{vipor}: 1 1>2的小提琴散点图！

install.packages("vipor")
library(vipor)

set.seed(1) # 设置随机种子，确保结果可重复
mydata_norm <- data.frame(
  x = rep("Mean = 0, sd = 1", 200), 
  y = rnorm(200, mean = 0, sd = 1) # 正态分布数据
)

set.seed(2) # 设置随机种子，确保结果可重复
mydata_bi <- data.frame(
  x = rep("Bimodal", 90), 
  y = c(rnorm(30, mean = -3), rnorm(60, mean = 3)) 
)

set.seed(3) # 设置随机种子，确保结果可重复
mydata_gamma <- data.frame(
  x = rep("The Gamma Distribution", 50), 
  y = rgamma(50, 1) # 生成50个伽马分布随机数
)

set.seed(4) # 设置随机种子，确保结果可重复
mydata_cauchy <- data.frame(
  x = rep("the Cauchy Distribution", 100), 
  y = rcauchy(100) # 生成100个柯西分布的随机数
)

summary(mydata_norm)
summary(mydata_bi)
summary(mydata_gamma)
summary(mydata_cauchy)

par(mfrow = c(1, 2))

vpPlot(mydata_norm$x, mydata_norm$y)
vpPlot(mydata_norm$x, mydata_norm$y, 
       pch = 23,      # 不同图形
       bg = "gold",   # 图形内填充颜色，只适用 pch = 21:25
       col = "black", # 图形的外圈线条颜色
       lwd = 1.5,     # 图形的线条粗细
       cex = 1.8)     # 图形的大小

par(mfrow = c(1, 3))

vpPlot(mydata_bi$x, mydata_bi$y)
vpPlot(mydata_gamma$x, mydata_gamma$y)
vpPlot(mydata_cauchy$x, mydata_cauchy$y)

summary(iris)

par(mfrow=c(1,1))

vpPlot(iris$Species, iris$Sepal.Length, 
       ylab = "Sepal_Length")

vpPlot(iris$Species, iris$Sepal.Width,
       ylab = "Sepal_Width", 
       pch = 10, 
       las = 2)

vpPlot(iris$Species, iris$Petal.Length, 
       ylab = "Petal_Length", 
       pch = 16, 
       cex = 0.6)

vpPlot(iris$Species, iris$Petal.Width, 
       ylab = "Petal_Width", 
       pch = 6)

[1]. https://github.com/sherrillmix/vipor