ggplot2-为折线图和条形图添加误差线

以ToothGrowth数据为例,进行处理

tg <- ToothGrowth
head(tg)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

library(ggplot2)

数据预处理

采用summarySE()(函数定义在本文末尾)对数据进行预处理,计算数据的 标准误差(Standard Error). > 标准误差区别于标准差(Standard Deviation) . SE为 standard error of the mean 可以参考: http://blog.csdn.net/tanzuozhev/article/details/50830928

# summarySE 计算标准差和标准误差以及95%的置信区间.

tgc <- summarySE(tg, measurevar="len", groupvars=c("supp","dose"))
tgc

##   supp dose  N   len       sd        se       ci
## 1   OJ  0.5 10 13.23 4.459709 1.4102837 3.190283
## 2   OJ  1.0 10 22.70 3.910953 1.2367520 2.797727
## 3   OJ  2.0 10 26.06 2.655058 0.8396031 1.899314
## 4   VC  0.5 10  7.98 2.746634 0.8685620 1.964824
## 5   VC  1.0 10 16.77 2.515309 0.7954104 1.799343
## 6   VC  2.0 10 26.14 4.797731 1.5171757 3.432090

折线图

绘制带有误差线和95%置信区间线的折线图和点图

# 带有标准误差线的折线图
# Standard error of the mean
ggplot(tgc, aes(x=dose, y=len, colour=supp)) + 
    geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.1) +
    geom_line() +
    geom_point()

# 对重叠的点,进行偏移处理(尽管这样可以将点分开便于观看,但是个人认为这并不科学)
pd <- position_dodge(0.1) # move them .05 to the left and right

ggplot(tgc, aes(x=dose, y=len, colour=supp)) + 
    geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.1, position=pd) +
    geom_line(position=pd) +
    geom_point(position=pd)

## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead

# 绘制带有95%置信区间的折线图
ggplot(tgc, aes(x=dose, y=len, colour=supp)) + 
    geom_errorbar(aes(ymin=len-ci, ymax=len+ci), width=.1, position=pd) +
    geom_line(position=pd) +
    geom_point(position=pd)

## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead

# 设置误差线的颜色,特别注意如果没有 group=supp,这个重合的误差线将不会偏移.

ggplot(tgc, aes(x=dose, y=len, colour=supp, group=supp)) + 
    geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
    geom_line(position=pd) +
    geom_point(position=pd, size=3)

## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead

下面是一个完整的带有标准误差线的图,geom_point 放在 geom_line之后,可以保证点被最后绘制,填充为白色.

ggplot(tgc, aes(x=dose, y=len, colour=supp, group=supp)) + 
    geom_errorbar(aes(ymin=len-se, ymax=len+se), colour="black", width=.1, position=pd) +
    geom_line(position=pd) +
    geom_point(position=pd, size=3, shape=21, fill="white") + # 21 is filled circle
    xlab("Dose (mg)") +
    ylab("Tooth length") +
    scale_colour_hue(name="Supplement type",    # Legend label, use darker colors
                     breaks=c("OJ", "VC"),
                     labels=c("Orange juice", "Ascorbic acid"),
                     l=40) +                    # Use darker colors, lightness=40
    ggtitle("The Effect of Vitamin C on\nTooth Growth in Guinea Pigs") +
    expand_limits(y=0) +                        # Expand y range
    scale_y_continuous(breaks=0:20*4) +         # Set tick every 4
    theme_bw() +
    theme(legend.justification=c(1,0),# 这一项很关键,如果没有这个参数,图例会偏移,读者可以试一试
          legend.position=c(1,0))               # Position legend in bottom right

## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead

条形图

绘制条形图与绘制折线图类似,但是必须要注意的是tgc$size必须被设置成 factor 类型,如果它是 数值型向量,那么将会出现错误. > 这是因为dose如果是 数值型向量将会作为连续型数据进行处理,而 因子型 变量被作为离散型数据进行处理.

# 转换为因子类型
tgc2 <- tgc
tgc2$dose <- factor(tgc2$dose)

# Error bars represent standard error of the mean
ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + 
    geom_bar(position=position_dodge(), stat="identity") +
    geom_errorbar(aes(ymin=len-se, ymax=len+se),
                  width=.2, # 设置误差线的宽度 
                  position=position_dodge(.9))

# 使用95%置信区间
ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + 
    geom_bar(position=position_dodge(), stat="identity") +
    geom_errorbar(aes(ymin=len-ci, ymax=len+ci),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))

完整的条形图

ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + 
    geom_bar(position=position_dodge(), stat="identity",
             colour="black", # Use black outlines,
             size=.3) +      # Thinner lines
    geom_errorbar(aes(ymin=len-se, ymax=len+se),
                  size=.3,    # Thinner lines
                  width=.2,
                  position=position_dodge(.9)) +
    xlab("Dose (mg)") +
    ylab("Tooth length") +
    scale_fill_hue(name="Supplement type", # Legend label, use darker colors
                   breaks=c("OJ", "VC"),
                   labels=c("Orange juice", "Ascorbic acid")) +
    ggtitle("The Effect of Vitamin C on\nTooth Growth in Guinea Pigs") +
    scale_y_continuous(breaks=0:20*4) +
    theme_bw()

## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA‘s
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)

    # 计算长度
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }

    # 以 groupvars 为组,计算每组的长度,均值,以及标准差
    # ddply 就是 dplyr 中的 group_by + summarise
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )

    # 重命名  
    datac <- plyr::rename(datac, c("mean" = measurevar))

    # 计算标准偏差
    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    # 计算置信区间
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}