read.csv('Titanic Data.csv')
getwd()
## [1] "C:/Users/Vimal/Desktop/Vimal/Dr. Sameer Mathur/Assignment"
titanicdata.df<-read.csv('Titanic Data.csv')
View(titanicdata.df)
dim(titanicdata.df)
## [1] 889   8

No. of passengers: 889

sum(titanicdata.df$Survived==1)
## [1] 340

No.of passengers survived= 340

table=with(titanicdata.df,table(Survived))
prop.table(table)*100
## Survived
##        0        1 
## 61.75478 38.24522

% of survived = 38.24522

xyz <- xtabs(~ Survived+Pclass, data=titanicdata.df)
xyz
##         Pclass
## Survived   1   2   3
##        0  80  97 372
##        1 134  87 119

134 first class survived

prop.table(xyz,2)*100
##         Pclass
## Survived        1        2        3
##        0 37.38318 52.71739 75.76375
##        1 62.61682 47.28261 24.23625

62.61682% of first class people survived

xy <- xtabs(~ Survived+Pclass+Sex, data=titanicdata.df)
 xy
## , , Sex = female
## 
##         Pclass
## Survived   1   2   3
##        0   3   6  72
##        1  89  70  72
## 
## , , Sex = male
## 
##         Pclass
## Survived   1   2   3
##        0  77  91 300
##        1  45  17  47
prop.table(xy,1)*100
## , , Sex = female
## 
##         Pclass
## Survived          1          2          3
##        0  0.5464481  1.0928962 13.1147541
##        1 26.1764706 20.5882353 21.1764706
## 
## , , Sex = male
## 
##         Pclass
## Survived          1          2          3
##        0 14.0255009 16.5755920 54.6448087
##        1 13.2352941  5.0000000 13.8235294
a <- xtabs(~ Survived+Sex, data=titanicdata.df)
prop.table(a,2)*100
##         Sex
## Survived   female     male
##        0 25.96154 81.10919
##        1 74.03846 18.89081
b <- xtabs(~Survived+Sex, data=titanicdata.df)
b
##         Sex
## Survived female male
##        0     81  468
##        1    231  109
chisq.test(b)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  b
## X-squared = 258.43, df = 1, p-value < 2.2e-16