library(readr)
library(dplyr)


#
# Load data
#

players <- read_csv("data/nba-players-2013-14.csv")
head(players)


#
# Player heights distributions
#

warriors <- players %>%
    filter(Team == "Warriors") %>%
    arrange(-Ht_inches)

# Bar chart
par(mar=c(5,10,5,5))
barplot(warriors$Ht_inches, 
        names.arg=warriors$Name, 
        horiz=TRUE, 
        border=NA, las=1, 
        main="Heights of Golden State Warriors, 2013-14")


# Dots on a continuous height scale
htrange <- range(warriors$Ht_inches)
cnts <- rep(0, htrange[2]-htrange[1]+1)
y <- c()
for (i in 1:dim(warriors)[1]) {
    cntindex <- warriors$Ht_inches[i] - htrange[1] + 1
    cnts[cntindex] <- cnts[cntindex] + 1
    y <- c(y, cnts[cntindex])
}
plot(warriors$Ht_inches, y,
     xlim = c(69, 87),
     ylim = c(0, 10),
     type="n", 
     main="Player heights", xlab="inches", ylab="count")
points(warriors$Ht_inches, y, pch=21, col=NA, bg="#999999")


# Dots for all players
htrange <- range(players$Ht_inches)
cnts <- rep(0, htrange[2]-htrange[1]+1)
y <- c()
for (i in 1:dim(players)[1]) {
    cntindex <- players$Ht_inches[i] - htrange[1] + 1
    cnts[cntindex] <- cnts[cntindex] + 1
    y <- c(y, cnts[cntindex])
}

par(las=1)
plot(players$Ht_inches, y,
     xlim = htrange,
     ylim = c(0, max(cnts)),
     type="n", 
     main="Player heights", xlab="inches", ylab="count")
points(players$Ht_inches, y, pch=21, col=NA, bg="#999999")

# Bar chart version of counts
barplot(cnts, names.arg=69:87, 
        main="Player heights", 
        xlab="inches", ylab="count", border=NA, las=1)


# Histogram
hist(players$Ht_inches, 
     breaks=seq(65, 90, 1),
     xlab="inches",
     main="NBA Player Heights")

# Vary bin sizes
par(mfrow=c(1,3), mar=c(3,3,3,3))
hist(players$Ht_inches, main="NBA Player Heights", xlab="inches", breaks=seq(65, 90, 1))
hist(players$Ht_inches, main="NBA Player Heights", xlab="inches", breaks=seq(65, 90, 2))
hist(players$Ht_inches, main="NBA Player Heights", xlab="inches", breaks=seq(65, 90, 5))