Creating Ternary Plot with R

1. Learning Outcome

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

2. Getting Started

2.1 Installing and loading the required libraries

Firstly, let’s install and load the required packages:

• tidyverse: an opinionated collection of R packages designed for data import, data wrangling and data exploration

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

• plotly: 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(tidyverse, ggtern, plotly)

2.2 Importing the data

We’ll use Singapore population data downloaded from Singapore Department of Statistics for this hands-on exercise. It contains data of Singapore population by age group and planning area from June 2000 to 2018. The data file is in csv format.

Let’s start by importing the data.

pop_data  <- read_csv("../../Data/respopagsex2000to2018_tidy.csv")

The data contains 108,126 rows and 5 columns:

• 3 character variables: planning area, subzone, age group
• 2 numerical variables: year, population

2.3 Preparing the data

Next, let’s create 3 new variables: young, active and old using age groups.

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)

In the new dataset, age groups has been transformed into columns. The 3 new columns are created in the last 3 colnumns.

3. Plotting Ternary Diagram with R

3.1 Plotting a static ternary diagram

Let’s create a simple static ternary diagram to visualize the 3 new columns we just created.

ggtern(data = agpop_mutated,
       aes(x = YOUNG, 
           y = ACTIVE, 
           z = OLD)) +
  geom_point()

Let’s make the plot easier to interpret.

ggtern(data = agpop_mutated,
       aes(x = YOUNG, 
           y = ACTIVE, 
           z = OLD)) +
  geom_point() +
  labs(title = "Population structure, 2015") +
  theme_rgbw()

3.2 Plotting an interative ternary diagram

Now, let’s plot an interactive ternary plot

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("Young"), 
  baxis = axis("Active"), 
  caxis = axis("Old")
)

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

This comes to the end of this hands-on exercise. I have learned to create ternary plots in R. Hope you enjoyed it, too!

See you in the next hands-on exercise 🥰