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 histoplot package. Thus it has been restored
here for enhanced backwards compatibility with boxplot.
As shown below for the iris dataset, histogram plots
show distribution information taking formula input that
boxplot implements but histoplot is unable to.
This demonstrates the customisation demonstrated in the main histoplot vignette using
histoplot syntax with the formula method commonly used for
boxplot, t.test, and lm.
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
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:
Plot colours can be further customised as with the original vioplot
package using the col argument:
However the vioplot (0.2) function is unable to colour
each histogram separately, thus this is enabled with a vectorised
col in histoplot (0.4):
histoplot(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)
Colours can also be customised for the histogram fill and border
separately using the col and border
arguments:
histoplot(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.
histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", rectCol="palevioletred", lineCol="violetred")
The same applies to the colour of the median point with
colMed:
### Combined customisation
These can be customised colours can be combined:
histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue", border="royalblue", rectCol="palevioletred", lineCol="violetred", colMed="violet")
These colour and shape settings can also be customised separately for each histogram:
histoplot(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))
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), ]A direct comparision of 2 datasets can be made with the
side argument and add = TRUE on the second
plot:
histoplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right")
histoplot(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")