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soccer.R
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soccer.R
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#' Number of cards given for each referee-player pair in soccer.
#'
#' A dataset containing card counts between 2,053 soccer players
#' playing in the first male divisions of England, Germany, France,
#' and Spain in the 2012-2013 season and 3,147 referees
#' that these players played under in professional matches.
#' The dataset contains other covariates including 2 independent
#' skin tone ratings per player.
#' Each line represents a player-referee pair.
#'
#' @format A data frame with 146,028 rows and 26 variables:
#' \describe{
#' \item{playerShort}{short player ID}
#' \item{player}{player name}
#' \item{club}{player club}
#' \item{leagueCountry}{country of player club (
#' England, Germany, France, and Spain)}
#' \item{birthday}{player birthday}
#' \item{height}{player height (in cm)}
#' \item{weight}{player weight (in kg)}
#' \item{position}{detailed player position}
#' \item{games}{number of games in the player-referee dyad}
#' \item{victories}{victories in the player-referee dyad}
#' \item{ties}{ties in the player-referee dyad}
#' \item{defeats}{losses in the player-referee dyad}
#' \item{goals}{goals scored by a player in the player-referee dyad}
#' \item{yellowCards}{number of yellow cards player received from referee}
#' \item{yellowReds}{number of yellow-red cards player received from referee}
#' \item{redCards}{number of red cards player received from referee}
#' \item{rater1}{skin rating of photo by rater 1
#' (5-point scale ranging from “very light skin” to “very dark skin”)}
#' \item{rater2}{skin rating of photo by rater 2
#' (5-point scale ranging from “very light skin” to “very dark skin”)}
#' \item{refNum}{unique referee ID number
#' (referee name removed for anonymizing purposes)}
#' \item{refCountry}{unique referee country ID number
#' (country name removed for anonymizing purposes)}
#' \item{meanIAT}{mean implicit bias score (using the race IAT)
#' for referee country, higher values correspond to faster
#' white | good, black | bad associations}
#' \item{nIAT}{sample size for race IAT in that particular country}
#' \item{seIAT}{standard error for mean estimate of race IAT}
#' \item{meanExp}{mean explicit bias score (using a racial thermometer task)
#' for referee country, higher values correspond to greater feelings of
#' warmth toward whites versus blacks}
#' \item{nExp}{sample size for explicit bias in that particular country}
#' \item{seExp}{standard error for mean estimate of explicit bias measure}
#' }
#' @details
#' The skin colour of each player was rated by two independent raters,
#' {rater1} and {rater2}, and the 5-point scale values were
#' scaled to 0 to 1 - i.e., 0, 0.25, 0.5, 0.75, 1.
#'
#' @source
#' Silberzahn, R., Uhlmann, E. L., Martin, D. P., Anselmi, P., Aust, F.,
#' Awtrey, E. C., … Nosek, B. A. (2018, August 24).
#' {Many analysts, one dataset: Making transparent how variations in analytical
#' choices affect results.} Retrieved from \url{https://osf.io/gvm2z/}
"soccer"