ggbeeswarm extends ggplot2 with violin point/beeswarm plots

Description

This package allows plotting of several groups of one dimensional data as a violin point/beeswarm plot in ggplot2 by arranging data points to resemble the underlying distribution. The development version of this package is on https://github.com/eclarke/ggbeeswarm.

Author(s)

Erik Clarke, erikclarke@gmail.com

See Also

position_quasirandom(), position_beeswarm()

Examples

  ggplot2::ggplot(ggplot2::mpg,aes(class, hwy)) + geom_quasirandom()
  # Generate fake data
  distro <- data.frame(
    'variable'=rep(c('runif','rnorm'),each=100),
    'value'=c(runif(100, min=-3, max=3), rnorm(100))
  )
  ggplot2::ggplot(distro,aes(variable, value)) + geom_quasirandom()
  ggplot2::ggplot(distro,aes(variable, value)) + geom_quasirandom(width=.1)

Points, jittered to reduce overplotting using the beeswarm package

Description

The beeswarm geom is a convenient means to offset points within categories to reduce overplotting. Uses the beeswarm package.

Usage

geom_beeswarm(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  ...,
  method = "swarm",
  cex = 1,
  side = 0L,
  priority = "ascending",
  fast = TRUE,
  dodge.width = NULL,
  corral = "none",
  corral.width = 0.9,
  preserve.data.axis = FALSE,
  groupOnX = NULL,
  orientation = NULL,
  beeswarmArgs = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

method: specifies the algorithm used to avoid overlapping points. The default "swarm" method places points in increasing order. If a point would overlap with an existing point, it is shifted sideways (along the group axis) by a minimal amount sufficient to avoid overlap.

While the "swarm" method places points in a predetermined order, the "compactswarm" method uses a greedy strategy to determine which point will be placed next. This often leads to a more tightly-packed layout. The strategy is very simple: on each iteration, a point that can be placed as close as possible to the non-data axis is chosen and placed. If there are two or more equally good points, priority is used to break ties.

The other 3 methods first discretise the values along the data axis, in order to create more efficient packing. The "square" method places points on a square grid, whereas "hex" uses a hexagonal grid. "centre"/"center" uses a square grid to produce a symmetric swarm. The number of break points for discretisation is determined by a combination of the available plotting area and the cex argument.

priority: controls the order in which points are placed, which generally has a noticeable effect on the plot appearance. "ascending" gives the 'traditional' beeswarm plot. "descending" is the opposite. "density" prioritizes points with higher local density. "random" places points in a random order. "none" places points in the order provided.

corral: By default, swarms from different groups are not prevented from overlapping, i.e. ⁠"corral = "none"⁠. Thus, datasets that are very large or unevenly distributed may produce ugly overlapping beeswarms. To control runaway points one can use the following methods. "gutter" collects runaway points along the boundary between groups. "wrap" implement periodic boundaries. "random" places runaway points randomly in the region. "omit" omits runaway points.

#' preserve.data.axis: In some cases, the underlying method may attempt to change the position of points along the ungrouped, or data, axis, in order to keep them from overlapping. To prevent this, set this option to TRUE.

See Also

geom_quasirandom() an alternative method, beeswarm::swarmx() how spacing is determined, ggplot2::geom_point() for regular, unjittered points, ggplot2::geom_jitter() for jittered points, ggplot2::geom_boxplot() for another way of looking at the conditional distribution of a variable

Examples

  # Generate fake data
  distro <- data.frame(
    'variable'=rep(c('runif','rnorm'),each=100),
    'value'=c(runif(100, min=-3, max=3), rnorm(100))
  )
  ggplot2::ggplot(distro,ggplot2::aes(variable, value)) +
    geom_beeswarm(priority='density',size=2.5)

Points, jittered to reduce overplotting using the vipor package

Description

The quasirandom geom is a convenient means to offset points within categories to reduce overplotting. Uses the vipor package.

Usage

geom_quasirandom(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  ...,
  method = "quasirandom",
  width = NULL,
  varwidth = FALSE,
  bandwidth = 0.5,
  nbins = NULL,
  dodge.width = NULL,
  groupOnX = NULL,
  orientation = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

See Also

vipor::offsetSingleGroup() how spacing is determined, ggplot2::geom_point() for regular, unjittered points, ggplot2::geom_jitter() for jittered points, ggplot2::geom_boxplot() for another way of looking at the conditional distribution of a variable

Examples

  # Generate fake data
  distro <- data.frame(
    'variable'=rep(c('runif','rnorm'),each=100),
    'value'=c(runif(100, min=-3, max=3), rnorm(100))
  )
  ggplot2::ggplot(distro, ggplot2::aes(variable, value)) + 
  geom_quasirandom(width=0.1)

An internal function to calculate new positions for geom_beeswarm

Description

An internal function to calculate new positions for geom_beeswarm

Usage

offset_beeswarm(
  data,
  yLim.expand,
  xRange,
  yRange,
  method = "swarm",
  cex = 1,
  side = 0L,
  priority = "ascending",
  fast = TRUE,
  corral = "none",
  corral.width = 0.2,
  preserve.data.axis = FALSE
)

Arguments

Details

method: specifies the algorithm used to avoid overlapping points. The default "swarm" method places points in increasing order. If a point would overlap with an existing point, it is shifted sideways (along the group axis) by a minimal amount sufficient to avoid overlap.

