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Customising Violin Plots with Formula Input

Tom Kelly

2024-07-05

Since boxplots have become the de facto standard for plotting the distribution of data most users are familiar with these and the formula input for dataframes. However this input is not available in the standard vioplot package. Thus it has been restored here for enhanced backwards compatibility with boxplot.

As shown below for the iris dataset, violin plots show distribution information taking formula input that boxplot implements but vioplot is unable to. This demonstrates the customisation demonstrated in the main vioplot vignette using vioplot syntax with the formula method commonly used for boxplot, t.test, and lm.

library("vioplot")
data(iris)
boxplot(Sepal.Length~Species, data = iris)

Whereas performing the same function does not work with vioplot (0.2).

devtools::install_version("vioplot", version = "0.2")
library("vioplot")
vioplot(Sepal.Length~Species, data = iris)
Error in min(data) : invalid 'type' (language) of argument

Plot Defaults

vioplot(Sepal.Length~Species, data = iris)

Another concern we see here is that the vioplot defaults are not aesthetically pleasing, with a rather glaring colour scheme unsuitable for professional or academic usage. Thus the plot default colours have been changed as shown here:

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length")

Plot colours: Violin Fill

Plot colours can be further customised as with the original vioplot package using the col argument:

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue")

Vectorisation

However the vioplot (0.2) function is unable to colour each violin separately, thus this is enabled with a vectorised col in vioplot (0.3):

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col=c("lightgreen", "lightblue", "palevioletred"))
legend("topleft", legend=c("setosa", "versicolor", "virginica"), fill=c("lightgreen", "lightblue", "palevioletred"), cex = 0.5)

Plot colours: Violin Lines and Boxplot

Colours can also be customised for the violin fill and border separately using the col and border arguments:

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue", border="royalblue")

Similarly, the arguments lineCol and rectCol specify the colours of the boxplot outline and rectangle fill. For simplicity the box and whiskers of the boxplot will always have the same colour.

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", rectCol="palevioletred", lineCol="violetred")

The same applies to the colour of the median point with colMed:

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", colMed="violet")

### Combined customisation

These can be customised colours can be combined:

vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue", border="royalblue", rectCol="palevioletred", lineCol="violetred", colMed="violet")

Vectorisation

These colour and shape settings can also be customised separately for each violin:

vioplot(Sepal.Length~Species, data = iris, main="Sepal Length", col=c("lightgreen", "lightblue", "palevioletred"), border=c("darkolivegreen4", "royalblue4", "violetred4"), rectCol=c("forestgreen", "blue", "palevioletred3"), lineCol=c("darkolivegreen", "royalblue", "violetred4"), colMed=c("green", "cyan", "magenta"), pchMed=c(15, 17, 19))

Enhanced Annotation

Here we demonstrate additional annotation features to display outliers and group sizes.

Labelling group size

Note that y-axes limits need to be adjusted to avoid overlaying text.

data("iris")
attach(iris)
## The following objects are masked from iris_small:
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_large:
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris2:
## 
##     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
vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length", ylab = "",
        col=c("lightgreen", "lightblue", "palevioletred"), ylim = c(0, max(Sepal.Length) * 1.1))
legend("bottomright", legend=c("setosa", "versicolor", "virginica"),
       fill=c("lightgreen", "lightblue", "palevioletred"), cex = 0.8)
add_labels(unlist(iris$Sepal.Length), iris$Species, height = 0.5, cex = 0.8)

#### Plotting outliers and medians

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         52

This adds outliers to the plot.

attach(iris2)
## The following objects are masked from iris (pos = 3):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_small:
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_large:
## 
##     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
vioplot(Sepal.Length~Species, data = iris2, main = "Sepal Length",
        col=c("lightgreen", "lightblue", "palevioletred"), ylim = c(min(Sepal.Length) * 0.9, max(Sepal.Length) * 1.1))
Sepal.medians <- sapply(unique(Species), function(sp) median(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 = "black", bars = "grey85", lwd = 2,
             fill = c("palegreen3", "lightblue3", "palevioletred3"))
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)

Annotation on split violins are shown here. See the split violin plot vignette for details on these parameters.

data(iris)
summary(iris2$Sepal.Width)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.800   3.000   3.151   3.400  12.000
table(iris2$Sepal.Width > mean(iris2$Sepal.Width))
## 
## FALSE  TRUE 
##    97    59
iris_large <- iris2[iris2$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 iris2 (pos = 3):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris (pos = 4):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_small:
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_large (pos = 6):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris2 (pos = 7):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris (pos = 8):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
vioplot(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 iris2 (pos = 4):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris (pos = 5):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_small (pos = 6):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris_large (pos = 7):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris2 (pos = 8):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
## The following objects are masked from iris (pos = 9):
## 
##     Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species
vioplot(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")

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They may not be fully stable and should be used with caution. We make no claims about them.