R offers several powerful plotting systems, with lattice and ggplot2 being two of the most popular. While these systems have different approaches to data visualization, there are ways to bridge concepts between them. One such concept is the use of shingles, a feature native to lattice, within ggplot2 visualizations. This article explores how to implement lattice-style shingles in ggplot2, providing R users with additional tools for creating informative and visually appealing plots in the R Programming Language.

What are Shingles?

In a lattice package shingles are used to divide continuous data into overlapping or non-overlapping intervals, which are then treated as factor levels for plotting. This allows for visualizing how data behaves across different ranges of a continuous variable. In ggplot2, this can be achieved using the cut() function to create these intervals.

Implementing Shingles in ggplot2

While ggplot2 doesn’t have a built-in shingle function, we can recreate this functionality using ggplot2’s flexible architecture. Here’s how to do it:

Creating a Shingle-like Function

First, let’s create a function that mimics the behavior of lattice’s shingles:

create_shingle <- function(x, intervals) {
  cuts <- cut(x, breaks = intervals, include.lowest = TRUE, dig.lab = 4)
  factor(cuts, ordered = TRUE)
}

Visualizing MPG vs. Weight with Horsepower Shingles

Let’s use the mtcars dataset to demonstrate this concept:

library(ggplot2)
library(dplyr)

# Sample data
data(mtcars)

# Create the plot
ggplot(mtcars, aes(x = wt, y = mpg, size = hp, color = factor(cyl))) +
  geom_point(alpha = 0.7) +
  scale_size_continuous(name = "Horsepower", range = c(1, 10)) +
  scale_color_brewer(name = "Cylinders", palette = "Set1") +
  labs(title = "Car Weight vs. MPG",
       x = "Weight (1000 lbs)",
       y = "Miles per Gallon") +
  theme_minimal()

Output:

shingles from lattice in ggplot2

In this example, we’ve created shingles for the horsepower variable using quartiles. The resulting plot shows the relationship between MPG and weight, with different colors representing different horsepower ranges. This allows us to see how the MPG-weight relationship varies across horsepower categories.

Faceted Plot with Shingles

We can take this concept further by combining shingles with faceting:

# Create shingles for both horsepower and displacement
mtcars$hp_shingle <- create_shingle(mtcars$hp, quantile(mtcars$hp, 
                                                        probs = seq(0, 1, by = 0.33)))
mtcars$disp_shingle <- create_shingle(mtcars$disp, 
                                      quantile(mtcars$disp, probs = seq(0, 1, by = 0.33)))

# Create the faceted plot
ggplot(mtcars, aes(x = wt, y = mpg, color = hp_shingle)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE) +
  facet_wrap(~ disp_shingle, scales = "free") +
  scale_color_brewer(palette = "Set1") +
  labs(title = "MPG vs. Weight, Conditioned on HP and Displacement Shingles",
       x = "Weight (1000 lbs)", y = "Miles per Gallon",
       color = "Horsepower Range") +
  theme_minimal() +
  theme(legend.position = "bottom")

Output:

shingles from lattice in ggplot2 in R

This more complex example uses shingles for both horsepower and displacement. The plot is faceted by displacement shingles, with horsepower shingles represented by color. This visualization allows us to examine the MPG-weight relationship across different ranges of both horsepower and displacement simultaneously.

Benefits and Considerations

• Flexibility: Using shingle-like divisions in ggplot2 allows for more flexible categorization of continuous variables.
• Interpretability: Shingles can make it easier to interpret patterns across different ranges of a variable.
• Complexity: While powerful, these plots can become complex. Care should be taken to ensure they remain interpretable.
• Customization: The intervals for shingles can be customized based on the data and the specific insights you’re seeking.

Conclusion

While shingles are not a native feature of ggplot2, we can effectively implement similar functionality, bridging the gap between lattice and ggplot2. This approach combines the powerful grammar of graphics from ggplot2 with the useful concept of shingles from lattice, providing R users with an expanded toolkit for data visualization.

By understanding and implementing these techniques, data scientists and analysts can create more nuanced and informative visualizations, potentially uncovering patterns and relationships in their data that might otherwise remain hidden.