![]() Let’s illustrate the raincloud plot, first with jittered points (more appropriate with large samples): 2 library(tidyverse) The advantage of this plot is that it illustrates, all at once, the distribution (with the density curve), the summary measures (first, second and third quartiles, and maximum/mininum without outliers thanks to the boxplot) and the number of observations (either via a dotplot or via jittered points). and the raw data in the form of a dotplot or jittered points.Geom_bar(aes(x = drv, fill = year), position = "dodge")Ī raincloud plot is a graph that combines 3 visualizations: ![]() To draw the bars next to each other for each group, use position = "dodge": ggplot(dat) + Geom_bar(aes(x = drv, fill = year), position = "fill") In order to compare proportions across groups, it is best to make each bar the same height using position = "fill": ggplot(dat) + We can also create a barplot with two qualitative variables: ggplot(dat) +Īes(x = drv, fill = year) + # fill by years Theme(legend.position = "none") # remove legend See below for more information.)Īgain, for a more appealing plot, we can add some colors to the bars with the fill argument: ggplot(dat) +Īes(x = drv, fill = drv) + # add colors to bars ![]() (Label for the x-axis can then easily be edited with the labs() function. If you want to order levels in an increasing order (i.e., category with the smallest frequency first), use the fct_rev() in addition to the fct_infreq() function: ggplot(dat) +Īes(x = fct_rev(fct_infreq(drv))) + # order by frequency library(forcats)Īes(x = fct_infreq(drv)) + # order by frequency To keep it short, graphics in R can be done in three ways, via the: R is known to be a really powerful programming language when it comes to graphics and visualizations (in addition to statistics and data science of course!).
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