive. \cr
#' general_fouls \tab numeric \tab The number of times a player had illegal contact with the opponent. \cr
#' general_ejections \tab numeric \tab The number of times a player or coach is removed from the game as a result of a serious offense. \cr
#' general_technical_fouls \tab numeric \tab The number of times an player or coach was called for a technical foul (unsportsmanlike conduct or violations). \cr
#' general_rebounds \tab numeric \tab The total number of rebounds (offensive and defensive). \cr
#' general_vorp \tab numeric \tab Value Over Replacement Player. \cr
#' general_minutes \tab numeric \tab The total number of minutes played. \cr
#' general_avg_minutes \tab numeric \tab The average number of minutes per game. \cr
#' general_fantasy_rating \tab numeric \tab The Fantasy Rating of a player. \cr
#' general_nba_rating \tab numeric \tab General nba rating. \cr
#' general_plus_minus \tab numeric \tab A player's estimated on-court impact on team performance measured in point differential per 100 possessions. \cr
#' general_avg_rebounds \tab numeric \tab The average rebounds per game. \cr
#' general_avg_fouls \tab numeric \tab The average fouls committed per game. \cr
#' general_avg_flagrant_fouls \tab numeric \tab The average number of flagrant fouls per game. \cr
#' general_avg_technical_fouls \tab numeric \tab The average number of technical fouls per game. \cr
#' general_avg_ejections \tab numeric \tab The average ejections per game. \cr
#' general_avg_disqualifications \tab numeric \tab The average number of disqualifications per game. \cr
#' general_assist_turnover_ratio \tab numeric \tab The average number of assists a player or team records per turnover. \cr
#' general_steal_foul_ratio \tab numeric \tab The average number of steals a player or team records per foul committed. \cr
#' general_block_foul_ratio \tab numeric \tab The average number of blocks a player or record per foul committed. \cr
#' general_avg_team_rebounds \tab numeric \tab The average number of rebounds for a team per game. \cr
#' general_total_rebounds \tab numeric \tab The total number of rebounds for a team or player. \cr
#' general_total_technical_fouls \tab numeric \tab The total number of technical fouls for a team or player. \cr
#' general_team_assist_turnover_ratio \tab numeric \tab The number of assists per turnover for a team. \cr
#' general_steal_turnover_ratio \tab numeric \tab The number of steals per turnover. \cr
#' general_avg48rebounds \tab numeric \tab The average number of rebounds per 48 minutes. \cr
#' general_avg48fouls \tab numeric \tab The average number of fouls committed per 48 minutes. \cr
#' general_avg48flagrant_fouls \tab numeric \tab The average number of flagrant fouls committed per 48 minutes. \cr
#' general_avg48technical_fouls \tab numeric \tab The average number of technical fouls committed per 48 minutes. \cr
#' general_avg48ejections \tab numeric \tab The average number of ejections per 48 minutes. \cr
#' general_avg48disqualifications \tab numeric \tab The average number of disqualifications per 48 minutes. \cr
#' general_games_played \tab numeric \tab Games Played. \cr
#' general_games_started \tab numeric \tab The number of games started by an athlete. \cr
#' general_double_double \tab numeric \tab The number of times double digit values were accumulated in 2 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' general_triple_double \tab numeric \tab The number of times double digit values were accumulated in 3 of the following categories: points, rebounds, assists, steals, and blocked shots. \cr
#' offensive_assists \tab numeric \tab The number of times a player who passes the ball to a teammate in a way that leads to a score by field goal, meaning that he or she was "assisting" in the basket. There is some judgment involved in deciding whether a passer should be credited with an assist. \cr
#' offensive_field_goals \tab numeric \tab Field Goal makes and attempts. \cr
#' offensive_field_goals_attempted \tab numeric \tab The number of times a 2pt field goal was attempted. \cr
#' offensive_field_goals_made \tab numeric \tab The number of times a 2pt field goal was made. \cr
#' offensive_field_goal_pct \tab numeric \tab The ratio of field goals made to field goals attempted: FGM / FGA. \cr
#' offensive_free_throws \tab numeric \tab Free Throw makes and attempts. \cr
#' offensive_free_throw_pct \tab numeric \tab The ratio of free throws made to free throws attempted: FTM / FTA. \cr
#' offensive_free_throws_attempted \tab numeric \tab The number of times a free throw was attempted. \cr
#' offensive_free_throws_made \tab numeric \tab The number of times a free throw was made. \cr
#' offensive_offensive_rebounds \tab numeric \tab The number of times when the offense obtains the possession of the ball after a missed shot. \cr
#' offensive_points \tab numeric \tab The number of points scored. \cr
#' offensive_turnovers \tab numeric \tab The number of times a player loses possession to the other team. \cr
#' offensive_three_point_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_three_point_field_goals_attempted \tab numeric \tab The number of times a 3pt field goal was attempted. \cr
#' offensive_three_point_field_goals_made \tab numeric \tab The number of times a 3pt field goal was made. \cr
#' offensive_total_turnovers \tab numeric \tab The number of turnovers plus team turnovers for the team. \cr
#' offensive_points_in_paint \tab numeric \tab The amount of points scored in the area known as "the Paint"(the rectangle between the foul line and the baseline). \cr
