getwd()
## [1] "C:/Users/Taruna/Documents"
To read and view data:
titanicdat.df <- read.csv("Titanic Data.csv")
View(titanicdat.df)
dim(titanicdat.df)
## [1] 889 8
Thus there were 889 passengers.
mytable<-with(titanicdat.df,table(Survived))
mytable
## Survived
## 0 1
## 549 340
Thus 340 passengers survived.
prop.table((mytable))*100
## Survived
## 0 1
## 61.75478 38.24522
Thus only 38.24% of passengers survived.
surviva <- xtabs(~ Survived+Pclass, data=titanicdat.df)
surviva
## Pclass
## Survived 1 2 3
## 0 80 97 372
## 1 134 87 119
Thus 134 firstclass passengers survived.
prop.table(surviva, 2)*100
## Pclass
## Survived 1 2 3
## 0 37.38318 52.71739 75.76375
## 1 62.61682 47.28261 24.23625
Thus 62.6% of firstclass passengers survived.
mytable <- xtabs(~ Survived+Pclass+Sex, data=titanicdat.df)
mytable
## , , 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(mytable,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
Thus 89 women from firstclass survived.
surviva <- xtabs(~ Survived+Sex, data=titanicdat.df)
prop.table(surviva,2)*100
## Sex
## Survived female male
## 0 25.96154 81.10919
## 1 74.03846 18.89081
Thus 74.03% of survivors were women.
final <- xtabs(~Survived+Sex, data=titanicdat.df)
final
## Sex
## Survived female male
## 0 81 468
## 1 231 109
chisq.test(final)
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: final
## X-squared = 258.43, df = 1, p-value < 2.2e-16
Thus proportion of women who survived was much greater than the proportion of men.
-END OF REPORT-