pacman::p_load(plotly, ggtern, tidyverse)Hands-on_Ex09_a
Creating Ternary Plot with R
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.
1.2 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.
The code chunks below will accomplish the task.
1.3 Data Preparation
1.3.1 The data
For the purpose of this hands-on exercise, the Singapore Residents by Planning AreaSubzone, Age Group, Sex and Type of Dwelling, June 2000-2018data 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.
1.3.2 Importing Data
To important respopagsex2000to2018_tidy.csv into R, read_csv() function of readr package will be used.
#Reading the data into R environment
pop_data <- read_csv("respopagsex2000to2018_tidy.csv") 1.3.3 Preparing the Data
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)1.4 Plotting Ternary Diagram with R
1.4.1 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()
1.4.2 Plotting an interative ternary diagram
The code below create an interactive ternary plot 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("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
)