R/data-documentation.R

In sportsR: A Comprehensive Collection of Sports and Athletics Datasets

# sportsR - A Comprehensive Collection of Sports and Athletics Datasets
# Version 0.1.0
# Copyright (C) 2026 Renzo Caceres Rossi
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.


#' English Football League Results 1888-2022
#'
#' This dataset, english_football, is a data frame containing results for
#' English soccer games in the top 4 tiers from the 1888/89 season to the
#' 2021/22 season. It includes information on match dates, seasons, home
#' and visiting teams, full-time scores, goals scored, division, tier, and
#' match outcomes.
#'
#' The dataset name has been kept as 'english_football' to avoid confusion
#' with other datasets in the R ecosystem. This naming convention helps
#' distinguish this dataset as part of the sportsR package and assists
#' users in identifying its specific characteristics.
#'
#' @name english_football
#' @format A data frame with 203956 observations and 12 variables:
#' \describe{
#'   \item{Date}{Character vector indicating the date of the match}
#'   \item{Season}{Numeric vector indicating the season}
#'   \item{home}{Character vector indicating the home team}
#'   \item{visitor}{Character vector indicating the visiting team}
#'   \item{FT}{Character vector indicating the full-time score}
#'   \item{hgoal}{Integer vector indicating the number of goals scored by the home team}
#'   \item{vgoal}{Integer vector indicating the number of goals scored by the visiting team}
#'   \item{division}{Character vector indicating the division}
#'   \item{tier}{Numeric vector indicating the tier}
#'   \item{totgoal}{Integer vector indicating the total number of goals scored in the match}
#'   \item{goaldif}{Integer vector indicating the goal difference}
#'   \item{result}{Character vector indicating the match result}
#' }
#' @source Data taken from the footBayes package version 2.0.0
#' @usage data(english_football)
#' @export
load("data/english_football.rda")
NULL




#' Italian Football League Results 1934-2022
#'
#' This dataset, italian_football, is a data frame containing results for
#' Italian soccer games in the top tier from the 1934/35 season to the
#' 2021/22 season. It includes information on match dates, seasons, home
#' and visiting teams, full-time scores, and goals scored.
#'
#' The dataset name has been kept as 'italian_football' to avoid confusion
#' with other datasets in the R ecosystem. This naming convention helps
#' distinguish this dataset as part of the sportsR package and assists
#' users in identifying its specific characteristics.
#'
#' @name italian_football
#' @format A data frame with 27684 observations and 8 variables:
#' \describe{
#'   \item{Date}{Date vector indicating the date of the match}
#'   \item{Season}{Numeric vector indicating the season}
#'   \item{home}{Character vector indicating the home team}
#'   \item{visitor}{Character vector indicating the visiting team}
#'   \item{FT}{Character vector indicating the full-time score}
#'   \item{hgoal}{Integer vector indicating the number of goals scored by the home team}
#'   \item{vgoal}{Integer vector indicating the number of goals scored by the visiting team}
#'   \item{tier}{Numeric vector indicating the tier}
#' }
#' @source Data taken from the footBayes package version 2.0.0
#' @usage data(italian_football)
#' @export
load("data/italian_football.rda")
NULL



