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##Violin Plots
Therefore violin plots are a powerful tool to assist researchers to visualise data, particularly in the quality checking and exploratory parts of an analysis. Violin plots have many benefits:
As shown below for the iris dataset, violin plots show
distribution information that the boxplot is unable to.
###General Set up
We set up the data with two categories (Sepal Width) as follows:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.800 3.000 3.057 3.300 4.400##
## FALSE TRUE
## 83 67iris_large <- iris[iris$Sepal.Width > mean(iris$Sepal.Width), ]
iris_small <- iris[iris$Sepal.Width <= mean(iris$Sepal.Width), ]###Boxplots
First we plot Sepal Length on its own:
An indirect comparison can be achieved with par:
{
par(mfrow=c(2,1))
boxplot(Sepal.Length~Species, data=iris_small, col = "lightblue")
boxplot(Sepal.Length~Species, data=iris_large, col = "palevioletred")
par(mfrow=c(1,1))
}
First we plot Sepal Length on its own:
An indirect comparison can be achieved with par:
{
par(mfrow=c(2,1))
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line")
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line")
par(mfrow=c(1,1))
}
An indirect comparison can be achieved with par:
{
par(mfrow=c(1,2))
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line")
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line")
par(mfrow=c(1,1))
}
A more direct comparision can be made with the side
argument and add = TRUE on the second plot:
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right")
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T)
title(xlab = "Species", ylab = "Sepal Length")
legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")
Custom axes labels are supported for split violin plots. However, you
must use these arguments on the first call of
vioplot.
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right", xlab = "Iris species", ylab = "Length", main = "Sepals", names=paste("Iris", levels(iris$Species)))
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T)
legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Width")
Note that this is disabled for the second vioplot call
to avoid overlaying labels.
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right")
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T, xlab = "Iris species", ylab = "Length", main = "Sepals", names=paste("Iris", levels(iris$Species)))## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, col = "lightblue", : Warning: names can only be changed on first call of vioplot (when add = FALSE)## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, col = "lightblue", : Warning: x-axis labels can only be changed on first call of vioplot (when add = FALSE)## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, col = "lightblue", : Warning: y-axis labels can only be changed on first call of vioplot (when add = FALSE)## Warning in vioplot.default(x, ...): Warning: names can only be changed on first call of vioplot (when add = FALSE)legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Width")
The line median option is more suitable for side by side comparisions but the point option is still available also:
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "point", side = "right", pchMed = 21, colMed = "palevioletred4", colMed2 = "palevioletred2")
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "point", side = "left", pchMed = 21, colMed = "lightblue4", colMed2 = "lightblue2", add = T)
title(xlab = "Species", ylab = "Sepal Length")
legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")
It may be necessary to include a points command to fix
the median being overwritten by the following plots:
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "point", side = "right", pchMed = 21, colMed = "palevioletred4", colMed2 = "palevioletred2")
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "point", side = "left", pchMed = 21, colMed = "lightblue4", colMed2 = "lightblue2", add = T)
points(1:length(levels(iris$Species)), as.numeric(sapply(levels(iris$Species), function(species) median(iris_large[grep(species, iris_large$Species),]$Sepal.Length))), pch = 21, col = "palevioletred4", bg = "palevioletred2")
title(xlab = "Species", ylab = "Sepal Length")
legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")
Similarly points could be added where a line has been used previously:
vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right", pchMed = 21, colMed = "palevioletred4", colMed2 = "palevioletred2")
vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", pchMed = 21, colMed = "lightblue4", colMed2 = "lightblue2", add = T)
points(1:length(levels(iris$Species)), as.numeric(sapply(levels(iris$Species), function(species) median(iris_large[grep(species, iris_large$Species),]$Sepal.Length))), pch = 21, col = "palevioletred4", bg = "palevioletred2")
points(1:length(levels(iris$Species)), as.numeric(sapply(levels(iris$Species), function(species) median(iris_small[grep(species, iris_small$Species),]$Sepal.Length))), pch = 21, col = "lightblue4", bg = "lightblue2")
title(xlab = "Species", ylab = "Sepal Length")
legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")
Here it is aesthetically pleasing and intuitive to interpret categorical differences in mean and variation in a continuous variable.
Here we add outliers and show annotation features.