#' offensive_brick_index \tab numeric \tab How many points a player costs his team with his shooting compared with the league average on a per-40-minute basis. ((52.8 - TS\%) x (FGA + (FTA x 0.44))) / (Min/40) . \cr
#' offensive_avg_field_goals_made \tab numeric \tab The average field goals made per game. \cr
#' offensive_avg_field_goals_attempted \tab numeric \tab The average field goals attempted per game. \cr
#' offensive_avg_three_point_field_goals_made \tab numeric \tab The average three point field goals made per game. \cr
#' offensive_avg_three_point_field_goals_attempted \tab numeric \tab The average three point field goals attempted per game. \cr
#' offensive_avg_free_throws_made \tab numeric \tab The average free throw shots made per game. \cr
#' offensive_avg_free_throws_attempted \tab numeric \tab The average free throw shots attempted per game. \cr
#' offensive_avg_points \tab numeric \tab The average number of points scored per game. \cr
#' offensive_avg_offensive_rebounds \tab numeric \tab The average offensive rebounds per game. \cr
#' offensive_avg_assists \tab numeric \tab The average assists per game. \cr
#' offensive_avg_turnovers \tab numeric \tab The average turnovers committed per game. \cr
#' offensive_offensive_rebound_pct \tab numeric \tab The percentage of the number of times they obtain the possession of the ball after a missed shot. \cr
#' offensive_estimated_possessions \tab numeric \tab An estimation of the number of possessions for a team or player. \cr
#' offensive_avg_estimated_possessions \tab numeric \tab The average number of estimated possessions per game for a team or player. \cr
#' offensive_points_per_estimated_possessions \tab numeric \tab The number of points per estimated possession for a team or player. \cr
#' offensive_avg_team_turnovers \tab numeric \tab The average number of turnovers for a team per game. \cr
#' offensive_avg_total_turnovers \tab numeric \tab The average number of total turnovers for a team per game. \cr
#' offensive_three_point_field_goal_pct \tab numeric \tab The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA. \cr
#' offensive_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made for a team or player. \cr
#' offensive_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted for a team or player. \cr
#' offensive_avg_two_point_field_goals_made \tab numeric \tab The number of 2-point field goals made per game for a team or player. \cr
#' offensive_avg_two_point_field_goals_attempted \tab numeric \tab The number of 2-point field goals attempted per game for a team or player. \cr
#' offensive_two_point_field_goal_pct \tab numeric \tab The percentage of 2-points fields goals made by a team or player. \cr
#' offensive_shooting_efficiency \tab numeric \tab The efficiency with which a team or player shoots the basketball. \cr
#' offensive_scoring_efficiency \tab numeric \tab The efficiency with which a team or player scores the basketball. \cr
#' offensive_avg48field_goals_made \tab numeric \tab The average number of fieldgoals made per 48 minutes. \cr
#' offensive_avg48field_goals_attempted \tab numeric \tab The average number of fieldgoals attempted per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_made \tab numeric \tab The average per number of 3-Pointers made per 48 minutes. \cr
#' offensive_avg48three_point_field_goals_attempted \tab numeric \tab The average number of 3-pointers attempted per 48 minutes. \cr
#' offensive_avg48free_throws_made \tab numeric \tab The average number of Free Throws made per 48 minutes. \cr
#' offensive_avg48free_throws_attempted \tab numeric \tab The average number of free throws attempted per 48 minutes. \cr
#' offensive_avg48points \tab numeric \tab The average number of points scored per 48 minutes. \cr
#' offensive_avg48offensive_rebounds \tab numeric \tab The average number of offenseive rebounds per 48 minutes. \cr
#' offensive_avg48assists \tab numeric \tab The average number of assists per 48 minutes. \cr
#' offensive_avg48turnovers \tab numeric \tab The average number of turnovers per 48 minutes. \cr
#' team_id \tab integer \tab Unique team identifier. \cr
#' team_guid \tab character \tab ESPN team GUID. \cr
#' team_uid \tab character \tab ESPN universal team identifier (UID format 's:40~l:...~t:...'). \cr
#' team_sdr \tab integer \tab ESPN team SDR identifier. \cr
#' team_slug \tab character \tab URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces'). \cr
#' team_location \tab character \tab Team city or location string. \cr
#' team_name \tab character \tab Full team display name (e.g. 'Las Vegas Aces'). \cr
#' team_abbreviation \tab character \tab Short team abbreviation (e.g. 'LAS'). \cr
#' team_display_name \tab character \tab Full team display name. \cr
#' team_short_display_name \tab character \tab Short team display name (e.g. 'Aces'). \cr
#' team_color \tab character \tab Team primary color (hex without leading '#'). \cr
#' team_alternate_color \tab character \tab Team alternate color (hex without leading '#'). \cr
#' team_is_active \tab logical \tab TRUE if the team is currently active. \cr
#' team_is_all_star \tab logical \tab TRUE if the row represents an All-Star team. \cr
#' logo_href \tab character \tab Team or league logo URL. \cr
#' logo_dark_href \tab character \tab Logo URL for dark backgrounds. \cr
#' }}
#' \if{latex}{See the HTML help or pkgdown reference for the column table.}
#'
#' @export
#' @keywords WNBA Player Stats
#' @family ESPN WNBA Functions
#'
#' @examples
#' \donttest{
#' try(espn_wnba_player_stats(athlete_id = 2529130, year = 2022))
#' }
espn_wnba_player_stats <- function(
athlete_id,
year,
season_type = 'regular',
total = FALSE
){
.args <- mget(setdiff(names(formals()), "..."))
