ggplot2:添加几何而不影响限制 [英] ggplot2: Adding a geom without affecting limits
问题描述
我想在ggplot密度图中添加其他几何图形,但不更改数据的显示限制,也不必通过自定义代码计算所需的限制.举个例子:
set.seed(12345)N = 1000d = data.frame(已测量= ifelse(rbernoulli(N,0.5),rpois(N,100),rpois(N,1)))d $ fit = dgeom(d $ measured,0.6)ggplot(d,aes(x =测量值))+ geom_density()+ geom_line(aes(y =适合),color ="blue")ggplot(d,aes(x =测量值))+ geom_density()+ geom_line(aes(y =适合),color ="blue")+ coord_cartesian(ylim = c(0,0.025))
在第一个图中,拟合曲线(非常不适合测量"的数据)使测量数据的形状变得模糊:我想裁剪图以包括来自第一个几何的所有数据,但是裁剪拟合曲线,如第二个图所示:
虽然我可以使用 coord_cartesian
生成第二个图,但这有两个缺点:
- 我必须通过自己的代码来计算限制(这很麻烦且容易出错)
- 通过我自己的代码计算限制与构面不兼容.(AFAIK)无法使用
coord_cartesian
提供每个方面的轴限制.但是,我需要将情节与facet_wrap(scales ="free")
结合使用
如果在计算坐标极限时未考虑第二个几何图形,将实现所需的输出-是否可能不使用自定义R代码计算极限?
问题
这可以与 facet_wrap()
组合:
d $ group<-rep(letters [1:2],500)#fake组ggplot(d,aes(x =测量值))+geom_density(aes(y = ..scaled ..))+geom_line(aes(y = fit_scaled),color ="blue")+facet_wrap(〜group,scales ="free")
不缩放数据的选项:
您可以使用
如果要比较相邻的组,则可以使用 facet_grid()
代替 facet_wrap()
,且 cols = 2
在 multiplot()
中:
multiplot(ggplot(d,aes(x =测量值))+geom_density()+facet_grid(group〜.,scales ="free"),ggplot(d,aes(x =测量值))+geom_line(aes(y = fit),color ="blue")+facet_grid(group〜.,scales ="free"),cols = 2)
它看起来像这样:
I would like to add additional geoms to a ggplot density plot, but without changing the displayed limits of the data and without having to compute the desired limits by custom code. To give an example:
set.seed(12345)
N = 1000
d = data.frame(measured = ifelse(rbernoulli(N, 0.5), rpois(N, 100), rpois(N,1)))
d$fit = dgeom(d$measured, 0.6)
ggplot(d, aes(x = measured)) + geom_density() + geom_line(aes(y = fit), color = "blue")
ggplot(d, aes(x = measured)) + geom_density() + geom_line(aes(y = fit), color = "blue") + coord_cartesian(ylim = c(0,0.025))
In the first plot, the fit curve (which fits the "measured" data quite badly) obscures the shape of the measured data: I would like to crop the plot to include all data from the first geom, but crop the fit curve, as in the second plot:
While I can produce the second plot with coord_cartesian
, this has two disadvantages:
- I have to compute the limits by my own code (which is cumbersome and error-prone)
- Computing the limits by my own code is not compatible with faceting. It is not possible (AFAIK) to provide per-facet axis limits with
coord_cartesian
. I however need to combine the plot withfacet_wrap(scales = "free")
The desired output would be achieved, if the second geom was not considered when computing coordinate limits - is that possible without computing the limits in custom R code?
The question R: How do I use coord_cartesian on facet_grid with free-ranging axis is related, but does not have a satisfactory answer.
One thing you could try is to scale fit
and use geom_density(aes(y = ..scaled..)
Scaling fit
between 0
and 1
:
d$fit_scaled <- (d$fit - min(d$fit)) / (max(d$fit) - min(d$fit))
Use fit_scaled
and ..scaled..
:
ggplot(d, aes(x = measured)) +
geom_density(aes(y = ..scaled..)) +
geom_line(aes(y = fit_scaled), color = "blue")
This can be combined with facet_wrap()
:
d$group <- rep(letters[1:2], 500) #fake group
ggplot(d, aes(x = measured)) +
geom_density(aes(y = ..scaled..)) +
geom_line(aes(y = fit_scaled), color = "blue") +
facet_wrap(~ group, scales = "free")
An option that does not scale the data:
You can use the function multiplot()
from http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
plots <- c(list(...), plotlist)
numPlots = length(plots)
if (is.null(layout)) {
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
for (i in 1:numPlots) {
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
With this function you can combine the two plots, which makes it easier to read them:
multiplot(
ggplot(d, aes(x = measured)) +
geom_density() +
facet_wrap(~ group, scales = "free"),
ggplot(d, aes(x = measured)) +
geom_line(aes(y = fit), color = "blue") +
facet_wrap(~ group, scales = "free")
)
This will give you:
And if you want to compare groups next to each other, you can use facet_grid()
instead of facet_wrap()
with cols = 2
in multiplot()
:
multiplot(
ggplot(d, aes(x = measured)) +
geom_density() +
facet_grid(group ~ ., scales = "free"),
ggplot(d, aes(x = measured)) +
geom_line(aes(y = fit), color = "blue") +
facet_grid(group ~ ., scales = "free"),
cols = 2
)
And it looks like this:
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