Hands-on Exercise 5d: Visual Multivariate Analysis with Parallel Coordinates Plot

Author

Wu Jiayan

Published

May 10, 2026

Modified

May 11, 2026

12  Visual Multivariate Analysis with Parallel Coordinates Plot

12.1 Overview

Parallel coordinates plot is a data visualisation specially designed for visualising and analysing multivariate, numerical data. It is ideal for comparing multiple variables together and seeing the relationships between them. For example, the variables contribute to Happiness Index. Parallel coordinates was invented by Alfred Inselberg in the 1970s as a way to visualize high-dimensional data. This data visualisation technique is more often found in academic and scientific communities than in business and consumer data visualizations. As pointed out by Stephen Few(2006), “This certainly isn’t a chart that you would present to the board of directors or place on your Web site for the general public. In fact, the strength of parallel coordinates isn’t in their ability to communicate some truth in the data to others, but rather in their ability to bring meaningful multivariate patterns and comparisons to light when used interactively for analysis.” For example, parallel coordinates plot can be used to characterise clusters detected during customer segmentation.

This hands-on exercise practices on:

  • plotting statistic parallel coordinates plots by using ggparcoord() of GGally package, and

  • plotting interactive parallel coordinates plots by using parallelPlot package.

12.2 Installing and Launching R Packages

For this exercise, the GGally, parallelPlot and tidyverse packages will be used.

The code chunks below are used to install and load the packages in R.

pacman::p_load(GGally, parallelPlot, tidyverse)

12.3 Data Preparation

In the code chunk below, read_csv() of readr package is used to import WHData-2018.csv into R and save it into a tibble data frame object called wh.

wh <- read_csv("data/WHData-2018.csv")

12.4.1 Plotting a simple parallel coordinates

Code chunk below shows a typical syntax used to plot a basic static parallel coordinates plot by using ggparcoord().

ggparcoord(data = wh, 
           columns = c(7:12))

Only two argument namely data and columns is used. Data argument is used to map the data object (i.e. wh) and columns is used to select the columns for preparing the parallel coordinates plot.

12.4.2 Plotting a parallel coordinates with boxplot

The basic parallel coordinates failed to reveal any meaning understanding of the World Happiness measures. Following sections makeover the plot by using a collection of arguments provided by ggparcoord().

ggparcoord(data = wh, 
           columns = c(7:12), 
           groupColumn = 2,
           scale = "uniminmax",
           alphaLines = 0.2,
           boxplot = TRUE, 
           title = "Parallel Coordinates Plot of World Happines Variables")

12.4.3 Parallel coordinates with facet

Since ggparcoord() is developed by extending ggplot2 package, we can combine some of the ggplot2 function when plotting a parallel coordinates plot.

In the code chunk below, facet_wrap() of ggplot2 is used to plot 10 small multiple parallel coordinates plots. Each plot represent one geographical region such as East Asia.

ggparcoord(data = wh, columns = c(7:12), groupColumn = 2, scale = "uniminmax", alphaLines = 0.2, boxplot = TRUE, title = "Multiple Parallel Coordinates Plots of World Happines Variables by Region") + facet_wrap(~ Region)

One of the aesthetic defect of the current design is that some of the variable names overlap on x-axis.

12.4.4 Rotating x-axis text label

To make the x-axis text label easy to read, let us rotate the labels by 30 degrees. We can rotate axis text labels using theme() function in ggplot2 as shown in the code chunk below

ggparcoord(data = wh, 
           columns = c(7:12), 
           groupColumn = 2,
           scale = "uniminmax",
           alphaLines = 0.2,
           boxplot = TRUE, 
           title = "Multiple Parallel Coordinates Plots of World Happines Variables by Region") +
  facet_wrap(~ Region) + 
  theme(axis.text.x = element_text(angle = 30))

Thing to learn from the code chunk above:

  • To rotate x-axis text labels, we use axis.text.x as argument to theme() function. And we specify element_text(angle = 30) to rotate the x-axis text by an angle 30 degree.

12.4.5 Adjusting the rotated x-axis text label

Rotating x-axis text labels to 30 degrees makes the label overlap with the plot and we can avoid this by adjusting the text location using hjust argument to theme’s text element with element_text(). We use axis.text.x as we want to change the look of x-axis text.

ggparcoord(data = wh, 
           columns = c(7:12), 
           groupColumn = 2,
           scale = "uniminmax",
           alphaLines = 0.2,
           boxplot = TRUE, 
           title = "Multiple Parallel Coordinates Plots of World Happines Variables by Region") +
  facet_wrap(~ Region) + 
  theme(axis.text.x = element_text(angle = 30, hjust=1))

12.5 Plotting Interactive Parallel Coordinates Plot: parallelPlot methods

parallelPlot is an R package specially designed to plot a parallel coordinates plot by using ‘htmlwidgets’ package and d3.js. This section demonstrates how to use functions provided in parallelPlot package to build interactive parallel coordinates plot.

12.5.1 The basic plot

The code chunk below plot an interactive parallel coordinates plot by using parallelPlot().

wh <- wh %>%
  select("Happiness score", c(7:12))
parallelPlot(wh,
             width = 320,
             height = 250)

Issue: some of the axis labels are too long.

12.5.2 Rotate axis label

In the code chunk below, rotateTitle argument is used to avoid overlapping axis labels.

parallelPlot(wh,
             rotateTitle = TRUE)

One of the useful interactive feature of parallelPlot is we can click on a variable of interest, for example Happiness score, the monotonous blue colour (default) will change a blues with different intensity colour scheme will be used.

12.5.3 Changing the colour scheme

Following code chunk changes the default blue colour scheme by using continousCS argument.

parallelPlot(wh,
             continuousCS = "YlOrRd",
             rotateTitle = TRUE)

12.5.4 Parallel coordinates plot with histogram

In the code chunk below, histoVisibility argument is used to plot histogram along the axis of each variables.

histoVisibility <- rep(TRUE, ncol(wh))
parallelPlot(wh,
             rotateTitle = TRUE,
             histoVisibility = histoVisibility)

12.6 References