We will attempt to make visualizations of the famous titanic data. We will be using the tidyverse for data manupulation and visualization. An almost similar version of the data used here can be found at Download
# Setting the working directory
setwd("C:/Users/Admin/Documents/Dell E6320/R Datasets")
# Loading the libraries
library(tidyverse)
library(extrafont)
library(extrafontdb)
# Loading the data
titanic_data <- read_csv("titanic_data.csv")
head(titanic_data)# A tibble: 6 x 7
PassengerId Survived Pclass Sex Age Fare Embarked
<dbl> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
1 1 0 3 male 22 7.25 S
2 2 1 1 female 38 71.3 C
3 3 1 3 female 26 7.92 S
4 4 1 1 female 35 53.1 S
5 5 0 3 male 35 8.05 S
6 6 0 3 male NA 8.46 Q
The column PassengerId can be removed. We also need to convert the Survived,Sex and Pclass to factors. Survived is coded in 0s and 1s. Os for died and 1s for survived. We can recode Survived appropritely.
titanic_data <- titanic_data %>%
select(-1) %>%
mutate(Survived = factor(Survived, levels = c(0, 1), labels = c("Died", "Survived")),
Sex = factor(Sex),
Pclass = factor(Pclass))
head(titanic_data)# A tibble: 6 x 6
Survived Pclass Sex Age Fare Embarked
<fct> <fct> <fct> <dbl> <dbl> <chr>
1 Died 3 male 22 7.25 S
2 Survived 1 female 38 71.3 C
3 Survived 3 female 26 7.92 S
4 Survived 1 female 35 53.1 S
5 Died 3 male 35 8.05 S
6 Died 3 male NA 8.46 Q
We may want to visualize the overall survival rates. We can do this using tables or graphs.
Died Survived
549 342
# Graph
titanic_data %>%
ggplot(aes(x = Survived)) +
geom_bar(width = 0.4) +
theme_classic() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman",face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold")
) +
labs(title = "Overall Survival Rates", x = NULL, y = "Passenger count")
A graph has a good visual impression compared to the table. Surival chances were low based on the graph.
We might want to visualize how the survival rates varied by sex. Did more males or females survive?
titanic_data %>%
ggplot(aes(x = Sex, fill = Survived)) +
geom_bar(width = 0.4) +
theme_classic() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman",face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(family = "Times New Roman")
) +
labs(title = "Survival rates by Sex", x = NULL, y = "Passenger count")
Based on the above plot, females had about 70% survival chance while males had about 30% survival chance.
Next, we investigate the survival rate by the passenger class.
titanic_data %>%
ggplot(aes(x = Pclass, fill = Survived)) +
geom_bar(width = 0.4) +
theme_classic() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman",face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(family = "Times New Roman")
) +
labs(title = "Survival rates by Passenger Class", x = NULL, y = "Passenger count")
We see that passengers in class 1, 2, 3 had about 70%, 45% and 30% survival chance respectively.
We might also be interested in visualizing how survival rates varied by age. We will use a histogram and a boxplot.
# Histogram
titanic_data %>%
ggplot(aes(x = Age, fill = Survived)) +
geom_histogram() +
theme_classic() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman",face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(family = "Times New Roman")
) +
labs(title = "Survival rates by Age")
Children less than 5 years had higher survival chances. Passengers aged 20-40 were more likely to die. Passengers aged about 65 - 75 had an almost 0 survival chance. One passenger aged 80 years survived.
# Boxplot
titanic_data %>%
ggplot(aes(x = Survived, y = Age)) +
geom_boxplot() +
theme_classic() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman", face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(family = "Times New Roman")
) +
labs(title = "Survival rates by Age", x = NULL)
Passengers who survived seems to have a lower median age.
Finally, we will investigate how different variables intercted with each other and with the survival rate.
titanic_data %>%
ggplot(aes(x = Sex, fill = Survived)) +
geom_bar(width = 0.4) +
facet_wrap(~ Pclass) +
theme_test() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman", face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(family = "Times New Roman")
) +
labs(title = "Survival rates Sex and Passenger class", x = NULL, y = "Passenger Count")
Females in 1,2 and 3 class had 90%,90% and 50% survival chances respectively comapred to 45%, 20% and 20% respectively for their males counterparts.
titanic_data %>%
ggplot(aes(x = Age, fill = Survived)) +
geom_histogram() +
facet_wrap(~Sex + Pclass) +
theme_test() +
theme(
plot.title = element_text(family = "Times New Roman", hjust = 0.5),
axis.text = element_text(family = "Times New Roman", face = "bold"),
axis.title = element_text(family = "Times New Roman", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(family = "Times New Roman")
) +
labs(title = "Survival rates Age, Sex and Passenger class")
Graphs are powerful visualization tools. In the titanic data, age, sex and passenger class were important predictors of survival. Identifying such predictors is important for predictive modelling.