Most basic
This is the most basic parallel coordinates chart you can build with R, the ggally packages and its ggparcoord() function.
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.
Note: here, a categoric variable is used to color lines, as specified in the groupColumn variable.
# Libraries
library(GGally)
# Data set is provided by R natively
data <- iris
# Plot
ggparcoord(data,
columns = 1:4, groupColumn = 5
) Custom color, theme, general appearance
This is pretty much the same chart as te previous one, except for the following customizations:
- color palette is improved thanks to the
viridispackage - title is added with
title, and customized intheme - dots are added with
showPoints - a bit of transparency is applied to lines with
alphaLines theme_ipsum()is used for the general appearance
# Libraries
library(hrbrthemes)
library(GGally)
library(viridis)
# Data set is provided by R natively
data <- iris
# Plot
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)
)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 scalinguniminmax→ Standardize to Min = 0 and Max = 1std→ Normalize univariately (substract mean & divide by sd)center→ Standardize and center variables
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("")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("")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("")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("")Highlight a group
Data visualization aims to highlight a story in the data. If you are interested in a specific group, you can highlight it as follow:
# Libraries
library(GGally)
library(dplyr)
# Data set is provided by R natively
data <- iris
# Plot
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("")