if (!(tolower(season_type) %in% c("regular","postseason"))) {
# Check if season_type is appropriate, if not regular
cli::cli_abort("Enter valid season_type: regular or postseason")
}
s_type <- ifelse(season_type == "postseason", 3, 2)
base_url <- "https://sports.core.api.espn.com/v2/sports/basketball/leagues/wnba/seasons/"
totals <- ifelse(total == TRUE, 0, "")
full_url <- paste0(
base_url,
year,
'/types/',s_type,
'/athletes/', athlete_id,
'/statistics/', totals
)
athlete_url <- paste0(
base_url,
year,
'/athletes/', athlete_id
)
df <- data.frame()
tryCatch(
expr = {
# Create the GET request and set response as res
res <- .retry_request(full_url)
# Check the result
check_status(res)
# Create the GET request and set response as res
athlete_res <- .retry_request(athlete_url)
# Check the result
check_status(athlete_res)
athlete_df <- athlete_res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_url <- athlete_df[["team"]][["$ref"]]
# Create the GET request and set response as res
team_res <- .retry_request(team_url)
# Check the result
check_status(team_res)
team_df <- team_res %>%
.resp_text() %>%
jsonlite::fromJSON(simplifyDataFrame = FALSE, simplifyVector = FALSE, simplifyMatrix = FALSE)
team_df[["links"]] <- NULL
team_df[["injuries"]] <- NULL
team_df[["record"]] <- NULL
team_df[["athletes"]] <- NULL
team_df[["venue"]] <- NULL
team_df[["groups"]] <- NULL
team_df[["ranks"]] <- NULL
team_df[["statistics"]] <- NULL
team_df[["leaders"]] <- NULL
team_df[["links"]] <- NULL
team_df[["notes"]] <- NULL
team_df[["franchise"]] <- NULL
team_df[["record"]] <- NULL
team_df[["college"]] <- NULL
team_df <- team_df %>%
purrr::map_if(is.list,as.data.frame) %>%
as.data.frame() %>%
dplyr::select(
-dplyr::any_of(
c("logos.width",
"logos.height",
"logos.alt",
"logos.rel..full.",
"logos.rel..default.",
"logos.lastUpdated",
"logos.width.1",
"logos.height.1",
"logos.alt.1",
"logos.rel..full..1",
"logos.rel..dark.",
"logos.lastUpdated.1",
"X.ref",
"X.ref.1",
"X.ref.2",
"X.ref.3"))) %>%
janitor::clean_names()
colnames(team_df)[1:14] <- paste0("team_",colnames(team_df)[1:14])
team_df <- team_df %>%
dplyr::rename(
"logo_href" = "logos_href",
"logo_dark_href" = "logos_href_1")
athlete_df[["links"]] <- NULL
athlete_df[["injuries"]] <- NULL
athlete_df[["birthPlace"]] <- NULL
athlete_df <- athlete_df %>%
purrr::map_if(is.list, as.data.frame) %>%
tibble::tibble(data = .data$.)
athlete_df <- athlete_df$data %>%
as.data.frame() %>%
dplyr::select(-dplyr::any_of(c("X.ref","X.ref.1","X.ref.2","X.ref.3","X.ref.4","X.ref.5","X.ref.6","X.ref.7","position.X.ref"))) %>%
janitor::clean_names() %>%
dplyr::rename(
"athlete_id" = "id",
"athlete_uid" = "uid",
"athlete_guid" = "guid",
"athlete_type" = "type")
# Get the content and return result as data.frame
df <- res %>%
.resp_text() %>%
jsonlite::fromJSON() %>%
purrr::pluck("splits") %>%
purrr::pluck("categories") %>%
tidyr::unnest("stats", names_sep = "_")
df <- df %>%
dplyr::mutate(
stats_category_name = paste0(.data$name, "_", .data$stats_name)) %>%
dplyr::select(
"stats_category_name",
"stats_value") %>%
tidyr::pivot_wider(
names_from = "stats_category_name",
values_from = "stats_value",
values_fn = dplyr::first) %>%
janitor::clean_names()
df <- athlete_df %>%
dplyr::bind_cols(df) %>%
dplyr::bind_cols(team_df)
df <- df %>%
dplyr::mutate_at(c(
"athlete_id",
"team_id",
"position_id",
"status_id",
"sdr",
"team_sdr"), as.integer) %>%
make_wehoop_data("ESPN WNBA Player Season Stats from ESPN.com",Sys.time())
},
error = function(e) .report_api_error(
e,
hint = "Invalid arguments or no player season stats data available!",
args = .args
),
warning = function(w) .report_api_warning(w, args = .args),
finally = {
}
)
return(df)
}
#' **Parse ESPN WNBA PBP, helper function**
#' @param resp Response object from the ESPN WNBA game summary endpoint
#' @return Returns a tibble
#' @importFrom lubridate with_tz ymd_hm
#' @export
helper_espn_wnba_pbp <- function(resp){
game_json <- resp %>%
jsonlite::fromJSON()
pbp_source <- game_json[["header"]][["competitions"]][["playByPlaySource"]]
plays <- game_json %>%
purrr::pluck("plays") %>%
dplyr::as_tibble()
if (pbp_source != "none" && nrow(plays) > 10) {
homeAway1 <- jsonlite::fromJSON(resp)[['header']][['competitions']][['competitors']][[1]][['homeAway']][1]
gameId <- as.integer(game_json[["header"]][["id"]])
season <- game_json[['header']][['season']][['year']]
season_type <- game_json[['header']][['season']][['type']]
game_date_time <- substr(game_json[['header']][['competitions']][['date']], 1,
nchar(game_json[['header']][['competitions']][['date']]) - 1) %>%
lubridate::ymd_hm() %>%
lubridate::with_tz(tzone = "America/New_York")
game_date <- as.Date(substr(game_date_time, 0, 10))
id_vars <- data.frame()
if (homeAway1 == "home") {
homeTeamId = as.integer(game_json[["header"]][["competitions"]][["competitors"]][[1]][['team']][['id']] %>%
purrr::pluck(1, .default = NA_integer_))
homeTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(1, .default = NA_character_))
homeTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamId = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['team']][['id']] %>%
purrr::pluck(2, .default = NA_integer_))
awayTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(2, .default = NA_integer_))
awayTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(2, .default = NA_character_)
id_vars <- data.frame(
homeTeamId,
homeTeamMascot,
homeTeamName,
homeTeamAbbrev,
homeTeamLogo,
homeTeamLogoDark,
homeTeamFullName,
homeTeamColor,
homeTeamAlternateColor,
homeTeamScore,
homeTeamWinner,
homeTeamRecord,
awayTeamId,
awayTeamMascot,
awayTeamName,
awayTeamAbbrev,
awayTeamLogo,
awayTeamLogoDark,
awayTeamFullName,
awayTeamColor,
awayTeamAlternateColor,
awayTeamScore,
awayTeamWinner,
awayTeamRecord
)
} else {
awayTeamId = as.integer(game_json[["header"]][["competitions"]][["competitors"]][[1]][['team']][['id']] %>%
purrr::pluck(1, .default = NA_integer_))
awayTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[1]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
awayTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(1, .default = NA_integer_))
awayTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(1, .default = NA_character_)
awayTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(1, .default = NA_character_)
homeTeamId = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['team']][['id']] %>%
purrr::pluck(2, .default = NA_integer_))
homeTeamMascot = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['name']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['location']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamAbbrev = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['abbreviation']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamLogo = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamLogoDark = game_json[['header']][['competitions']][['competitors']][[1]][['team']][['logos']][[2]][['href']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamFullName = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["displayName"]] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["color"]] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamAlternateColor = game_json[['header']][['competitions']][['competitors']][[1]][['team']][["alternateColor"]] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamScore = as.integer(game_json[['header']][['competitions']][['competitors']][[1]][['score']] %>%
purrr::pluck(2, .default = NA_integer_))
homeTeamWinner = game_json[['header']][['competitions']][['competitors']][[1]][['winner']] %>%
purrr::pluck(2, .default = NA_character_)
homeTeamRecord = game_json[['header']][['competitions']][['competitors']][[1]][['record']][[1]][['summary']] %>%
purrr::pluck(2, .default = NA_character_)
id_vars <- data.frame(
homeTeamId,
homeTeamMascot,
homeTeamName,
homeTeamAbbrev,
homeTeamLogo,
homeTeamLogoDark,
homeTeamFullName,
homeTeamColor,
homeTeamAlternateColor,
homeTeamScore,
homeTeamWinner,
homeTeamRecord,
awayTeamId,
awayTeamMascot,
awayTeamName,
awayTeamAbbrev,
awayTeamLogo,
awayTeamLogoDark,
awayTeamFullName,
awayTeamColor,
awayTeamAlternateColor,
awayTeamScore,
awayTeamWinner,
awayTeamRecord
)
}
game_json <- game_json %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE)
plays <- game_json %>%
purrr::pluck("plays")
if (("coordinate.x" %in% names(plays)) && ("coordinate.y" %in% names(plays))) {
plays <- plays %>%
dplyr::mutate(
# convert types
coordinate.x = as.double(.data$coordinate.x),
coordinate.y = as.double(.data$coordinate.y),
# Free throws are adjusted automatically to 19' from baseline, which
# corresponds to 13.75' from the center of the basket (originally
# the center of the basket is (25, 0))
coordinate.y = dplyr::case_when(
stringr::str_detect(.data$type.text, "Free Throw") ~ 13.75,
TRUE ~ .data$coordinate.y
),
coordinate.x = dplyr::case_when(
stringr::str_detect(.data$type.text, "Free Throw") ~ 25,
TRUE ~ .data$coordinate.x
),
coordinate_x_transformed = dplyr::case_when(
.data$team.id == homeTeamId ~ -1 * (.data$coordinate.y - 41.75),
TRUE ~ .data$coordinate.y - 41.75
),
coordinate_y_transformed = dplyr::case_when(
.data$team.id == homeTeamId ~ -1 * (.data$coordinate.x - 25),
TRUE ~ .data$coordinate.x - 25
)
) %>%
dplyr::rename(
"coordinate.x.raw" = "coordinate.x",
"coordinate.y.raw" = "coordinate.y",
"coordinate.x" = "coordinate_x_transformed",
"coordinate.y" = "coordinate_y_transformed"
)
}
## Written this way for compliance with data repository processing
if ("participants" %in% names(plays)) {
plays <- plays %>%
tidyr::unnest_wider("participants")
suppressWarnings(
aths <- plays %>%
dplyr::group_by(.data$id) %>%
dplyr::select(
"id",
"athlete.id") %>%
tidyr::unnest_wider("athlete.id", names_sep = "_")
)
names(aths) <- c("play.id", paste0("athlete.id.", seq_len(ncol(aths) - 1)))
