The data I'm working with are highly skewed, with the vast majority of data concentrated at 0. It seems really hard to highlight the differences between these kind of distributions:
gamma1 <- rgamma(10000, shape=0.05, rate=1) gamma2 <- rgamma(10000, shape=0.055, rate=0.98) gamma3 <- rgamma(10000, shape=0.06, rate=0.95) c(mean(gamma1), mean(gamma2), mean(gamma3))  0.04845668 0.05253655 0.05797983 ks.test(gamma1, gamma2) Two-sample Kolmogorov-Smirnov test data: gamma1 and gamma2 D = 0.0433, p-value = 1.44e-08 alternative hypothesis: two-sided ks.test(gamma2, gamma3) Two-sample Kolmogorov-Smirnov test data: gamma2 and gamma3 D = 0.0456, p-value = 1.864e-09 alternative hypothesis: two-sided ks.test(gamma1, gamma3) Two-sample Kolmogorov-Smirnov test data: gamma1 and gamma3 D = 0.0798, p-value < 2.2e-16 alternative hypothesis: two-sided
As most of the data is at 0, histograms are not very helpful for seeing the differences between the distributions (not to mention the fact there doesn't seem to be a convenient way to plot a histogram with multiple distributions in R, see https://github.com/hadley/ggplot2/issues/1081):
Violin plots seem to misrepresent the shape of the distributions (they look a lot more normal-like than they really are) and since the means are very low, the boxes are almost invisible:
Since these plot aren't really showing anything useful, I was wondering if there is a better way to visualise skewed distributions?