今天给大家介绍一个好看又简单的散点图展示方法，叫做Beeswarm图（也称为列散点图或小提琴散点图），是一种绘制会重叠的点的方法，使它们从重叠变成彼此相邻。 除了减少过度绘图之外，它还有助于可视化每个点（类似于小提琴图）上的数据密度，同时仍单独显示每个数据点。
一、beeswarn包
代码语言：javascript复制install.packages("beeswarm")
library(beeswarm)1. beeswarm
stripchart(decrease ~ treatment, data = OrchardSprays,
vertical = TRUE, log = "y", method = 'jitter', jitter = 0.2, cex = 1,
pch = 16, col = rainbow(8),
main = 'stripchart')
beeswarm(decrease ~ treatment, data = OrchardSprays,
log = TRUE, pch = 16, col = rainbow(8),
main = 'beeswarm')
可以看到在第一个图中，有很多散点是重复的，而在Beeswarm图中，将这些重复的点分散开，变成邻近的点。
2. 定义点的不同颜色
data(breast)
beeswarm(time_survival ~ ER, data = breast,
#breast数据，根据ER分组，用time_survival 值绘制散点图
pch = 16, pwcol = 1 as.numeric(event_survival),
#pch点的形状，pwcol 根据event_survival分组颜色
#使用pwpch，pwcol和pwbg来控制每个单独数据点的“逐点”特性，这里可以给每个点赋值颜色参数
xlab = "", ylab = "Follow-up time (months)",
labels = c("ER neg", "ER pos"))
legend("topright", legend = c("Yes", "No"), #右上角图例
title = "Censored", pch = 16, col = 1:2) # pch 图例中点的形状
myCol <- lapply(distributions, function(x) cut(x, breaks = quantile(x), labels = FALSE))
#将distributions的数据划分为四个范围，定义成1-4的数值，然后用这个数据定义颜色
beeswarm(distributions, pch = 16, pwcol = myCol)
legend("bottomright", legend = 1:4, pch = 16, col = 1:4, title = "Quartile")
3. 绘制点的四种方法比较
代码语言：javascript复制set.seed(123)
distro <- list(runif = runif(100, min = -3, max = 3),
rnorm = rnorm(100)) ##随机生成数据
beeswarm(distro, col = 2:3, pch = 16,method = "square",
main = "method = square") #图1
beeswarm(distro, col = 2:3, pch = 16,method = "swarm",
main = "method = swarm") #图2
beeswarm(distro, col = 2:3, pch = 16,method = "center",
main = "method = center") #图3
beeswarm(distro, col = 2:3, pch = 16,method = "hex",
main = "method = hex") #图4
4. 五种corral方法比较
代码语言：javascript复制beeswarm(distributions,
pch = 21, col = c(1,7,5), bg = "#8B2323",
# col圆点边的颜色，bg圆点填充颜色
corral = "gomit",
# 图二gutter，图三wrap，图四random，图五gomit，
main = "corral = gomit")
5. 将beeswarms与 "boxplot"或 "bxplot"结合起来
代码语言：javascript复制boxplot(len ~ dose, data = ToothGrowth,
outline = FALSE,
main = 'boxplot beeswarm')
beeswarm(len ~ dose, data = ToothGrowth,
col = 4, pch = 16, add = TRUE)
beeswarm(len ~ dose, data = ToothGrowth,
col = 4, pch = 16,
main = 'beeswarm bxplot')
bxplot(len ~ dose, data = ToothGrowth, add = TRUE)
6. "side" and "priority"
代码语言：javascript复制beeswarm(distributions, col = 2:4, main = 'Default') #默认
beeswarm(distributions, col = 2:4, side = -1, main = 'side = -1') #点集中在左侧
beeswarm(distributions, col = 2:4, side = 1, main = 'side = 1') #点集中在右侧
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"')
二、ggbeeswarm包绘制蜂群图
ggbeeswarm提供了两种使用ggplot2创建蜂群图的方法。 一个是geom_quasirandom(),另一个是geom_beeswarm()
代码语言：javascript复制install.packages("ggbeeswarm")
library(ggbeeswarm)
library(ggplot2)1.geom_quasirandom()
（1）
代码语言：javascript复制ggplot(iris,aes(Species, Sepal.Length)) geom_jitter()
#geom_jitter() 函数是用来消除点的重合的一种方法
ggplot(iris,aes(Species, Sepal.Length)) geom_quasirandom() #geom_quasirandom默认值
ggplot(mpg,aes(class, hwy)) geom_quasirandom() #默认值
ggplot(mpg,aes(hwy, class)) geom_quasirandom(groupOnX=FALSE) #坐标轴转换
ggplot(mpg,aes(class, hwy)) geom_quasirandom(varwidth = TRUE)
#有的组只有几个点，用varwidth = TRUE调整宽度
sub_mpg <- mpg[mpg$class %in% c("midsize", "pickup", "suv"),]
ggplot(sub_mpg, aes(class, displ, color=factor(cyl))) geom_quasirandom(dodge.width=1)
#闪避，dodge.width 调整组内的不同颜色亚组的距离
（2）几种散点的分布方式
代码语言：javascript复制ggplot(iris, aes(Species, Sepal.Length)) geom_quasirandom(method = "tukey")
ggtitle("Tukey texture") #图1，图基
ggplot(iris, aes(Species, Sepal.Length)) geom_quasirandom(method = "tukeyDense")
ggtitle("Tukey density") #图2，图基 密度
ggplot(iris, aes(Species, Sepal.Length)) geom_quasirandom(method = "frowney")
ggtitle("Banded frowns") #图3，皱眉型（看起来不高兴的样子）
ggplot(iris, aes(Species, Sepal.Length)) geom_quasirandom(method = "smiley")
ggtitle("Banded smiles") #图4，微笑型
ggplot(iris, aes(Species, Sepal.Length)) geom_quasirandom(method = "pseudorandom")
ggtitle("Jittered density") #图5，伪随机
ggplot(iris, aes(Species, Sepal.Length)) geom_beeswarm()
ggtitle("Beeswarm") #图6，蜂群图
2. geom_beeswarm()
代码语言：javascript复制ggplot(iris,aes(Species, Sepal.Length)) geom_beeswarm() #默认
ggplot(mpg,aes(hwy, class)) geom_beeswarm(size=0.5,groupOnX=FALSE)
#size改变点大小，groupOnX 坐标轴转换
ggplot(mpg,aes(hwy, class)) geom_beeswarm(size=.5,groupOnX=FALSE)
scale_y_discrete(expand=expand_scale(add=c(0.5,1)))
#scale_y_discrete是对y轴离散变量进行处理，expand 表示扩展坐标轴显示范围
sub_mpg <- mpg[mpg$class %in% c("midsize", "pickup", "suv"),]
ggplot(sub_mpg, aes(class, displ, color=factor(cyl)))
geom_beeswarm(dodge.width=0.5)
小编总结：
虽然方法很简单，但是只要我们根据自己的数据仔细调整颜色和背景等，就可以画出好看又高级的展示图呢~
