how to use triangle.plot in r from ade4

Lussy04

3 min read

·

24-01-2025

1

0

Share

The ade4 package in R is a treasure trove for ecologists and statisticians, offering a wide array of functions for multivariate analysis. One particularly useful tool is triangle.plot, which provides a powerful way to visualize compositional data – data representing proportions that sum to a constant (often 100%). This article will guide you through the intricacies of using triangle.plot effectively, providing practical examples and explanations. Understanding how to use triangle.plot allows for clear and informative representation of proportional data.

Understanding Compositional Data and the Ternary Diagram

Before diving into triangle.plot, let's grasp the concept of compositional data. This type of data is characterized by its parts summing to a whole. Think of soil composition (sand, silt, clay), species abundance in a community, or the proportion of different minerals in a rock sample. These proportions, expressed as percentages or fractions, are what triangle.plot excels at visualizing. The visualization is achieved using a ternary diagram (also known as a Gibbs triangle), a three-sided plot where each corner represents one component of the composition.

Installing and Loading Necessary Packages

First, ensure you have ade4 installed. If not, use:

install.packages("ade4")

Then, load the package:

library(ade4)

We'll also use ggplot2 for enhanced plotting capabilities (optional but recommended):

library(ggplot2)

Basic triangle.plot Usage: A Simple Example

Let's create a simple example to illustrate the function's core functionality. We'll generate some sample compositional data:

data <- data.frame(
  A = c(0.2, 0.4, 0.6, 0.8, 0.1, 0.3, 0.5, 0.7, 0.9),
  B = c(0.7, 0.5, 0.3, 0.1, 0.8, 0.6, 0.4, 0.2, 0.0),
  C = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)
)

Note that each row sums to 1. Now, let's plot this using triangle.plot:

triangle.plot(data)

This generates a basic ternary plot. Each point represents a single observation from our data, its position determined by the proportions of A, B, and C.

Enhancing the Plot: Adding Labels and Titles

The basic plot is functional, but we can improve its clarity significantly. Let's add labels and a title:

triangle.plot(data,
              main = "Compositional Data Visualization", # Plot title
              labels = c("Component A", "Component B", "Component C"), # Corner labels
              show.points = TRUE, #Display points
              addaxes = TRUE) #Add axes

This code produces a much more informative plot with clear labels and title.

Adding Color and Grouping: Visualizing Categories

Often, your compositional data is associated with groups or categories. Let's assume each data point belongs to a specific group:

data$Group <- factor(rep(c("Group 1", "Group 2"), each = 5))

Now, let's color the points according to their group:

triangle.plot(data[,1:3], 
              main = "Compositional Data by Group",
              labels = c("Component A", "Component B", "Component C"),
              col = data$Group, # Color points by group
              show.points = TRUE,
              addaxes = TRUE)
              
legend("topleft", legend = levels(data$Group), col = 1:length(levels(data$Group)), pch = 1, cex = 0.8)

This visualizes the differences in composition between the two groups.

Using ggplot2 for Advanced Customization

For more sophisticated plotting options, integrate ggplot2:

library(ggplot2)
#Convert to long format for ggplot2 compatibility
library(tidyr)
data_long <- data %>% pivot_longer(cols = c("A", "B", "C"), names_to = "Component", values_to = "Proportion")

#Create the ternary plot with ggplot2
ggplot(data_long, aes(x = A, y = B, z = C, color = Group)) +
  geom_point() +
  scale_color_manual(values = c("Group 1" = "blue", "Group 2" = "red")) +
  ggtern::geom_tern_point()+
  ggtern::theme_tern() + #Apply ternary theme
  ggtern::labs(title = "Compositional Data Visualization using ggplot2",
              x = "Component A", y = "Component B", z = "Component C")

This gives you access to a wider array of customization options offered by ggplot2. Remember to install the ggtern package if you haven't already (install.packages("ggtern")).

Conclusion

triangle.plot in ade4 is an invaluable tool for visualizing compositional data. By mastering its functionalities, you can create informative and visually appealing plots that effectively communicate patterns and differences within your data. Using ggplot2 allows for even greater control over the aesthetics and information displayed, ultimately enhancing the communicative power of your visualizations. Remember to always clearly label your axes and provide a descriptive title to ensure your plot is easily understood by your audience.