ggplot: How to Have a Secondary Y Axis Independent from the First?

Are you tired of dealing with messy and confusing plots in R? Do you struggle to visualize multiple variables with different units on the same graph? Worry no more! In this article, we’ll delve into the world of ggplot and explore the magic of secondary y-axes, showing you how to create stunning plots with independent y-axes.

What’s the Big Deal About Secondary Y-Axes?

In many cases, you’ll want to plot multiple variables with different units on the same graph. For instance, you might want to show the relationship between temperature (in degrees Celsius) and humidity (in percentage) over time. Using a single y-axis would be a nightmare, making it difficult to interpret the data. That’s where secondary y-axes come in handy!

Secondary y-axes allow you to create separate scales for each variable, making it easy to visualize and compare the data. But, did you know that ggplot has some quirks when it comes to secondary y-axes? Yep, it’s not as straightforward as you might think. That’s why we’re here to guide you through the process.

Setting Up the Stage: ggplot Basics

Before we dive into the world of secondary y-axes, let’s quickly review the basics of ggplot. If you’re already familiar with ggplot, feel free to skip this section.

library(ggplot2)

# Sample data
df <- data.frame(x = c(1, 2, 3, 4, 5),
                 y1 = c(10, 20, 30, 40, 50),
                 y2 = c(100, 150, 200, 250, 300))

# Basic ggplot
ggplot(df, aes(x, y1)) +
  geom_line()

This code creates a simple line plot with x on the x-axis and y1 on the y-axis. Easy peasy!

The Magic of Secondary Y-Axes

Now, let’s add a secondary y-axis for the y2 variable. You might think it’s as simple as adding another geom_line() with aes(y2), but nope! ggplot has some tricks up its sleeve. Here’s the correct way to do it:

ggplot(df, aes(x, y1)) +
  geom_line() +
  scale_y_continuous(name = "y1 axis", sec.axis = sec_axis(~./10, name = "y2 axis"))

Whoa! What’s going on here? We’ve added a secondary y-axis using the sec.axis argument within the scale_y_continuous() function. The formula ~./10 is crucial – it tells ggplot to scale the y2 values by dividing them by 10. This way, the secondary y-axis will show the correct values for y2.

Understanding the Formula: ~./10

The formula ~./10 might look like magic, but it’s actually quite simple. In R, the ~/ symbol is used to define a formula. In this case, we’re dividing the y2 values by 10 using the ./ operator. This allows us to scale the secondary y-axis correctly.

Think of it like this: if you want to plot y2 with the same range as y1, you’d need to divide y2 by a certain value (in this case, 10) to bring it to the same scale. The formula ~./10 does exactly that!

Customizing the Secondary Y-Axis

Now that we have our secondary y-axis, let’s customize it to our heart’s content! You can modify the appearance and behavior of the secondary y-axis using various arguments within the sec_axis() function.

ggplot(df, aes(x, y1)) +
  geom_line() +
  scale_y_continuous(name = "y1 axis", 
                       sec.axis = sec_axis(~./10, 
                                          name = "y2 axis",
                                          breaks = c(100, 200, 300),
                                          labels = c("100K", "200K", "300K")))

In this example, we’ve customized the secondary y-axis by:

• Specifying custom breaks (c(100, 200, 300)) to control the tick marks
• Defining custom labels (c(“100K”, “200K”, “300K”)) to change the axis labels

Feel free to experiment with different arguments and options to customize your secondary y-axis!

Common Pitfalls and Troubleshooting

When working with secondary y-axes in ggplot, you might encounter some common issues. Fear not, dear reader! We’ve got you covered.

Issue 1: Misaligned Axis Labels

Sometimes, the axis labels might not align properly. This is usually due to the default alignment of the axis labels. To fix this, simply add the argument hjust (for horizontal justification) or vjust (for vertical justification) to the sec_axis() function.

ggplot(df, aes(x, y1)) +
  geom_line() +
  scale_y_continuous(name = "y1 axis", 
                     sec.axis = sec_axis(~./10, 
                                        name = "y2 axis",
                                        hjust = 1))

In this example, we’ve set hjust = 1 to right-align the axis labels.

Issue 2: Overlapping Axis Labels

When dealing with multiple axis labels, they might overlap and become unreadable. To avoid this, you can adjust the size and angle of the axis labels using the element_text() function.

ggplot(df, aes(x, y1)) +
  geom_line() +
  scale_y_continuous(name = "y1 axis", 
                     sec.axis = sec_axis(~./10, 
                                        name = "y2 axis")) +
  theme(axis.text.y = element_text(angle = 90, hjust = 1, size = 8))

In this example, we’ve rotated the axis labels by 90 degrees, right-aligned them, and reduced their size to 8 points.

Conclusion

Voilà! You’ve now mastered the art of creating secondary y-axes in ggplot. Remember, the key to success lies in understanding the formula ~./10 and customizing the sec_axis() function to your heart’s content.

With these newfound skills, you’ll be able to create stunning plots that showcase multiple variables with different units. Happy plotting, and don’t forget to experiment with different customization options to make your plots truly shine!

Keyword	Description
ggplot	A popular data visualization library in R
secondary y-axis	A secondary axis on the right side of the plot, often used for variables with different units
sec_axis()	A function in ggplot used to create a secondary y-axis

By the way, if you’re looking for more ggplot goodness, be sure to check out our other articles on data visualization with ggplot!

Frequently Asked Question

Get ready to unlock the secrets of ggplot and master the art of secondary y-axes!