Parallel coordinates plot

definition - mistake - related - code

Definition


Parallel plot or parallel coordinates plot allows to compare the feature of several individual observations (series) on a set of numeric variables. Each vertical bar represents a variable and often has its own scale. (The units can even be different). Values are then plotted as series of lines connected across each axis.


The ├Čris dataset provides four features (each represented with a vertical line) for 150 flower samples (each represented with a color line). Samples are grouped in three species. The chart below highlights efficiently that setosa has smaller Petals, but its sepal tends to be wider.

# Libraries
library(tidyverse)
library(hrbrthemes)
library(patchwork)
library(GGally)
library(viridis)

# Data set is provided by R natively
data <- iris

# Plot
data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    showPoints = TRUE, 
    title = "Parallel Coordinate Plot for the Iris Data",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    plot.title = element_text(size=10)
  )

Note: Parallel plot is the equivalent of a spider chart, but with cartesian coordinates. Thus, it is often prefered.

What for


A parallel plot allows to study the features of samples for several quantitative variables. Its strength is that the variables can even be completely different: different ranges and even different units.

In the graphic above flower features were grouped in species, and all variables were normalized and sharing the same unit (cm). Here is another example where diamonds are compared for 4 variables that share different units, like the price in $ or depth in %. Note the use of scaling to be able to compare them.

diamonds %>% 
  sample_n(10) %>%
    ggparcoord(
      columns = c(1,5:7), 
      groupColumn = 2, 
      #order = "anyClass",
      showPoints = TRUE, 
      title = "Diamonds features",
      alphaLines = 0.3
      ) + 
    scale_color_viridis(discrete=TRUE) +
    theme_ipsum()+
    theme(
      plot.title = element_text(size=10)
    )

Variation


Here is an overview of the parallel coordinates features you can play with:

# Plot
p1 <- data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    scale="globalminmax",
    showPoints = TRUE, 
    title = "No scaling",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    legend.position="none",
    plot.title = element_text(size=10)
  ) +
  xlab("")
  
p2 <- data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    scale="uniminmax",
    showPoints = TRUE, 
    title = "Standardize to Min = 0 and Max = 1",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    legend.position="none",
    plot.title = element_text(size=10)
  ) +
  xlab("")


p3 <- data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    scale="std",
    showPoints = TRUE, 
    title = "Normalize univariately (substract mean & divide by sd)",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    legend.position="none",
    plot.title = element_text(size=10)
  ) +
  xlab("")


p4 <- data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    scale="center",
    showPoints = TRUE, 
    title = "Standardize and center variables",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    legend.position="none",
    plot.title = element_text(size=10)
  ) +
  xlab("")


p1 + p2 + p3 + p4 + plot_layout(ncol = 2)



# Plot
p1 <- data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = c(1:4),
    showPoints = TRUE, 
    title = "Original",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    legend.position="Default",
    plot.title = element_text(size=10)
  ) +
  xlab("")
  
p2 <- data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    showPoints = TRUE, 
    title = "Re-ordered",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) +
  theme_ipsum()+
  theme(
    legend.position="none",
    plot.title = element_text(size=10)
  ) +
  xlab("")

p1 + p2 


# Plot
data %>% 
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    showPoints = TRUE, 
    title = "Original",
    alphaLines = 0.3
    ) + 
  scale_color_manual(values=c( "#69b3a2", "grey", "grey") ) +
  theme_ipsum()+
  theme(
    legend.position="Default",
    plot.title = element_text(size=10)
  ) +
  xlab("")

Common mistakes


Related


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A work by Yan Holtz for data-to-viz.com