ggplot2-为折线图和条形图添加误差线
本文更新地址: http://blog.csdn.net/tanzuozhev/article/details/51106089
本文在 http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/ 的基础上加入了自己的理解
采用ggplot2绘制折线图和条形图,并添加误差线.
ggplot2只能处理 data.frame数据,每列作为一个变量,是一个指标.
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 0.5library(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)
}