# add outliers to demo data
iris2 <- iris
iris2 <- rbind(iris2, c(7, 1, 0, 0, "setosa"))
iris2 <- rbind(iris2, c(1, 10, 0, 0, "setosa"))
iris2 <- rbind(iris2, c(9, 2, 0, 0, "versicolor"))
iris2 <- rbind(iris2, c(2, 12, 0, 0, "versicolor"))
iris2 <- rbind(iris2, c(10, 1, 0, 0, "virginica"))
iris2 <- rbind(iris2, c(12, 7, 0, 0, "virginica"))
iris2$Species <- factor(iris2$Species)
iris2$Sepal.Length <- as.numeric(iris2$Sepal.Length)
iris2$Sepal.Width <- as.numeric(iris2$Sepal.Width)
table(iris2$Species)##
## setosa versicolor virginica
## 52 52 52Annotation on split violins are shown here. See the main violin plot vignette for details on these parameters.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.800 3.000 3.151 3.400 12.000##
## FALSE TRUE
## 97 59iris_large <- iris2[iris2$Sepal.Width > mean(iris2$Sepal.Width), ]
iris_small <- iris2[iris2$Sepal.Width <= mean(iris2$Sepal.Width), ]
attach(iris_large)## The following objects are masked from iris_small (pos = 3):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_large (pos = 4):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris2 (pos = 5):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris (pos = 6):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_small (pos = 7):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_large (pos = 8):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris2 (pos = 9):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris (pos = 10):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Speciesvioplot(Sepal.Length~Species, data=iris_large, plotCentre = "line", side = "right", col=c("lightgreen", "lightblue", "palevioletred"), ylim = c(min(iris2$Sepal.Length) * 0.9, max(iris2$Sepal.Length) * 1.1),
names=c("setosa", "versicolor", "virginica"))
Sepal.medians <- sapply(unique(Species), function(sp) median(iris_large$Sepal.Length[Species == sp]))
# highlights medians
points(x = c(1:length(Sepal.medians)), y = Sepal.medians, pch = 21, cex = 1.25, lwd = 2,
col = "white", bg = c("forestgreen", "lightblue4", "palevioletred4"))
# plots outliers above 2 SD
add_outliers(unlist(iris_large$Sepal.Length), iris2$Species, cutoff = 2,
col = c("palegreen3", "lightblue3", "palevioletred3"), bars = "grey85", lwd = 2,
fill = "grey85")
legend("bottomright", legend=c("setosa", "versicolor", "virginica"),
fill=c("palegreen3", "lightblue3", "palevioletred3"), cex = 0.6)
add_labels(unlist(iris2$Sepal.Length), iris2$Species, height = 0.5, cex = 0.8)
attach(iris_small)## The following objects are masked from iris_large (pos = 3):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_small (pos = 4):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_large (pos = 5):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris2 (pos = 6):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris (pos = 7):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_small (pos = 8):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris_large (pos = 9):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris2 (pos = 10):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species## The following objects are masked from iris (pos = 11):
##
## Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Speciesvioplot(Sepal.Length~Species, data=iris_small, plotCentre = "line", side = "left", add = T, col=c("palegreen1", "lightblue1", "palevioletred1"), ylim = c(min(Sepal.Length) * 0.9, max(Sepal.Length) * 1.1),
names=c("setosa", "versicolor", "virginica"))## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, plotCentre = "line", : Warning: names can only be changed on first call of vioplot (when add = FALSE)## Warning in vioplot.default(x, ...): Warning: names can only be changed on first call of vioplot (when add = FALSE)Sepal.medians <- sapply(unique(Species), function(sp) median(iris_small$Sepal.Length[Species == sp]))
# highlights medians
points(x = c(1:length(Sepal.medians)), y = Sepal.medians, pch = 21, cex = 1.25, lwd = 2,
col = "white", bg = c("forestgreen", "lightblue4", "palevioletred4"))
# plots outliers above 2 SD
add_outliers(unlist(iris2$Sepal.Length), iris2$Species, cutoff = 2,
col = c("palegreen3", "lightblue3", "palevioletred3"), bars = "grey85", lwd = 2,
fill = "grey50")
legend("bottomright", legend=c("setosa", "versicolor", "virginica"),
fill=c("lightgreen", "lightblue", "palevioletred"), cex = 0.6)
add_labels(unlist(iris2$Sepal.Length), iris2$Species, height = 0.5, cex = 0.8)
# add legend and titles
legend("topleft", fill = c("lightblue2", "lightblue3"), legend = c("small", "large"), title = "Sepal Width")
title(xlab = "Species", ylab = "Sepal Length")
These extensions to vioplot here are based on those
provided here:
These have previously been discussed on the following sites:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.