Die*_*nne 16 r histogram ggplot2
ggplot2挑战的latticist需要帮助:在直方图中请求变量per-facet中断的语法是什么?
library(ggplot2)
d = data.frame(x=c(rnorm(100,10,0.1),rnorm(100,20,0.1)),par=rep(letters[1:2],each=100))
# Note: breaks have different length by par
breaks = list(a=seq(9,11,by=0.1),b=seq(19,21,by=0.2))
ggplot(d, aes(x=x) ) +
geom_histogram() + ### Here the ~breaks should be added
facet_wrap(~ par, scales="free")
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根据特殊要求,并说明为什么我不是一个伟大的ggplot粉丝,lattice版本
library(lattice)
d = data.frame(x=c(rnorm(100,10,0.1),rnorm(100,20,0.1)),par=rep(letters[1:2],each=100))
# Note: breaks have different length by par
myBreaks = list(a=seq(8,12,by=0.1),b=seq(18,22,by=0.2))
histogram(~x|par,data=d,
panel = function(x,breaks,...){
# I don't know of a generic way to get the
# grouping variable with histogram, so
# this is not very generic
par = levels(d$par)[which.packet()]
breaks = myBreaks[[par]]
panel.histogram(x,breaks=breaks,...)
},
breaks=NULL, # important to force per-panel compute
scales=list(x=list(relation="free")))
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koh*_*ske 13
这是一个替代方案:
hls <- mapply(function(x, b) geom_histogram(data = x, breaks = b),
dlply(d, .(par)), myBreaks)
ggplot(d, aes(x=x)) + hls + facet_wrap(~par, scales = "free_x")
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如果你需要缩小x的范围,那么
hls <- mapply(function(x, b) {
rng <- range(x$x)
bb <- c(rng[1], b[rng[1] <= b & b <= rng[2]], rng[2])
geom_histogram(data = x, breaks = bb, colour = "white")
}, dlply(d, .(par)), myBreaks)
ggplot(d, aes(x=x)) + hls + facet_wrap(~par, scales = "free_x")
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我认为不可能在每个方面给出不同的断点.
作为解决方法,您可以创建两个图,然后使用grid.arrange()库中的函数gridExtra将它们放在一起.要设置断点geom_histogram()使用binwidth=,并为箱的宽度一个值.
p1<-ggplot(subset(d,par=="a"), aes(x=x) ) +
geom_histogram(binwidth=0.1) +
facet_wrap(~ par)
p2<-ggplot(subset(d,par=="b"), aes(x=x) ) +
geom_histogram(binwidth=0.2) +
facet_wrap(~ par)
library(gridExtra)
grid.arrange(p1,p2,ncol=2)
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