我准备好以下数据集来绘制误差线和线图
> growth
treatment class variable N value sd se ci
1 elevated Dominant RBAI2012 18 0.014127713 0.009739951 0.002295728 0.004843564
2 elevated Dominant RBAI2013 18 0.021869978 0.013578741 0.003200540 0.006752549
3 elevated Codominant RBAI2012 40 0.011564725 0.013718591 0.002169100 0.004387418
4 elevated Codominant RBAI2013 41 0.011471512 0.011091167 0.001732149 0.003500804
5 elevated Subordinate RBAI2012 24 0.004419784 0.009286883 0.001895677 0.003921507
6 elevated Subordinate RBAI2013 24 0.004397105 0.008704831 0.001776866 0.003675728
7 ambient Dominant RBAI2012 13 0.025836265 0.011880315 0.003295007 0.007179203
8 ambient Dominant RBAI2013 13 0.025992636 0.015162901 0.004205432 0.009162850
9 ambient Codominant RBAI2012 26 0.018067329 0.011830940 0.002320238 0.004778620
10 ambient Codominant RBAI2013 26 0.015595275 0.012467140 0.002445007 0.005035587
11 ambient Subordinate RBAI2012 33 0.006073904 0.008287442 0.001442658 0.002938599
12 ambient Subordinate RBAI2013 35 0.003239033 0.006846507 0.001157271 0.002351857
我尝试了以下代码,产生了这个情节:
p <- ggplot(growth,aes(class,value,colour=treatment,group=variable))
pd<-position_dodge(.9)
# se= standard error; ci=confidence interval
p + geom_errorbar(aes(ymin=value-se,ymax=value+se),width=.1,position=pd,colour="black") + geom_point(position=pd,size=4) + geom_line(position=pd) +
theme_bw() + theme(legend.position=c(1,1),legend.justification=c(1,1))
线条应该在每个x轴类别中链接相同颜色的点,但显然它们不会。拜托,你能不能帮我正确划线(例如蓝色和红色,红色,红色,“主导”级,不同的线条为“共显”级。 另外,你知道如何在x标签中包含我正在分组的变量(即“RBAI2012”,“RBAI2013”? 非常感谢
答案 0 :(得分:1)
为了区分“变量”的不同级别,您可以引入第四个aes
stetic:shape
。首先定义一个新的分组变量,即'treatment'和'variable'的组合,它有四个级别。将组,颜色和形状映射到此变量。然后使用scale_colour_manual
和scale_shape_manual
设置两个级别的颜色,这对应于两个级别的“处理”。同样,定义两个“变量”形状。
growth$grp <- paste0(growth$treatment, growth$variable)
ggplot(data = growth, aes(x = class, y = value, group = grp,
colour = grp, shape = grp)) +
geom_point(size = 4, position = pd) +
geom_line(position = pd) +
geom_errorbar(aes(ymin = value - se, ymax = value + se), colour = "black",
position = pd, width = 0.1) +
scale_colour_manual(name = "Treatment:Variable",
values = c("red", "red","blue", "blue")) +
scale_shape_manual(name = "Treatment:Variable",
values = c(19, 17, 19, 17))
theme_bw() +
theme(legend.position = c(1,1), legend.justification = c(1,1))
答案 1 :(得分:0)
一种选择是使用像这样的分面图:
p <- ggplot(growth, aes(x = class, y = value, group = treatment, color = treatment))
p + geom_point(size = 4) + facet_grid(. ~ variable) + geom_errorbar(aes(ymin=value-se,ymax=value+se),width=.1,colour="black") + geom_line()
如果你想在一张图上,另一种选择是定义一个结合治疗和变量的新变量:
growth$treatment_variable <- paste(growth$treatment, growth$variable)
p <- ggplot(growth, aes(x = class, y = value, group = treatment_variable, colour = treatment_variable))
pd<-position_dodge(.2)
p + geom_point(size = 4, position=pd) + geom_errorbar(aes(ymin=value-se, ymax=value+se), width=.1, position=pd, colour="black") + geom_line(position=pd)
答案 2 :(得分:0)
您有太多的分组变量(variable
和treatment
)并将它们包含在单个图中可能会有点令人困惑。您可能想要使用分面,如下所示:
p <- ggplot(growth,aes(class,value,colour=treatment,group=treatment))
pd<-position_dodge(.9)
p +
geom_errorbar(aes(ymin=value-se,ymax=value+se),width=.1,position=pd,colour="black") +
geom_point(position=pd,size=4) + geom_line(position=pd) +
theme_bw() + theme(legend.position=c(1,1),legend.justification=c(1,1)) +
facet_grid(variable~treatment)
答案 3 :(得分:0)
可以这样做,但你需要破解它,因为你基本上在geom_line()
上绘制不同的分组(变量+处理),而不是geom_point()
和geom_errorbar()
调用
您需要使用ggplot_build()
取回呈现的数据,并根据现有点数据绘制geom_line()
,按颜色分组:
p <- ggplot(growth) # move the aes() into the individual charts
pd<-position_dodge(.9) # leave dodge as is
se<-0.01 # faked this
p <- p +
geom_point(aes(x=factor(class),y=value,colour=treatment,group=variable),position=pd,size=4) +
theme_bw() + theme(legend.position=c(1,1),legend.justification=c(1,1)) +
geom_errorbar(aes(x=factor(class),ymin=value-se,ymax=value+se,colour=treatment,group=variable),position=pd,width=.1,colour="black")
b<-ggplot_build(p)$data[[1]] # get the ggpolt rendered data for this panel
p + geom_line(data=b,aes(x,y,group=colour), color=b$colour) # plot the lines