Customizing Legend Labels in ggplot2

Introduction to ggplot2 and Custom Legends

ggplot2 is a powerful data visualization package for R, which provides a coherent system for describing and building graphs. One common task when creating visualizations with ggplot2 is customizing the legend labels to make them more meaningful or specific to your dataset.

This tutorial covers how to effectively customize legend labels in ggplot2 plots using different techniques, ensuring that your data visualization communicates precisely what you intend.

Transforming Data for Better Visualization

Before diving into legend customization, it’s essential to understand a crucial step: transforming your data. Often, ggplot2 works best with data in a long format rather than wide format. The melt() function from the reshape2 package is commonly used to achieve this transformation.

Example: Data Transformation

Consider a dataset containing temperature readings across different categories:

# Sample dataset
T999 <- runif(10, 100, 200)
T888 <- runif(10, 200, 300)
TY <- runif(10, 20, 30)
df <- data.frame(T999, T888, TY)

# Transforming the data from wide to long format
library(reshape2)
dfm <- melt(df, id = "TY")

By converting your dataset into a long format (dfm), we map each observation to its corresponding category (e.g., T999, T888).

Customizing Legend Labels

Using scale_*_manual()

To customize legend labels directly, use the scale_color_manual() or scale_fill_manual() functions. These allow you to specify both the colors and the labels for your legends.

library(ggplot2)

# Creating a scatter plot with custom color and label
ggplot(data = dfm, aes(x = TY, y = value, color = variable)) + 
  geom_point(size=5) +
  labs(title = "Temperatures\n", x = "TY [°C]", y = "Txxx", color = "Legend Title\n") +
  scale_color_manual(labels = c("T999", "T888"), values = c("blue", "red")) +
  theme_bw() +
  theme(axis.text.x = element_text(size = 14), axis.title.x = element_text(size = 16),
        axis.text.y = element_text(size = 14), axis.title.y = element_text(size = 16),
        plot.title = element_text(size = 20, face = "bold", color = "darkgreen"))

Using guides() and labs()

The guides() function provides another way to customize legends by specifying guide properties for each aesthetic.

# Creating a scatterplot with custom legend using guides()
ggplot(data = dfm, aes(x = TY, y = value, color = variable)) + 
  geom_point(size=5) +
  labs(title = "Temperatures\n", x = "TY [°C]", y = "Txxx") +
  scale_color_manual(labels = c("T999", "T888"), values = c("blue", "red")) +
  theme_bw() +
  guides(color = guide_legend(title = "my title"))

Alternatively, labs() can be used to label aesthetics directly within the plotting function:

# Using labs to customize legend labels
ggplot(data = dfm, aes(x = TY, y = value, color = variable)) + 
  geom_point(size=5) +
  labs(title = "Temperatures\n", x = "TY [°C]", y = "Txxx", color = "Legend Title")

Best Practices and Tips

1. Consistency: Ensure your legend labels are consistent with the data categories to avoid confusion.
2. Clarity: Use clear, descriptive labels that make sense at a glance.
3. Aesthetic Mapping: Remember that ggplot2 maps aesthetics (like color or shape) to variables in your dataset. Customizing these mappings can greatly enhance the interpretability of your plots.

Conclusion

Customizing legend labels is an essential part of creating effective visualizations with ggplot2. By using functions like scale_color_manual(), guides(), and labs(), you have robust tools to tailor legends according to your needs, enhancing both clarity and impact in data storytelling.