Summary  
This chapter explains how to construct basic data visualizations by mapping data variables to plot aesthetics and adding appropriate geometric layers (e.g., bars, scatter, lines, histograms, boxplots) using ggplot2.

General domain of usage  
Business analytics

When you want to visualize your data in R, **ggplot2** offers a variety of common plot types, each suited to different data and questions. **Scatter plots** are ideal when you want to show the relationship between two numerical variables, such as height versus weight. **Bar charts** are best for comparing values across categories, like sales by product or region. **Line charts** help you see trends over time, such as monthly revenue. **Histograms** are used to explore the distribution of a single numerical variable, while **boxplots** summarize distributions and highlight outliers. Choosing the right plot type depends on your data: use scatter plots for two continuous variables, bar charts for categorical comparisons, and histograms or boxplots for distributions.

# Create a hardcoded data frame for sales by product category
sales_data <- data.frame(
  Category = c("Books", "Electronics", "Clothing", "Toys"),
  Sales = c(120, 300, 150, 80)
)

# Build a bar chart using ggplot2
library(ggplot2)
ggplot(sales_data, aes(x = Category, y = Sales)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Sales by Product Category", x = "Category", y = "Sales")

To understand how this bar chart is constructed, look at each part of the code. First, the `sales_data` data frame holds two columns: `Category` (**categorical**) and `Sales` (**numerical**). The `ggplot` function initializes the plot, mapping `Category` to the x-axis and `Sales` to the y-axis using `aes()`. The `geom_bar` layer tells **ggplot2** to draw bars for each category. By setting `stat = "identity"`, you instruct **ggplot2** to use the actual sales values instead of counting occurrences. The `fill` argument colors the bars. Finally, `labs` adds helpful labels. Mapping the categorical variable (`Category`) to the x-axis ensures each bar represents a distinct category, making the chart easy to interpret.

Which plot types are best suited for categorical versus numerical data?

What is the main purpose of `geom_bar` in the bar chart code sample above?

Why is it important to map categorical variables to the x-axis in a bar chart, as explained above?

This beginner-level course introduces data visualization in R using the powerful ggplot2 package. Learn how to create, customize, and interpret a variety of charts and plots essential for effective data analysis. The course covers the fundamentals of ggplot2, guiding you through building basic plots, enhancing them with themes and annotations, and understanding how to choose the right visualizations for different data types.

Learn the foundational concepts of ggplot2, including its grammar of graphics, basic plot structure, and how to create your first visualizations using hardcoded data frames.

Delve into customizing your plots with colors, themes, and labels to make your visualizations more informative and visually appealing.

Explore more advanced plot types in ggplot2, such as boxplots and faceted plots, to analyze distributions and compare groups.

Learn how to annotate your plots and choose the right visualization to effectively communicate your data's story.

Building Basic Plots

1. Which plot types are best suited for categorical versus numerical data?

2. What is the main purpose of `geom_bar` in the bar chart code sample above?

3. Why is it important to map categorical variables to the x-axis in a bar chart, as explained above?

Building Basic Plots

1. Which plot types are best suited for categorical versus numerical data?

2. What is the main purpose of geom_bar in the bar chart code sample above?

3. Why is it important to map categorical variables to the x-axis in a bar chart, as explained above?

2. What is the main purpose of `geom_bar` in the bar chart code sample above?