A Comprehensive Guide to Manually Setting Legends in ggplot2

Introduction

In data visualization applications, users often face the challenge of manually setting legends in ggplot2, especially for complex plots containing multiple graphical elements. The original code lacked a legend, prompting a deep analysis of the legend generation mechanism.

Core Concept: Mapping Aesthetics to Generate Legends

In ggplot2, legends are automatically generated when aesthetics such as colour or fill are mapped to variables within the aes() function. The initial code specified colours as constants, leading to missing legends. The solution involves mapping colours to string values in aes() and then defining these using scale_colour_manual() and scale_fill_manual() functions.

Solution: Implementing Manual Legend

To manually set up a legend, one must map colours to string variables in the geom_* functions and then use scale_*_manual() to set the colours and names. Below is a rewritten example code based on the solution:

cols <- c("LINE1"="#f04546", "LINE2"="#3591d1", "BAR"="#62c76b")
ggplot(data = data, aes(x = a)) + 
  geom_bar(stat = "identity", aes(y = h, fill = "BAR"), colour = "#333333") + 
  geom_line(aes(y = b, group = 1, colour = "LINE1"), size = 1.0) + 
  geom_point(aes(y = b, colour = "LINE1"), size = 3) + 
  geom_errorbar(aes(ymin = d, ymax = e, colour = "LINE1"), width = 0.1, size = 0.8) + 
  geom_line(aes(y = c, group = 1, colour = "LINE2"), size = 1.0) + 
  geom_point(aes(y = c, colour = "LINE2"), size = 3) + 
  geom_errorbar(aes(ymin = f, ymax = g, colour = "LINE2"), width = 0.1, size = 0.8) + 
  scale_colour_manual(name = "Error Bars", values = cols) + 
  scale_fill_manual(name = "Bar", values = cols) + 
  ylab("Symptom severity") + xlab("PHQ-9 symptoms") + 
  ylim(0, 1.6) + 
  theme_bw() + 
  theme(axis.title.x = element_text(size = 15, vjust = -0.2)) + 
  theme(axis.title.y = element_text(size = 15, vjust = 0.3))

Explanation: In this code, by setting colour and fill to string values in aes() and mapping them via scale_*_manual() functions, a legend with specified colours and names is generated. This approach allows for flexible control over the legend's appearance.

Advanced Techniques: Customizing Legend Appearance

In the basic solution, the legend may have a black background. To address this, use the override.aes parameter in the guide_legend() function. For example, adding guide = guide_legend(override.aes = aes(fill = NA)) in scale_colour_manual() sets the fill to null, thereby removing the black background. This is a key fine-tuning technique that can be used to merge or adjust the legend's visual properties.

Conclusion

Manually setting legends in ggplot2 requires careful mapping of aesthetics and the use of scale functions. Through these methods, users can freely control the colours, names, and appearance of legends to suit different data visualization needs. This process elevates the flexibility of data visualization to a new level.