您能否推荐在任何可用的 R 包中使用四个变量可视化数据的最佳方法。
也就是说,我有两个分类变量(人口(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 维图。
有什么建议吗?
最佳答案
我会先逐个绘制变量,然后一起绘制, 从全体人口开始,逐步 将数据分成不同的组。
# 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
)
关于r - R 的多元图形推荐,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/9393986/