Learning to Reorder Facets in ggplot2: A Step-by-Step Guide

Mastering Custom Facet Ordering in ggplot2

The ggplot2 package, an integral component of the Tidyverse within the R programming language, provides powerful tools for creating sophisticated statistical graphics. A cornerstone of complex data visualization is the concept of faceting, which allows users to split a plot into multiple subplots based on the discrete values of one or more categorical variables. This technique is invaluable for comparing distributions or relationships across different subsets of data simultaneously. While ggplot2 handles the organization of these panels automatically, controlling the exact sequence in which they appear is often crucial for storytelling and logical data presentation, especially when the default alphabetical or appearance-based order is misleading or suboptimal for interpretation.

Customizing the order of these facets moves beyond simple default plotting and allows the analyst to impose a meaningful structure derived from domain knowledge, statistical hierarchy, or aesthetic preference. For instance, if data represents stages of development or levels of treatment intensity, forcing the facets into a specific chronological or ordinal sequence significantly enhances the clarity of the resulting visualization. Achieving this specific control requires interacting directly with how ggplot2 interprets the categorical variables used for faceting.

The fundamental mechanism for specifying a non-default order revolves around R’s treatment of categorical data types. Since ggplot2 relies on the inherent properties of the data structures it receives, we must explicitly define the permissible orderings for the faceting variable. The primary tool we utilize for this purpose is the combination of the facet_grid() or facet_wrap() function with the native R function factor(), specifically leveraging its levels argument. This powerful pairing ensures that the visualization reflects the desired logical flow without requiring permanent modification to the underlying data structure itself.

Understanding Default Facet Ordering and the Need for Customization

When applying faceting functions like facet_grid or facet_wrap(), ggplot2 utilizes the categorical variable provided to generate distinct panels. By default, the package organizes these panels based on one of two methods, depending on the variable type. If the faceting variable is a character string (chr), the facets are typically ordered alphabetically (A, B, C…). If the variable is already defined as a factor, the order follows the internal level structure of that factor, which, if not explicitly set, defaults to alphabetical order derived from when the factor was created.

While this automatic arrangement is convenient for quick inspections, it frequently fails when the categories possess an intrinsic, non-alphabetical ranking. Consider categories like “Low,” “Medium,” and “High,” which alphabetically would be ordered “High,” “Low,” “Medium.” Displaying these in the default order disrupts the natural progression, making visual comparison difficult. If the data represents four teams—A, B, C, D—and we wish to display them in performance order (e.g., C, D, A, B), relying on the default order (A, B, C, D) defeats the purpose of the careful arrangement.

Therefore, mastering custom ordering is not merely an aesthetic choice; it is a critical component of effective data communication. By imposing a logical sequence, we guide the viewer’s eye and streamline the cognitive process required to extract insights from the faceted visualization. This control is achieved by temporarily converting the faceting variable into an ordered factor variable, ensuring that ggplot2 respects the analyst’s chosen display sequence when constructing the plot layout.

The Role of Factors and Levels in R

In the R programming language, a factor variable is the standard way to store categorical data, whether it is nominal (e.g., gender, country) or ordinal (e.g., size: small, medium, large). Unlike simple character vectors, factors possess a predefined set of unique values known as “levels.” It is the order of these internal levels that dictates how functions like ggplot2 will handle sorting, plotting, and statistical modeling related to that variable.

When a character vector is coerced into a factor without specifying the order, R typically sets the levels alphabetically. The true power of the factor data type, however, lies in the ability to manually define the order of these levels using the levels argument within the factor() function. This operation is non-destructive; it does not change the actual data points (e.g., ‘C’ is still ‘C’), but it changes how R ranks and processes that category relative to others.

To change the order of facets within ggplot2 without altering the original dataframe, we leverage this capability directly within the plotting function call. We wrap the faceting variable inside the factor() function, supplying the desired sequence of categories via the levels parameter. This temporary modification tells ggplot2 exactly how to arrange the panels for that specific visualization. The general syntax for achieving this custom ordering using facet_grid() is outlined below:

The following basic syntax is used to specify the order of facets in ggplot2:

p +
  facet_grid(~factor(my_variable, levels=c('val1', 'val2', 'val3', ...)))

In this syntax, p represents the initial plot object, my_variable is the categorical column used for faceting, and levels contains a character vector defining the precise sequence in which the facets should appear, moving from left to right or top to bottom, depending on the faceting orientation.

Practical Implementation: Defining the Dataset

To illustrate this technique, we will use a sample dataset representing performance metrics (points and assists) across four hypothetical teams (A, B, C, D). This example provides a clear, manageable structure for demonstrating how the facet ordering can be manipulated independently of the data storage order. Understanding the initial structure of the data is essential before attempting any visualization modification.

