Title:	The Bee Swarm Plot, an Alternative to Stripchart
Description:	The bee swarm plot is a one-dimensional scatter plot like "stripchart", but with closely-packed, non-overlapping points.
Version:	0.4.0
Date:	2021-05-07
Imports:	stats, graphics, grDevices, utils
NeedsCompilation:	yes
License:	Artistic-2.0
URL:	https://github.com/aroneklund/beeswarm
BugReports:	https://github.com/aroneklund/beeswarm/issues
Packaged:	2021-05-07 08:52:45 UTC; aron
Author:	Aron Eklund [aut, cre], James Trimble [aut]
Maintainer:	Aron Eklund <aroneklund@gmail.com>
Repository:	CRAN
Date/Publication:	2021-06-01 21:40:02 UTC

Bee swarm plot

Description

Create a bee swarm plot. A bee swarm plot is a one-dimensional scatter plot similar to stripchart, but with various methods to separate coincident points such that each point is visible. Also, beeswarm introduces additional features unavailable in stripchart, such as the ability to control the color and plotting character of each point.

Usage

beeswarm(x, ...)

## S3 method for class 'formula'
beeswarm(formula, data = NULL, subset, na.action = NULL, 
         pwpch = NULL, pwcol = NULL, pwbg = NULL, pwcex = NULL, dlab, glab, ...)

## Default S3 method:
beeswarm(x, 
    method = c("swarm", "compactswarm", "center", "hex", "square"), 
    vertical = TRUE, horizontal = !vertical, 
    cex = 1, spacing = 1, breaks = NULL,
    labels, at = NULL, 
    corral = c("none", "gutter", "wrap", "random", "omit"),
    corralWidth, side = 0L, 
    priority = c("ascending", "descending", "density", "random", "none"),
    fast = TRUE,
    pch = par("pch"), col = par("col"), bg = NA, 
    pwpch = NULL, pwcol = NULL, pwbg = NULL, pwcex = NULL,
    do.plot = TRUE, add = FALSE, axes = TRUE, log = FALSE,
    xlim = NULL, ylim = NULL, dlim = NULL, glim = NULL,
    xlab = NULL, ylab = NULL, dlab = "", glab = "",
    ...)

Arguments

Details

Several methods for placing the points are available; each method uses a different algorithm to avoid overlapping points.

The default method, swarm, places points in increasing order. If a point would overlap an existing point, it is shifted sideways (along the group axis) by a minimal amount sufficient to avoid overlap. breaks is ignored.

The other three methods first discretize the values along the data axis, in order to create more efficient packing: square places the points on a square grid, whereas hex uses a hexagonal grid. center uses a square grid to produce a symmetric swarm. By default, the number of breakpoints for discretization is determined by a combination of the available plotting area and the plotting character size. The discretization of the data can be explicitly controlled using breaks. If breaks is set to NA, the data will not be grouped into intervals; this may be a sensible option if the data is already discrete.

In contrast to most other plotting functions, changing the size of the graphics device will often change the position of the points.

The plotting characters and colors can be controlled in two ways. First, the arguments pch, col and bg can specify plotting characters and colors in the same way as stripchart and boxplot: in short, the arguments apply to each group as a whole (and are recycled if necessary).

Alternatively, the “point-wise” characteristics of each individual data point can be controlled using pwpch, pwcol, and pwbg, which override pch, col and bg if these are also specified. Likewise, pwcex controls the size of each point relative to the default (which may be adjusted by cex). Notably, the point layout algorithm is applied without considering the point-wise arguments; thus setting pwcex larger than 1 will usually result in partially overlapping points. These arguments can be specified as a list or vector. If supplied using the formula method, the arguments can be specified as part of the formula interface; i.e. they are affected by data and subset.

The dlab and glab labels may be used instead of xlab and ylab if those are not specified. dlab applies to the continuous data axis (the Y axis unless horizontal is TRUE); glab to the group axis. Likewise, dlim and glim can be used to specify limits of the axes instead of xlim or ylim.

This function is intended to be mostly compatible with calls to stripchart or boxplot. Thus, code that works with these functions should work with beeswarm with minimal modification.

By default, swarms from different groups are not prevented from overlapping. Thus, large data sets, or data sets with uneven distributions, may produce somewhat unpleasing beeswarms. If this is a problem, consider reducing cex. Another approach is to control runaway points (those that would be plotted outside a region allotted to each group) with the corral argument: The default, "none", does not control runaway points. "gutter" collects runaway points along the boundary between groups. "wrap" implements periodic boundaries. "random" places runaway points randomly in the region. "omit" omits runaway points. See Examples below.

When using the "swarm" method, priority controls the order in which the points are placed; this generally has a noticeable effect on the resulting appearance. "ascending" gives the "traditional" beeswarm plot in which the points are placed in an ascending order. "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.

