8/31/2023 0 Comments Add title ggplot2 scatter plotThe 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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