#' ATP Matches in 2019
#'
#' This dataset, atp_matches_2019, is a data frame containing match-level data
#' for men's professional tennis matches played on the ATP Tour during 2019. It
#' includes information on tournament details, court and surface conditions,
#' player rankings and points, set-by-set scores, and betting odds from multiple
#' bookmakers for each match.
#'
#' The dataset name has been kept as 'atp_matches_2019' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name atp_matches_2019
#' @format A data frame with 2610 observations and 36 variables:
#' \describe{
#'   \item{ATP}{Integer vector indicating the ATP tournament identification number}
#'   \item{Location}{Character vector indicating the city where the tournament was played}
#'   \item{Tournament}{Character vector indicating the name of the tournament}
#'   \item{Date}{Character vector indicating the date the match was played}
#'   \item{Series}{Character vector indicating the ATP series or category of the tournament}
#'   \item{Court}{Character vector indicating whether the match was played indoors or outdoors}
#'   \item{Surface}{Character vector indicating the court surface (e.g., Hard, Clay, Grass)}
#'   \item{Round}{Character vector indicating the round of the tournament}
#'   \item{Best.of}{Integer vector indicating the maximum number of sets played (3 or 5)}
#'   \item{Winner}{Character vector indicating the name of the match winner}
#'   \item{Loser}{Character vector indicating the name of the match loser}
#'   \item{WRank}{Character vector indicating the ATP ranking of the winner}
#'   \item{LRank}{Character vector indicating the ATP ranking of the loser}
#'   \item{WPts}{Character vector indicating the ATP ranking points of the winner}
#'   \item{LPts}{Character vector indicating the ATP ranking points of the loser}
#'   \item{W1}{Integer vector indicating the games won by the winner in set 1}
#'   \item{L1}{Integer vector indicating the games won by the loser in set 1}
#'   \item{W2}{Integer vector indicating the games won by the winner in set 2}
#'   \item{L2}{Integer vector indicating the games won by the loser in set 2}
#'   \item{W3}{Integer vector indicating the games won by the winner in set 3}
#'   \item{L3}{Integer vector indicating the games won by the loser in set 3}
#'   \item{W4}{Integer vector indicating the games won by the winner in set 4}
#'   \item{L4}{Integer vector indicating the games won by the loser in set 4}
#'   \item{W5}{Integer vector indicating the games won by the winner in set 5}
#'   \item{L5}{Integer vector indicating the games won by the loser in set 5}
#'   \item{Wsets}{Integer vector indicating the total number of sets won by the winner}
#'   \item{Lsets}{Integer vector indicating the total number of sets won by the loser}
#'   \item{Comment}{Character vector indicating the match outcome status (e.g., Completed, Retired, Walkover)}
#'   \item{B365W}{Numeric vector indicating the Bet365 odds for the winner}
#'   \item{B365L}{Numeric vector indicating the Bet365 odds for the loser}
#'   \item{PSW}{Numeric vector indicating the Pinnacle Sports odds for the winner}
#'   \item{PSL}{Numeric vector indicating the Pinnacle Sports odds for the loser}
#'   \item{MaxW}{Numeric vector indicating the maximum odds offered by any bookmaker for the winner}
#'   \item{MaxL}{Numeric vector indicating the maximum odds offered by any bookmaker for the loser}
#'   \item{AvgW}{Numeric vector indicating the average odds offered across bookmakers for the winner}
#'   \item{AvgL}{Numeric vector indicating the average odds offered across bookmakers for the loser}
#' }
#' @source Data taken from the welo package version 0.1.4
#' @usage data(atp_matches_2019)
#' @export
load("data/atp_matches_2019.rda")
NULL

