Analysis of titanic problems

library(vcd)

2.b Read the data

Load the titanic data file

ti.df <- read.csv(paste("Titanic Data.csv", sep=""))

View the data

View(ti.df)

3.a to count the total number of passengers on board the Titanic.

library(psych)
describe(ti.df$Age) #where n is total no of passengers

##    vars   n  mean    sd median trimmed  mad min max range skew kurtosis
## X1    1 889 29.65 12.97   29.7   29.22 9.34 0.4  80  79.6 0.43     0.96
##      se
## X1 0.43

3.a(alternate way)

mytable <- with(ti.df, table(Survived))
margin.table(mytable)

## [1] 889

3.b To count the number of passengers onboard who survived.

mytable <- with(ti.df, table(Survived))
mytable  #where 1= survival

## Survived
##   0   1 
## 549 340

3.c to measure the percentage of passengers who survived the sinking of the Titanic.

prop.table(mytable)*100 #where 1= survival

## Survived
##        0        1 
## 61.75478 38.24522

3.d to count the number of first-class passengers who survived the sinking of the Titanic

mytable <- xtabs(~ Survived+Pclass, data=ti.df)
mytable # here 1=survival , and for class 1= ist class

##         Pclass
## Survived   1   2   3
##        0  80  97 372
##        1 134  87 119

3.e to measure the percentage of first-class passengers who survived the sinking of the Titanic

margin.table(mytable)  #to get the total no of passengers

## [1] 889

prop.table(mytable)*100 #to get the percentage of first class passenger who survived to total no of passenger

##         Pclass
## Survived         1         2         3
##        0  8.998875 10.911136 41.844769
##        1 15.073116  9.786277 13.385827

3.f to count the number of females from First-Class who survived the sinking of the Titanic

mytable <- xtabs(~ Survived+Sex+Pclass, data=ti.df)
ftable(mytable) #here 1=survival , and for class 1= ist class

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

3.g to measure the percentage of survivors who were female

mytable <- xtabs(~ Survived+Sex, data=ti.df)
margin.table(mytable)  #for reference for total no of passengers

## [1] 889

prop.table(mytable)*100

##         Sex
## Survived    female      male
##        0  9.111361 52.643420
##        1 25.984252 12.260967

3.h to measure the percentage of females on board the Titanic who survived with total no of female

margin.table(mytable, 2)

## Sex
## female   male 
##    312    577

prop.table(mytable, 2) #where 1= survived

##         Sex
## Survived    female      male
##        0 0.2596154 0.8110919
##        1 0.7403846 0.1889081

3.i Run a Pearson’s Chi-squared test to test the following hypothesis:

Hypothesis: The proportion of females onboard who survived the sinking of the Titanic was higher than the proportion of males onboard who survived the sinking of the Titanic.

mytable <- xtabs(~ Sex+Survived, data=ti.df)
addmargins(mytable) #to just have a guess or to review

##         Survived
## Sex        0   1 Sum
##   female  81 231 312
##   male   468 109 577
##   Sum    549 340 889

chisq.test(mytable)

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

as after running the Chi-squared test

we can say(p < 0.01) which means the hypothesis is correct.