Titanic Case

#-----------Titanic Case Analysis-------------------

#1 Reading Data 
 titanic.df <- read.csv(paste("Titanic Data.csv", sep =""))

#2 Viewing data
 View(titanic.df)

#3 number of passengers on board
 dim(titanic.df)

## [1] 889   8

#4 number of passangers survived the sinking of titanic
 table(titanic.df$Survived)

## 
##   0   1 
## 549 340

#5 percentage of passengers who survived the sinking of titanic
 mytable <- table(titanic.df$Survived)
 mytable 

## 
##   0   1 
## 549 340

 prop <- prop.table(mytable)
 propPer <- prop*100
 propPer

## 
##        0        1 
## 61.75478 38.24522

 propPer[2]

##        1 
## 38.24522

#6 number of first class passenger who survived the sinking
 mytab <- xtabs(~ Survived + Pclass, data=titanic.df)
 addmargins(mytab)

##         Pclass
## Survived   1   2   3 Sum
##      0    80  97 372 549
##      1   134  87 119 340
##      Sum 214 184 491 889

 nrow(subset(titanic.df, Survived == 1 & Pclass == 1))

## [1] 134

 surviversByClass <- xtabs(~ Survived + Pclass, data=titanic.df)
 prop.table(surviversByClass, 2)

##         Pclass
## Survived         1         2         3
##        0 0.3738318 0.5271739 0.7576375
##        1 0.6261682 0.4728261 0.2423625

#7 percentage of first class passenger who survived the sinking 
 100*prop.table(surviversByClass, 2)[2,1]

## [1] 62.61682

#8 number of females from First-Class who survived the sinking of the Titanic
 mytable1 <- xtabs(~ Survived + Sex + Pclass, data=titanic.df)
 mytable1

## , , Pclass = 1
## 
##         Sex
## Survived female male
##        0      3   77
##        1     89   45
## 
## , , Pclass = 2
## 
##         Sex
## Survived female male
##        0      6   91
##        1     70   17
## 
## , , Pclass = 3
## 
##         Sex
## Survived female male
##        0     72  300
##        1     72   47

 ftable(mytable1)[3]

## [1] 89

#9 percentage of survivors who were female
 surviversBySex <- xtabs(~ Survived + Sex, data=titanic.df)
 surviversBySex

##         Sex
## Survived female male
##        0     81  468
##        1    231  109

 surviversBySex [2]

## [1] 231

 propSur <- prop.table(surviversBySex,1)
 propSurPer <- propSur*100
 propSurPer[2,1]

## [1] 67.94118

#10 Chi- Square Test
 chisq.test(surviversBySex)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  surviversBySex
## X-squared = 258.43, df = 1, p-value < 2.2e-16