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9.1 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.

By the end of this hands-on exercise, you will gain hands-on experience on:

plotting statistic parallel coordinates plots by using ggparcoord() of GGally package, and
plotting interactive parallel coordinates plots by using parallelPlot package.

9.2 Getting Started

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.

Code

pacman::p_load(GGally, parallelPlot, tidyverse)

package 'parallelPlot' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\sandr\AppData\Local\Temp\Rtmp6v7eNp\downloaded_packages

9.3 Importing Data

In this hands-on exercise, the World Happinees 2018 (http://worldhappiness.report/ed/2018/) data will be used. The data set is download at https://s3.amazonaws.com/happiness-report/2018/WHR2018Chapter2OnlineData.xls. The original data set is in Microsoft Excel format. It has been extracted and saved in csv file called WHData-2018.csv.

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.

Code

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

9.4 Plotting Static Parallel Coordinates Plot

In this section, you will learn how to plot static parallel coordinates plot by using ggparcoord() of GGally package. Before getting started, it is a good practice to read the function description in detail.

9.4.1 Plotting a simple parallel coordinates

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

Code

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

Notice that 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.

9.4.2 Plotting a parallel coordinates with boxplot

The basic parallel coordinates failed to reveal any meaning understanding of the World Happiness measures. In this section, you will learn how to makeover the plot by using a collection of arguments provided by ggparcoord().

Code

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

Things to learn from the code chunk above.

groupColumn argument is used to group the observations (i.e. parallel lines) by using a single variable (i.e. Region) and colour the parallel coordinates lines by region name.
scale argument is used to scale the variables in the parallel coordinate plot by using uniminmax method. The method univariately scale each variable so the minimum of the variable is zero and the maximum is one.
alphaLines argument is used to reduce the intensity of the line colour to 0.2. The permissible value range is between 0 to 1.
boxplot argument is used to turn on the boxplot by using logical TRUE. The default is FALSE.
title argument is used to provide the parallel coordinates plot a title.

9.4.3 Parallel coordinates with facet

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

Code

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.

9.4.4 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

Code

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.

9.4.5 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.

Code

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))

9.5 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. In this section, you will learn how to use functions provided in parallelPlot package to build interactive parallel coordinates plot.

9.5.1 The basic plot

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

Code

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

Notice that some of the axis labels are too long. You will learn how to overcome this problem in the next step.

9.5.2 Rotate axis label

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

Code

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.

9.5.3 Changing the colour scheme

We can change the default blue colour scheme by using continousCS argument as shown in the code chunl below.

Code

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

9.5.4 Parallel coordinates plot with histogram

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

Code

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

--- title: "Hands On Exercise 9C" subtitle: "Visual Multivariate Analysis with Parallel Coordinates Plot" format: html date: 06/19/2025 date-format: long date-modified: last-modified editor: visual execute: eval: true echo: true warning: false freeze: true --- [*Note: Please toggle between 'Show' and 'Hide' above based on your preference for viewing the R codes.*]{.underline} # 9.1 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](http://www.math.tau.ac.il/~aiisreal/) 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](https://www.perceptualedge.com/articles/b-eye/parallel_coordinates.pdf)(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. By the end of this hands-on exercise, you will gain hands-on experience on: - plotting statistic parallel coordinates plots by using [`ggparcoord()`](https://ggobi.github.io/ggally/reference/ggparcoord.html) of [**GGally**](https://r4va.netlify.app/chap15) package, and - plotting interactive parallel coordinates plots by using [**parallelPlot**](https://cran.r-project.org/web/packages/parallelPlot/) package. # 9.2 Getting Started 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. ```{r} pacman::p_load(GGally, parallelPlot, tidyverse) ``` # 9.3 Importing Data In this hands-on exercise, the World Happinees 2018 (http://worldhappiness.report/ed/2018/) data will be used. The data set is download at https://s3.amazonaws.com/happiness-report/2018/WHR2018Chapter2OnlineData.xls. The original data set is in Microsoft Excel format. It has been extracted and saved in csv file called **WHData-2018.csv**. 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*. ```{r} wh <- read_csv("data/WHData-2018.csv") ``` # 9.4 Plotting Static Parallel Coordinates Plot In this section, you will learn how to plot static parallel coordinates plot by using [`ggparcoord()`](https://ggobi.github.io/ggally/reference/ggparcoord.html) of **GGally** package. Before getting started, it is a good practice to read the function description in detail. ## 9.4.1 Plotting a simple parallel coordinates Code below shows a typical syntax used to plot a basic static parallel coordinates plot by using `ggparcoord()`. ```{r} ggparcoord(data = wh, columns = c(7:12)) ``` Notice that 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. ## 9.4.2 Plotting a parallel coordinates with boxplot The basic parallel coordinates failed to reveal any meaning understanding of the World Happiness measures. In this section, you will learn how to makeover the plot by using a collection of arguments provided by `ggparcoord()`. ```{r} ggparcoord(data = wh, columns = c(7:12), groupColumn = 2, scale = "uniminmax", alphaLines = 0.2, boxplot = TRUE, title = "Parallel Coordinates Plot of World Happines Variables") ``` Things to learn from the code chunk above. - `groupColumn` argument is used to group the observations (i.e. parallel lines) by using a single variable (i.e. Region) and colour the parallel coordinates lines by region name. - `scale` argument is used to scale the variables in the parallel coordinate plot by using `uniminmax` method. The method univariately scale each variable so the minimum of the variable is zero and the maximum is one. - `alphaLines` argument is used to reduce the intensity of the line colour to 0.2. The permissible value range is between 0 to 1. - `boxplot` argument is used to turn on the boxplot by using logical `TRUE`. The default is `FALSE`. - `title` argument is used to provide the parallel coordinates plot a title. ## 9.4.3 Parallel coordinates with facet Since `ggparcoord()` is developed by extending **ggplot2** package, we can combination use some of the ggplot2 function when plotting a parallel coordinates plot. In the code 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. ```{r} 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. ## 9.4.4 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 ```{r} 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. ## 9.4.5 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. ```{r} 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)) ``` # 9.5 Plotting Interactive Parallel Coordinates Plot: parallelPlot methods [**parallelPlot**](https://cran.r-project.org/web/packages/parallelPlot/) is an R package specially designed to plot a parallel coordinates plot by using ‘htmlwidgets’ package and [d3.js](https://d3js.org/). In this section, you will learn how to use functions provided in parallelPlot package to build interactive parallel coordinates plot. ## 9.5.1 The basic plot The code chunk below plot an interactive parallel coordinates plot by using `parallelPlot()`. ```{r} wh <- wh %>% select("Happiness score", c(7:12)) parallelPlot(wh, width = 320, height = 250) ``` Notice that some of the axis labels are too long. You will learn how to overcome this problem in the next step. ## 9.5.2 Rotate axis label In the code chunk below, `rotateTitle` argument is used to avoid overlapping axis labels. ```{r} 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. ## 9.5.3 Changing the colour scheme We can change the default blue colour scheme by using `continousCS` argument as shown in the code chunl below. ```{r} parallelPlot(wh, continuousCS = "YlOrRd", rotateTitle = TRUE) ``` ## 9.5.4 Parallel coordinates plot with histogram In the code chunk below, `histoVisibility` argument is used to plot histogram along the axis of each variables. ```{r} histoVisibility <- rep(TRUE, ncol(wh)) parallelPlot(wh, rotateTitle = TRUE, histoVisibility = histoVisibility) ```