ggally
is a ggplot2
extension. It allows
to build
parallel coordinates charts thanks
to the ggparcoord()
function. Check several
reproducible examples in this post.
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
)
This is pretty much the same chart as te previous one, except for the following customizations:
viridis
package
title
, and customized in
theme
showPoints
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 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("")
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("")