Hands-on Exercise 5a- Creating Ternary Plots with R

Published

February 5, 2024

Modified

February 24, 2024

5.1 Overview

Ternary plots are a way to display the distribution and variability of three-part compositional data. (For example, the proportion of aged, economy active and young population or sand, silt, and clay in soil.)

It’s display is a triangle with sides scaled from 0 to 1. Each side represents one of the three components. A point is plotted so that a line drawn perpendicular from the point to each leg of the triangle intersect at the component values of the point.

In this hands-on, we will learn how to build ternary plot programmatically using R for visualising and analysing population structure of Singapore.

This hands-on exercise consists of four steps:

  • Install and launch tidyverse and ggtern packages.

  • Derive three new measures using mutate() function of dplyr package.

  • Build a static ternary plot using ggtern() function of ggtern package.

  • Build an interactive ternary plot using plot-ly() function of Plotly R package.

5.2 Installing and launching R packages

For this exercise, two main R packages will be used in this hands-on exercise, they are:

  • ggtern, a ggplot extension specially designed to plot ternary diagrams. The package will be used to plot static ternary plots.

  • Plotly R, an R package for creating interactive web-based graphs via plotly’s JavaScript graphing library, plotly.js . The plotly R libary contains the ggplotly function, which will convert ggplot2 figures into a Plotly object.

pacman::p_load(plotly, ggtern, tidyverse)

5.3 Data Preparation

For the purpose of this hands-on exercise, the Singapore Residents by Planning AreaSubzone, Age Group, Sex and Type of Dwelling, June 2000-2018 data will be used.

#Reading the data into R environment
pop_data <- read_csv("data/respopagsex2000to2018_tidy.csv")

We will use the mutate() function of dplyr package to derive three new measures, namely: young, active, and old.

#Deriving the young, economy active and old measures
agpop_mutated <- pop_data %>%
  mutate(`Year` = as.character(Year))%>%
  spread(AG, Population) %>%
  mutate(YOUNG = rowSums(.[4:8]))%>%
  mutate(ACTIVE = rowSums(.[9:16]))  %>%
  mutate(OLD = rowSums(.[17:21])) %>%
  mutate(TOTAL = rowSums(.[22:24])) %>%
  filter(Year == 2018)%>%
  filter(TOTAL > 0)

5.4 Plotting Ternary Diagram with R

5.4.1 Plotting a static ternary diagram

Using ggtern() function of ggtern package to create a simple ternary plot.

#Building the static ternary plot
ggtern(data=agpop_mutated,aes(x=ACTIVE,y=OLD, z=YOUNG)) +
  geom_point()

#Building the static ternary plot
ggtern(data=agpop_mutated, aes(x=ACTIVE,y=OLD, z=YOUNG)) +
  geom_point() +
  labs(title="Population structure, 2015") +
  theme_rgbw()

5.4.2 Plotting an interactive ternary diagram

Using plot_ly() function of Plotly R.

# reusable function for creating annotation object
label <- function(txt) {
  list(
    text = txt, 
    x = 0.1, y = 1,
    ax = 0, ay = 0,
    xref = "paper", yref = "paper", 
    align = "center",
    font = list(family = "serif", size = 15, color = "white"),
    bgcolor = "#b3b3b3", bordercolor = "black", borderwidth = 2
  )
}

# reusable function for axis formatting
axis <- function(txt) {
  list(
    title = txt, tickformat = ".0%", tickfont = list(size = 10)
  )
}

ternaryAxes <- list(
  aaxis = axis("Active"), 
  baxis = axis("Old"), 
  caxis = axis("Young")
)

# Initiating a plotly visualization 
plot_ly(
  agpop_mutated, 
  a = ~ACTIVE, 
  b = ~OLD, 
  c = ~YOUNG, 
  color = I("black"), 
  type = "scatterternary"
) %>%
  layout(
    annotations = label("Ternary Markers"), 
    ternary = ternaryAxes
  )

5.4.3 Plotting Practise

Below are some additional plots created for practise.

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