for (nm in paste0("athlete.id.", 1:3)) {
if (!nm %in% names(aths)) aths[[nm]] <- NA_character_
}
aths <- aths[, c("play.id", paste0("athlete.id.", 1:3))]
plays <- plays %>%
dplyr::bind_cols(aths) %>%
janitor::clean_names() %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"athlete_id_1",
"athlete_id_2",
"athlete_id_3"
)), ~as.integer(.x)))
}
## Written this way for compliance with data repository processing
if (!("homeTeamName" %in% names(plays))) {
plays <- plays %>%
dplyr::bind_cols(id_vars)
}
plays <- plays %>%
dplyr::select(-dplyr::any_of(c("athlete.id", "athlete_id"))) %>%
janitor::clean_names() %>%
dplyr::mutate(
game_id = gameId,
season = season,
season_type = season_type,
game_date = game_date,
game_date_time = game_date_time) %>%
dplyr::rename(dplyr::any_of(c(
"athlete_id_1" = "participants_0_athlete_id",
"athlete_id_2" = "participants_1_athlete_id",
"athlete_id_3" = "participants_2_athlete_id")))
plays <- plays %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"athlete_id_1",
"athlete_id_2",
"athlete_id_3",
"type_id",
"team_id"
)), ~as.integer(.x)))
plays_df <- plays %>%
make_wehoop_data("ESPN WNBA Play-by-Play Information from ESPN.com", Sys.time())
return(plays_df)
}
}
#' **Parse ESPN WNBA Team Box, helper function**
#' @rdname helper_espn_wnba_pbp
#' @param resp Response object from the ESPN WNBA game summary endpoint
#' @return Returns a tibble
#' @importFrom lubridate with_tz ymd_hm
#' @export
helper_espn_wnba_team_box <- function(resp){
game_json <- resp %>%
jsonlite::fromJSON()
gameId <- as.integer(game_json[["header"]][["id"]])
game_date_time <- substr(game_json[['header']][['competitions']][['date']], 1,
nchar(game_json[['header']][['competitions']][['date']]) - 1) %>%
lubridate::ymd_hm() %>%
lubridate::with_tz(tzone = "America/New_York")
game_date <- as.Date(substr(game_date_time, 0, 10))
# ESPN's header `boxscoreAvailable` flag is unreliable for archival games
# (the WBB twin drops pre-2014 boxscores over it), so availability is
# derived from the payload itself; the statistics-length check below
# remains the real gate.
box_score_available <- length(game_json[["boxscore"]][["teams"]]) > 0
if (box_score_available == TRUE) {
teams_box_score_df <- game_json[["boxscore"]][["teams"]] %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE)
if (length(teams_box_score_df[["statistics"]][[1]]) > 0) {
# Teams info columns and values
teams_df <- game_json[["header"]][["competitions"]][["competitors"]][[1]]
homeAway1 <- teams_df[["homeAway"]][1]
homeAway1_team.id <- as.integer(teams_df[["id"]][1])
homeAway1_team.score <- as.integer(teams_df[["score"]][1])
homeAway1_team.winner <- teams_df[["winner"]][1]
homeAway2 <- teams_df[["homeAway"]][2]
homeAway2_team.id <- as.integer(teams_df[["id"]][2])
homeAway2_team.score <- as.integer(teams_df[["score"]][2])
homeAway2_team.winner <- teams_df[["winner"]][2]
# Pivoting the table values for each team from long to wide
statistics_df_1 <- teams_box_score_df[["statistics"]][[1]] %>%
tibble::tibble() %>%
dplyr::select("name", "displayValue") %>%
tidyr::spread("name", "displayValue")
statistics_df_2 <- teams_box_score_df[["statistics"]][[2]] %>%
tibble::tibble() %>%
dplyr::select("name", "displayValue") %>%
tidyr::spread("name", "displayValue")
# Assigning values to the correct data frame rows - 1
statistics_df_1$team.homeAway <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
homeAway1,
homeAway2
)
statistics_df_1$team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
as.integer(homeAway1_team.score),
as.integer(homeAway2_team.score)
)
statistics_df_1$team.winner <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
homeAway1_team.winner,
homeAway2_team.winner
)
statistics_df_1$team.id <- as.integer(teams_box_score_df[["team.id"]][[1]])
statistics_df_1$team.uid <- teams_box_score_df[["team.uid"]][[1]]
statistics_df_1$team.slug <- teams_box_score_df[["team.slug"]][[1]]
statistics_df_1$team.location <- teams_box_score_df[["team.location"]][[1]]
statistics_df_1$team.name <- teams_box_score_df[["team.name"]][[1]]
statistics_df_1$team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[1]]
statistics_df_1$team.displayName <- teams_box_score_df[["team.displayName"]][[1]]
statistics_df_1$team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[1]]
statistics_df_1$team.color <- teams_box_score_df[["team.color"]][[1]]
statistics_df_1$team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[1]]
statistics_df_1$team.logo <- teams_box_score_df[["team.logo"]][[1]]
statistics_df_1$opponent.team.id <- as.integer(teams_box_score_df[["team.id"]][[2]])
statistics_df_1$opponent.team.uid <- teams_box_score_df[["team.uid"]][[2]]
statistics_df_1$opponent.team.slug <- teams_box_score_df[["team.slug"]][[2]]
statistics_df_1$opponent.team.location <- teams_box_score_df[["team.location"]][[2]]
statistics_df_1$opponent.team.name <- teams_box_score_df[["team.name"]][[2]]
