Hands-on_Ex09_1 - 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.)

The 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, I will build ternary plot programmatically using R for visualising and analysing population structure of Singapore. Here are the 4 steps:

1. Install and launch tidyverse and ggtern packages.
2. Derive three new measures using mutate() function of dplyr package.
3. Build a static ternary plot using ggtern() function of ggtern package.
4. Build an interactive ternary plot using plot-ly() function of Plotly R package.

2 Install and Launch R Packages

2 main R packages will be used.

R Package	Overview
ggtern	a ggplot extension that plots ternary diagrams. The package will be used to plot static ternary plots.
Plotply R	an R package for creating interactive web-based graphs via plotly’s JavaScript graphing library, plotly.js. The plotly R library contains the ggplotly function, which will convert ggplot2 figures into a Plotly object.
tidyverse	selected tidyverse family packages: readr, dplyr and tidyr are installed and loaded.

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

pacman::p_load(plotly, ggtern, tidyverse)

The downloaded binary packages are in
    /var/folders/nr/x4l8hvc562g81px_9hrd30_r0000gn/T//RtmpWg1pAz/downloaded_packages

3 Data Preparation

3.1 Data

The Singapore Residents by Planning AreaSubzone, Age Group, Sex and Type of Dwelling, June 2000-2018 data will be used.

File name: respopagsex2000to2018_tidy.csv

Import data

pop_data <- read_csv("data/respopagsex2000to2018_tidy.csv")
head(pop_data)

# A tibble: 6 × 5
  PA         SZ                     AG      Year Population
  <chr>      <chr>                  <chr>  <dbl>      <dbl>
1 Ang Mo Kio Ang Mo Kio Town Centre AGE0-4  2011        290
2 Ang Mo Kio Ang Mo Kio Town Centre AGE0-4  2012        270
3 Ang Mo Kio Ang Mo Kio Town Centre AGE0-4  2013        260
4 Ang Mo Kio Ang Mo Kio Town Centre AGE0-4  2014        250
5 Ang Mo Kio Ang Mo Kio Town Centre AGE0-4  2015        260
6 Ang Mo Kio Ang Mo Kio Town Centre AGE0-4  2016        250

Prepare data

Use mutate() function of dplyr package to derive 3 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) %>% #turn the values in Population col into AG cols.
  mutate(YOUNG = rowSums(.[4:8])) %>% #Age 0-24
  mutate(ACTIVE = rowSums(.[9:16])) %>% #Age 25-64
  mutate(OLD = rowSums(.[17:21])) %>% #Age >65
  mutate(TOTAL = rowSums(.[22:24])) %>% #Age 
  filter(Year == 2018) %>%
  filter(TOTAL >0)

🎯 Useful mutate functions

• +, -, log(), etc., for their usual mathematical meanings

• lead(), lag()

• dense_rank(), min_rank(), percent_rank(), row_number(), cume_dist(), ntile()

• cumsum(), cummean(), cummin(), cummax(), cumany(), cumall()

• na_if(), coalesce()

• if_else(), recode(), case_when()

Grouped tibbles

Because mutating expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped mutate:

starwars %>%
  select(name, mass, species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

With the grouped equivalent:

starwars %>%
  select(name, mass, species) %>%
  group_by(species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

The former normalises mass by the global average whereas the latter normalises by the averages within species levels.

4 Plot Ternary Diagram with R

4.1 Plot a static ternary diagram

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

Classic

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

theme_rgbw()

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

Interactive

Use plot_ly() to create an interactive plot.:

# Function for creating annotation object too
label <- function(txt) {
  list(
    text = txt, 
    x = 0.1, y = 1, #Position of the annotation in the plot
    ax = 0, ay = 0, #annotation has no arrow
    xref = "paper", yref = "paper",  #Positioning is relative to the entire figure, not data points
    align = "center",
    font = list(family = "Calibri", size = 15, color = "white"),
    bgcolor = "#000000", # Background color of the annotation box (light gray)
    bordercolor = "black", 
    borderwidth = 2
  )
}

# Function for creating axis formatting too
axis <- function(txt) {
  list(
    title = txt, tickformat = ".0%", tickfont = list(size = 10)
  )
}

ternaryAxes <- list(
  aaxis = axis("Young"), 
  baxis = axis("Active"), 
  caxis = axis("Old")
)

# Initiate 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
  )