# 
#  lecture-13.r
#  vqv
#  
#  Created by Vincent Vu on 2011-10-10.
#  This work is licensed under a Creative Commons 
#  Attribution-NonCommercial-ShareAlike 3.0 Unported License.
# 

# masters.Rdata contains a single object, 'masters2011', that is a list with
# 3 components: 'leaderboard', 'scorecard', 'course'
load('masters2011.RData')

runningTotal <- function(df, par = masters2011$course$par) {
    # Reorder the rows of the data frame by the round number 
    # (so that the scores are in chronological order)
    # and extract the scores as a matrix
    x <- as.matrix(df[order(df$round), 1:18])
    n <- nrow(x)

    # Convert from an 18 x n matrix to a vector of length 18*n, rowwise
    x <- as.vector(t(x))

    # Calculate the running over/under score
    x <- cumsum(x - rep(par, n))

    return(x)
}


# ==================================================================
# = Look at all players that made the cut (played in all 4 rounds) =
# ==================================================================
madecut <- subset(masters2011$leaderboard, position != 'CUT')$player
df <- subset(masters2011$scorecard, player %in% madecut)
df <- droplevels(df)


# ===================================
# = Split, apply, combine with plyr =
# ===================================
library(plyr)
scores <- daply(df, 'player', runningTotal)


countPlayers <- function(x) {
    data.frame(count = length(unique(x$player)))
}

ddply(df, 'country', countPlayers)


# ==============
# = Strategy 2 =
# ==============
scorePlayer <- function(x) {
    par <- masters2011$course$par
    data.frame(score = sum(x[1:18] - masters2011$course$par))
}

scores <- ddply(df, c('player', 'round'), scorePlayer)
scores <- merge(df, scores)
daply(scores, c('country', 'round'), function(x) median(x$score))