Adjusting Plot Widths In Ggarrange: A Comprehensive Guide

Hey data enthusiasts! Ever found yourself wrestling with the layout of your plots when using ggarrange in R? Specifically, have you wanted to tweak the width of individual plots within your combined figure? Well, you're in the right place! This guide dives deep into the art of controlling plot widths when using ggarrange from the ggpubr package, and similar approaches in packages like cowplot. We'll explore various methods, from simple adjustments to more complex customizations, ensuring your visualizations look exactly as you envision them. Let's get started, shall we?

Understanding the Challenge: Default Plot Widths in ggarrange

So, the first thing to understand is how ggarrange handles plot widths by default. When you arrange multiple plots using ggarrange, it tries to create a visually balanced layout. By default, it often distributes the available width equally among the plots in each row or column. This is great for quick and easy arrangements, but what if you want one plot to be wider than the others? Or if you're struggling with plots that are either too cramped or awkwardly spaced? That's where the customization magic begins.

Often, when using ggarrange directly, you won't find a straightforward argument to control individual plot widths. The function primarily focuses on the overall layout (number of rows, columns) and basic spacing. This means we'll need to employ a bit of creativity, using either additional arguments or a different functions to achieve the desired width variations. The good news is that there are several effective ways to achieve this control, letting you fine-tune your plots to perfection.

Let's face it, controlling plot widths can be a game-changer for your visualizations. Getting the right proportions can make a huge difference in how effectively your data is communicated. You might want one plot to dominate the space, highlighting its importance, while another plot acts as a subtle complement. Or maybe your plots have different aspect ratios and need adjusting. Whatever your needs, knowing how to control those widths is a must-have skill in your data visualization arsenal!

Method 1: Using the widths Argument (and its limitations)

One of the most direct approaches involves the widths argument within ggarrange. This argument is designed to adjust the relative widths of plots within each column. Similarly, the heights argument lets you control the relative heights within each row. However, there's a crucial detail to remember: these arguments work with the relative widths, not the absolute ones. This mean that you specify a proportion, like 1:2 to indicate that one plot should be twice as wide as another within a given row or column.

Let's illustrate with an example. Suppose you have three plots (p1, p2, p3) and want the third plot to be twice as wide as the first two:

library(ggpubr)

ggarrange(p1, p2, p3, 
          ncol = 3, widths = c(1, 1, 2))

In this case, widths = c(1, 1, 2) tells ggarrange to make the third plot (p3) twice as wide as the first two. Keep in mind that this argument applies to each row of plots. If you're arranging plots in multiple rows and columns, you will have to consider how the width adjustments interact across the rows and columns.

While widths is super helpful, it’s important to know its limitations. If you have a more complex layout, with plots arranged in different configurations, you might need alternative techniques. For example, If you have plots with legends, the relative sizes might not always look as expected. Also, you might need additional customization to fine-tune the spacing and alignment of the plots.

Method 2: The Power of cowplot::plot_grid for Advanced Width Control

If you need more granular control over plot widths, the cowplot package's plot_grid function is your best friend. Unlike ggarrange, plot_grid provides explicit arguments for setting the widths (and heights) of individual plots, making it ideal for complex layouts. plot_grid offers a more flexible and powerful way to arrange plots. Here's a simple example:

library(cowplot)

plot_grid(p1, p2, p3, 
          rel_widths = c(1, 1, 2))

Here, rel_widths takes a vector of relative widths, just like the widths argument in ggarrange. This means we're still using proportions to define the width relationship. This gives you precise control over the proportions of the plots. The plots don't have to be arranged in a simple grid-like structure, and you can combine plots of different sizes. This function is super handy when you have plots with varying complexities or when you're trying to highlight specific data features.

However, keep in mind the difference between rel_widths and absolute widths. You're still defining relative sizes, not pixel-by-pixel widths. This is normally what you want for consistent scaling across different output sizes.

Another great feature of plot_grid is its ability to handle plots with legends much more smoothly than some ggarrange configurations. You'll often find that legends are automatically positioned better, and that overall layout feels more polished.

Method 3: Adjusting Plot Margins and Theme Elements

Besides directly controlling widths, sometimes adjusting plot margins and theme elements can also indirectly influence the perceived width and overall appearance of your plots. This is especially useful if you want to create a more spacious or compact layout without explicitly changing the relative sizes of the plots.

Here's how you can do it:

Adjusting Plot Margins

Use theme() to adjust the margins around the plot elements:

library(ggplot2)

p1 + theme(plot.margin = margin(t = 1, r = 1, b = 1, l = 1, "cm"))

The plot.margin argument in theme() controls the margins around the entire plot area. The parameters t, r, b, and l represent top, right, bottom, and left margins, respectively. Adjusting these margins can add or remove whitespace around the plot, which can affect how the plot fits within the ggarrange or plot_grid layout.

Modifying Axis Titles, Labels, and Legends

Another useful tip involves tweaking the size and position of axis titles, labels, and legends. By reducing the size or repositioning these elements, you can potentially free up some space, making your plots appear less cramped. For example, you can adjust the size of axis titles:

p1 + theme(axis.title = element_text(size = 10))

This will reduce the space occupied by your title.

Theme Elements

You can also use these elements to fine-tune spacing and appearance:

# Remove panel borders
p1 + theme(panel.border = element_blank())

# Adjust panel spacing
p1 + theme(panel.spacing = unit(0.5, "cm"))

By using these adjustments in conjunction with ggarrange or plot_grid, you can achieve a level of control that lets you create truly customized and visually appealing plots.

Troubleshooting Common Issues

Let's face it: sometimes things don't go as planned. Here's a rundown of common issues you might encounter when trying to control plot widths, and how to fix them:

Plots Overlapping

If plots overlap, check your widths and rel_widths values. Ensure they are appropriate for the number of plots and the desired layout. If you're using theme adjustments, make sure that the margins are not too large, causing the plots to exceed the available space.

Legends Cropping

Legends can sometimes be cropped when using ggarrange. Try using plot_grid, which often handles legends more effectively. You might also experiment with legend.position and adjusting the plot margins to avoid cropping. Consider using a combined legend for multiple plots if the legends take up too much space.

Uneven Spacing

If spacing looks uneven, double-check that your widths or rel_widths are correctly specified. Also consider using the hjust and vjust arguments within theme() to fine-tune the alignment of plot elements. You can also adjust the spacing parameters in plot_grid to get the perfect appearance.

Unexpected Behavior

If you're getting unexpected results, the first step is always to simplify your code. Start with a very basic example and add complexity incrementally. Also, ensure your plots have the right dimensions to start with, and experiment with saving them at different sizes to see how the layouts behave. Check for any conflicting theme settings that might be overriding your intended adjustments.

Conclusion: Mastering Plot Widths for Stunning Visualizations

Well, there you have it! You now have the skills and knowledge to effectively manage and manipulate the width of your plots when using ggarrange and plot_grid in R. Whether you are aiming for simple adjustments, creating complex multi-plot layouts, or fine-tuning the aesthetics of your visualizations, the techniques discussed here will help you bring your data stories to life.

Remember, the key is to experiment! Don't be afraid to try different approaches and see what works best for your specific data and visualization goals. With a little practice, you'll be able to create stunning, well-designed plots that effectively communicate your findings. Happy plotting, and happy data-ing, folks!