Who is Prioritized in Titanic Evacuation? Visualization
Reza Desita
4/1/2021
1 About Titanic
RMS Titanic was a British passenger liner operated by the White Star Line that sank in the North Atlantic Ocean on 15 April 1912, after striking an iceberg during her maiden voyage from Southampton to New York City. Of the estimated 2,224 passengers and crew aboard, more than 1,500 died, making the sinking at the time one of the deadliest of a single ship and the deadliest peacetime sinking of a superliner or cruise ship to date.
RMS Titanic was the largest ship afloat at the time she entered service and was the second of three Olympic-class ocean liners operated by the White Star Line. She was built by the Harland and Wolff shipyard in Belfast. Thomas Andrews, chief naval architect of the shipyard at the time, died in the disaster.
1.1 Data Set Up
#Data Input
titanic<- read.csv("data/train.csv")
str(titanic)## '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 : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ 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 : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : chr "S" "C" "S" "S" ...#Tidying Data
titanic$Sex <- as.factor(titanic$Sex)
titanic$Pclass <- as.factor(titanic$Pclass)
titanic$Embarked <- as.factor(titanic$Embarked)#Checking NA
colSums(is.na(titanic))## PassengerId Survived Pclass Name Sex Age
## 0 0 0 0 0 177
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 0 0 0#Drop NAs
titanic <- na.omit(titanic)# Change value of Survived
titanic$Survived <- sapply(X = as.character(titanic$Survived), FUN = switch,
"0" = "Not Survived",
"1" = "Survived")
table(titanic$Survived)##
## Not Survived Survived
## 424 290titanic$Survived <- as.factor(titanic$Survived)1.1.1 Data Overview
summary(titanic)## PassengerId Survived Pclass Name Sex
## Min. : 1.0 Not Survived:424 1:186 Length:714 female:261
## 1st Qu.:222.2 Survived :290 2:173 Class :character male :453
## Median :445.0 3:355 Mode :character
## Mean :448.6
## 3rd Qu.:677.8
## Max. :891.0
## Age SibSp Parch Ticket
## Min. : 0.42 Min. :0.0000 Min. :0.0000 Length:714
## 1st Qu.:20.12 1st Qu.:0.0000 1st Qu.:0.0000 Class :character
## Median :28.00 Median :0.0000 Median :0.0000 Mode :character
## Mean :29.70 Mean :0.5126 Mean :0.4314
## 3rd Qu.:38.00 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :80.00 Max. :5.0000 Max. :6.0000
## Fare Cabin Embarked
## Min. : 0.00 Length:714 : 2
## 1st Qu.: 8.05 Class :character C:130
## Median : 15.74 Mode :character Q: 28
## Mean : 34.69 S:554
## 3rd Qu.: 33.38
## Max. :512.33Description of the data :
PassengerId : ID of Passenger Survived : Survival - 0 = Did not survived - 1 = Survived Pclass : Ticket Class - 1 = 1st = Upper - 2 = 2nd = Middle - 3 = 3rd = Lower Name : Passenger name Sex : Passenger sex Age : Passenger age SibSp : No. of siblings boarding the Titanic Parch : No. of parents or children boarding the Titanic Ticket : Ticket Number Fare : Passenger fare Cabin : Cabin number Embarked : Port of Embarkation - C = Cherbourg - Q = Queenstown - S = Southampton
1.1.2 Survival Rate
1.1.2.1 How many survived?
#Check Data of Survived Passenger
titanic_survived <- titanic[titanic$Survived == "Survived",]
nrow(titanic_survived)## [1] 290342 people survived the Titanic.
1.1.2.2 Does Sex affect the rate of survival?
graphics::pie(xtabs(~ Sex, titanic_survived), main = "Sex of Survived Passengers")
female <- titanic_survived[titanic_survived$Sex == "female",]
aggregate(Age ~ Sex, female, mean)## Sex Age
## 1 female 28.84772The data shown that there are a total of 891 passenger boarding the Titanic. When the ship sank, it shown that there are only 324 passenger survived. 549 others did not survived the accident. From 324 survived passenger, there are more female passengers (233 passengers) since the procedure of evacuation is to save women and children first. The average of female passengers survived are around the age of 28 years old.
