Introduction to Marginal Histogram Scatterplots with Lattice Package

As a data visualization enthusiast, you’ve likely come across various techniques for creating informative and visually appealing plots. One such technique is the marginal histogram scatterplot, which provides a unique perspective on the relationship between two variables by displaying histograms along the margins of a scatterplot. In this article, we’ll explore how to create a marginal histogram scatterplot using the lattice package in R.

Background on Lattice Package and ggplot2

The lattice package is a powerful tool for creating complex and customized plots in R. While it has gained popularity in recent years, its usage has been somewhat overshadowed by ggplot2. However, both packages have their strengths and weaknesses. The lattice package provides more flexibility and control over the plot structure, while ggplot2 offers a more declarative approach to data visualization.

Why Marginal Histogram Scatterplots?

Marginal histogram scatterplots are particularly useful for visualizing the marginal distributions of two variables. By displaying histograms along the margins of a scatterplot, we can gain insight into the shape and characteristics of each distribution. This technique is especially useful when working with high-dimensional datasets or when trying to identify patterns in the data.

Creating a Marginal Histogram Scatterplot using Lattice Package

To create a marginal histogram scatterplot using the lattice package, we’ll follow these steps:

1. Load the necessary libraries: lattice and ggplot2
2. Prepare our dataset: We’ll use the built-in mtcars dataset in R.
3. Create the main scatterplot: Use xyplot(hp ~ mpg, mtcars)
4. Create the histograms: Use hist(~ mpg, mtcars)

Here’s the code:

data(mtcars)
library(lattice)

# Create the main scatterplot
scatter <- xyplot(hp ~ mpg, mtcars)

# Create the histograms
hist1 <- histogram(~ mpg, data = mtcars, plot.type = "density")
hist2 <- histogram(~ hp, data = mtcars, plot.type = "density")

# Combine the plots into a single lattice plot
plot(scatter, more = TRUE, split = c(1, 2, 1, 2))
plot(hist1, more = FALSE, split = c(1, 1, 1, 2))
plot(hist2, more = FALSE, split = c(2, 2, 1, 2))

Understanding the split Argument

The split argument in the lattice package controls how the plots are arranged. In our example, we used c(1, 2, 1, 2) to create a 2x2 plot structure. The first two elements (1 and 2) specify the position of the scatterplot along the x-axis, while the last two elements (1 and 2) specify the position of the histograms.

Fine-Tuning the Plot

As we can see from the example code, we didn’t cover how to rotate one histogram or create a sideways histogram. To achieve this, you’ll need to experiment with different values for the split argument and explore other options in the lattice package documentation.

Conclusion

Creating marginal histograms scatterplots using the lattice package is a powerful technique for visualizing two-variable relationships. By combining the benefits of both lattice and ggplot2 packages, we can create complex and informative plots that reveal hidden patterns in our data. With practice and experimentation, you’ll become proficient in crafting custom plots that communicate your message effectively.

Additional Resources

For further exploration of lattice package functions and options, refer to the lattice documentation. Additionally, reading the ?histogram documentation page can provide valuable insights into manipulating plot positions and other features.

Last modified on 2023-05-31