【搬运】用ggplot2作带有误差线的折线图和柱状图

 

## 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)
}

library(ggplot2)
tg <- ToothGrowth

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

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

pd <- position_dodge(0.1) # move them .05 to the left and right
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


tgc2 <- tgc
tgc2$dose <- factor(tgc2$dose)
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()                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                

 

转载自ggplot2-为折线图和条形图添加误差线

其他学习材料

R语言可视化学习笔记之添加p-value和显著性标记

R语言可视化学习笔记之添加p-value和显著性标记(排版更好)

 

2022年7月24日更新

ggplot2软件包的stat_summary是可以可以直接实现添加误差线的。

ggplot(data_m, aes(x=Group, y=TB, fill = Salinity)) +  
  stat_summary(fun = mean, geom = "bar", position = position_dodge(0.9), color = "black") +  
  stat_summary(fun.data = mean_se, geom = "errorbar", position = position_dodge(0.9), width = 0.2) + 
  labs(x = "", y = "", legend = NULL)