Summary  
This chapter covers the concept of faceting in data visualization, showing how to split a single plot into multiple subplots based on one or more categorical variables using functions like facet_wrap() and facet_grid() for easier group-wise comparison.

General domain of usage  
Exploratory analysis of used car sales data.

Faceting is a powerful way to break a complex plot into multiple simpler ones, allowing you to compare data across different categories. Instead of creating separate plots manually for each group (e.g., each fuel type), `ggplot2` offers functions like `facet_wrap()` and `facet_grid()` to automatically generate subplots for categorical variables. This makes trends, outliers, and patterns easier to detect across subgroups.

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.

Faceting

Awesome!

Faceting