pacman::p_load(tidyverse, plotly)
library(ggtern)Hands-on Exercise 5A
13 Creating Ternary Plot
13.1 Overview and Learning Outcomes
This hands-on exercise is based on Chapter 13 of the R for Visual Analytics book.
Ternary plots are a way of displaying the distribution and variability of three-part compositional data. Its 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 intersects at the component values of the point.
In this hands-on exercise, a ternary plot is created to visualise and analyse the population structure of Singapore. The learning outcomes are:
Install and launch tidyverse and ggtern packages.
Derive three new measures using the
mutate()function in the dplyr package.Build a static ternary plot using the
ggtern()function in the ggtern package.Build an interactive ternary plot using the
plot_ly()function in the Plotly R package.
13.2 Getting Started
13.2.1 Installing and Loading Required Libraries
In this hands-on exercise, the following R packages are used:
tidyverse (i.e. readr, tidyr, dplyr) for performing data science tasks such as importing, tidying, and wrangling data;
ggtern (ggplot extension) for plotting ternary diagrams; and
plotly for creating interactive web-based graphs via plotly’s JavaScript graphing library, plotly.js.
The code chunk below uses the p_load() function in the pacman package to check if the packages are installed. If yes, they are then loaded into the R environment. If no, they are installed, then loaded into the R environment.
require(devtools)
install_version("ggtern", version = "3.4.1", repos = "http://cran.us.r-project.org")
library(ggtern)13.2.2 Importing Data
The dataset for this hands-on exercise is imported into the R environment using the read_csv() function in the readr package and stored as the R object, pop_data. It contains data regarding Singapore Residents by Planning Area Subzone, Age Group, Sex and Type of Dwelling, June 2000-2018.
pop_data = read_csv("data/respopagsex2000to2018_tidy.csv") The tibble data frame, pop_data, has 5 columns and 108,126 rows.
13.2.3 Preparing Data
The mutate() function in the dplyr package is then used to derive three new measures - young, active, and old.
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)13.3 Plotting Ternary Diagram
13.3.1 Plotting Static Ternary Diagram
The ggtern() function in the ggtern package is used to create a simple ternary plot.
ggtern(data=agpop_mutated,
aes(x=YOUNG,y=ACTIVE, z=OLD)) +
geom_point()The labels and a colour theme are then added to enhance the plot.
ggtern(data=agpop_mutated,
aes(x=YOUNG,y=ACTIVE, z=OLD)) +
geom_point() +
labs(title="Population Structure, 2018") +
theme_rgbw()13.3.2 Plotting Interactive Ternary Diagram
The plot_ly() function in the plotly package is then used to create an interactive ternary plot.
# 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 visualisation
plot_ly(
agpop_mutated,
a = ~YOUNG,
b = ~ACTIVE,
c = ~OLD,
color = I("black"),
type = "scatterternary") %>%
layout(annotations = label("Ternary Markers"),
ternary = ternaryAxes)~~~ End of Hands-on Exercise 5A ~~~