Understanding the Issue with Legend Labels in R Shaded Maps

When creating shaded maps in R using the ggplot2 or maptools libraries, it’s common to encounter issues with legend labels displaying incorrect information, such as showing the same interval multiple times. This can be particularly frustrating when working with continuous variables and need to distinguish between different intervals of values.

In this article, we’ll delve into the world of R shaded maps, exploring the underlying concepts and technical details that contribute to this issue. We’ll examine how to identify and resolve the problem, providing practical examples and code snippets along the way.

Introduction to Quantile Intervals

Before diving into the specifics of legend labels, let’s review some fundamental concepts in R and ggplot2. The classIntervals function is used to create quantile intervals for continuous variables. These intervals are created by dividing the data into equal-sized bins based on the specified number of breaks (or intervals). The resulting classes represent distinct ranges of values within the data.

# Load required libraries
library(gpclib)
library(maptools)     # loads sp library too
library(RColorBrewer) # creates nice color schemes

# Create a sample dataset with continuous variable 'mediresp'
set.seed(123456)
x = 1:30
y = 30*runif(30)
plotvar = sample(5+10*c(rep(NA,20),runif(10)))

In this example, we create a sample dataset with a continuous variable mediresp and specify the number of breaks (8) when calling classIntervals. The resulting classes are stored in the class object.

Identifying the Problem

The issue with legend labels displaying the same interval multiple times arises from the way R handles quantile intervals. When you create a shaded map using ggplot2, it uses the class intervals to determine the color palette and corresponding legend entries. However, if the number of breaks is too large, or if there are many zero values in the data, the legend labels can become redundant.

# Create a sample dataset with continuous variable 'mediresp'
set.seed(123456)
x = 1:30
y = 30*runif(30)
plotvar = rep(NA,20) + 0:10

# Define the number of breaks (8)
nclr = 8

# Calculate quantile intervals using classIntervals
class <- classIntervals(plotvar, nclr, style="quantile")

# Extract the color palette and legend entries
plotclr <- brewer.pal(nclr,"BuPu")
colcode <- findColours(class, plotclr)

In this example, we create a sample dataset with a continuous variable mediresp and specify 10 intervals using classIntervals. The resulting classes are stored in the class object.

Resolving the Issue

To resolve the issue of legend labels displaying the same interval multiple times, you can try the following approaches:

1. Reduce the number of breaks: Decrease the number of quantile intervals to reduce redundancy in the legend labels.
2. Use a different color palette: Choose a color palette that doesn’t produce many zero values or close-to-zero values.
3. Manually adjust the legend: Add an extra item to the legend to indicate what happens to items with NA colors.

# Reduce the number of breaks (4)
nclr = 4

# Calculate quantile intervals using classIntervals
class <- classIntervals(plotvar, nclr, style="quantile")

# Extract the color palette and legend entries
plotclr <- brewer.pal(nclr,"BuPu")
colcode <- findColours(class, plotclr)

# Manually add an extra item to the legend
legend(-119, 42, legend=c("Missing",names(attr(colcode, "table"))),
    fill=c("white",attr(colcode, "palette")), cex=0.6, bty="n")

Conclusion

Resolving issues with legend labels in R shaded maps requires an understanding of quantile intervals and how to handle redundant labels. By reducing the number of breaks or using a different color palette, you can minimize redundancy and ensure accurate representation of your data. Additionally, manually adjusting the legend can provide a clear indication of what happens to items with NA colors.

Remember to always inspect your data and plots carefully to catch any issues before sharing or presenting your work. Happy plotting!

Last modified on 2023-07-23