Understanding ggplot2: geom_smooth Confidence Band Limitations

Introduction to ggplot2 and the Problem at Hand

The geom_smooth function in R’s ggplot2 package is a powerful tool for creating regression lines and confidence bands on scatterplots. However, there have been instances where users have encountered an issue with their confidence bands not extending all the way to the edges of the graph, even when using the fullrange=TRUE parameter. In this post, we’ll delve into the cause of this problem and explore possible solutions.

Problem Statement

The problem arises because of how ggplot2 handles the positioning of its geometric elements, including confidence bands. When a dataset’s range exceeds the boundaries set by scale_x/y_continuous, points outside these ranges are excluded from plotting to prevent visual clutter. However, when generating confidence bands using geom_smooth, the function does not inherently extend these bands beyond the plotted area.

Solution: Understanding coord_cartesian

The solution lies in introducing coord_cartesian into the plotting process. This function overrides the default scaling and limits imposed by scale_x/y_continuous, ensuring that all data points are included, regardless of their proximity to the plot boundaries. By using both scale_x/y_continuous for setting ranges and coord_cartesian for actual plotting, we can effectively control the extent of our confidence bands.

Background: Scales and Coordinate Systems

Before we dive deeper into code examples, let’s briefly discuss how scales and coordinate systems interact within ggplot2:

• Scales: These are used to set the limits, continuity, and other properties of various axes in a plot. For instance, scale_x_continuous and scale_y_continuous determine the range for continuous variables on the x-axis and y-axis respectively.
• Coordinate System: This refers to how ggplot2 arranges its geometric elements within the plotting area. The default behavior is to exclude points outside the set limits from being plotted.

Exploring Code Examples

To illustrate this concept, we’ll create three plots that demonstrate different combinations of scale_x/y_continuous and coord_cartesian.

# Create a simple plot with only scale_x/y_continuous
p1 = ggplot(mtcars, aes(wt, mpg, colour=factor(am))) + 
  geom_smooth(fullrange=TRUE, method="lm") +
  scale_x_continuous(expand=c(0,0), limits=c(0,10)) +
  scale_y_continuous(expand=c(0,0), limits=c(0,100)) +
  ggtitle("scale_x/y_continuous")

# Create a plot with coord_cartesian added
p2 = ggplot(mtcars, aes(wt, mpg, colour=factor(am))) + 
  geom_smooth(fullrange=TRUE, method="lm") +
  scale_x_continuous(expand=c(0,0), limits=c(0,10)) +
  scale_y_continuous(expand=c(0,0), limits=c(0,100)) +
  coord_cartesian(xlim=c(0,10), ylim=c(0,100)) +
  ggtitle("Add coord_cartesian; same y-range")

# Create a plot with expanded y-limits and coord_cartesian
p3 = ggplot(mtcars, aes(wt, mpg, colour=factor(am))) + 
  geom_smooth(fullrange=TRUE, method="lm") +
  scale_x_continuous(expand=c(0,0), limits=c(0,10)) +
  scale_y_continuous(expand=c(0,0), limits=c(-50,100)) +
  coord_cartesian(xlim=c(0,10), ylim=c(0,100)) +
  ggtitle("Add coord_cartesian; expanded y-range")

# Combine plots for comparison
gridExtra::grid.arrange(p1, p2, p3)

Conclusion

By understanding the role of coord_cartesian in controlling plotting ranges and extending confidence bands to plot edges, users can effectively create high-quality visualizations with confidence. It’s essential to experiment with different combinations of scaling functions and coordinate systems to achieve desired outcomes.

Remember that when working with ggplot2, a thorough grasp of its core concepts is crucial for crafting visually stunning and informative plots.

Last modified on 2024-01-29