Working with Lattice Heatmaps in R: Excluding the Colorbar

In this article, we will explore how to create heatmaps using the lattice package in R and discuss ways to exclude the colorbar from these plots.

Introduction to the lattice Package

The lattice package is a popular data visualization library for R that provides various tools and methods for creating high-quality, publication-grade graphics. It includes support for creating heatmaps, scatterplots, box plots, and more.

Heatmaps are particularly useful for displaying relationships between two variables, such as correlations or density plots. However, one common issue with these plots is the presence of a colorbar that can clutter the visualization.

Understanding Colorkeys in Lattice Heatmaps

Colorkeys are used to display the colorscale associated with each data point on the heatmap. They are typically displayed next to the plot and show the mapping between the colors used in the plot and specific values or ranges of values.

In R, colorkeys can be controlled using various options available in the levelplot function from the lattice package. One option that allows you to exclude the colorbar is setting colorkey = FALSE.

Using colorkey = FALSE

To use the colorkey = FALSE option, we need to specify it within the levelplot function when creating our heatmap.

Here’s an example code snippet that demonstrates how to create a simple heatmap without a colorbar:

x <- seq(pi/4, 5 * pi, length.out = 100)
y <- seq(pi/4, 5 * pi, length.out = 100)
r <- as.vector(sqrt(outer(x^2, y^2, "+")))
grid <- expand.grid(x=x, y=y)
grid$z <- cos(r^2) * exp(-r/(pi^3))

levelplot(z ~ x * y, grid, cuts = 50, scales = list(log = "e"), 
          xlab = "", ylab = "", main = "Weird Function", sub = "with log scales",
          colorkey = FALSE, region = TRUE)

This code creates a simple heatmap with a logarithmic scale using lattice. The colorkey option is set to FALSE, which excludes the colorbar from the plot.

Creating Custom Heatmaps

While colorkey = FALSE works well for most cases, you may need to create custom heatmaps that require more control over the colorbar. In this case, you can use additional options available in the levelplot function, such as colorbar, cals, and scale.

For example, if you want to remove the colorbar completely but keep a scaling indicator on one side of the plot, you can set colorbar = TRUE with the cal option:

x <- seq(pi/4, 5 * pi, length.out = 100)
y <- seq(pi/4, 5 * pi, length.out = 50)
r <- as.vector(sqrt(outer(x^2, y^2, "+")))
grid <- expand.grid(x=x, y=y)
grid$z <- cos(r^2) * exp(-r/(pi^3))

levelplot(z ~ x * y, grid, cuts = 50, scales = list(log = "e"), 
          xlab = "", ylab = "", main = "Weird Function", sub = "with log scales",
          colorbar = TRUE, cal = "(x)", region = TRUE)

This code creates a heatmap with a scaling indicator on the left side of the plot.

Best Practices for Customizing Colorbars

When creating custom heatmaps that require manual control over the colorbar, it’s essential to keep in mind some best practices:

• Use colorkey or colorbar options when needed.
• Consider setting cals option to remove unscaled values from the colorbar.
• Use scale function with log argument for logarithmic scales.

By following these tips, you can create high-quality heatmaps that are both informative and visually appealing.

Conclusion

In this article, we explored how to exclude the colorbar from lattice heatmaps in R using various options available within the levelplot function. We discussed best practices for customizing colorbars and provided examples of creating simple and complex heatmaps with controlled colorbars.

Whether you’re working on a specific project or simply looking to improve your data visualization skills, understanding how to create high-quality lattice heatmaps is an essential part of any R development workflow.

Last modified on 2024-05-08