自己找到了解决方案。也许有人可以使用它:
#step 1: preparing data
ageMetaData <- ddply(data,~group,summarise,
mean=mean(age),
sd=sd(age),
min=min(age),
max=max(age),
median=median(age),
Q1=summary(age)['1st Qu.'],
Q3=summary(age)['3rd Qu.']
)
#step 2: correction for outliers
out <- data.frame() #initialising storage for outliers
for(group in 1:length((levels(factor(data$group))))){
bps <- boxplot.stats(data$age[data$group == group],coef=1.5)
ageMetaData[ageMetaData$group == group,]$min <- bps$stats[1] #lower wisker
ageMetaData[ageMetaData$group == group,]$max <- bps$stats[5] #upper wisker
if(length(bps$out) > 0){ #adding outliers
for(y in 1:length(bps$out)){
pt <-data.frame(x=group,y=bps$out[y])
out<-rbind(out,pt)
}
}
}
#step 3: drawing
p <- ggplot(ageMetaData, aes(x = group,y=mean))
p <- p + geom_errorbar(aes(ymin=min,ymax=max),linetype = 1,width = 0.5) #main range
p <- p + geom_crossbar(aes(y=median,ymin=Q1,ymax=Q3),linetype = 1,fill='white') #box
# drawning outliers if any
if(length(out) >0) p <- p + geom_point(data=out,aes(x=x,y=y),shape=4)
p <- p + scale_x_discrete(name= "Group")
p <- p + scale_y_continuous(name= "Age")
p
分位数数据结果很难看,但有效。也许还有另一种方式。结果如下所示:

还稍微改进了箱线图:
- 添加了第二个较小的虚线误差条以反映 sd 范围。
- 添加点以反映均值
- 移除背景
也许这对某人也可能有用:
p <- ggplot(ageMetaData, aes(x = group,y=mean))
p <- p + geom_errorbar(aes(ymin=min,ymax=max),linetype = 1,width = 0.5) #main range
p <- p + geom_crossbar(aes(y=median,ymin=Q1,ymax=Q3),linetype = 1,fill='white') #box
p <- p + geom_errorbar(aes(ymin=mean-sd,ymax=mean+sd),linetype = 3,width = 0.25) #sd range
p <- p + geom_point() # mean
# drawning outliers if any
if(length(out) >0) p <- p + geom_point(data=out,aes(x=x,y=y),shape=4)
p <- p + scale_x_discrete(name= "Group")
p <- p + scale_y_continuous(name= "Age")
p + opts(panel.background = theme_rect(fill = "white",colour = NA))
结果是:

以及具有较小范围的相同数据(箱线图coef = 0.5)
