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titanic.df <- read.csv(paste("Titanic.csv"))
View(titanic.df)

Number of Passengers :

passengers <- titanic.df[,1]
length(passengers)
## [1] 889
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
glimpse(titanic.df)
## Observations: 889
## Variables: 8
## $ Survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1,...
## $ Pclass   <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2,...
## $ Sex      <fctr> male, female, female, female, male, male, male, male...
## $ Age      <dbl> 22.0, 38.0, 26.0, 35.0, 35.0, 29.7, 54.0, 2.0, 27.0, ...
## $ SibSp    <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0,...
## $ Parch    <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0,...
## $ Fare     <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51....
## $ Embarked <fctr> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S...

Number of Survivors :

survivors <-filter(titanic.df,Survived==1)
length(survivors[,1])
## [1] 340

Percentage of Survivors :

prop.table(table(titanic.df$Survived))
## 
##         0         1 
## 0.6175478 0.3824522

Number of first class survivors :

firstsurvivors <-filter(survivors,Pclass==1)
length(firstsurvivors[,1])
## [1] 134

Percentage of passengers in first class who survived :

firstclass <-filter(titanic.df,Pclass==1)
prop.table(table(firstclass$Survived))
## 
##         0         1 
## 0.3738318 0.6261682

Number of Females in First Class who survived :

femalesfirst <- filter(firstsurvivors,Sex=='female')
length(femalesfirst[,1])
## [1] 89

Percentage of Survivors who were female :

prop.table(table(survivors$Sex))
## 
##    female      male 
## 0.6794118 0.3205882

Percentage of Females who survived :

females <-filter(titanic.df,Sex=='female')
prop.table(table(females$Survived))
## 
##         0         1 
## 0.2596154 0.7403846

Pearson Chi Square Test :

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

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.