1.1.2.3 Does ticket class affect the rate of survival?
graphics::barplot(xtabs(~ Pclass, titanic_survived), main = "Ticket Class of Survived Passengers")
#Check ratio of the survived passengers of each class
summary(titanic_survived$Pclass)## 1 2 3
## 122 83 85summary(titanic$Pclass)## 1 2 3
## 186 173 355ratio1 <- 136 / 216 * 100
ratio1 # Upper Class## [1] 62.96296ratio2 <- 87 / 184 * 100
ratio2 # Middle Class## [1] 47.28261ratio3 <- 119 / 491 * 100
ratio3 # Lower Class## [1] 24.23625The data also shown that most of the survived passengers are from the Upper Class (136 people). But to be precise, from the calculation of survival rate ratio of each classes, it also shown that the Upper Class gets more than 50% survival rate, and the Lower Class only got 24% of survival rate.
1.1.2.4 Does Age affect the rate of survival?
graphics::barplot(xtabs(~ Age, titanic_survived), main = "Age of Survived Passengers")
It also shown that the mean of age of survival in the Titanic accident are around 28 years old. This could also happen because those mostly survived are female.
1.1.2.5 Does the Port of Embarkament affect the rate of survival?
summary(titanic$Embarked)## C Q S
## 2 130 28 554summary(titanic_survived$Embarked)## C Q S
## 2 79 8 201#Ratio of Survival Rate based on Port of Embarkation
ratioC <- 93 / 168 * 100
ratioC## [1] 55.35714ratioQ <- 30 / 77 * 100
ratioQ## [1] 38.96104ratioS <- 217 / 644 * 100
ratioS## [1] 33.69565# Ticket Class of each Port of Embarkation
cherbourg <- titanic_survived[titanic_survived$Embarked == "C",]
summary(cherbourg$Pclass)## 1 2 3
## 53 8 18queenstown <- titanic_survived[titanic_survived$Embarked == "Q",]
summary(queenstown$Pclass)## 1 2 3
## 1 1 6southampton <- titanic_survived[titanic_survived$Embarked == "S",]
summary(southampton$Pclass)## 1 2 3
## 66 74 61From the calculation, it shows that passengers from Cherbourg, France has the highest survival rate (55%). It could also be because of female:male ratio. Other than that, most passengers from Cherbourg also in Upper Class.
female <- titanic_survived[titanic_survived$Sex == "female",]
summary(female$Embarked)## C Q S
## 2 55 7 133- Cherbourg
summary(cherbourg$Sex)## female male
## 55 24summary(cherbourg$Pclass)## 1 2 3
## 53 8 18nrow(cherbourg[cherbourg$Sex == "female" & cherbourg$Pclass == "1",]) # No. of survived female passengers of Cherbourg in Upper Class## [1] 37After more inspection, it shows interesting result. It turns out that most female passengers came from Southampton, but the reason why passengers from Cherbourg has the highest survival was because all female passengers of Cherbourg survived (64 people).
- Queenstown
summary(queenstown$Sex)## female male
## 7 1summary(queenstown$Pclass)## 1 2 3
## 1 1 6nrow(queenstown[queenstown$Sex == "female" & queenstown$Pclass == "1",])## [1] 1Upper Class female of Queenstown also survived.
- Southampton
summary(southampton$Sex)## female male
## 133 68summary(southampton$Pclass)## 1 2 3
## 66 74 61nrow(southampton[southampton$Sex == "female" & southampton$Pclass == "1",])## [1] 42Not all Upper Class female of Southampton survived.
1.1.2.6 Which female passengers survived?
nrow(female[female$Age >= 28,]) # survived female passengers (above 28 yo)## [1] 102nrow(female[female$Age >= 28 & female$Parch >= 1,]) # survived female passengers (above 28 yo) and with parents/child on board## [1] 30# Ticket Class of survived female passengers
summary(female$Pclass)## 1 2 3
## 82 68 47From 233 female passengers who survived, 138 of them have the age above average (28 years old). Only 33 of them have a parents or children on board. 91 of them are from the Upper Class.
