1 Introduction

The data is about biography of passengers of Titanic. It contains information about gender, who survived or not, ticket class, age, total sibling that aboard the titanic, number of parents aboard, passenger fare, and port of embarkation.
This analysis is to see following objectives :
1. How Many passengers survived the Titanic based on class?
2. How Many passengers survived the Titanic based on Sex?
3. How Many passengers survived the Titanic based on Port Embarked?

The step of analysis is as followed :

1.1 Import libraries :

library(ggplot2)
library(GGally)
library(ggthemes)
library(ggpubr)
library(lubridate)
library(readr) 
library(dplyr)
library(tidyr)
library(glue)
library(plotly)
library(tidyverse)

1.2 1. Data loading

train <- read.csv("D:/Meinari/algoritma/Tugas/titanic/train.csv")

1.3 2. Data Checking

summary(train)

##   PassengerId       Survived          Pclass     
##  Min.   :  1.0   Min.   :0.0000   Min.   :1.000  
##  1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000  
##  Median :446.0   Median :0.0000   Median :3.000  
##  Mean   :446.0   Mean   :0.3838   Mean   :2.309  
##  3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000  
##  Max.   :891.0   Max.   :1.0000   Max.   :3.000  
##                                                  
##                                     Name         Sex           Age       
##  Abbing, Mr. Anthony                  :  1   female:314   Min.   : 0.42  
##  Abbott, Mr. Rossmore Edward          :  1   male  :577   1st Qu.:20.12  
##  Abbott, Mrs. Stanton (Rosa Hunt)     :  1                Median :28.00  
##  Abelson, Mr. Samuel                  :  1                Mean   :29.70  
##  Abelson, Mrs. Samuel (Hannah Wizosky):  1                3rd Qu.:38.00  
##  Adahl, Mr. Mauritz Nils Martin       :  1                Max.   :80.00  
##  (Other)                              :885                NA's   :177    
##      SibSp           Parch             Ticket         Fare       
##  Min.   :0.000   Min.   :0.0000   1601    :  7   Min.   :  0.00  
##  1st Qu.:0.000   1st Qu.:0.0000   347082  :  7   1st Qu.:  7.91  
##  Median :0.000   Median :0.0000   CA. 2343:  7   Median : 14.45  
##  Mean   :0.523   Mean   :0.3816   3101295 :  6   Mean   : 32.20  
##  3rd Qu.:1.000   3rd Qu.:0.0000   347088  :  6   3rd Qu.: 31.00  
##  Max.   :8.000   Max.   :6.0000   CA 2144 :  6   Max.   :512.33  
##                                   (Other) :852                   
##          Cabin     Embarked
##             :687    :  2   
##  B96 B98    :  4   C:168   
##  C23 C25 C27:  4   Q: 77   
##  G6         :  4   S:644   
##  C22 C26    :  3           
##  D          :  3           
##  (Other)    :186

str(train)

## 'data.frame':    891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
##  $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
##  $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
##  $ Ticket     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
##  $ Embarked   : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...

colSums(is.na(train))

## PassengerId    Survived      Pclass        Name         Sex         Age 
##           0           0           0           0           0         177 
##       SibSp       Parch      Ticket        Fare       Cabin    Embarked 
##           0           0           0           0           0           0

1.4 3. Variable Description

Passengerid : unique ID number to each passenger
Survived : Passenger survive (1) or died(0)
Pclass : Passenger class
Name : name
Sex : Gender of passenger
Age : age of passenger
Sibsp : Number of siblings/spouses
Parch : Number of parents/ children
Ticket : Ticket number
Fare : Amount of money spent on ticket
Cabin : Cabin category
Embarked : Port where passenger embarked (C= Cherbourg, Q= Queenstown, S=Southampton)

1.5 4. Data Processing

head(train)

unique(train$Pclass)

## [1] 3 1 2

unique(train$Age)

##  [1] 22.00 38.00 26.00 35.00    NA 54.00  2.00 27.00 14.00  4.00 58.00 20.00
## [13] 39.00 55.00 31.00 34.00 15.00 28.00  8.00 19.00 40.00 66.00 42.00 21.00
## [25] 18.00  3.00  7.00 49.00 29.00 65.00 28.50  5.00 11.00 45.00 17.00 32.00
## [37] 16.00 25.00  0.83 30.00 33.00 23.00 24.00 46.00 59.00 71.00 37.00 47.00
## [49] 14.50 70.50 32.50 12.00  9.00 36.50 51.00 55.50 40.50 44.00  1.00 61.00
## [61] 56.00 50.00 36.00 45.50 20.50 62.00 41.00 52.00 63.00 23.50  0.92 43.00
## [73] 60.00 10.00 64.00 13.00 48.00  0.75 53.00 57.00 80.00 70.00 24.50  6.00
## [85]  0.67 30.50  0.42 34.50 74.00

unique(train$Cabin)

