Summary  
The chapter explains how to use faceting functions to split data by one or two categorical variables and arrange the resulting subsets into multi-panel plots using a wrapped or grid layout.  

General domain of usage  
Data visualization

Faceting is a powerful feature in `ggplot2` that allows you to split your data into subsets and display the results in multiple panels within a single plot. This technique is especially useful when you want to compare patterns, trends, or distributions across different groups or categories side by side. By organizing similar plots in a grid or wrapped layout, you can quickly spot differences and similarities that might be hidden in a single, overplotted chart. Faceting helps you communicate complex, group-wise relationships in your data with clarity and efficiency.

library(ggplot2)

# Sample dataset: mtcars, comparing mpg by cyl and gear
p <- ggplot(mtcars, aes(x = hp, y = mpg)) +
  geom_point()

# Using facet_wrap to create panels by number of cylinders
p_wrap <- p + facet_wrap(~cyl)
print(p_wrap)

# Using facet_grid to create a grid of panels by cylinders and gears
p_grid <- p + facet_grid(gear ~ cyl)
print(p_grid)

You will often choose between `facet_wrap` and `facet_grid` depending on the structure of your grouping variables and the story you wish to tell with your visualization. Use `facet_wrap` when you have a single categorical variable and want to display panels in a wrapped sequence, making efficient use of space. This approach is ideal when the number of groups is moderate and you do not need to compare across two dimensions. On the other hand, `facet_grid` is best when you want to compare data across two categorical variables, arranging panels in a grid defined by rows and columns. This makes it easier to explore interactions between two factors, such as comparing trends across both gender and region, or in the case above, across both the number of gears and cylinders.

library(testthat)

source("user_code.R")

test_that("plot uses correct axes", {
    p <- create_facet_plot()
    mapping <- p$mapping
    expect_true(!is.null(mapping$x) && rlang::as_name(mapping$x) == "Sepal.Length",
        info = "x-axis should be Sepal.Length"
    )
    expect_true(!is.null(mapping$y) && rlang::as_name(mapping$y) == "Sepal.Width",
        info = "y-axis should be Sepal.Width"
    )
})

test_that("plot uses facet_wrap by Species", {
    p <- create_facet_plot()
    facet <- p$facet
    expect_true(inherits(facet, "FacetWrap"),
        info = "Faceting function should be facet_wrap"
    )
    facet_vars <- names(facet$params$facets)
    expect_true("Species" %in% facet_vars,
        info = "Faceting should be done by Species column"
    )
})

test_that("plot shows one panel per unique Species", {
    p <- create_facet_plot()
    n_panels <- length(unique(ggplot2::ggplot_build(p)$layout$layout$PANEL))
    n_species <- length(unique(iris$Species))
    expect_equal(n_panels, n_species,
        info = "There should be one panel per unique Species value"
    )
})


test_main.R

Master the art of data visualization in R using the powerful ggplot2 package. This course guides you from the basics of plotting to advanced visualization techniques, enabling you to create insightful and visually appealing graphics for data analysis and presentation.

Comprehensive exploration of information visualization in R using ggplot2, from foundational concepts to advanced techniques.

Faceting for Multi-Panel Plots

Solution