我想制作一个点范围图,其中各组的点不会相互堆叠。情节应如下所示::
我做躲避的最佳尝试是在躲闪论证中使用向量:
library(ggplot2)
dat <- structure(list(Treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"),
Temp = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L), .Label = c("10", "20"), class = "factor"), Rep = c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), Meas = c(3L,
2L, 2L, 2L, 6L, 4L, 4L, 3L, 5L, 1L, 2L, 3L), SD = c(2L, 3L,
2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 1L)), .Names = c("Treatment",
"Temp", "Rep", "Meas", "SD"), row.names = c(NA, -12L), class = "data.frame")
ggplot(dat, aes(x = Treatment, y = Meas, ymin = Meas - SD/2, ymax = Meas + SD/2)) +
geom_linerange(aes(color = Temp), position=position_dodge(width=c(0.6,0.4)), size = 1, alpha = 0.5) +
geom_point(aes(color = Temp, shape = Temp), position=position_dodge(width=c(0.6,0.4)), size = 3) +
theme_bw()
这导致如下所示的情节。但是,所有的点都没有被躲避,我必须在Illustrator中移动点和误差条以获得上面的绘图。 有没有办法在ggplot2
的两个级别上使用道奇参数?
答案 0 :(得分:5)
从逻辑上思考,position_dodge
更适合酒吧。它在一个因子级别上与lineranges
一起使用,但是在第二级,很难定义线之间的最小距离。虽然您可以对因子进行数字区分,然后为标签添加适当的位置。
dat1<-cbind(dat,aux=rep(1,length(dat[,1])))
dat1<-within(dat1, {aux = unlist(by(aux,Treatment,cumsum))})
dat1$aux<-dat1$aux+as.numeric(dat1$Treatment)*10
ggplot(dat1, aes( x=aux, y = Meas, ymin = Meas - SD/2, ymax = Meas + SD/2)) +
geom_linerange(aes(color = Temp), size = 1, alpha = 0.5) +geom_point(aes(color = Temp, shape = Temp))+
scale_x_continuous("Treatment",breaks=c(13.5,23.5), labels=c("A","B")) + # here you define coordinates for A and B
theme_bw()
答案 1 :(得分:2)
这不会解决position_dodge()
的问题,但会解决此问题。
在原始数据框中添加了新变量x
。它包含点/线范围的x
坐标。数据框中的值应该是您想要绘制它们的顺序。
dat$x<-c(0.85,0.9,0.95,1.05,1.1,1.15,1.85,1.9,1.95,2.05,2.1,2.15)
现在将此新变量用作x
值,并使用scale_x_continuos()
设置中断和标签,以便按比例获得A
和B
。
ggplot(dat, aes(x = x, y = Meas, ymin = Meas - SD/2, ymax = Meas + SD/2)) +
geom_linerange(aes(color = Temp), size = 1, alpha = 0.5) +
geom_point(aes(color = Temp, shape = Temp), size = 3) +
theme_bw()+
scale_x_continuous("Treatment",breaks=c(1,2),labels=c("A","B"),limits=c(0.5,2.5))