Parallel coordinates chart with ggally in R

To analyze and visualize high-dimensional data, one can use Parallel Coordinates. A background is drawn consisting of n parallel lines, often vertical and evenly spaced, to display a set of points in an n-dimensional space. A point in n-dimensional space is represented by a polyline with vertices on parallel axes; the i^th coordinate of the point corresponds to the position of the vertex on the i^th axis.

This representation is similar to time series visualization, except that it is used with data that does not have a natural order because the axes do not correlate to points in time. As a result, several axis layouts may be of interest.

Parallel coordinates chart with ggally in R Programming Language

In R programming Language, using the ggally packages, and the ggparcoord() function one can create the simplest basic parallel coordinates chart.

The input dataset must be a data frame containing multiple numeric variables, each of which will serve as a vertical axis on the chart. The function’s columns argument specifies the number of columns for these variables.

Plotting the Parallel Coordinate plot with IRIS Data

Here we are going to plot the parallel Coordinate plot with the iris dataset for this we will create dataframe from this dataset and then we will plot parallel Coordinate using ggparcoord() method.

Syntax: ggparcoord( data, columns, groupColumn)

Parameters:

• data: Dataset to plot
• columns: Vector of variables (either names or indices) to be axes in the plot
• groupColumn: Single variable to group (color) by

R

# libraries
library(GGally)

# default data in R
data <- iris

# glimpse of the data
head(data)

# plotting the Parallel Coordinates
ggparcoord(data, # data
           columns = 1:3, # plotting first 3 columns
           groupColumn = 5) # target parameter

Output:

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Customising Theme and Color

For customizing the theme and color within the plot we will use viridis and hrbrthemes package for color palette and themes for axis and plot.

R

# Libraries
library(GGally)
library(viridis)    # provide the color palette
library(hrbrthemes) # provides themes for axis and plot

# default data in R
data <- iris

# glimpse of the data
head(data)

# plotting the Parallel Coordinates
ggparcoord(data, # data
           columns = 1:3, # plotting first 3 columns
           alphaLines = .4, # transparency of the color
           groupColumn = 5, order = "anyClass",
           showPoints = TRUE) +
  theme(
    plot.title = element_text(size=10)
  )

Output:

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa