Geom Tile Layers in ggplot: Adding a Second Layer for Outlining or Dimming

When working with geometric objects like tiles in a heatmap using geom_tile from the ggplot2 package, it can be challenging to add additional layers that complement or modify the original visualization. In this article, we will explore how to add a second layer on top of an existing tile layer for outlining or dimming specific areas.

Introduction

The geom_tile function in ggplot creates a matrix of colored tiles based on the values of a continuous variable. While it provides a straightforward way to visualize data, sometimes additional layers are needed to better understand the underlying patterns or relationships. In this scenario, we will demonstrate how to add a second tile layer that outlines specific areas or dims out others.

Understanding `geom_tile`

Before we dive into adding multiple layers, let’s quickly review the basics of geom_tile. The function takes in several key arguments:

aes(x = value1, y = value2): specifies the x and y coordinates for the tiles. In this case, we are using the values from our data frames.
fill = expression(value3): specifies the color of each tile based on the third value in the dataset.
colour = "grey20": sets the border color of the tiles.

Creating a Sample Dataset

To demonstrate the concept, let’s create a sample dataset with two data frames, df1 and df2. The first data frame (df1) contains continuous values that will be used to generate the heatmap. The second data frame (df2) has the same structure as the first but with a different set of values.

library(ggplot2)
n <- 4
df1 <- data.frame(x = rep(letters[1:n], times = n),
                  y = rep(1:n, each = n),
                  z = rnorm(n ^ 2)) 

df2 <- data.frame(x = rep(letters[1:n], times = n),
                  y = rep(1:n, each = n),
                  z = FALSE) 
df2$z[c(2,14)] <- TRUE

Adding the First Tile Layer

First, we will create a heatmap using geom_tile on our data frame df1.

p1 <- ggplot(df1, aes(x = x, y = y))
p1 <- p1 + geom_tile(aes(fill = z), colour = "grey20")
p1 <- p1 + scale_fill_gradient2(low = "darkgreen", 
                                mid = "white", 
                                high = "darkred",
                                breaks = c(min(df1$z), max(df1$z)),
                                labels = c("Low", "High"))

Adding a Second Tile Layer

To add the second tile layer, we need to specify the color and size of the border for the outline or dimming effect. We will use the factor function from R’s dplyr package to convert the logical values in df2 into binary (TRUE/ FALSE) variables.

library(dplyr)
p1 + geom_tile(data = df2,
               aes(colour = factor(z, c(TRUE, FALSE))),
               size = factor(z, c(TRUE, FALSE))) +
  scale_colour_manual("z", values = c("blue4", "white")) + 
  scale_size_manual("z", values = c(3, 0))

In the above code snippet, we have added a second geom_tile layer to our existing plot (p1). The colour aesthetic is set based on the binary conversion of df2's z-values using factor(z, c(TRUE, FALSE)). This produces a blue border around the areas where z equals TRUE and a white fill.

Exploring Alternative Approaches

There are other ways to achieve the desired outcome. For example, you could overlay another heatmap on top of the original layer using the following approach:

p1 + geom_tile(data = df2,
               aes(fill = z), colour = "grey20")

However, this method can result in visually confusing layers if the tile sizes and colors are different. The alternative approach described above provides a more cohesive appearance.

Conclusion

Adding multiple layers to a ggplot visualization using geom_tile can help reveal additional insights or patterns within your data. By understanding how to control the color and size of the borders for specific areas, you can create visually appealing heatmaps that effectively communicate complex information.

Last modified on 2024-02-24