#' WTA Matches in 2019
#'
#' This dataset, wta_matches_2019, is a data frame containing match-level data
#' for women's professional tennis matches played on the WTA Tour during 2019. It
#' includes information on tournament details, court and surface conditions,
#' player rankings and points, set-by-set scores, and betting odds from multiple
#' bookmakers for each match.
#'
#' The dataset name has been kept as 'wta_matches_2019' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name wta_matches_2019
#' @format A data frame with 2472 observations and 32 variables:
#' \describe{
#'   \item{WTA}{Integer vector indicating the WTA tournament identification number}
#'   \item{Location}{Character vector indicating the city where the tournament was played}
#'   \item{Tournament}{Character vector indicating the name of the tournament}
#'   \item{Date}{Character vector indicating the date the match was played}
#'   \item{Tier}{Character vector indicating the WTA tier or category of the tournament}
#'   \item{Court}{Character vector indicating whether the match was played indoors or outdoors}
#'   \item{Surface}{Character vector indicating the court surface (e.g., Hard, Clay, Grass)}
#'   \item{Round}{Character vector indicating the round of the tournament}
#'   \item{Best.of}{Integer vector indicating the maximum number of sets played}
#'   \item{Winner}{Character vector indicating the name of the match winner}
#'   \item{Loser}{Character vector indicating the name of the match loser}
#'   \item{WRank}{Character vector indicating the WTA ranking of the winner}
#'   \item{LRank}{Character vector indicating the WTA ranking of the loser}
#'   \item{WPts}{Character vector indicating the WTA ranking points of the winner}
#'   \item{LPts}{Character vector indicating the WTA ranking points of the loser}
#'   \item{W1}{Integer vector indicating the games won by the winner in set 1}
#'   \item{L1}{Integer vector indicating the games won by the loser in set 1}
#'   \item{W2}{Integer vector indicating the games won by the winner in set 2}
#'   \item{L2}{Integer vector indicating the games won by the loser in set 2}
#'   \item{W3}{Integer vector indicating the games won by the winner in set 3}
#'   \item{L3}{Integer vector indicating the games won by the loser in set 3}
#'   \item{Wsets}{Integer vector indicating the total number of sets won by the winner}
#'   \item{Lsets}{Integer vector indicating the total number of sets won by the loser}
#'   \item{Comment}{Character vector indicating the match outcome status (e.g., Completed, Retired, Walkover)}
#'   \item{B365W}{Numeric vector indicating the Bet365 odds for the winner}
#'   \item{B365L}{Numeric vector indicating the Bet365 odds for the loser}
#'   \item{PSW}{Numeric vector indicating the Pinnacle Sports odds for the winner}
#'   \item{PSL}{Numeric vector indicating the Pinnacle Sports odds for the loser}
#'   \item{MaxW}{Numeric vector indicating the maximum odds offered by any bookmaker for the winner}
#'   \item{MaxL}{Numeric vector indicating the maximum odds offered by any bookmaker for the loser}
#'   \item{AvgW}{Numeric vector indicating the average odds offered across bookmakers for the winner}
#'   \item{AvgL}{Numeric vector indicating the average odds offered across bookmakers for the loser}
#' }
#' @source Data taken from the welo package version 0.1.4
#' @usage data(wta_matches_2019)
#' @export
load("data/wta_matches_2019.rda")
NULL


#' Golden State Warriors Basketball - 2016
#'
#' This dataset, warriors_2016, is a data frame containing game-by-game team
#' statistics for the Golden State Warriors during the 2016 NBA season. It
#' includes information on game location, opponent, win/loss outcome, points
#' scored, and detailed shooting, rebounding, and other box score statistics
#' for both the Warriors and their opponents.
#'
#' The dataset name has been kept as 'warriors_2016' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name warriors_2016
#' @format A data frame with 82 observations and 33 variables:
#' \describe{
#'   \item{Game}{Integer vector indicating the game number in the season}
#'   \item{Date}{Factor w/ 82 levels indicating the date the game was played}
#'   \item{Location}{Factor w/ 2 levels indicating whether the game was played at home or away}
#'   \item{Opp}{Factor w/ 29 levels indicating the opposing team}
#'   \item{Win}{Factor w/ 2 levels indicating whether the Warriors won or lost the game}
#'   \item{Points}{Integer vector indicating the points scored by the Warriors}
#'   \item{OppPoints}{Integer vector indicating the points scored by the opponent}
#'   \item{FG}{Integer vector indicating the number of field goals made by the Warriors}
#'   \item{FGA}{Integer vector indicating the number of field goals attempted by the Warriors}
#'   \item{FG3}{Integer vector indicating the number of three-point field goals made by the Warriors}
#'   \item{FG3A}{Integer vector indicating the number of three-point field goals attempted by the Warriors}
#'   \item{FT}{Integer vector indicating the number of free throws made by the Warriors}
#'   \item{FTA}{Integer vector indicating the number of free throws attempted by the Warriors}
#'   \item{Rebounds}{Integer vector indicating the total rebounds by the Warriors}
#'   \item{OffReb}{Integer vector indicating the offensive rebounds by the Warriors}
#'   \item{Assists}{Integer vector indicating the assists by the Warriors}
#'   \item{Steals}{Integer vector indicating the steals by the Warriors}
#'   \item{Blocks}{Integer vector indicating the blocks by the Warriors}
#'   \item{Turnovers}{Integer vector indicating the turnovers committed by the Warriors}
#'   \item{Fouls}{Integer vector indicating the personal fouls committed by the Warriors}
#'   \item{OppFG}{Integer vector indicating the number of field goals made by the opponent}
#'   \item{OppFGA}{Integer vector indicating the number of field goals attempted by the opponent}
#'   \item{OppFG3}{Integer vector indicating the number of three-point field goals made by the opponent}
#'   \item{OppFG3A}{Integer vector indicating the number of three-point field goals attempted by the opponent}
#'   \item{OppFT}{Integer vector indicating the number of free throws made by the opponent}
#'   \item{OppFTA}{Integer vector indicating the number of free throws attempted by the opponent}
#'   \item{OppRebounds}{Integer vector indicating the total rebounds by the opponent}
#'   \item{OppOffReb}{Integer vector indicating the offensive rebounds by the opponent}
#'   \item{OppAssists}{Integer vector indicating the assists by the opponent}
#'   \item{OppSteals}{Integer vector indicating the steals by the opponent}
#'   \item{OppBlocks}{Integer vector indicating the blocks by the opponent}
#'   \item{OppTurnovers}{Integer vector indicating the turnovers committed by the opponent}
#'   \item{OppFouls}{Integer vector indicating the personal fouls committed by the opponent}
#' }
#' @source Data taken from the Lock5Data package version 4.0.1
#' @usage data(warriors_2016)
#' @export
load("data/warriors_2016.rda")
NULL