1.1.2.7 How many children under 17 years old survived?
nrow(titanic[titanic$Age < 17,]) # No of passengers under 17 y.o.## [1] 100nrow(titanic_survived[titanic_survived$Age < 17,]) # No. of survived passengers under 17 y.o.## [1] 55Only 107 children under 17 years old survived the Titanic. But this is not an accurate data as there are so many NAs in the Age column which means there are a lot of unidentified passengers.
1.2 Visualization
library(ggplot2)theme_titanic <- function() {
theme(
plot.background = element_rect(fill = "snow2"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.background = element_rect(color = "orange2"),
axis.ticks = element_blank(),
legend.background = element_rect(fill = "snow2"),
plot.title = element_text(size = 18)
)
}1.2.1 Who Survived?
ggplot(titanic, aes(x = Survived,
y = Sex,
fill = Sex)) +
geom_bar(position="stack", stat="identity") +
facet_grid(~ Pclass) +
labs(title = "Survival based on Gender and Ticket Class",
x = "",
y = "") +
guides(size = F) +
theme_titanic()
library(viridis)## Loading required package: viridisLite# Based on Age, Sex and Pclass
ggplot(titanic_survived, aes(x = Pclass,
y = Age
)) +
geom_boxplot() +
geom_jitter(aes(col = Age)) +
geom_boxplot(alpha=0.5) +
labs(title = "Survival Based on Ticket Class and Age",
x= "Ticket Class",
y= "Age",
col = "Age") +
theme_titanic()
### How many survived on each Embakation Port?
library(leaflet)
# install.packages("leaflet.minicharts")
library(leaflet.minicharts)titanic_agg <- aggregate(Survived ~ Embarked, titanic_survived, length)
titanic_agg$latitude <- c(0, 49.644577, 51.850334, 50.8965)
titanic_agg$longitude <- c(0, -1.605079, -8.294286, -1.3968)
titanic_agg <- titanic_agg[-c(1),]1.2.2 Survived Passengers based on Embarkation Port
# Survival based on Embarkament Port
map1 <- leaflet(data = titanic_agg)
map1 <- addTiles(map1)
map1 <- addMarkers(map1,
lng = ~longitude,
lat = ~latitude)
map1 <- addMinicharts(map = map1,
lng = titanic_agg$longitude,
lat = titanic_agg$latitude,
chartdata = titanic_agg$Survived,
showLabels = TRUE,
width = 80
)
map11.2.3 Conclusion
From all the analysis, it can be concluded that female who is around the age of 20 something has the highest chance of survival in the Titanic. Women and children traveling in Upper and Middle Class were given priority for getting into the lifeboats and most of them were saved. Upper class passengers had 62% of survival rate. The Port of Embarkament did not have a significant difference of survival rate, mostly passengers were only identified by their ticket class.
The less number of survival in Titanic tragedy also happened because of the lack of Emergency Preparation. Some specific failures in emergency preparedness before the sinking of the Titanic included: - Not enough lifeboats for all passengers and crew, perhaps because the builders considered the ship “unsinkable”! - No lifeboat drills had been conducted, and many people did not know where to go or what to do. - Many of the first lifeboats to leave the Titanic were not full and some occupants were reluctant to pull other people from the icy water for fear of capsizing their lifeboat. - The decision to abandon ship was delayed while the captain and crew assessed damage. Had the captain started evacuation earlier, before people began to panic, more lifeboats may have been filled in a more orderly evacuation.
1.3 References
https://www.kaggle.com/c/titanic/data?select=train.csv
https://www.shiftcomm.com/insights/never-let-go-titanic-survival-101/#:~:text=Port%20of%20Embarkation&text=The%20three%20ports%20were%20Queesntown,this%20region%20survived%20the%20accident
https://www.aiche.org/sites/default/files/2012-07-Beacon-English.pdf