Summary  
This chapter covers constructing and customizing bar plots in ggplot2 by mapping variables to aesthetics, specifying stat transformations for counts or aggregated values, and styling plots with fills, colors, themes, and labels, including stacked and grouped bar layouts.  

General domain of usage  
Categorical data visualization

### Why Use Bar Plots?
Bar plots are one of the most common ways to visualize categorical data. They help us quickly:

- Compare counts or frequencies of categories;

- Visualize group-wise summaries (like average price per fuel type);

- Understand relationships between two categorical variables using grouped or stacked bars.

Whether you're showing the number of cars by fuel type or comparing transmission modes across fuels, bar plots make categorical comparisons clear and intuitive.

### Bar Plot Syntax in ggplot2
```
ggplot(data = df, aes(x = category)) +
  geom_bar()
```
- If only `x` is provided, `geom_bar()` automatically counts the number of observations;

- If `y` is also provided, you need to add `stat = "identity"` to use actual values.

### Example: Count of Cars by Fuel Type
```
ggplot(df, aes(x = fuel)) + 
  geom_bar(fill = "lightblue", color = "red") +
  labs(title = "Car Distribution by Fuel Type", x = "Fuel Type", y = "Count") +
  theme_minimal()
```
This plot shows the number of cars available for each type of fuel.

What does `geom_bar()` do when only the `x` variable is provided?


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.

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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 Bar Plots

Why Use Bar Plots?

Bar Plot Syntax in ggplot2

Example: Count of Cars by Fuel Type

Awesome!

Creating Bar Plots

Why Use Bar Plots?

Bar Plot Syntax in ggplot2

Example: Count of Cars by Fuel Type