Parallel Coordinates Plot in ggplot2

How to make Parallel Coordinates Plot in ggplot2 with Plotly.

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Default parallel coordinates plot

The input dataset must be a data frame with several numeric variables, each being used as a vertical axis on the chart. Columns number of these variables are specified in the columns argument of the function.

For this example, a categoric variable is used to color lines, as specified in the groupColumn variable.

library(plotly)
library(GGally)

data <- iris

p <- ggparcoord(data,
    columns = 1:4, groupColumn = 5
    )

ggplotly(p)

Custom colour, theme and style

This is the same chart as the previous one, except for the following customizations:

color palette is improved thanks to the viridis package
title is added with title, and customized in theme
dots are added with showPoints
a bit of transparency is applied to lines with alphaLines
theme_ipsum() is used for the general appearance

library(plotly)
library(GGally)
library(viridis)
library(hrbrthemes)

data <- iris

p <- ggparcoord(data,
    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)
  )

ggplotly(p)

Scaling

Scaling transforms the raw data to a new scale that is common with other variables. It is a crucial step to compare variables that do not have the same unit, but can also help otherwise as shown in the example below.

The ggally package offers a scale argument. Four possible options are applied on the same dataset below:

globalminmax → No scaling
uniminmax → Standardize to Min = 0 and Max = 1
std → Normalize univariately (substract mean & divide by sd)
center → Standardize and center variables

library(plotly)
library(GGally)
library(viridis)
library(hrbrthemes)

data <- iris

p <- ggparcoord(data,
    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=13)
  ) +
  xlab("")

ggplotly(p)

Standardize to Min = 0 and Max = 1:

library(plotly)
library(GGally)
library(viridis)
library(hrbrthemes)

data <- iris

p <- ggparcoord(data,
    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=13)
  ) +
  xlab("")

ggplotly(p)

Normalize univariately (substract mean & divide by sd):

library(plotly)
library(GGally)
library(viridis)
library(hrbrthemes)

data <- iris

p <- ggparcoord(data,
    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=13)
  ) +
  xlab("")

ggplotly(p)

Standardize and center variables:

library(plotly)
library(GGally)
library(viridis)
library(hrbrthemes)

data <- iris

p <- ggparcoord(data,
    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=13)
  ) +
  xlab("")

ggplotly(p)

Highlight a group

library(plotly)
library(GGally)
data <- iris

p <- data %>%
  arrange(desc(Species)) %>%
  ggparcoord(
    columns = 1:4, groupColumn = 5, order = "anyClass",
    showPoints = TRUE, 
    title = "Original",
    alphaLines = 1
    ) + 
  scale_color_manual(values=c( "#69b3a2", "#E8E8E8", "#E8E8E8") ) +
  theme_ipsum()+
  theme(
    legend.position="Default",
    plot.title = element_text(size=10)
  ) +
  xlab("")

ggplotly(p)

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)