statistics_df_1$opponent.team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[2]]
statistics_df_1$opponent.team.displayName <- teams_box_score_df[["team.displayName"]][[2]]
statistics_df_1$opponent.team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[2]]
statistics_df_1$opponent.team.color <- teams_box_score_df[["team.color"]][[2]]
statistics_df_1$opponent.team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[2]]
statistics_df_1$opponent.team.logo <- teams_box_score_df[["team.logo"]][[2]]
statistics_df_1$opponent.team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][1]) == as.integer(homeAway1_team.id),
as.integer(homeAway2_team.score),
as.integer(homeAway1_team.score)
)
# Assigning values to the correct data frame rows - 2
statistics_df_2$team.homeAway <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
homeAway2,
homeAway1
)
statistics_df_2$team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
as.integer(homeAway2_team.score),
as.integer(homeAway1_team.score)
)
statistics_df_2$team.winner <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
homeAway2_team.winner,
homeAway1_team.winner
)
statistics_df_2$team.id <- as.integer(teams_box_score_df[["team.id"]][[2]])
statistics_df_2$team.uid <- teams_box_score_df[["team.uid"]][[2]]
statistics_df_2$team.slug <- teams_box_score_df[["team.slug"]][[2]]
statistics_df_2$team.location <- teams_box_score_df[["team.location"]][[2]]
statistics_df_2$team.name <- teams_box_score_df[["team.name"]][[2]]
statistics_df_2$team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[2]]
statistics_df_2$team.displayName <- teams_box_score_df[["team.displayName"]][[2]]
statistics_df_2$team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[2]]
statistics_df_2$team.color <- teams_box_score_df[["team.color"]][[2]]
statistics_df_2$team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[2]]
statistics_df_2$team.logo <- teams_box_score_df[["team.logo"]][[2]]
statistics_df_2$opponent.team.id <- as.integer(teams_box_score_df[["team.id"]][[1]])
statistics_df_2$opponent.team.uid <- teams_box_score_df[["team.uid"]][[1]]
statistics_df_2$opponent.team.slug <- teams_box_score_df[["team.slug"]][[1]]
statistics_df_2$opponent.team.location <- teams_box_score_df[["team.location"]][[1]]
statistics_df_2$opponent.team.name <- teams_box_score_df[["team.name"]][[1]]
statistics_df_2$opponent.team.abbreviation <- teams_box_score_df[["team.abbreviation"]][[1]]
statistics_df_2$opponent.team.displayName <- teams_box_score_df[["team.displayName"]][[1]]
statistics_df_2$opponent.team.shortDisplayName <- teams_box_score_df[["team.shortDisplayName"]][[1]]
statistics_df_2$opponent.team.color <- teams_box_score_df[["team.color"]][[1]]
statistics_df_2$opponent.team.alternateColor <- teams_box_score_df[["team.alternateColor"]][[1]]
statistics_df_2$opponent.team.logo <- teams_box_score_df[["team.logo"]][[1]]
statistics_df_2$opponent.team.score <- ifelse(
as.integer(teams_box_score_df[["team.id"]][2]) == as.integer(homeAway2_team.id),
as.integer(homeAway1_team.score),
as.integer(homeAway2_team.score)
)
complete_statistics_df <- statistics_df_1 %>%
dplyr::bind_rows(statistics_df_2)
# Assigning game/season level data to team box score and converting types
complete_statistics_df$season <- game_json[["header"]][["season"]][["year"]]
complete_statistics_df$season_type <- game_json[["header"]][["season"]][["type"]]
complete_statistics_df$game_date <- as.Date(substr(game_json[["header"]][["competitions"]][["date"]], 0, 10))
complete_statistics_df$game_id <- as.integer(gameId)
complete_statistics_df$game_date_time <- game_date_time
complete_statistics_df$game_date <- game_date
suppressWarnings(
complete_statistics_df <- complete_statistics_df %>%
tidyr::separate("fieldGoalsMade-fieldGoalsAttempted",
into = c("fieldGoalsMade", "fieldGoalsAttempted"),
sep = "-") %>%
tidyr::separate("freeThrowsMade-freeThrowsAttempted",
into = c("freeThrowsMade", "freeThrowsAttempted"),
sep = "-") %>%
tidyr::separate("threePointFieldGoalsMade-threePointFieldGoalsAttempted",
into = c("threePointFieldGoalsMade", "threePointFieldGoalsAttempted"),
sep = "-") %>%
dplyr::mutate(dplyr::across(c(
"fieldGoalPct",
"freeThrowPct",
"threePointFieldGoalPct"
), ~as.numeric(.x))) %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"assists",
"blocks",
"defensiveRebounds",
"fieldGoalsMade",
"fieldGoalsAttempted",
"flagrantFouls",
"fouls",
"freeThrowsMade",
"freeThrowsAttempted",
"offensiveRebounds",
"steals",
"teamTurnovers",
"technicalFouls",
"threePointFieldGoalsMade",
"threePointFieldGoalsAttempted",
"totalRebounds",
"totalTechnicalFouls",
"totalTurnovers",
"turnovers"
)), ~as.integer(.x)))
)
team_box_score <- complete_statistics_df %>%
janitor::clean_names() %>%
dplyr::select(dplyr::any_of(c(
"game_id",
"season",
"season_type",
"game_date",
"game_date_time",
"team_id",
"team_uid",
"team_slug",
"team_location",
"team_name",