#' Golden State Warriors Basketball - 2019
#'
#' This dataset, warriors_2019, is a data frame containing game-by-game team
#' statistics for the Golden State Warriors during the 2019 NBA season. It
#' includes information on game location, opponent, win/loss outcome, points
#' scored, and detailed shooting, rebounding, and other box score statistics
#' for both the Warriors and their opponents.
#'
#' The dataset name has been kept as 'warriors_2019' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name warriors_2019
#' @format A data frame with 82 observations and 33 variables:
#' \describe{
#'   \item{Game}{Integer vector indicating the game number in the season}
#'   \item{Date}{Factor w/ 82 levels indicating the date the game was played}
#'   \item{Location}{Factor w/ 2 levels indicating whether the game was played at home or away}
#'   \item{Opp}{Factor w/ 29 levels indicating the opposing team}
#'   \item{Win}{Factor w/ 2 levels indicating whether the Warriors won or lost the game}
#'   \item{Points}{Integer vector indicating the points scored by the Warriors}
#'   \item{FG}{Integer vector indicating the number of field goals made by the Warriors}
#'   \item{FGA}{Integer vector indicating the number of field goals attempted by the Warriors}
#'   \item{FG3}{Integer vector indicating the number of three-point field goals made by the Warriors}
#'   \item{FG3A}{Integer vector indicating the number of three-point field goals attempted by the Warriors}
#'   \item{FT}{Integer vector indicating the number of free throws made by the Warriors}
#'   \item{FTA}{Integer vector indicating the number of free throws attempted by the Warriors}
#'   \item{Rebounds}{Integer vector indicating the total rebounds by the Warriors}
#'   \item{OffReb}{Integer vector indicating the offensive rebounds by the Warriors}
#'   \item{Assists}{Integer vector indicating the assists by the Warriors}
#'   \item{Steals}{Integer vector indicating the steals by the Warriors}
#'   \item{Blocks}{Integer vector indicating the blocks by the Warriors}
#'   \item{Turnovers}{Integer vector indicating the turnovers committed by the Warriors}
#'   \item{Fouls}{Integer vector indicating the personal fouls committed by the Warriors}
#'   \item{OppPoints}{Integer vector indicating the points scored by the opponent}
#'   \item{OppFG}{Integer vector indicating the number of field goals made by the opponent}
#'   \item{OppFGA}{Integer vector indicating the number of field goals attempted by the opponent}
#'   \item{OppFG3}{Integer vector indicating the number of three-point field goals made by the opponent}
#'   \item{OppFG3A}{Integer vector indicating the number of three-point field goals attempted by the opponent}
#'   \item{OppFT}{Integer vector indicating the number of free throws made by the opponent}
#'   \item{OppFTA}{Integer vector indicating the number of free throws attempted by the opponent}
#'   \item{OppRebounds}{Integer vector indicating the total rebounds by the opponent}
#'   \item{OppOffReb}{Integer vector indicating the offensive rebounds by the opponent}
#'   \item{OppAssists}{Integer vector indicating the assists by the opponent}
#'   \item{OppSteals}{Integer vector indicating the steals by the opponent}
#'   \item{OppBlocks}{Integer vector indicating the blocks by the opponent}
#'   \item{OppTurnovers}{Integer vector indicating the turnovers committed by the opponent}
#'   \item{OppFouls}{Integer vector indicating the personal fouls committed by the opponent}
#' }
#' @source Data taken from the Lock5Data package version 4.0.1
#' @usage data(warriors_2019)
#' @export
load("data/warriors_2019.rda")
NULL