We begin by constructing a simple data frame in the R programming language. The data frame contains three key variables: team (the categorical variable we will use for faceting), points, and assists. Notice that the team data, as entered, follows an alphabetical sequence (A, A, B, B, C, C, D, D).

The following R code snippet generates and displays our example data frame:

#create data frame
df <- data.frame(team=c('A', 'A', 'B', 'B', 'C', 'C', 'D', 'D'),
                 points=c(8, 14, 20, 22, 25, 29, 30, 31),
                 assists=c(10, 5, 5, 3, 8, 6, 9, 12))

#view data frame
df

  team points assists
1    A      8      10
2    A     14       5
3    B     20       5
4    B     22       3
5    C     25       8
6    C     29       6
7    D     30       9
8    D     31      12

This dataframe, named df, provides the necessary input for our visualization exercise. The goal will be to create a scatterplot showing the relationship between assists and points, separated by the team variable, and then deliberately rearrange the order of the resulting team panels.

Visualizing Default Facet Arrangement (Example 1)

Before imposing a custom order, it is helpful to observe the default behavior of ggplot2 when using facet_grid on a character variable. When the team variable is treated as a simple character vector, ggplot2 defaults to organizing the panels alphabetically (A, B, C, D) or based on the order of appearance in the dataset if the variable had been defined as an unordered factor. Since the data frame lists ‘A’ first, followed by ‘B’, ‘C’, and ‘D’, the default plot will reflect this arrangement, ensuring that panels are laid out sequentially from left to right.

The following code snippet demonstrates the creation of multiple scatter plots, one for each team, using the facet_grid() function. We specify the formula .~team to arrange the panels horizontally based on the team variable.

library(ggplot2)

#create multiple scatter plots using facet_grid
ggplot(df, aes(assists, points)) +
  geom_point() +
  facet_grid(.~team)

As expected, the resulting graph displays the facets in the default order: Team A, followed by Team B, Team C, and finally Team D. This is the simplest visualization, reflecting the inherent structure of the data and the alphabetical nature of the categorical variable.

Implementing Custom Ordering Using factor() (Example 2)

If our analytical goal requires presenting the teams in a different sequence—perhaps C, D, A, B—to reflect a specific narrative (e.g., ranking by average points), we must intervene by explicitly defining the factor levels. We achieve this by embedding the factor() function directly within the facet_grid() call. This is the most efficient and cleanest approach, as it avoids modifying the original data frame, keeping the data manipulation separate from the plotting logic.

Inside facet_grid(), we replace the variable name team with factor(team, levels = c('C', 'D', 'A', 'B')). This instructs ggplot2 to temporarily treat the team variable as a factor whose levels are precisely ordered C, D, A, B. The order specified within the levels argument is paramount; the facets will appear exactly in that sequence across the visualization panel.

The complete code for implementing this custom order is provided below. Note the subtle but crucial difference in how the facet_grid() argument is constructed compared to the previous example:

library(ggplot2)

#create multiple scatter plots using facet_grid with specific order
ggplot(df, aes(assists, points)) +
  geom_point() +
  facet_grid(~factor(team, levels=c('C', 'D', 'A', 'B')))

The resulting plot successfully displays the scatter plots in the analyst-defined order: C, D, A, B. This confirms the effectiveness of using the factor() function with the levels argument to override the default alphabetical arrangement in ggplot2 faceting operations.

Advantages and Conclusion

The primary advantage of employing this method—defining the factor levels directly within the facet_grid() or facet_wrap() call—is its non-destructive nature. By embedding the factor definition, we avoid making permanent alterations to the source dataframe df. The original team column remains a character vector or an unordered factor (if it was defined as such initially), preserving the integrity of the data for subsequent analyses that might require a different ordering or no ordering at all. This approach adheres to best practices in R programming language by keeping data manipulation steps localized to the specific function where they are needed.

Furthermore, this technique provides immense flexibility. If the user needed to generate several different visualizations of the same data, each requiring a unique facet order, they could simply change the character vector specified in the levels argument for each respective plot call. This is far more efficient than creating multiple temporary columns or modifying the base factor definition repeatedly. It ensures that the visualization is highly tailored to the interpretive needs of the moment.

In summary, controlling the display order of panels in faceted ggplot2 visualizations is achieved by leveraging R’s mechanism for handling categorical data. By wrapping the faceting variable in the factor() function and specifying a custom sequence using the levels parameter, analysts can transition from default arrangements to logically structured, highly communicative statistical graphics, enhancing the quality and narrative power of their data visualization efforts.

Additional Resources

For further reading and advanced customization techniques related to factors, faceting, and data visualization in R, consult the following resources:

• The official facet_grid documentation provides detailed information on all arguments and usage patterns.
• Resources on handling and manipulating factor variable data types in R, which are essential for controlling ordering in visualizations.
• Comprehensive guides on the forcats package, which offers advanced tools for factor manipulation within the Tidyverse.