对于连续型数据轴和离散型数据轴

# Some sample data
dat <- read.table(header=TRUE, text=‘
     cond result
  control     10
treatment   11.5
‘)

library(ggplot2)

# 基础图形
bp <- ggplot(dat, aes(x=cond, y=result)) +
    geom_bar(position=position_dodge(), stat="identity")
bp

# 加入一条水平线
bp + geom_hline(aes(yintercept=12))

# linetype 设置线型为 虚线
bp + geom_hline(aes(yintercept=12), colour="#990000", linetype="dashed")

依据分类情况,加入直线

# Draw separate hlines for each bar. First add another column to dat
dat$hline <- c(9,12)
dat

##        cond result hline
## 1   control   10.0     9
## 2 treatment   11.5    12

# Need to re-specify bp, because the data has changed
bp <- ggplot(dat, aes(x=cond, y=result)) +
    geom_bar(position=position_dodge(), stat="identity")

# 对每个条形加入切割线
bp + geom_errorbar(aes(y=hline, ymax=hline, ymin=hline), colour="#AA0000")

bp + geom_errorbar(width=0.5, # 设置线宽度
                   aes(y=hline, ymax=hline, ymin=hline), colour="#AA0000")

# 又一次定义数据
dat_hlines <- data.frame(cond=c("control","treatment"), hline=c(9,12))
dat_hlines

##        cond hline
## 1   control     9
## 2 treatment    12

# 条形图的数据来自于 dat, 可是 线的数据来自于 dat_hlines
bp + geom_errorbar(data=dat_hlines, aes(y=hline, ymax=hline, ymin=hline), colour="#AA0000")

直线穿过各组数据

这里实际上是有4条线, 可是前两条线与后两条线的数值同样,看上去是两条线.

dat <- read.table(header=TRUE, text=‘
     cond group result hline
  control     A     10     9
treatment     A   11.5    12
  control     B     12     9
treatment     B     14    12
‘)
dat

##        cond group result hline
## 1   control     A   10.0     9
## 2 treatment     A   11.5    12
## 3   control     B   12.0     9
## 4 treatment     B   14.0    12

# Define basic bar plot
bp <- ggplot(dat, aes(x=cond, y=result, fill=group)) +
    geom_bar(position=position_dodge(), stat="identity")
bp

# The error bars get plotted over one another -- there are four but it looks
# like two
bp + geom_errorbar(aes(y=hline, ymax=hline, ymin=hline), linetype="dashed")

每组数据分别划线

dat <- read.table(header=TRUE, text=‘
     cond group result hline
  control     A     10    11
treatment     A   11.5    12
  control     B     12  12.5
treatment     B     14    15
‘)

# Define basic bar plot
bp <- ggplot(dat, aes(x=cond, y=result, fill=group)) +
    geom_bar(position=position_dodge(), stat="identity")
bp

bp + geom_errorbar(aes(y=hline, ymax=hline, ymin=hline), linetype="dashed",
                   position=position_dodge())

为连续型数据轴加入直线

dat <- read.table(header=TRUE, text=‘
      cond xval yval
   control 11.5 10.8
   control  9.3 12.9
   control  8.0  9.9
   control 11.5 10.1
   control  8.6  8.3
   control  9.9  9.5
   control  8.8  8.7
   control 11.7 10.1
   control  9.7  9.3
   control  9.8 12.0
 treatment 10.4 10.6
 treatment 12.1  8.6
 treatment 11.2 11.0
 treatment 10.0  8.8
 treatment 12.9  9.5
 treatment  9.1 10.0
 treatment 13.4  9.6
 treatment 11.6  9.8
 treatment 11.5  9.8
 treatment 12.0 10.6
‘)

library(ggplot2)

Basic lines

# 基本散点图
sp <- ggplot(dat, aes(x=xval, y=yval, colour=cond)) + geom_point()

# 水平线
sp + geom_hline(aes(yintercept=10))

# 垂直虚线
sp + geom_hline(aes(yintercept=10)) +
    geom_vline(aes(xintercept=11.5), colour="#BB0000", linetype="dashed")

为均值自己主动划线

# Add colored lines for the mean xval of each group
sp + geom_hline(aes(yintercept=10)) +
     geom_line(stat="vline", xintercept="mean")

# 依据 cond 分面
spf <- sp + facet_grid(. ~ cond)
spf

# 在全部的面上划线
spf + geom_hline(aes(yintercept=10))

假设想为不同的分面, 加入不同的直线, 这里有两种设置. 一种是又一次生成一个满足数据须要的 data.frame 还有一种是使用 geom_line() 的stat和xintercept

# 第一种方法
dat_vlines <- data.frame(cond=levels(factor(dat$cond)), xval=c(10,11.5))
dat_vlines

##        cond xval
## 1   control 10.0
## 2 treatment 11.5

spf + geom_hline(aes(yintercept=10)) +
      geom_vline(aes(xintercept=xval), data=dat_vlines,
                    colour="#990000", linetype="dashed")