What's the difference between facet_wrap() and facet_grid() in ggplot2?

ggplot2 Faceting: Understanding facet_wrap() vs. facet_grid()

Explore the key differences between facet_wrap() and facet_grid() in ggplot2 for creating multi-panel plots, and learn when to use each for effective data visualization.

Faceting is a powerful feature in ggplot2 that allows you to split your data into subsets based on one or more categorical variables and then plot each subset in its own panel. This is incredibly useful for exploring relationships within different groups of your data. ggplot2 offers two primary functions for faceting: facet_wrap() and facet_grid(). While both achieve multi-panel plots, they differ significantly in how they arrange these panels and the types of relationships they are best suited to display.

Introduction to Faceting in ggplot2

Before diving into the specifics of facet_wrap() and facet_grid(), it's important to understand the core concept of faceting. Imagine you have a dataset of car mileage, and you want to see how fuel efficiency (mpg) varies with engine displacement (disp) for different numbers of cylinders (cyl) and drive types (drv). Instead of creating separate plots for each combination, faceting allows you to generate a single plot with multiple sub-plots, each representing a unique combination of your faceting variables.

flowchart TD
    A[Raw Data] --> B{Group by Categorical Variable(s)}
    B --> C[Create Subsets of Data]
    C --> D[Plot Each Subset in Separate Panel]
    D --> E[Arrange Panels using facet_wrap() or facet_grid()]
    E --> F[Multi-Panel Plot]

Conceptual flow of the faceting process in ggplot2.

facet_wrap(): Wrapping Panels for a Single Variable

facet_wrap() is designed for faceting by one or more discrete variables, arranging the panels in a way that 'wraps' them into approximately rectangular layouts. It's ideal when you have a single primary categorical variable you want to break down your plot by, or when you have multiple variables but don't need to explicitly map them to rows and columns.

library(ggplot2)

# Example using facet_wrap() with a single variable
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_wrap(~ class)

# Example using facet_wrap() with multiple variables (concatenated)
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_wrap(~ cyl + drv)

Using facet_wrap() to facet by car class and by a combination of cylinders and drive type.

Key Characteristics of facet_wrap()

1. Single Formula Input: Takes a single formula, typically ~ variable or ~ var1 + var2. When multiple variables are provided, they are combined into a single faceting variable.
2. Automatic Layout: ggplot2 automatically determines the number of rows and columns to arrange the panels, aiming for a compact layout. You can suggest the number of rows or columns using nrow or ncol arguments, but it's often best to let ggplot2 decide.
3. Independent Scales (by default): By default, facet_wrap() allows scales (x and y axes) to vary across panels, which can be useful for highlighting patterns within each subset. You can control this with the scales argument (e.g., scales = "free_x", "free_y", "free", or "fixed").
4. No Empty Panels: facet_wrap() only creates panels for combinations of variables that actually exist in your data, avoiding empty plots.

facet_grid(): Defining Rows and Columns Explicitly

facet_grid() is used when you want to explicitly arrange your panels in a 2D grid, with one variable defining the rows and another defining the columns. This is particularly useful for comparing two categorical variables simultaneously and observing their interactions.

library(ggplot2)

# Example using facet_grid() with rows and columns
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(drv ~ cyl)

# Example using facet_grid() with only rows
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(drv ~ .)

# Example using facet_grid() with only columns
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(. ~ cyl)

Using facet_grid() to arrange plots by drive type (rows) and cylinders (columns).

Key Characteristics of facet_grid()

1. Two-Sided Formula Input: Takes a formula of the form rows ~ columns. The variable(s) before ~ define the rows, and the variable(s) after ~ define the columns. Use . to indicate no faceting along a dimension (e.g., .~cyl for columns only).
2. Fixed Layout: The layout is strictly determined by the unique combinations of the row and column variables. This means if a combination doesn't exist in your data, facet_grid() will still create an empty panel for it.
3. Shared Scales (by default): By default, facet_grid() fixes the scales across all panels within a row (for y-axis) or column (for x-axis), making comparisons easier. You can override this with the scales argument, similar to facet_wrap().
4. Empty Panels Possible: As mentioned, facet_grid() will create panels for all possible combinations of the faceting variables, even if some combinations have no data. This can be useful for highlighting missing data or potential combinations.

Choosing Between facet_wrap() and facet_grid()

The choice between facet_wrap() and facet_grid() depends on your data and the message you want to convey. Here's a quick guide:

• Use facet_wrap() when:

  • You have one or a few faceting variables and want a compact, automatically arranged layout.
  • You don't need to explicitly compare variables along fixed row/column dimensions.
  • You prefer to avoid empty panels.
  • You want scales to be independent by default (though this can be changed).

• Use facet_grid() when:

  • You have two distinct categorical variables that you want to compare directly, one defining rows and the other defining columns.
  • You need a strict, fixed grid layout, even if it means showing empty panels.
  • You want scales to be consistent across rows/columns by default for easier comparison.
  • You want to visualize all possible combinations of two variables, even those without data.

flowchart TD
    A[Start] --> B{Number of Faceting Variables?}
    B -->|One or few| C{Layout Preference?}
    B -->|Two distinct| D{Explicit Row/Column Comparison Needed?}

    C -->|Compact, auto-wrap| E[Use facet_wrap()]
    C -->|Strict grid, all combinations| F[Use facet_grid()]

    D -->|Yes| F
    D -->|No, just group| E

Decision tree for choosing between facet_wrap() and facet_grid().

Understanding these differences will allow you to create more effective and insightful multi-panel plots in ggplot2, tailoring your visualizations to the specific questions you're trying to answer with your data.