Warning! This post describes some intuitions behind the idea of the functional boxplots. I think that it is a very useful technique, but all statistical tools should be used with caution. Reading only one blog post might be not enough to apply them in practice. At the end of the post, I added an information about useful resources covering this topic in a more rigid way. A classical boxplot is an excellent tool for the quick summary of the data.
My first package published on CRAN - DepthProc recently hit 20k downloads. library(cranlogs) library(ggplot2) downloads <- cran_downloads("DepthProc", from = "2014-08-21", to = "2018-06-10") ggplot(downloads) + geom_line(aes(x = date, y = cumsum(count))) + ylab("Downloads") + xlab("Date") + theme_bw() + ggtitle("DepthProc", "Download stats") There are some jumps on the line. I wondered if they all occurred just after the package release (old users updates to the new versions). Here’s some code to check this.