Part I: R with ggplot2 The ggplot2 packages for R has some fantastic features for very powerful, flexible, and aesthetic data visualisation (If you are not familiar with the packages, you can have a look at some of the capabilities here: [http://docs.ggplot2.org/current/]). It is also relatively easy to export figures in a way that matches any journals figure specifications.
I will use the sample dataset ‘iris’ that is included with R for the following demonstration. This dataset contains data on petal length of three iris species among other measures.
# Loading the data
data(iris)
# Producing a basic plot
ggplot(data=iris,aes(x=factor(Species), y=Petal.Length)) +
geom_boxplot() +
xlab('Species') +
ylab('petal length [cm]')
For publication, we would probably like to make the main features of the plot a bit bolder, control the font size of the axis labels, and use a white background:
figure <- ggplot(data=iris,aes(x=factor(Species), y=Petal.Length)) +
geom_boxplot(width=0.5,lwd=1,fill='grey') +
xlab('Species') +
ylab('petal length [cm]') +
theme_bw() +
theme(axis.text=element_text(size=12),
axis.title=element_text(size=13))
We might also want to include annotations that indicate results of statistical analyses. Here is a one-way ANOVA to compare petal length between species followed by post-hoc t-tests to determine differences between species pairs:
# one-way ANOVA:
summary(aov(data=iris,Petal.Length ~ Species))
# t-test single contrasts:
t.test(iris$Petal.Length[iris$Species=='setosa'],iris$Petal.Length[iris$Species=='versicolor'],paired=FALSE)
t.test(iris$Petal.Length[iris$Species=='setosa'],iris$Petal.Length[iris$Species=='virginica'],paired=FALSE)
t.test(iris$Petal.Length[iris$Species=='versicolor'],iris$Petal.Length[iris$Species=='virginica'],paired=FALSE)
This analysis indicates that there are significant differences between all species in petal length. Next, we will add information about the group differences to the boxplot for the convenience of the reader:
figure +
geom_segment(aes(x=1, y=7, xend=2, yend=7), size=0.1) +
geom_segment(aes(x=2, y=7.2, xend=3, yend=7.2), size=0.1) +
geom_segment(aes(x=1, y=7.4, xend=3, yend=7.4), size=0.1) +
annotate("text",x=1.5, y=7,label="*",size=8) +
annotate("text",x=2.5, y=7.2,label="*",size=8) +
annotate("text",x=2, y=7.5, label="*",size=8)
The final step is to export the figure with properties that match the specifications of the publisher. As an example, I will export a figure with 4cm height and 3cm width at 300 dpi resolution in PNG format:
ggsave(figure,file=‘/Users/joebathelt/Example.png’, width=3,height=4,dpi=300,limitsize=TRUE)
