# R-Code for March 31st.  

#See MArch 29th R Code for an legend and explanation of the below data. We know have 4 new categories.

#Legned

#F=Female passengers
#M=Male passengers
#A=Alive, i.e. survivors
#D=Dead, i.e.victims
#1=First class  passengers
#2=Second class  passengers
#3=Third class  passengers
#C=Crew
# AF=Female and Survivor 
# AD=Female and Victim 
# AF=Male and Survivor 
# AF=Male and Victim


# Rows AF,AM,DF,DM
# Columns 1,2,3, C


A=rbind(
c(98,57,85,26),
c(104,61,93,186),
c(63,84,270,83),
c(60,83,258,590))
M1=margin.table(A,1) 
F1=diag(1/M1)%*%A
M2=margin.table(A,2) 
F2=A%*%diag(1/M2)


###################

# Goal: Find the percentage of female survivors in each class.

#The female survivors are: 

A[1,]

#First notice the Females in each class are: 

A[1,]+A[3,]


# Hence, the percentage of female survivors in each class (Call them PF)  are: 
 
PF=A[1,]/(A[1,]+A[3,])

# Exercise:  the percentage of male survivors in each class  (Call them PF)   are? 

#Answer: 

PM=A[2,]/(A[2,]+A[4,])


# We can compare the percentage of female survivors in each class.

pie(PF,labels=c("First","Second","Third","Crew"),main="Percentage of female survivors in each class")

# With the percentage of female survivors in each class

quartz()

pie(PM,labels=c("First","Second","Third","Crew"),main="Percentage of male survivors in each class")

# Question 1:  Compare PM and PF with the percentage of female verse male survivors (over all), what happened?

# Question 2:  Are these really "pie charts"?  Why or why not?

# Question 3: Do our "pie charts" indicate that something is independent of something else?  If so, then  what things are independent and how is this indicated?