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

Author

Cai Jingheng

Published

February 3, 2024

Modified

February 6, 2024

1 Overview

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.

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

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.

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.

We will also need to ensure that selected tidyverse family packages namely: readr, dplyr and tidyr are also installed and loaded.

In this exercise, version 3.2.1 of ggplot2 will be installed instead of the latest version of ggplot2. This is because the current version of ggtern package is not compatible to the latest version of ggplot2.

The code chunks below will accomplish the task.

Code

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

Code

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

Code

library(ggtern)

3 Data Preparation

3.1 The data

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. The data set has been downloaded and included in the data sub-folder of the hands-on exercise folder. It is called respopagsex2000to2018_tidy.csv and is in csv file format.

3.2 Importing Data

To important respopagsex2000to2018_tidy.csv into R, read_csv() function of readr package will be used.

Code

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

3.3 Preparing the Data

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

Code

#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)

4 Plotting Ternary Diagram with R

4.1 Plotting a static ternary diagram

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

Code

#Building the static ternary plot
ggtern(data=agpop_mutated,aes(x=YOUNG,y=ACTIVE, z=OLD)) +
  geom_point(color = "#3459e6")

Code

#Building the static ternary plot
ggtern(data=agpop_mutated, aes(x=YOUNG,y=ACTIVE, z=OLD)) +
  geom_point(color = "#3459e6") +
  labs(title="Population structure, 2015") +
  theme_rgbw()

4.2 Plotting an interative ternary diagram

The code below create an interactive ternary plot using plot_ly() function of Plotly R.

Code

# 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 = "white", 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("#3459e6"), 
  type = "scatterternary"
) %>%
  layout(
    annotations = label("Ternary Markers"), 
    ternary = ternaryAxes
  )

--- title: "Hands-on Exercise 5A: Creating Ternary Plot with R" author: "Cai Jingheng" date: "Feb,3,2024" date-modified: "last-modified" toc: true number-sections: true format: html: code-fold: true code-tools: true warning: false --- ## **Overview** 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. In this hands-on, you will learn how to build ternary plot programmatically using R for visualising and analysing population structure of Singapore. 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. ## **Installing and launching R packages** For this exercise, two main R packages will be used in this hands-on exercise, they are: - [**ggtern**](http://www.ggtern.com/), a ggplot extension specially designed to plot ternary diagrams. The package will be used to plot static ternary plots. - [**Plotly R**](https://plot.ly/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. We will also need to ensure that selected **tidyverse** family packages namely: **readr**, **dplyr** and **tidyr** are also installed and loaded. In this exercise, version 3.2.1 of **ggplot2** will be installed instead of the latest version of **ggplot2**. This is because the current version of **ggtern** package is not compatible to the latest version of **ggplot2**. The code chunks below will accomplish the task. ```{r} pacman::p_load('plotly', 'tidyverse') ``` ```{r} #| eval: false require(devtools) install_version("ggtern", version = "3.4.1", repos = "http://cran.us.r-project.org") ``` ```{r} library(ggtern) ``` ## **Data Preparation** ### **The data** For the purpose of this hands-on exercise, the [Singapore Residents by Planning AreaSubzone, Age Group, Sex and Type of Dwelling, June 2000-2018](https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data) data will be used. The data set has been downloaded and included in the data sub-folder of the hands-on exercise folder. It is called *respopagsex2000to2018_tidy.csv* and is in csv file format. ### **Importing Data** To important *respopagsex2000to2018_tidy.csv* into R, ***read_csv()*** function of **readr** package will be used. ```{r} #Reading the data into R environment pop_data <- read_csv("data/respopagsex2000to2018_tidy.csv") ``` ### **Preparing the Data** Next, use the ***mutate()*** function of **dplyr** package to derive three new measures, namely: young, active, and old. ```{r} #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. ```{r} #Building the static ternary plot ggtern(data=agpop_mutated,aes(x=YOUNG,y=ACTIVE, z=OLD)) + geom_point(color = "#3459e6") ``` ```{r} #Building the static ternary plot ggtern(data=agpop_mutated, aes(x=YOUNG,y=ACTIVE, z=OLD)) + geom_point(color = "#3459e6") + labs(title="Population structure, 2015") + theme_rgbw() ``` ### **Plotting an interative ternary diagram** The code below create an interactive ternary plot using ***plot_ly()*** function of **Plotly R**. ```{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 = "white", 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("#3459e6"), type = "scatterternary" ) %>% layout( annotations = label("Ternary Markers"), ternary = ternaryAxes ) ```