"team_abbreviation",
"team_display_name",
"team_short_display_name",
"team_color",
"team_alternate_color",
"team_logo",
"team_home_away",
"team_score",
"team_winner")),
tidyr::everything()) %>%
make_wehoop_data("ESPN WNBA Team Box Information from ESPN.com", Sys.time())
return(team_box_score)
}
}
}
#' **Parse ESPN WNBA Player Box, helper function**
#' @rdname helper_espn_wnba_pbp
#' @param resp Response object from the ESPN WNBA game summary endpoint
#' @return Returns a tibble
#' @importFrom lubridate with_tz ymd_hm
#' @export
helper_espn_wnba_player_box <- function(resp){
game_json <- resp %>%
jsonlite::fromJSON(flatten = TRUE)
players_box_score_df <- game_json[["boxscore"]][["players"]] %>%
jsonlite::toJSON() %>%
jsonlite::fromJSON(flatten = TRUE) %>%
as.data.frame()
gameId <- as.integer(game_json[["header"]][["id"]])
season <- game_json[["header"]][["season"]][["year"]]
season_type <- game_json[["header"]][["season"]][["type"]]
game_date_time <- substr(game_json[['header']][['competitions']][['date']], 1,
nchar(game_json[['header']][['competitions']][['date']]) - 1) %>%
lubridate::ymd_hm() %>%
lubridate::with_tz(tzone = "America/New_York")
game_date <- as.Date(substr(game_date_time, 0, 10))
boxScoreAvailable <- game_json[["header"]][["competitions"]][["boxscoreAvailable"]]
boxScoreSource <- game_json[["header"]][["competitions"]][["boxscoreSource"]]
# This is checking if [[athletes]][[1]]'s stat rebounds is able to be converted to a numeric value
# without introducing NA's
suppressWarnings(
valid_stats <- players_box_score_df[["statistics"]][[1]][["athletes"]][[1]][["stats"]][[1]] %>%
purrr::pluck(7) %>%
as.numeric()
)
# Payload presence replaces ESPN's unreliable header `boxscoreAvailable`
# flag (archival games carry stats while the flag says FALSE); the athlete
# and stat validity conjuncts below remain the real gate.
if (length(game_json[["boxscore"]][["players"]]) > 0 &&
length(players_box_score_df[["statistics"]][[1]][["athletes"]][[1]]) > 1 &&
!is.na(valid_stats)) {
players_df <- players_box_score_df %>%
tidyr::unnest("statistics") %>%
tidyr::unnest("athletes")
if (length(players_box_score_df[["statistics"]]) > 1 &&
length(players_df$stats[[1]]) > 0) {
players_df <- jsonlite::fromJSON(jsonlite::toJSON(game_json[["boxscore"]][["players"]]), flatten = TRUE) %>%
tidyr::unnest("statistics") %>%
tidyr::unnest("athletes")
stat_cols <- players_df$keys[[1]]
stats <- players_df$stats
stats_df <- as.data.frame(do.call(rbind,stats))
colnames(stats_df) <- stat_cols
suppressWarnings(
stats_df <- stats_df %>%
tidyr::separate("fieldGoalsMade-fieldGoalsAttempted",
into = c("fieldGoalsMade", "fieldGoalsAttempted"),
sep = "-") %>%
tidyr::separate("freeThrowsMade-freeThrowsAttempted",
into = c("freeThrowsMade", "freeThrowsAttempted"),
sep = "-") %>%
tidyr::separate("threePointFieldGoalsMade-threePointFieldGoalsAttempted",
into = c("threePointFieldGoalsMade", "threePointFieldGoalsAttempted"),
sep = "-") %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"minutes",
"fieldGoalPct",
"freeThrowPct",
"threePointFieldGoalPct"
)), ~as.numeric(.x))) %>%
dplyr::mutate(dplyr::across(dplyr::any_of(c(
"assists",
"blocks",
"defensiveRebounds",
"fieldGoalsMade",
"fieldGoalsAttempted",
"flagrantFouls",
"fouls",
"freeThrowsMade",
"freeThrowsAttempted",
"offensiveRebounds",
"steals",
"teamTurnovers",
"technicalFouls",
"threePointFieldGoalsMade",
"threePointFieldGoalsAttempted",
"rebounds",
"totalTechnicalFouls",
"totalTurnovers",
"turnovers",
"points"
)), ~as.integer(.x)))
)
players_df_did_not_play <- players_df %>%
dplyr::filter(.data$didNotPlay) %>%
dplyr::select(dplyr::any_of(c(
"starter",
"ejected",
"didNotPlay",
"reason",
"active",
"athlete.displayName",
"athlete.jersey",
"athlete.id",
"athlete.shortName",
"athlete.headshot.href",
"athlete.position.name",
"athlete.position.abbreviation",
"team.displayName",
"team.shortDisplayName",
"team.location",
"team.name",
"team.logo",
"team.id",
"team.uid",
"team.slug",
"team.abbreviation",
"team.color",
"team.alternateColor"
)))
players_df <- players_df %>%
dplyr::filter(!.data$didNotPlay) %>%
dplyr::select(dplyr::any_of(c(
"starter",
"ejected",
"didNotPlay",
"reason",
"active",
"athlete.displayName",
"athlete.jersey",
"athlete.id",
"athlete.shortName",
"athlete.headshot.href",
"athlete.position.name",
"athlete.position.abbreviation",
"team.displayName",
"team.shortDisplayName",
"team.location",
"team.name",
"team.logo",
"team.id",
"team.uid",
"team.slug",
"team.abbreviation",
"team.color",
"team.alternateColor"
)))
players_df <- stats_df %>%
dplyr::bind_cols(players_df) %>%
dplyr::bind_rows(players_df_did_not_play)
players_df <- players_df %>%
dplyr::select(dplyr::any_of(c(
"athlete.displayName",
"team.shortDisplayName")),
tidyr::everything()) %>%
janitor::clean_names() %>%
dplyr::mutate(
game_id = gameId,
season = season,
season_type = season_type,
game_date = game_date,
