#Load train.csv data file

file_path <- “C:\Users\fardm\OneDrive\Desktop\PhD\train.csv” train_data <- read.csv(“C:\Users\fardm\OneDrive\Desktop\PhD\train.csv”)

install.packages(“ggplot2”) library(ggplot2) install.packages(“qqplotr”) library(qqplotr)

Create four-panel visualization

par(mfrow=c(2, 2)) # Increase plotting area size par(mar = c(4, 4, 2, 2)) # Adjust the margins

Convert Age to numeric (in case it’s not already)

train_data\(Age <- as.numeric(train_data\)Age)

Histogram Age

hist(train_data$Age, main=“Histogram of Age”, xlab=“Age”, col=“lightblue”, border=“black”)

Probability Plot (Q-Q plot) Age

qqnorm(train_data\(Age, main="Q-Q Plot of Age") qqline(train_data\)Age, col = 2)

Dot Plot Age

dotchart(train_data$Age, main=“Dot Plot of Age”, xlab=“Age”)

Box Plot Age

boxplot(train_data$Age, main=“Box Plot of Age”, ylab=“Age”, col=“lightgreen”, border=“black”)

Convert Age to numeric (in case it’s not already)

train_data\(FoodCourt <- as.numeric(train_data\)FoodCourt)

Histogram FoodCourt

hist(train_data$FoodCourt, main=“Histogram of FoodCourt”, xlab=“FoodCourt”, col=“lightblue”, border=“black”)

Probability Plot (Q-Q plot) FoodCourt

qqnorm(train_data\(FoodCourt, main="Q-Q Plot of FoodCourt") qqline(train_data\)FoodCourt, col = 2)

Dot Plot FoodCourt

dotchart(train_data$FoodCourt, main=“Dot Plot of FoodCourt”, xlab=“FoodCourt”)

Box Plot FoodCourt

boxplot(train_data$FoodCourt, main=“Box Plot of FoodCourt”, ylab=“FoodCourt”, col=“lightgreen”, border=“black”

Bar Plot for HomePlanet

ggplot(train_data, aes(HomePlanet))+ geom_bar(fill= “lightblue”) + labs(title = “Distribution of HomePlanet”, x = “HomePlanet”, y = “Count”)

Bar Plot for VIP

ggplot(train_data, aes(VIP))+ geom_bar(fill = “lightgreen”) + labs(title = “Distribution of VIP Status”, x = “VIP”, y = “Count”)