1. Setting working directory to access the csv file
#1) Setting working directory
setwd("C:/Users/HP/Downloads/Intern/Titanic")

Working Directory Set.

  1. Reading the data from csv file and creation of a data-frame:
#2) Reading the data using read.csv and creation of dataframe
titanic.df <- read.csv(paste("Titanic Data.csv", sep=""))

titanic.df is created.

  1. Viewing the contents of titanic.df data-frame.
#3) Viewing the data frame in R
View(titanic.df)

Matrix of rows and columns are visible to us.

  1. Finding the number of passengers who were onboard the Titanic:
#4) Number of Passengers onboard the Titanic
length(titanic.df$Survived)
## [1] 889
  1. Finding number of passengers who survived the sinkning of titanic:
#5) Number of Passengers who survived the sinking of Titanic

passengers_survived.df <- titanic.df[which(titanic.df$Survived=='1'),]
length(passengers_survived.df$Survived)
## [1] 340
  1. Percentage of passengers who survived the sinking of the ship:
#6) Percentage of passengers who survived the sinking of Titanic

mytable<-xtabs(~Survived,data=titanic.df)
prop.table(mytable)*100
## Survived
##        0        1 
## 61.75478 38.24522
  1. No. of first class passengers who survived the sinking of titanic:
#7) No. of first class passengers who survived the sinking of titanic

fclass_passengers_survived.df <- titanic.df[which(titanic.df$Survived=='1' & titanic.df$Pclass=='1'),]
length(fclass_passengers_survived.df$Survived)
## [1] 134
  1. percentage of first-class passengers who survived the sinking of the Titanic
#8)  percentage of first-class passengers who survived the sinking of the Titanic

mytable<-xtabs(~Survived+Pclass,data=titanic.df)
mytable
##         Pclass
## Survived   1   2   3
##        0  80  97 372
##        1 134  87 119
prop.table(mytable)*100
##         Pclass
## Survived         1         2         3
##        0  8.998875 10.911136 41.844769
##        1 15.073116  9.786277 13.385827
  1. number of females from First-Class who survived the sinking of the Titanic
#9)  number of females from First-Class who survived the sinking of the Titanic

fclass_female_passengers_survived.df <- titanic.df[which(titanic.df$Survived=='1' & titanic.df$Pclass=='1' & titanic.df$Sex=="female"),]
length(fclass_female_passengers_survived.df$Survived)
## [1] 89
  1. percentage of survivors who were female
#10) percentage of survivors who were female

survivors.df<-titanic.df[which(titanic.df$Survived=='1'),]
mytable<-xtabs(~Survived+Sex,data=survivors.df)
prop.table(mytable)*100
##         Sex
## Survived   female     male
##        1 67.94118 32.05882
#---------------------------------------------------------------
# Another way

  mytable<-xtabs(~Survived+Sex,data=titanic.df)
  prop.table(mytable,1) *100
##         Sex
## Survived   female     male
##        0 14.75410 85.24590
##        1 67.94118 32.05882
  1. percentage of females on board the Titanic who survived
#11) percentage of females on board the Titanic who survived

females.df<-titanic.df[which(titanic.df$Sex=="female"),]
mytable<-xtabs(~Survived,data=females.df)
prop.table(mytable)*100
## Survived
##        0        1 
## 25.96154 74.03846
#---------------------------------------------------------------
# Another way

mytable<-xtabs(~Survived+Sex,data=titanic.df)
prop.table(mytable,2) *100
##         Sex
## Survived   female     male
##        0 25.96154 81.10919
##        1 74.03846 18.89081

  1. 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.

#12) 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=titanic.df)
addmargins(mytable)
##         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
  1. R to create a table showing the average age of the survivors and the average age of the people who died
#-------------------------------------------------------------------------------------------------------
#WEEK 2 DAY 1

#13)  R to create a table showing the average age of the survivors and the average age of the people who died.

aggregate(titanic.df$Age,list(titanic.df$Survived),mean)
##   Group.1        x
## 1       0 30.41530
## 2       1 28.42382

  1. R to run a t-test to test the following hypothesis:

    H2: The Titanic survivors were younger than the passengers who died.

#14)  R to run a t-test to test the following hypothesis:
#H2: The Titanic survivors were younger than the passengers who died.

t.test(titanic.df$Age~titanic.df$Survived, data=titanic.df)
## 
##  Welch Two Sample t-test
## 
## data:  titanic.df$Age by titanic.df$Survived
## t = 2.1816, df = 667.56, p-value = 0.02949
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1990628 3.7838912
## sample estimates:
## mean in group 0 mean in group 1 
##        30.41530        28.42382
#In this test, p-value came 0.029 (here p < 0.05) which suggests significant difference between the means of the two samples and we can reject the null hypothesis.

In this test, p-value came 0.029 (here p < 0.05) which suggests significant difference between the means of the two samples and this is indicative of rejection of the the null hypothesis.