Correlation Matrix and Heatmap Visualization

# File Name: correlation_matrix_heatmap.R

# Author: Gerard King - www.gerardking.dev

# Title: Correlation Matrix and Heatmap Visualization

# Description: This R program generates a synthetic dataset with multiple variables, computes

# the correlation matrix, and visualizes it as a heatmap. This is useful for exploring relationships

# between variables in datasets.

# Use Cases:

# - Identifying correlated features in a dataset

# - Analyzing the strength of relationships between variables

# - Visualizing large datasets to identify patterns and outliers

# Audience:

# - Data scientists and analysts performing exploratory data analysis (EDA)

# - Students learning about correlation and data visualization

# - Researchers identifying key relationships in multivariate datasets

# Blue Team Uses:

# - Analyzing correlations between system metrics (e.g., CPU usage, memory, network traffic)

# - Detecting potential multicollinearity in system performance data

# - Identifying relationships between different security events or logs

# Red Team Uses:

# - Identifying potential dependencies between attack vectors and vulnerabilities

# - Analyzing correlated patterns in simulated attack data

# - Visualizing relationships between different attack strategies or techniques

# Current Date: 2025-03-06

# Load necessary library for visualization

library(ggplot2)

library(reshape2)

library(RColorBrewer)

# Set seed for reproducibility

set.seed(101)

# Generate a synthetic dataset with 5 variables

data <- data.frame(

  var1 = rnorm(100),

  var2 = rnorm(100),

  var3 = rnorm(100),

  var4 = rnorm(100),

  var5 = rnorm(100)

)

# Compute the correlation matrix

cor_matrix <- cor(data)

# Melt the correlation matrix for ggplot

melted_cor_matrix <- melt(cor_matrix)

# Create a heatmap of the correlation matrix

ggplot(melted_cor_matrix, aes(Var1, Var2, fill = value)) +

  geom_tile() +

  scale_fill_gradient2(midpoint = 0, low = "blue", high = "red", mid = "white") +

  theme_minimal() +

  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +

  labs(title = "Correlation Matrix Heatmap",

       x = "Variables",

       y = "Variables",

       fill = "Correlation")

# Print the correlation matrix to the console

cat("Correlation Matrix:\n")

print(cor_matrix)

# Print the current date for reference

cat("Date of execution:", Sys.Date(), "\n")