Usage examples for ggbeeswarm
In ggbeeswarm: Categorical Scatter (Violin Point) Plots

The basics

This is the simplest example of using geom_quasirandom to generate violin scatter plots:

library(ggbeeswarm)
set.seed(12345)
n <- 100
dat <- data.frame(
  data = rnorm(n*2),
  class = rep(c('a', 'b'), n)
)

ggplot(dat, aes(x = class, y = data)) +
  geom_quasirandom()

The usual ggplot2 options can be used:

ggplot(dat, aes(x = class, y = data)) +
  geom_quasirandom(aes(color = class))

Additional factors can be shown on the categorical axis by setting dodge.width, which creates smaller violin plots at each category (akin to ggplot2::geom_jitterdodge) and allows data to be compared within groups:

ggplot(dat, aes(x = group, y = data, color = data > 0)) +
  geom_quasirandom(dodge.width = 0.8)

This also works on the y-axis automatically:

ggplot(dat, aes(y = group, x = data, color = data > 0)) +
  geom_quasirandom(dodge.width = 0.8)

These examples also hold for geom_beeswarm. Note that for the beeswarm methods, the cex argument should be specified in order to create well-spaced swarms:

ggplot(dat, aes(x = group, y = data, color = data > 0)) +
  geom_beeswarm(dodge.width = 0.8, cex=2)

Both geom_beeswarm and geom_quasirandom also work with facets:

dat2 <- dat
dat2$group <- rnorm(n*2) > 0
ggplot(dat2, aes(x = class, y = data)) +
  geom_quasirandom(dodge.width = 0.8) +
   facet_wrap(facets = c("group"))

ggplot(dat2, aes(x = class, y = data)) +
  geom_beeswarm(dodge.width = 0.8, cex=3) +
   facet_wrap(facets = c("group"))

Options

There are several ways to plot grouped one-dimensional data combining points and density estimation including:

pseudorandom: The kernel density is estimated then points are distributed uniform randomly within the density estimate for a given bin. Selection of an appropriate number of bins does not greatly affect appearance but coincidental clumpiness is common.
alternating within bins: The kernel density is estimated then points are distributed within the density estimate for a given bin evenly spaced with extreme values alternating from right to left e.g. max, 3rd max, ..., 4th max, 2nd max. If maximums are placed on the outside then these plots often form consecutive "smiley" patterns. If minimums are placed on the outside then "frowny" patterns are generated. Selection of the number of bins can have large effects on appearance important.
tukey: An algorithm described by Tukey and Tukey in "Strips displaying empirical distributions: I. textured dot strips" using constrained permutations of offsets to distribute the data.
quasirandom: The kernel density is estimated then points are distributed quasi-randomly using the von der Corput sequence within the density estimate for a given bin. Selection of an appropriate number of bins does not greatly affect appearance and position does not depend on plotting parameters.
beeswarm: The package beeswarm provides methods for generating a "beeswarm" plot where points are distributed so that no points overlap. Kernel density is not calculated although the resulting plot does provide an approximate density estimate. Selection of an appropriate number of bins affects appearance and plot and point sizes must be known in advance.

The first four options are included within geom_quasirandom using the method= argument and beeswarm plots are generated using geom_beeswarm:

  library(gridExtra)
  dat <- list(
    'Normal'=rnorm(50),
    'Dense normal'= rnorm(500),
    'Bimodal'=c(rnorm(100), rnorm(100,5)),
    'Trimodal'=c(rnorm(100), rnorm(100,5),rnorm(100,-3))
  )
  labs<-rep(names(dat),sapply(dat,length))
  labs<-factor(labs,levels=unique(labs))
  dat<-unlist(dat)
  p1<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(alpha=.2) +
    ggtitle('quasirandom') + labs(x='') +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  p2<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='pseudorandom',alpha=.2) +
    ggtitle('pseudorandom') + labs(x='') +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  p3<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='smiley',alpha=.2) +
    ggtitle('smiley') + labs(x='') +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  p4<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='frowney',alpha=.2) +
    ggtitle('frowney') + labs(x='') +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  p5<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='tukey',alpha=.2) +
    ggtitle('tukey') + labs(x='') +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  p6<-ggplot(mapping=aes(labs, dat)) +
    geom_beeswarm(alpha=.2,size=.75) +
    ggtitle('geom_beeswarm') + labs(x='') +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  grid.arrange(p1, p2, p3, p4, p5, p6, ncol=3)

quasirandom calls vipor::offsetX which calls stats::density to compute kernel density estimates. The tightness of the fit can be adjusted with the bandwidth option and the width of the offset with width. nbins to adjust the number of bins used in the kernel density is also provided; this can usually be left at its default when using quasirandom offsets but is useful for non-quasirandom methods:

  library(gridExtra)
  p1<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(bandwidth=2,alpha=.2) +
    ggtitle('bandwidth=2') + labs(x='')
  p2<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(bandwidth=.1,alpha=.2) +
    ggtitle('bandwidth=.1') + labs(x='')
  p3<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(width=.1,alpha=.2) +
    ggtitle('width=.1') + labs(x='')
  p4<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(nbins=100,alpha=.2) +
    ggtitle('nbins=100') + labs(x='')
  grid.arrange(p1, p2, p3, p4, ncol=1)

The frowney or smiley methods are sensitive to the number of bins, so the argument nbins is more useful/necessary with them:

  p1<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='smiley',alpha=.2) +
    ggtitle('Default (n/5)') + labs(x='')
  p2<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='smiley',nbins=50,alpha=.2) +
    ggtitle('nbins=50') + labs(x='')
  p3<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='smiley',nbins=100,alpha=.2) +
    ggtitle('nbins=100') + labs(x='')
  p4<-ggplot(mapping=aes(labs, dat)) +
    geom_quasirandom(method='smiley',nbins=250,alpha=.2) +
    ggtitle('nbins=250') + labs(x='')
  grid.arrange(p1, p2, p3, p4, ncol=1)

The varwidth argument scales the width of a group by the square root of the number of observations in that group (as in the function boxplot):

  dat <- list(
    '10 points'=rnorm(10),
    '50 points'=rnorm(50,2),
    '200 points'=c(rnorm(400), rnorm(100,5)),
    '5000 points'= rnorm(5000,1)
  )
  labs<-rep(names(dat),sapply(dat,length))
  labs<-factor(labs,levels=unique(labs))
  dat<-unlist(dat)
  ggplot(mapping=aes(labs, dat)) + geom_quasirandom(alpha=.3,varwidth=TRUE)

Real data

library(dplyr)
set.seed(1234)
diamonds2 <- diamonds %>%
  group_by(cut) %>%
  sample_n(size = 100)

ggplot(diamonds2, aes(x = clarity, y = carat, color = price)) +
  geom_quasirandom(size=1, varwidth = TRUE, width=0.7) +
  facet_grid(rows=vars(cut)) +
  scale_color_viridis_b(option = "A", end = 0.8)
#  facet_wrap(facets=vars(carat_gt_1), scales = "free_y")

ggplot(diamonds2, aes(x = clarity, y = carat, color = price)) +
  geom_beeswarm(size=1, cex=1.3) +
  facet_grid(rows=vars(cut)) +
  scale_color_viridis_b(option = "A", end = 0.8)

An example using the beaver1 and beaver2 data from the datasets package:

  beaver<-data.frame(
    'Temperature'=c(beaver1$temp,beaver2$temp),
    'Beaver'=rep(
      c('Beaver 1','Beaver 2'),
      c(nrow(beaver1),nrow(beaver2))
    )
  )
  ggplot(beaver,mapping=aes(Beaver, Temperature)) + geom_quasirandom()