Survival analysis is a crucial tool in actuarial science, used to model time-to-event data. This presentation explores various statistical techniques applied to a breast cancer dataset.

The dataset used in this analysis contains information related to breast cancer cases. It includes several variables such as Age,Race,Marital Status, T Stage, N Stage. Let’s begin by examining the structure and initial rows of the dataset:

library(ggplot2)
ggplot(data, aes(x=Age)) + 
  geom_histogram(binwidth=5, fill="blue", color="black", alpha=0.7) +
  labs(title="Distribution of Age", x="Age (years)", y="Frequency")

#Boxplot of Age by Grade

ggplot(data, aes(x=factor(Grade), y=Age)) +
  geom_boxplot(fill="blue", color="black", alpha=0.7) +
  labs(title="Boxplot of Age by Grade", x="Grade", y="Age (years)")

#Density Plot of Age

ggplot(data, aes(x=Age)) +
  geom_density(fill="blue", alpha=0.7) +
  geom_vline(aes(xintercept=mean(Age)), color="red", linetype="dashed", size=1) +
  labs(title="Density Plot of Age with Mean Line", x="Age (years)", y="Density")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

#R Code Example

# Example code to create a plot
ggplot(data, aes(x=Age, y=Grade)) +
  geom_point() +
  labs(title="Scatter Plot of Age vs. Grade", x="Age (years)", y="Grade")