Customizing Plot Symbols and Legends in R Base Plots

In this article, we’ll explore how to use multiple plot symbols on the same symbol in a base R plot and customize legends for them.

Introduction

R’s plot() function is a powerful tool for creating a wide range of plots. One common requirement when working with these plots is to add additional elements like points or lines to customize the appearance of the graph. However, by default, R can make it challenging to create legends that are easily readable and distinguishable from one another.

Background

The plot() function in R uses a variety of arguments to control its behavior, including plotting symbols (pch), point sizes (cex), colors (col), and more. These options allow users to fine-tune their plots to meet specific needs, but they can also lead to complicated code when dealing with multiple plot elements.

For example, consider a scenario where you want to plot both a solid red circle and an overlapped black point on the same symbol. By default, R’s legend() function is unable to handle overlapping symbols without causing confusion.

The Problem

The provided Stack Overflow question illustrates this problem, demonstrating how legends cannot be added but instead overlap when using multiple plotting symbols (pch = 19 for the red circle and pch = 1 for the black point). This issue arises because R's legend()` function relies on a single plotting symbol to determine which element of the plot corresponds to each data point.

Solution: Using bty = "n"

The solution lies in using the bty argument within the plot() function. By setting bty to "n", we tell R to not print any borders around the plot area, effectively creating space for our custom legends.

Here’s an updated code snippet that demonstrates how this works:

# Create a new figure with no borders
plot(1, pch = 19, cex = 3, col = "red")
points(1, pch = 1, cex = 3, col = "black", lwd = 2)

# Create the first legend for the red symbol
legend("top",
       "sym",
       bty = "n",
       pch = 19,
       col = c("red"),
       cex = 2,
       pt.cex = 4)

# Create the second legend for the black point
legend("top",
       "sym",
       bty = "n",
       pch = 1,
       col = c("black"),
       cex = 2,
       pt.cex = 4)

By using bty = "n", we create space between the plot symbols and their corresponding legends, allowing us to distinguish one symbol from another.

Customizing Legend Behavior

While setting bty to "n" resolves the issue of overlapping symbols in legends, it doesn’t address other potential problems with legend behavior. For instance, R’s default behavior for points is to create a legend entry when the data point falls within a certain range (typically ±2 standard deviations from the mean). However, this range can vary depending on the plot type and parameters.

To customize legend behavior further, you can use the pt.cex argument in conjunction with bty = "n". By setting pt.cex, you can control the size of point symbols in the legend, which helps distinguish between similar plotting symbols.

Here’s an updated example that demonstrates how to customize legend behavior using pt.cex:

# Create a new figure with no borders and larger points in the legend
plot(1, pch = 19, cex = 3, col = "red")
points(1, pch = 1, cex = 3, col = "black", lwd = 2)

# Create the first legend for the red symbol with larger point size
legend("top",
       "sym",
       bty = "n",
       pch = 19,
       col = c("red"),
       cex = 4,
       pt.cex = 8) # Increased pt.cex to make the legend more distinguishable

# Create the second legend for the black point
legend("top",
       "sym",
       bty = "n",
       pch = 1,
       col = c("black"),
       cex = 4,
       pt.cex = 8) # Increased pt.cex to make the legend more distinguishable

By adjusting pt.cex, we can fine-tune the appearance of our legends and ensure they’re easily readable even in complex plots.

Conclusion

Using bty = "n" and customizing pt.cex allows us to create base R plots with multiple plot symbols that are easily distinguishable from one another. This technique is essential for creating clear and informative graphics, especially when working with large datasets or intricate plot structures.

By understanding how plotting symbols interact with legends in R and using the right techniques to customize legend behavior, you can unlock a world of creative possibilities in your data visualization projects.

Last modified on 2024-06-15