How to Easily Order Facets in ggplot2 for Clear Data Visualization

Understanding Factor Levels and Facet Ordering

In the context of the R programming language, a factor is a data structure used to store categorical data. The crucial aspect of factors is their defined set of unique values, known as “levels.” Unlike simple character strings, factors maintain an internal order corresponding to these levels. When ggplot2 receives a categorical variable for faceting, it checks if that variable is a factor. If it is, ggplot2 respects the internal level order defined by the factor; if it is a character vector, ggplot2 typically converts it internally and applies default sorting (usually alphabetical).

To manipulate the display order of facets, the analyst must ensure the grouping variable is treated as a factor and that the order of its levels matches the desired arrangement of the panels. This is the cornerstone of customizing facet order, whether you are using facet_wrap() or facet_grid(). If the data grouping variable is already a factor, you may use functions like relevel() or factor() with the levels argument to redefine its sequence prior to plotting. If the variable is still stored as text, the preferred method is to convert it immediately to a factor while specifying the correct order.

The flexibility provided by this approach ensures that the visualization accurately reflects any inherent or desired logical hierarchy within the data. For instance, if plotting performance across different organizational levels (e.g., Junior, Senior, Manager), forcing an alphabetical sort (Manager, Junior, Senior) would be illogical. By setting the factor levels explicitly (Junior, Senior, Manager), the faceted plot aligns perfectly with the organizational structure, enhancing readability and interpretability for the audience.

The Core Syntax for Custom Facet Ordering

The most robust and common method for establishing a custom facet order involves applying the factor() function directly within the faceting layer. This method avoids permanently altering the underlying data frame, instead applying the specific order only within the context of the plot generation. This technique is highly efficient for one-off visualizations or when experimenting with different panel arrangements without modifying the original data structure.

The general syntax employs the facet_grid() or facet_wrap() functions and utilizes the levels argument inside the factor() call. By listing the categorical values in the exact sequence they should appear in the visualization, we override the default internal sorting mechanism of ggplot2.

You can use the following basic syntax to specify the order of facets in ggplot2, typically used when faceting along the horizontal axis in facet_grid:

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

In this structure, my_variable is the column name being used for faceting, and the comma-separated list of strings ('val1', 'val2', etc.) defines the precise sequence of the panels. It is critical that every unique value present in my_variable that you wish to display is included in the levels argument, and they must be spelled exactly as they appear in the data. Omission of a level will result in that category not being displayed in the facet grid.

The following example demonstrates this powerful syntax in a practical application, showing how to transform a plot with default ordering into one that follows a custom, predefined sequence.

Setting Up the Example Data Frame in R

To illustrate the process of custom facet ordering, we will work with a simple data frame containing simulated statistics for different teams. This data structure includes a categorical variable (team) that we will use for faceting, and two numerical variables (points and assists) that will form the basis of our scatter plot. Defining this data frame in R is the essential first step before visualization.

The data frame, df, tracks performance metrics across four teams (A, B, C, D), with two observations per team:

#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

In this initial state, the team variable is a character vector. When ggplot2 processes this data, it will likely sort the facets alphabetically (A, B, C, D) or based on the order of first appearance in the dataset (A, B, C, D, in this specific case, which happens to align with the alphabetical order).

Visualizing the Default Facet Order

Before implementing the custom ordering, it is instructive to observe the default behavior of the faceting function. We will use facet_grid() to create individual scatter plots for each team, mapping assists to the x-axis and points to the y-axis. This visualization serves as our baseline against which the customized result will be compared.

The following code utilizes the facet_grid() function, applying the team variable without any explicit factor level manipulation:

library(ggplot2)

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

The syntax facet_grid(.~team) specifies that the panels should be arranged horizontally based on the values in the team column. As anticipated, the output graphic organizes the panels according to the default sorting rules applied by ggplot2 to the character vector, resulting in the sequence A, B, C, D from left to right. This default arrangement, derived from the variable’s intrinsic character or appearance order, may not always be the most effective for data presentation.

By default, ggplot2 places the scatter plots in order based on which values appear first in the team variable in the data frame, which in this case corresponds to the alphabetical sequence. While this is straightforward, it lacks the necessary control when a specific, non-alphabetical or non-chronological order is mandated by the analysis context.

Implementing Custom Order Using Factor Levels

To deviate from the default sorting, we introduce the crucial step of converting the team variable into a factor variable directly within the facet_grid() call. This is achieved using the factor() function, where we explicitly define the levels argument. The sequence of values provided to levels dictates the precise order of the resulting facet panels.

Suppose our analytical goal is to present the data in the order C, D, A, B—perhaps prioritizing the teams with the highest observed average points, or following an external comparison standard. We simply list these teams in the desired sequence within the levels argument:

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')))

This single line of code, nested within the faceting function, executes the temporary conversion and ordering. The facet_grid() function then uses this newly ordered factor to arrange the plot panels. This methodology is highly efficient because it is contained entirely within the visualization step, minimizing the risk of modifying the source data or requiring intermediate data manipulation steps.

Upon reviewing the generated visualization, we can clearly observe that the scatter plots are now arranged precisely according to the custom order specified within the levels argument: C, followed by D, then A, and finally B. This confirmation demonstrates the successful application of factor level manipulation for controlling facet presentation.

Advantages and Best Practices of In-Function Ordering

The primary advantage of using the factor(..., levels=c(...)) approach directly within the facet_grid() or facet_wrap() function is that we do not actually modify the underlying source data frame. The manipulation of levels is temporary and exists only within the scope of the plotting command. This ensures data integrity is maintained, adhering to best practices in reproducible data analysis where raw data should ideally remain untouched.

Instead of modifying the original column, we only change the levels within the faceting function’s scope. This is particularly useful in workflows involving many plots or iterative analysis, where permanently changing the factor levels of a variable might interfere with subsequent calculations or summaries that rely on the default alphabetical or numeric order.

For more complex scenarios, such as ordering based on a numerical summary statistic (e.g., ordering teams by average points), an analyst would typically first calculate the summary statistic, then use helper functions from packages like forcats (part of the Tidyverse) to redefine the factor levels based on that calculated order, and finally pass the newly ordered factor variable to ggplot2. However, for manual, predefined orders, the direct factor(..., levels) method remains the cleanest and most explicit approach.

Extending Control to Facet_Wrap() and Axis Scaling

While our example focuses on facet_grid(), the exact same principle of managing factor levels applies to facet_wrap(). The only difference is the layout geometry: facet_wrap() wraps panels into a 2D layout, whereas facet_grid() enforces a strict grid based on two variables (rows and columns).

The original introductory text mentioned the scales argument within facet_wrap(). While scales does not control order, it is an important related concept when customizing facet appearance. The scales argument allows the user to specify whether the x and y axes should be the same across all panels (scales = "fixed", the default) or vary (scales = "free", "free_x", or "free_y"). Using scales = "free" can dramatically improve the visual comparison when the data ranges vastly differ between facets, but it is entirely independent of the panel ordering mechanism defined by factor levels.

In conclusion, mastery over faceting in ggplot2 requires a solid understanding of how R handles categorical data through factor levels. By taking explicit control over these levels, the data scientist ensures that visualizations are not only aesthetically pleasing but also logically structured to support compelling data narratives, moving beyond simple default sorting mechanisms.