While the "swarm" method places points in a predetermined order, the "compactswarm" method uses a greedy strategy to determine which point will be placed next. This often leads to a more tightly-packed layout. The strategy is very simple: on each iteration, a point that can be placed as close as possible to the non-data axis is chosen and placed. If there are two or more equally good points, priority is used to break ties.

The other 3 methods first discretise the values along the data axis, in order to create more efficient packing. The "square" method places points on a square grid, whereas "hex" uses a hexagonal grid. "centre"/"center" uses a square grid to produce a symmetric swarm. The number of break points for discretisation is determined by a combination of the available plotting area and the cex argument.

priority: controls the order in which points are placed, which generally has a noticeable effect on the plot appearance. "ascending" gives the 'traditional' beeswarm plot. "descending" is the opposite. "density" prioritizes points with higher local density. "random" places points in a random order. "none" places points in the order provided.

corral: By default, swarms from different groups are not prevented from overlapping, i.e. ⁠"corral = "none"⁠. Thus, datasets that are very large or unevenly distributed may produce ugly overlapping beeswarms. To control runaway points one can use the following methods. "gutter" collects runaway points along the boundary between groups. "wrap" implement periodic boundaries. "random" places runaway points randomly in the region. "omit" omits runaway points.

preserve.data.axis: In some cases, the underlying method may attempt to change the position of points along the ungrouped, or data, axis, in order to keep them from overlapping. To prevent this, set this option to TRUE.

See Also

geom_beeswarm(), position_quasirandom(), beeswarm::swarmx()

Other position adjustments: position_beeswarm(), position_quasirandom()

Arrange points using the ⁠\link[beeswarm]⁠ package.

Description

Arrange points using the ⁠\link[beeswarm]⁠ package.

Usage

position_beeswarm(
  method = "swarm",
  cex = 1,
  side = 0L,
  priority = "ascending",
  fast = TRUE,
  orientation = NULL,
  groupOnX = NULL,
  dodge.width = 0,
  corral = "none",
  corral.width = 0.2,
  preserve.data.axis = FALSE
)

Arguments

Details

method: specifies the algorithm used to avoid overlapping points. The default "swarm" method places points in increasing order. If a point would overlap with an existing point, it is shifted sideways (along the group axis) by a minimal amount sufficient to avoid overlap.

While the "swarm" method places points in a predetermined order, the "compactswarm" method uses a greedy strategy to determine which point will be placed next. This often leads to a more tightly-packed layout. The strategy is very simple: on each iteration, a point that can be placed as close as possible to the non-data axis is chosen and placed. If there are two or more equally good points, priority is used to break ties.

The other 3 methods first discretise the values along the data axis, in order to create more efficient packing. The "square" method places points on a square grid, whereas "hex" uses a hexagonal grid. "centre"/"center" uses a square grid to produce a symmetric swarm. The number of break points for discretisation is determined by a combination of the available plotting area and the cex argument.

priority: controls the order in which points are placed, which generally has a noticeable effect on the plot appearance. "ascending" gives the 'traditional' beeswarm plot. "descending" is the opposite. "density" prioritizes points with higher local density. "random" places points in a random order. "none" places points in the order provided.

corral: By default, swarms from different groups are not prevented from overlapping, i.e. ⁠"corral = "none"⁠. Thus, datasets that are very large or unevenly distributed may produce ugly overlapping beeswarms. To control runaway points one can use the following methods. "gutter" collects runaway points along the boundary between groups. "wrap" implement periodic boundaries. "random" places runaway points randomly in the region. "omit" omits runaway points.

#' preserve.data.axis: In some cases, the underlying method may attempt to change the position of points along the ungrouped, or data, axis, in order to keep them from overlapping. To prevent this, set this option to TRUE.

See Also

geom_beeswarm(), position_quasirandom(), beeswarm::swarmx()

Other position adjustments: offset_beeswarm(), position_quasirandom()

Arrange points using quasirandom noise to avoid overplotting

Description

Arrange points using quasirandom noise to avoid overplotting

Usage

position_quasirandom(
  method = "quasirandom",
  width = NULL,
  varwidth = FALSE,
  bandwidth = 0.5,
  nbins = NULL,
  dodge.width = 0,
  orientation = NULL,
  groupOnX = NULL,
  na.rm = FALSE
)

Arguments

See Also

vipor::offsetSingleGroup(), geom_quasirandom()

Other position adjustments: offset_beeswarm(), position_beeswarm()