Whereas 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.

Value

A data frame with plotting information, invisibly.

See Also

stripchart, boxplot

Examples

 
  
  ## One of the examples from 'stripchart'
  beeswarm(decrease ~ treatment,
    data = OrchardSprays, log = TRUE, 
    pch = 16, col = rainbow(8))

  ## One of the examples from 'boxplot', with a beeswarm overlay
   boxplot(len ~ dose, data = ToothGrowth,
              main = "Guinea Pigs' Tooth Growth",
              xlab = "Vitamin C dose mg",
              ylab = "Tooth length")  
   beeswarm(len ~ dose, data = ToothGrowth, col = 2, add = TRUE)

  ## Compare the 5 methods
  op <- par(mfrow = c(2,3))
  for (m in c("swarm", "compactswarm", "center", "hex", "square")) {
    beeswarm(len ~ dose, data = ToothGrowth, method = m, main = m)
  }
  par(op)

  ## Demonstrate the use of 'pwcol'
  data(breast)
  beeswarm(time_survival ~ ER, data = breast,
    pch = 16, pwcol = 1 + as.numeric(event_survival),
    xlab = "", ylab = "Follow-up time (months)",
    labels = c("ER neg", "ER pos"))
  legend("topright", legend = c("Yes", "No"),
    title = "Censored", pch = 16, col = 1:2)

  ## The list interface
  distributions <- list(runif = runif(200, min = -3, max = 3), 
                        rnorm = rnorm(200),
                        rlnorm = rlnorm(200, sdlog = 0.5))
  beeswarm(distributions, col = 2:4)

  ## Demonstrate 'pwcol' with the list interface 
  myCol <- lapply(distributions, function(x) cut(x, breaks = quantile(x), labels = FALSE))
  beeswarm(distributions, pch = 16, pwcol = myCol)
  legend("bottomright", legend = 1:4, pch = 16, col = 1:4, title = "Quartile")

  ## Demonstrate the 'corral' methods
  par(mfrow = c(2,3))
  beeswarm(distributions, col = 2:4, 
    main = 'corral = "none" (default)')
  beeswarm(distributions, col = 2:4, corral = "gutter", 
    main = 'corral = "gutter"')
  beeswarm(distributions, col = 2:4, corral = "wrap", 
    main = 'corral = "wrap"')
  beeswarm(distributions, col = 2:4, corral = "random", 
    main = 'corral = "random"')
  beeswarm(distributions, col = 2:4, corral = "omit", 
    main = 'corral = "omit"')  
 
  ## Demonstrate 'side' and 'priority'
  par(mfrow = c(2,3))
  beeswarm(distributions, col = 2:4, 
    main = 'Default')
  beeswarm(distributions, col = 2:4, side = -1, 
    main = 'side = -1')
  beeswarm(distributions, col = 2:4, side = 1, 
    main = 'side = 1')
  beeswarm(distributions, col = 2:4, priority = "descending", 
    main = 'priority = "descending"')
  beeswarm(distributions, col = 2:4, priority = "random", 
    main = 'priority = "random"')  
  beeswarm(distributions, col = 2:4, priority = "density", 
    main = 'priority = "density"')  

  ## Demonstrate 'side' and 'priority' for compact method
  par(mfrow = c(2,3))
  beeswarm(distributions, col = 2:4, method = "compactswarm",
    main = 'Default')
  beeswarm(distributions, col = 2:4, method = "compactswarm", side = -1, 
    main = 'side = -1')
  beeswarm(distributions, col = 2:4, method = "compactswarm", side = 1, 
    main = 'side = 1')
  beeswarm(distributions, col = 2:4, method = "compactswarm",
    priority = "descending",  main = 'priority = "descending"')
  beeswarm(distributions, col = 2:4, method = "compactswarm",
    priority = "random",  main = 'priority = "random"')  
  beeswarm(distributions, col = 2:4, method = "compactswarm",
    priority = "density",  main = 'priority = "density"')  

  ## Demonstrate pwcol, pwpch, pwbg, and pwcex
  beeswarm(mpg ~ cyl, data = mtcars, cex = 3, 
    pwcol = gear, pwbg = am + 1, pwpch = gear + 18, pwcex = hp / 335)
  

Lymph-node-negative primary breast tumors

Description

Tumor molecular measurements and outcome from breast cancer patients.

Usage

data(breast)

Format

A data frame with 286 observations on the following 5 variables.

ER

Estrogen receptor status (factor with levels neg, pos)

ESR1

Expression of the ESR1 gene (numeric)

ERBB2

Expression of the ERBB2 gene (numeric)

time_survival

Time in months (numeric)

event_survival

Coded event: 0 = censored, 1 = metastasis (numeric)

Details

ER, ESR1, and ERBB2 were measured on a tumor specimen taken at surgery (time = 0).