##   [1]                 C85             C123            E46            
##   [5] G6              C103            D56             A6             
##   [9] C23 C25 C27     B78             D33             B30            
##  [13] C52             B28             C83             F33            
##  [17] F G73           E31             A5              D10 D12        
##  [21] D26             C110            B58 B60         E101           
##  [25] F E69           D47             B86             F2             
##  [29] C2              E33             B19             A7             
##  [33] C49             F4              A32             B4             
##  [37] B80             A31             D36             D15            
##  [41] C93             C78             D35             C87            
##  [45] B77             E67             B94             C125           
##  [49] C99             C118            D7              A19            
##  [53] B49             D               C22 C26         C106           
##  [57] C65             E36             C54             B57 B59 B63 B66
##  [61] C7              E34             C32             B18            
##  [65] C124            C91             E40             T              
##  [69] C128            D37             B35             E50            
##  [73] C82             B96 B98         E10             E44            
##  [77] A34             C104            C111            C92            
##  [81] E38             D21             E12             E63            
##  [85] A14             B37             C30             D20            
##  [89] B79             E25             D46             B73            
##  [93] C95             B38             B39             B22            
##  [97] C86             C70             A16             C101           
## [101] C68             A10             E68             B41            
## [105] A20             D19             D50             D9             
## [109] A23             B50             A26             D48            
## [113] E58             C126            B71             B51 B53 B55    
## [117] D49             B5              B20             F G63          
## [121] C62 C64         E24             C90             C45            
## [125] E8              B101            D45             C46            
## [129] D30             E121            D11             E77            
## [133] F38             B3              D6              B82 B84        
## [137] D17             A36             B102            B69            
## [141] E49             C47             D28             E17            
## [145] A24             C50             B42             C148           
## 148 Levels:  A10 A14 A16 A19 A20 A23 A24 A26 A31 A32 A34 A36 A5 A6 A7 ... T

table(train$Pclass)

## 
##   1   2   3 
## 216 184 491

levels(train$Embarked)

## [1] ""  "C" "Q" "S"

# Change label if embarked
train2<- train %>% 
  mutate(Embarked = as.factor(case_when(Embarked == "" ~ "other",
                              Embarked == "C" ~ "Cherbourg",
                              Embarked == "Q" ~ "Queenstown",
                              Embarked == "S" ~ "Southampton"))) %>% 
  mutate(Survived = as.factor(case_when(Survived == 1 ~ "Survived",
                                        Survived == 0 ~ "Died")))

2 Data Analysis

2.1 How Many passengers survived the Titanic based on passenger’s class?

train1 <- train2 %>%
  group_by(Pclass, Survived) %>% 
  summarise(total=n()) %>% 
  ungroup() %>% 
    mutate(text = glue(
    "Number of Survival/Death = {total}"
  ))


plot1 <- ggplot(data = train1, aes(x= reorder(Pclass, total), y=total, text = text))+
  geom_col(aes(fill= Survived), position = "dodge")+
  theme()+
  labs(title="Titanic's Passenger Survived based on their class",  x="Class", y="Number of Survival", caption="Made by Meinari Claudia")

ggplotly(plot1, tooltip = "text")

Conclusion 1 : so based on class, passengers who on class 3 have number of death highest, 372 people.

2.2 How Many passengers survived the Titanic based on Sex?

train3 <- train2 %>% 
  group_by(Sex, Survived) %>% 
  summarise(total=n()) %>% 
  ungroup() %>% 
    mutate(text=glue(
    "Number of Survival/Death = {total}"
  ))

plot2 <- ggplot(data = train3, aes(x= reorder(Sex, total), y=total, text=text))+
  geom_col(aes(fill=Survived), position = "dodge")+
  theme()+
  labs(title="Titanic's Passenger Survived based on Sex",  x="Sex", y="Number of Survival", caption="Made by Meinari Claudia")

ggplotly(plot2, tooltip="text")

Conclusion 2 : based on gender, number death of male (468) exceeded female (81). the survival ratio of male was 19% compare to female was 74%.

2.3 How Many passengers survived the Titanic based on Port Embarked?

train4 <- train2 %>% 
  group_by(Embarked, Survived) %>% 
  summarise(total = n()) %>% 
  ungroup() %>% 
  mutate(text=glue(
    "Number of Survival/Death = {total}"
  ))


plot3 <- ggplot(data = train4, aes(x= reorder(Embarked, total), y=total, text=text))+
  geom_col(aes(fill= Survived), position = "dodge")+
  theme()+
  labs(title="Titanic's Passenger Survived based on Port Embarked",  x="Port Embarked", y="Number of Survival", caption="Made by Meinari Claudia")

ggplotly(plot3, tooltip = "text")

Conclusion3 : based on Port Embarked, most passengers of Titanic embarked at Southampton followed by Cherbourg and Queenstown. However, the biggest death ratio came from them who embarked at Southampton (66%).

Who Did Survive from Titanic

Meinari

February 10, 2020