Understanding ggplot2’s geom_text_repel and Overcoming Text Overlap Issues

When working with geospatial data, it is not uncommon to encounter cases where text labels overlap with each other due to their proximity on the plot. This can lead to a cluttered and visually unappealing representation of the data. In this post, we will delve into the world of ggplot2’s geom_text_repel function and explore how to overcome issues related to text overlapping.

Introduction to ggplot2’s geom_text_repel

The geom_text_repel function in ggplot2 is used to add text labels to a plot. It takes advantage of the underlying geometry engine to position each label separately, allowing for more flexibility in customizing their placement and appearance. However, when dealing with large datasets or complex plots, this flexibility can sometimes result in labels overlapping with each other.

The Problem: Text Overlapping

In the provided Stack Overflow example, we see an instance of text overlapping occurring on a facet grid plot. Specifically, the label “ZNF717” is only partially displayed due to its proximity to the top edge of the panel. This issue can be challenging to resolve, especially when working with large datasets or complex layouts.

A Closer Look at geom_text_repel’s Parameters

To better understand how geom_text_repel works and how it can be customized, let’s take a closer look at its parameters:

• direction: Specifies the direction of repulsion. When set to “x”, labels are pushed away from each other horizontally. When set to “y”, labels are pushed away from each other vertically.
• hjust: Specifies the horizontal justification for the text labels. Values range from 0 (left) to 1 (right).
• vjust: Specifies the vertical justification for the text labels. Values range from -1 (top) to 1 (bottom).
• size: Specifies the size of the text labels.

By adjusting these parameters, we can control how the labels are positioned and spaced out on the plot.

Solution: Modifying Default Coordinates

One approach to resolving the issue is to modify the default coordinates of the plot. This can be achieved by using the coord_cartesian function with the clip argument set to "off". This tells ggplot2 not to clip any elements that extend beyond the plot boundaries.

Here’s an example code snippet that demonstrates this approach:

library(ggplot2)
library(ggrepel)
library(dplyr)

mtcars %>% 
  rowwise() %>% 
  mutate(label="test_label") %>% 
  mutate(facet=runif(n = n(),min = 1,max=20)) %>% 
  ggplot(aes(x=disp,y=hp,label=label)) + 
  geom_text_repel(direction = "y", hjust = 0.5, size = 2) + 
  facet_wrap(~facet, nrow = 2) +
  coord_cartesian(clip = "off")

In this code snippet, we use coord_cartesian with clip = "off" to disable clipping and allow labels that extend beyond the plot boundaries.

The Problem with Facets: Overlapping Labels

Another issue arises when working with facets. Since each facet is self-contained, the repulsion mechanism applied by geom_text_repel cannot prevent overlap between labels across different facets. In the provided example, we see an instance of this problem occurring in the mtcars dataset.

Solution: Rotating Text Labels

One approach to resolving this issue is to rotate text labels using the angle aesthetic. This can help to reduce overlap by positioning the labels at an angle that minimizes their intersection.

Here’s an updated code snippet that demonstrates this approach:

library(ggplot2)
library(ggrepel)
library(dplyr)

mtcars %>% 
  rowwise() %>% 
  mutate(label="test_label") %>% 
  mutate(facet=runif(n = n(),min = 1,max=20)) %>% 
  ggplot(aes(x=disp,y=hp,label=label)) + 
  geom_text_repel(direction = "y", hjust = 0.5, size = 2) +
  facet_wrap(~facet, nrow = 2) +
  coord_cartesian(clip = "off") +
  labs(angle = 45)

In this code snippet, we use the labs function to set the angle of the text labels to 45 degrees. This helps to reduce overlap by positioning the labels at an angle that minimizes their intersection.

Conclusion

When working with geospatial data and complex plots, issues related to text overlapping can be challenging to resolve. However, by understanding how geom_text_repel works and customizing its parameters, we can overcome these issues and create visually appealing representations of our data.

Additionally, modifying default coordinates or using alternative approaches such as rotating text labels can help to address specific challenges. By combining these techniques, we can create high-quality visualizations that effectively communicate complex information.

In the next section, we’ll explore additional strategies for customizing geom_text_repel and improving its performance when working with large datasets.

Last modified on 2023-08-12