Adjusting Color Scale to Fit Wide Range of Data with ggplot2

When working with data that spans a wide range, it’s common to encounter problems where the existing color scale is not suitable for visualizing the entire dataset. This can lead to information loss in certain regions or “burnt out” areas where extreme values dominate.

In this post, we’ll explore how to adjust the color scale of ggplot2 to better visualize data with a wide range. We’ll examine different techniques and tools available in R’s ggplot2 package for creating effective visualizations that balance representation of both high and low-value ranges.

Understanding the Problem

The provided example dataset shows agegroups 1 through 5, along with corresponding incidence values across four days (day1, day2, day3, and day4). The resulting heatmap clearly demonstrates how some age groups have very few data points compared to others. As a result, when using scale_fill_gradient or its variant scale_fill_gradient2, extreme values in the upper range can cause all the lower-value colors to be “burnt out” (appearing as white or gray).

Finding a Solution: Using scale_fill_gradient2

The solution is to utilize scale_fill_gradient2, which offers more flexibility in setting up color maps and transforming the data scale. The main difference between this function and its predecessor, scale_fill_gradient, lies in the additional options available for specifying the midpoint value of the gradient.

Here’s how to apply it:

library(tidyverse)
df <- tribble(
  ~agegroup, ~day1, ~day2, ~day3, ~day4,
  1, 20, 50, 21, 24,
  2, 23, 60, 25, 25,
  3, 26, 80, 14, 50,
  4, 23, 250, 300, 500,
  5, 50, 80, 280, 290,
)
df %>% 
  pivot_longer(!agegroup, names_to = "day", values_to = "incidence") %>% 
  ggplot(aes(x = day, y = agegroup), group = agegroup) + 
  geom_bin2d(aes(fill = incidence), stat = 'identity') + 
  scale_fill_gradient2(low = "blue",
                       mid = "white",
                       high = "red",
                       midpoint = 100)

This code sets the midpoint value of the gradient to be at 100, effectively adjusting the color map so that low values have a blue hue and high values are colored red. This setup balances visual representation of both lower-value data points (blue) and higher-value data points (red).

Transformation with Logarithmic Scale

Another approach involves applying logarithmic transformation to the incidence column before creating the heatmap:

library(tidyverse)
df <- tribble(
  ~agegroup, ~day1, ~day2, ~day3, ~day4,
  1, 20, 50, 21, 24,
  2, 23, 60, 25, 25,
  3, 26, 80, 14, 50,
  4, 23, 250, 300, 500,
  5, 50, 80, 280, 290,
)
df %>% 
  pivot_longer(!agegroup, names_to = "day", values_to = "incidence") %>% 
  ggplot(aes(x = day, y = agegroup), group = agegroup) + 
  geom_bin2d(aes(fill = log10(incidence)), stat = 'identity') + 
  scale_fill_gradient2(low = "blue",
                       mid = "white",
                       high = "red")

By taking the logarithm of incidence values and applying it to aes(fill = log10(incidence)), we adjust the visual range, ensuring that both low and high values are represented effectively.

Conclusion

When dealing with data having a wide range of values, finding an appropriate color scale can be a challenging task. The solution involves utilizing scale_fill_gradient2 for adjusting the gradient’s behavior or transforming the data using logarithmic scaling techniques. These strategies allow for better visualization of both low and high-value ranges in heatmaps created with ggplot2.

Note that this is not an exhaustive guide to color scaling in R, but it should help you get started on finding an approach suitable for your specific use case.

Last modified on 2024-08-26