ESR1 and ERBB2 expression values were determined by microarray probe sets 205225_at and 216836_s_at using RMA-normalized data.

Source

Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005 Feb 19-25;365(9460):671-9.

Examples

  data(breast)

  with(breast, 
    plot(ESR1, ERBB2, col = as.numeric(ER))
  )

Plot quantile lines

Description

Plot lines indicating the specified quantiles for each group. This function is intended as a simplified interpretation of boxplot, which can be combined with a beeswarm (or stripchart) plot.

Usage

bxplot(x, ...)

## S3 method for class 'formula'
bxplot(formula, data = NULL, ..., subset, na.action = NULL)

## Default S3 method:
bxplot(x, probs = c(0.25, 0.5, 0.75),
    vertical = TRUE, horizontal = !vertical, add = FALSE,
    col = par("col"), lty = par("lty"), lwd = NULL, 
    at = NULL, width = 0.75, ...)

Arguments

Details

This function is intended as a minimalistic interpration of boxplot; however, the quantiles plotted by bxplot are not necessarily the same as the hinges plotted by a boxplot.

Notice that specifying a vector of graphical parameters such as lwd or col will refer to each of probs, not to each group in the data (as one might expect by analogy with boxplot).

If lwd is NULL, and if the probs includes 0.5, lwd will be set to 3 times par("lwd") for probs=0.5, and par("lwd") for the others. (Thus something resembling the median line and hinges of a boxplot is produced by default.)

Value

None.

Examples

  ## bxplot on bottom
  beeswarm(len ~ dose, data = ToothGrowth)
  bxplot(len ~ dose, data = ToothGrowth, add = TRUE)
  
  ## bxplot on top
  bxplot(decrease ~ treatment, data = OrchardSprays, probs = 0.5, col = 2)
  beeswarm(decrease ~ treatment, data = OrchardSprays, add = TRUE)

  ## Show deciles 
  data(breast)
  bxplot(time_survival ~ event_survival, data = breast, 
    probs = seq(0, 1, by = 0.1), col = rainbow(10))
  beeswarm(time_survival ~ event_survival, data = breast, 
    pch = 21, bg = "gray75", add = TRUE)

Adjust 1-d data to separate coincident points

Description

Take a series of points lying in a horizontal or vertical line, and jitter them in the other dimension such that no points are overlapping.

Usage

swarmx(x, y, 
    xsize = xinch(0.08, warn.log = FALSE), 
    ysize = yinch(0.08, warn.log = FALSE),
    log = NULL, cex = par("cex"), side = 0L, 
    priority = c("ascending", "descending", "density", "random", "none"),
    fast = TRUE, compact = FALSE)
swarmy(x, y, 
    xsize = xinch(0.08, warn.log = FALSE), 
    ysize = yinch(0.08, warn.log = FALSE),
    log = NULL, cex = par("cex"), side = 0L, 
    priority = c("ascending", "descending", "density", "random", "none"),
    fast = TRUE, compact = FALSE)

Arguments

Details

For swarmx, the input coordinates must lie in a vertical line. For swarmy, the input coordinates must lie in a horizontal line.

swarmx adjusts coordinates to the left or right; swarmy adjusts coordinates up or down.

priority controls the order in which the points are placed; this has generally has a noticeable effect on the resulting appearance. "ascending" gives the "traditional" beeswarm plot in which the points are placed in an ascending order. "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.

When compact is FALSE, points are placed in a predetermined order. When compact is TRUE, a greedy strategy is used 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.

Usually it makes sense to call this function after a plotting device has already been set up (e.g. when adding points to an existing plot), so that the default values for xsize, ysize, and log will be appropriate.

Value

A data frame with columns x and y with the new coordinates.

See Also

beeswarm, jitter

Examples

 

## Plot points in one dimension
index <- rep(0, 100)
values <- rnorm(100)
plot(index, values, xlim = c(-0.5, 2.5))
points(swarmx(index + 1, values), col = 2)
points(swarmx(index + 2, values, cex = 1.5), col = 3, cex = 1.5)

## Try the horizontal direction, with a log scale
plot(values, index, log = "x", ylim = c(-1, 2))
points(swarmy(values, index + 1), col = 2)

## Newer examples using "side", "priority", and "compact"
plot(c(-0.5, 3.5), range(values), type = 'n')
points(swarmx(index + 0, values), col = 1)
points(swarmx(index + 0.9, values, side = -1), col = 2)
points(swarmx(index + 1.1, values, side =  1, priority = "descending"), col = 3)
points(swarmx(index + 2  , values, priority = 'density'), col = 4)
points(swarmx(index + 3  , values, priority = 'random'), col = 5)
points(swarmx(index + 3  , values, priority = 'random', compact = TRUE), col = 5)