Correlation matrices can be overwhelming to interpret just by looking at numbers. Heatmaps provide a visual way to see the strength and direction of relationships between variables.

Why Use a Correlation Heatmap?

A correlation heatmap provides a visual way to examine relationships between numeric variables. By using colors to represent the strength and direction of correlations, it becomes much easier to identify strong or weak associations at a glance. This is particularly helpful when working with many variables, as it can quickly reveal patterns, highlight multicollinearity, and guide further analysis.

Visualizing Correlation Matrix with Heatmap

First, you need to create a correlation matrix for visualization:

# Select numeric columns
numeric_df <- df[, c("selling_price", "km_driven", "max_power", "mileage", "engine")]
# Compute correlation matrix
cor_matrix <- cor(numeric_df, use = "complete.obs")

Then, you can use the ggcorrplot() function to build a plot from it:

ggcorrplot(cor_matrix,
           method = "square",
           type = "full",
           lab = TRUE,
           lab_size = 5,
           colors = c("red", "white", "forestgreen"),
           title = "Correlation Heatmap",
           ggtheme = ggplot2::theme_light())

This function has multiple parameters that can be used to change the style of the plot:

• method = "square" makes each cell a square block;
• lab = TRUE overlays the correlation values on each block;
• colors indicate direction: red (negative), white (neutral), green (positive);
• theme_light() gives the plot a clean, minimal style.