Question

您能否推荐使用任何可用R包中的四个变量来可视化数据的最佳方法。

即，我有两个分类变量（种群（12）和字符（50））和两个连续变量（100个个体（矩阵中的行）的每个字符长度测量的平均值和变异系数）。所以它基本上是一个12x50x100x100维度图。

有什么建议吗？

Answer 1

我会先逐个绘制变量，然后一起绘制，从整个人口开始逐步将数据切分为不同的组。

# Sample data
n1 <- 6   # Was: 12
n2 <- 5   # Was: 50
n3 <- 10  # Was: 100
d1 <- data.frame(
  population = rep(LETTERS[1:n1], each=n2*n3),
  character = rep(1:n2, each=n3, times=12),
  id = 1:(n1*n2*n3),
  mean = rnorm(n1*n2*n3),
  var  = rchisq(n1*n2*n3, df=5)
)
# Not used, but often useful with ggplot2
library(reshape2)
d2 <- melt(d1, id.vars=c("population","character","id"))

# Look at the first variable
library(lattice)
densityplot( ~ mean, data=d1 )
densityplot( ~ mean, groups=population, data=d1 )
densityplot( ~ mean | population, groups=character, data=d1 )

# Look at the second variable
densityplot( ~ var, data=d1 )
densityplot( ~ var, groups=population, data=d1 )
densityplot( ~ var | population, groups=character, data=d1 )

# Look at both variables
xyplot( mean ~ var, data=d1 )
xyplot( mean ~ var, groups=population, data=d1 )
xyplot( mean ~ var | population, groups=character, data=d1 )

# The plots may be more readable with lines rather than points
xyplot( 
  mean ~ var | population, groups = character, 
  data = d1, 
  panel = panel.superpose, panel.groups = panel.loess
)

Answer 2

如果要在数据的一个维度或另一个维度上绘制一系列“切片”，请考虑lattice。为什么不突然转到http://addictedtor.free.fr/graphiques/，看看有人写了一些代码来创建你想要的那种图形？

R的多变量图形推荐

2 个答案: