Adding a Box for NA Values to the ggplot Legend for a Continuous Map

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Introduction

In this article, we will explore how to add a box for missing values (NA) in a continuous map using the ggplot2 package in R. We will discuss two approaches: one that involves splitting the value variable into a discrete scale and another that uses a separate color scale with a manual color mapping.

Understanding the Problem

The original poster has a map with a legend gradient and wants to add a box for the NA values. They have tried various workarounds but are looking for a more elegant solution.

Background

In ggplot2, maps can be created using the geom_polygon function, which requires a continuous variable (value) that defines the color palette. However, when there are missing values (NA), ggplot2 does not provide an automatic way to handle them in the legend.

Approach 1: Discrete Scale

One possible solution is to split the value variable into a discrete scale using the cut() function. This will allow us to create a separate color palette with “NA” as one of the distinct colors.

Code

library(ggplot2)
map <- map_data("world")
map$value <- setNames(sample(-50:50, length(unique(map$region)), TRUE), 
                     unique(map$region))[map$region]
map[map$region == "Russia", "value"] <- NA
ggplot() +
  geom_polygon(data = map,
               aes(long, lat, group = group, fill = discrete_value)) +
  scale_fill_brewer(palette = "RdYlBu", na.value = "black") +
  coord_quickmap()

In this code, we first split the value variable into a discrete scale using cut(). We then create a new column called discrete_value and use it as the fill variable in the map. Finally, we set the na.value argument to “black” to ensure that NA values are displayed as black boxes.

Approach 2: Separate Color Scale

Another possible solution is to use a separate color scale with a manual color mapping. This approach allows us to retain the original color gradient and colorbar-style legend while adding a box for the NA value.

Code

p2 <- ggplot() +
  geom_polygon(data = map, aes(long, lat, group = group, fill = value)) +
  scale_fill_gradient2(low = "brown3", mid = "cornsilk1", high = "turquoise4",
                       limits = c(-50, 50),
                       na.value = "black") +
  geom_point(aes(x = -100, y = -50, size = "NA"), shape = NA, colour = "black") +
  guides(size = guide_legend("NA", override.aes = list(shape = 15, size = 10)))

In this code, we create a new map with the same value variable as before. However, instead of using geom_polygon, we use geom_point() to draw a black box for each NA value. We then use guides(size) to override the default legend behavior and display the NA values as a single color.

Conclusion

In conclusion, both approaches have their advantages and disadvantages. The first approach involves splitting the value variable into a discrete scale, which can be more intuitive but may not work well with all types of data. The second approach uses a separate color scale with a manual color mapping, which allows for more control over the legend but may require additional setup.

Ultimately, the choice between these approaches depends on the specific requirements and constraints of your project.

Last modified on 2025-03-28