game_date_time = game_date_time)
teams_df <- game_json[["header"]][["competitions"]][["competitors"]][[1]]
homeAway1 <- teams_df[["homeAway"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.id <- as.integer(teams_df[["id"]] %>%
purrr::pluck(1, .default = NA_integer_))
homeAway1_team.location <- teams_df[["team.location"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.name <- teams_df[["team.name"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.abbreviation <- teams_df[["team.abbreviation"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.displayName <- teams_df[["team.displayName"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.logos <- teams_df[["team.logos"]][[1]][["href"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.color <- teams_df[["team.color"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.alternateColor <- teams_df[["team.alternateColor"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.winner <- teams_df[["winner"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway1_team.score <- as.integer(teams_df[["score"]] %>%
purrr::pluck(1, .default = NA_integer_))
homeAway2 <- teams_df[["homeAway"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.id <- as.integer(teams_df[["id"]] %>%
purrr::pluck(2, .default = NA_integer_))
homeAway2_team.location <- teams_df[["team.location"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.name <- teams_df[["team.name"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.abbreviation <- teams_df[["team.abbreviation"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.displayName <- teams_df[["team.displayName"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.logos <- teams_df[["team.logos"]][[2]][["href"]] %>%
purrr::pluck(1, .default = NA_character_)
homeAway2_team.color <- teams_df[["team.color"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.alternateColor <- teams_df[["team.alternateColor"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.winner <- teams_df[["winner"]] %>%
purrr::pluck(2, .default = NA_character_)
homeAway2_team.score <- as.integer(teams_df[["score"]] %>%
purrr::pluck(2, .default = NA_integer_))
players_df <- players_df %>%
dplyr::mutate(
home_away = ifelse(.data$team_id == homeAway1_team.id, homeAway1, homeAway2),
team_winner = ifelse(.data$team_id == homeAway1_team.id, homeAway1_team.winner, homeAway2_team.winner),
team_score = ifelse(.data$team_id == homeAway1_team.id, homeAway1_team.score, homeAway2_team.score),
opponent_team_id = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.id, homeAway1_team.id),
opponent_team_name = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.name, homeAway1_team.name),
opponent_team_location = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.location, homeAway1_team.location),
opponent_team_display_name = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.displayName, homeAway1_team.displayName),
opponent_team_abbreviation = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.abbreviation, homeAway1_team.abbreviation),
opponent_team_logo = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.logos, homeAway1_team.logos),
opponent_team_color = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.color, homeAway1_team.color),
opponent_team_alternate_color = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.alternateColor, homeAway1_team.alternateColor),
opponent_team_score = ifelse(.data$team_id == homeAway1_team.id, homeAway2_team.score, homeAway1_team.score),
) %>%
dplyr::arrange(.data$home_away)
player_box_score <- players_df %>%
dplyr::select(dplyr::any_of(c(
"game_id",
"season",
"season_type",
"game_date",
"game_date_time",
"athlete_id",
"athlete_display_name",
"team_id",
"team_name",
"team_location",
"team_short_display_name",
"minutes",
"field_goals_made",
"field_goals_attempted",
"three_point_field_goals_made",
"three_point_field_goals_attempted",
"free_throws_made",
"free_throws_attempted",
"offensive_rebounds",
"defensive_rebounds",
"rebounds",
"assists",
"steals",
"blocks",
"turnovers",
"fouls",
"plus_minus",
"points",
"starter",
"ejected",
"did_not_play",
"reason",
"active",
"athlete_jersey",
"athlete_short_name",
"athlete_headshot_href",
"athlete_position_name",
"athlete_position_abbreviation",
"team_display_name",
"team_uid",
"team_slug",
"team_logo",
"team_abbreviation",
"team_color",
"team_alternate_color",
"home_away",
"team_winner",
"team_score",
"opponent_team_id",
"opponent_team_name",
"opponent_team_location",
"opponent_team_display_name",
"opponent_team_abbreviation",
"opponent_team_logo",
"opponent_team_color",
"opponent_team_alternate_color",
"opponent_team_score"
))) %>%
dplyr::mutate_at(c(
"athlete_id",
"team_id",
"team_score",
"opponent_team_score"
), as.integer) %>%
make_wehoop_data("ESPN WNBA Player Box Information from ESPN.com", Sys.time())
return(player_box_score)
}
}
}
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