Using Labeller to Add Units to Strip Labels with ggplot2 and Facet Wrap

Faceting plots in ggplot2 is a powerful way to visualize multiple datasets alongside each other. However, when working with categorical variables that contain units or labels, manually specifying the label vector can be cumbersome and prone to errors. In this article, we will explore how to use the labeller function within ggplot2 to automatically add units to strip labels.

Introduction

The facet_wrap function in ggplot2 is used to create a faceted plot with multiple panels. The labeller argument allows us to specify a custom labeling function for each facet. This function can be used to transform the facet levels into desired labels, such as adding units to categorical variables.

Background

In the provided example from Stack Overflow, the user is using labeller to add units (“mg”) to strip labels in a boxplot faceted plot. The code uses an anonymous function (a lambda) within labeller to apply the transformation.

ggplot(df) +
  geom_boxplot(aes(y = output))+
  facet_wrap(vars(group), labeller = labeller(group = ~ paste(.x, "mg")))

In this example, .x represents the current facet level being processed. The anonymous function ~ paste(.x, "mg") takes the current facet level and appends “mg” to it.

Using Labeller with Anonymous Functions

We can use an anonymous function within labeller to apply more complex transformations, such as adding units to categorical variables. Here’s an example:

library(ggplot2)

ggplot(df) +
  geom_boxplot(aes(y = output))+
  facet_wrap(vars(group), labeller = labeller(group = ~ paste(.x, "mL")))

In this example, the anonymous function ~ paste(.x, "mL") adds “mL” to the end of each facet level.

Using Lambda Functions with Labeller

While anonymous functions are convenient, they can also be less readable than explicit lambda functions. Here’s an equivalent example using a named lambda function:

library(ggplot2)

ggplot(df) +
  geom_boxplot(aes(y = output))+
  facet_wrap(vars(group), labeller = labeller(group = function(x) paste(x, "mg")))

In this example, we define the transformation as an explicit lambda function using function(x). This can make the code more readable and maintainable.

Handling Missing Values

When working with categorical variables that contain missing values, we need to handle these cases carefully. One approach is to use the ifelse function to substitute missing values with a specific value, such as “Unknown”. Here’s an example:

library(ggplot2)

ggplot(df) +
  geom_boxplot(aes(y = output))+
  facet_wrap(vars(group), labeller = labeller(group = ~ ifelse(.x == "", "Unknown", paste(.x, "mg"))))

In this example, we use ifelse to substitute missing values ("") with the string “Unknown”. The transformation is then applied using the paste function.

Conclusion

Using labeller with anonymous functions or lambda functions allows us to easily add units to strip labels in faceted plots. By exploring different transformation options, such as named lambda functions and handling missing values, we can create more robust and maintainable code. With these techniques, you’ll be able to create high-quality visualizations with minimal fuss.

Advanced Example: Using Labeller with Multiple Variables

When working with multiple variables in a facet plot, we may need to apply different transformations or units to each variable. Here’s an example that uses labeller with two separate lambda functions:

library(ggplot2)

ggplot(df) +
  geom_boxplot(aes(y = output1))+
  geom_boxplot(aes(y = output2))+
  facet_wrap(vars(group), labeller = labeller(group = ~ paste(.x, "mg"), vars(output1)))

In this example, we define two separate lambda functions using labeller. The first function applies the transformation for the group variable, while the second function applies the transformation for the output1 variable. This approach allows us to customize the transformations for each variable individually.

Additional Tips and Tricks

• When working with categorical variables that contain missing values, make sure to handle these cases carefully using functions like ifelse.
• Use named lambda functions or explicit anonymous functions to improve code readability and maintainability.
• Experiment with different transformation options, such as paste, gsub, and strsplit, to find the best approach for your specific use case.

By following these tips and techniques, you’ll be able to create high-quality visualizations with minimal fuss and maximum effectiveness.

Last modified on 2023-05-05