Summary  
This chapter explains how to use ggplot2’s geom_density to generate kernel density estimates for visualizing the distribution of a numeric variable and how to compare multiple groups by mapping a fill aesthetic and adjusting transparency with the alpha parameter.

General domain of usage  
Exploratory data analysis

### Why Use Density Plots?
A **density plot** is a smoothed version of a histogram. It is useful to:

- Understand the **distribution** of a numeric variable;

- **Compare distributions** across groups (like fuel types);

- Spot peaks, skewness, and spread.

Unlike histograms, **density plots** estimate the probability of a value occurring within a range.

### Basic Syntax for Density Plots in ggplot2
```
ggplot(data = df, aes(x = variable)) +
  geom_density()
```
For group comparisons, use `fill = group_variable` and adjust transparency using `alpha`.

### Example: Selling Price Distribution
```
ggplot(df, aes(x = selling_price)) + 
  geom_density(fill = "blue") + 
  labs(title = "Density Plot of Selling Prices", 
       x = "Selling Price", 
       y = "Density")
```
This plot shows how car prices are distributed, revealing where most prices cluster and how prices are spread.

What argument controls the transparency of overlapping density plots?


Gain practical experience in data analysis with R by learning how to clean, transform, and visualize datasets. Explore essential workflows such as selecting and filtering data, handling missing values, and summarizing results. Build confidence in preparing data for insights, reporting, and deeper statistical exploration.

Explore the foundations of data analysis with R. Learn how to install the tools, load and inspect datasets, select and filter information, sort and transform data, handle missing values, and summarize results for deeper insights.

Learn to create compelling visualizations with ggplot2. Build bar charts, histograms, density plots, and scatter plots, then customize and refine them with styling options and faceting to reveal deeper insights in your data.

Strengthen your understanding of statistics for data analysis. Apply descriptive measures, identify and treat outliers, and use correlation techniques with visual tools like heatmaps and scatter plots to uncover meaningful relationships.

Creating Density Plots

Why Use Density Plots?

Basic Syntax for Density Plots in ggplot2

Example: Selling Price Distribution

Awesome!

Creating Density Plots

Why Use Density Plots?

Basic Syntax for Density Plots in ggplot2

Example: Selling Price Distribution