#############################################################
### Code to read in the Bulls data for Problem 1
#############################################################

bulls<-read.table("https://people.stat.sc.edu/hitchcock/bulldata.txt",header=T)
attach(bulls)
# bulls contains all the variables in the data set (including Breed)

bulls.numeric <- bulls[,-1]
# bulls.numeric contains the numeric variables in the data set (everything except Breed)

# Might be helpful:
bull.labels <- row.names(bulls.numeric)

#############################################################
### Code to read in the NBA data for Problem 2
#############################################################

# Read this basic data file in, but we will remove some variables
# and create some other variables for analysis.
myNBAplayers2022 <- read.csv("https://people.stat.sc.edu/hitchcock/NBAplayers2022.csv",header=T)
myNBAdata<-myNBAplayers2022[,-c(1,2,4,30)]

# Creating some "per-game" or "per-minute-played" variables:
myNBAdata$MinGame <- myNBAdata$MP/myNBAdata$G
myNBAdata$ORBMin <- myNBAdata$ORB/myNBAdata$Min
myNBAdata$DRBMin <- myNBAdata$DRB/myNBAdata$Min
myNBAdata$ASTMin <- myNBAdata$AST/myNBAdata$Min
myNBAdata$STLMin <- myNBAdata$STL/myNBAdata$Min
myNBAdata$BLKMin <- myNBAdata$BLK/myNBAdata$Min
myNBAdata$TOVMin <- myNBAdata$TOV/myNBAdata$Min
myNBAdata$PFMin <- myNBAdata$PF/myNBAdata$Min
myNBAdata$PTSMin <- myNBAdata$PTS/myNBAdata$Min

# Removing some unneeded variables:
myNBAdata_final <- myNBAdata[,-c(2,4,5,6,8,9,11,12,14,15,16,18:26)]
attach(myNBAdata_final)

# NOTE: myNBAdata_final is the numeric data set that should be analyzed.

# The following code creates some character vectors that may be useful to you.  
# It's up to you whether you want to make use of them.

PlayerName <- myNBAplayers2022[,1]
PlayerName_short <- myNBAplayers2022[,30]
key <- cbind(PlayerName, PlayerName_short)
# you can print 'key' to see which "short" codes go with which players' names.

Team <- myNBAplayers2022[,4]
Position <- myNBAplayers2022[,2]