#' Baseball Team Statistics (2019)
#'
#' This dataset, mlb_teams_2019, is a data frame containing season-level team
#' statistics for Major League Baseball teams during the 2019 season. It
#' includes information on league affiliation, wins, and offensive statistics
#' such as runs, hits, home runs, RBI, stolen bases, walks, strikeouts, and
#' batting average.
#'
#' The dataset name has been kept as 'mlb_teams_2019' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name mlb_teams_2019
#' @format A data frame with 30 observations and 14 variables:
#' \describe{
#'   \item{Team}{Factor w/ 30 levels indicating the name of the MLB team}
#'   \item{League}{Factor w/ 2 levels indicating the league the team belongs to (American or National)}
#'   \item{Wins}{Integer vector indicating the number of games won by the team}
#'   \item{Runs}{Integer vector indicating the total number of runs scored by the team}
#'   \item{Hits}{Integer vector indicating the total number of hits by the team}
#'   \item{Doubles}{Integer vector indicating the total number of doubles hit by the team}
#'   \item{Triples}{Integer vector indicating the total number of triples hit by the team}
#'   \item{HomeRuns}{Integer vector indicating the total number of home runs hit by the team}
#'   \item{RBI}{Integer vector indicating the total number of runs batted in by the team}
#'   \item{StolenBases}{Integer vector indicating the total number of stolen bases by the team}
#'   \item{CaughtStealing}{Integer vector indicating the total number of times the team was caught stealing}
#'   \item{Walks}{Integer vector indicating the total number of walks drawn by the team}
#'   \item{Strikeouts}{Integer vector indicating the total number of strikeouts by the team}
#'   \item{BattingAvg}{Numeric vector indicating the team's overall batting average}
#' }
#' @source Data taken from the Lock5Data package version 4.0.1
#' @usage data(mlb_teams_2019)
#' @export
load("data/mlb_teams_2019.rda")
NULL


#' Baseball Team Statistics (2024)
#'
#' This dataset, mlb_teams_2024, is a data frame containing season-level team
#' statistics for Major League Baseball teams during the 2024 season. It
#' includes information on league affiliation, wins, and offensive statistics
#' such as runs, hits, home runs, RBI, stolen bases, walks, strikeouts, and
#' batting average.
#'
#' The dataset name has been kept as 'mlb_teams_2024' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name mlb_teams_2024
#' @format A data frame with 30 observations and 14 variables:
#' \describe{
#'   \item{Team}{Character vector indicating the name of the MLB team}
#'   \item{League}{Character vector indicating the league the team belongs to (American or National)}
#'   \item{Wins}{Integer vector indicating the number of games won by the team}
#'   \item{Runs}{Integer vector indicating the total number of runs scored by the team}
#'   \item{Hits}{Integer vector indicating the total number of hits by the team}
#'   \item{Doubles}{Integer vector indicating the total number of doubles hit by the team}
#'   \item{Triples}{Integer vector indicating the total number of triples hit by the team}
#'   \item{HomeRuns}{Integer vector indicating the total number of home runs hit by the team}
#'   \item{RBI}{Integer vector indicating the total number of runs batted in by the team}
#'   \item{StolenBases}{Integer vector indicating the total number of stolen bases by the team}
#'   \item{CaughtStealing}{Integer vector indicating the total number of times the team was caught stealing}
#'   \item{Walks}{Integer vector indicating the total number of walks drawn by the team}
#'   \item{Strikeouts}{Integer vector indicating the total number of strikeouts by the team}
#'   \item{BattingAvg}{Numeric vector indicating the team's overall batting average}
#' }
#' @source Data taken from the Lock5Data package version 4.0.1
#' @usage data(mlb_teams_2024)
#' @export
load("data/mlb_teams_2024.rda")
NULL


