Hands-on Exercise 5a: Creating Ternary Plot with R

What Will I Learn?

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

Take note

Ternary plots are a way of displaying 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.

The 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.

Getting Started

Loading the R packages

pacman::p_load('plotly', 'tidyverse')

require(devtools)
install_version("ggtern", version = "3.4.1", repos = "http://cran.us.r-project.org")

library(ggtern)

Data Preparation

In this hands-on exercise, Exam_data.csv will be used.

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

Next, 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)

Plotting Ternary Diagram with R

Plotting a static ternary diagram

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

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

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

Plotting an interative ternary diagram

# 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("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
  )