#' PGA Tournament Data
#'
#' This dataset, pga_results, is a data frame containing player-level results
#' and performance statistics from PGA Tour tournaments. It includes information
#' on tournament and player identifiers, scoring, fantasy points (DraftKings,
#' FanDuel, and SuperDraft), cut status, finishing position, tournament details
#' such as course, date, purse and season, and strokes gained statistics across
#' different aspects of the game.
#'
#' The dataset name has been kept as 'pga_results' to avoid confusion with
#' other datasets in the R ecosystem. This naming convention helps distinguish this
#' dataset as part of the sportsR package and assists users in identifying its
#' specific characteristics.
#'
#' @name pga_results
#' @format A data frame with 3676 observations and 34 variables:
#' \describe{
#'   \item{Player_initial_last}{Character vector indicating the player's name in initial-last format}
#'   \item{tournament.id}{Integer vector indicating the tournament identification number}
#'   \item{player.id}{Integer vector indicating the player identification number}
#'   \item{hole_par}{Integer vector indicating the par for the hole}
#'   \item{strokes}{Integer vector indicating the number of strokes taken}
#'   \item{hole_DKP}{Numeric vector indicating the DraftKings points earned per hole}
#'   \item{hole_FDP}{Numeric vector indicating the FanDuel points earned per hole}
#'   \item{hole_SDP}{Integer vector indicating the SuperDraft points earned per hole}
#'   \item{streak_DKP}{Integer vector indicating the DraftKings streak bonus points}
#'   \item{streak_FDP}{Numeric vector indicating the FanDuel streak bonus points}
#'   \item{streak_SDP}{Integer vector indicating the SuperDraft streak bonus points}
#'   \item{n_rounds}{Integer vector indicating the number of rounds played}
#'   \item{made_cut}{Integer vector indicating whether the player made the cut}
#'   \item{pos}{Integer vector indicating the player's finishing position}
#'   \item{finish_DKP}{Integer vector indicating the DraftKings points earned for finishing position}
#'   \item{finish_FDP}{Integer vector indicating the FanDuel points earned for finishing position}
#'   \item{finish_SDP}{Integer vector indicating the SuperDraft points earned for finishing position}
#'   \item{total_DKP}{Numeric vector indicating the total DraftKings points earned}
#'   \item{total_FDP}{Numeric vector indicating the total FanDuel points earned}
#'   \item{total_SDP}{Integer vector indicating the total SuperDraft points earned}
#'   \item{player}{Character vector indicating the full name of the player}
#'   \item{tournament.name}{Character vector indicating the name of the tournament}
#'   \item{course}{Character vector indicating the name of the golf course}
#'   \item{date}{Character vector indicating the date of the tournament}
#'   \item{purse}{Numeric vector indicating the total prize money offered at the tournament}
#'   \item{season}{Integer vector indicating the season or year of the tournament}
#'   \item{no_cut}{Integer vector indicating whether the tournament had no cut}
#'   \item{Finish}{Character vector indicating the player's final finishing position}
#'   \item{sg_putt}{Numeric vector indicating strokes gained putting}
#'   \item{sg_arg}{Numeric vector indicating strokes gained around the green}
#'   \item{sg_app}{Numeric vector indicating strokes gained approach}
#'   \item{sg_ott}{Numeric vector indicating strokes gained off the tee}
#'   \item{sg_t2g}{Numeric vector indicating strokes gained tee to green}
#'   \item{sg_total}{Numeric vector indicating total strokes gained}
#' }
#' @source Data taken from the ISAR package version 1.0.5
#' @usage data(pga_results)
#' @export
load("data/pga_results.rda")
NULL