共 10 篇文章

R语言之可视化（23）高亮某一元素

R语言之可视化（24）生成带P值得箱线图

R语言之可视化（25）绘制相关图（ggcorr包）

R语言之可视化（26）ggplot2绘制饼图

R语言之可视化（27）ggplot2绘制线图

R语言之可视化（28）蜜蜂图

R语言之可视化（30）扫地僧easystats（1）

R语言之可视化（31）扫地僧easystats（2）相关性分析

R语言之可视化（32）之ggtext：提高ggplot2的文本呈现

R语言之可视化（33）绘制差异基因分析统计图

清单首页R语言之可视化23-33文章详情

清单「R语言之可视化23-33」 05/10

R语言之可视化（27）ggplot2绘制线图

用户1359560

基本线图

数据

数据来源于 ToothGrowth 数据集

df <- data.frame(dose=c("D0.5", "D1", "D2"),
                len=c(4.2, 10, 29.5))
head(df)

##   dose  len
## 1 D0.5  4.2
## 2   D1 10.0
## 3   D2 29.5

len : Tooth length
dose : Dose in milligrams (0.5, 1, 2)

带点线图

library(ggplot2)
# Basic line plot with points
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line()+
  geom_point()
# Change the line type
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(linetype = "dashed")+
  geom_point()
# Change the color
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(color="red")+
  geom_point()

你可以添加一个箭头

library(grid)
# Add an arrow
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(arrow = arrow())+
  geom_point()
# Add a closed arrow to the end of the line
myarrow=arrow(angle = 15, ends = "both", type = "closed")
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(arrow=myarrow)+
  geom_point()

同样也可以用geom_step() or geom_path()将数值连接起来

ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_step()+
  geom_point()
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_path()+
  geom_point()

geom_line : 根据X轴数值连接
geom_path() : 根据初始数值连接
geom_step : 通过阶梯连接起来

多分组线图

数据

df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("D0.5", "D1", "D2"),2),
                len=c(6.8, 15, 33, 4.2, 10, 29.5))
head(df2)

##   supp dose  len
## 1   VC D0.5  6.8
## 2   VC   D1 15.0
## 3   VC   D2 33.0
## 4   OJ D0.5  4.2
## 5   OJ   D1 10.0
## 6   OJ   D2 29.5

len : Tooth length
dose : Dose in milligrams (0.5, 1, 2)
supp : Supplement type (VC or OJ) 如下图所示：通过不同的分组，绘制了两个线图

# Line plot with multiple groups
ggplot(data=df2, aes(x=dose, y=len, group=supp)) +
  geom_line()+
  geom_point()
# Change line types
ggplot(data=df2, aes(x=dose, y=len, group=supp)) +
  geom_line(linetype="dashed", color="blue", size=1.2)+
  geom_point(color="red", size=3)

不同分组使用不同的类型的线

# Change line types by groups (supp)
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point()
# Change line types and point shapes
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point(aes(shape=supp))

也可以通过 scale_linetype_manual()手段设置线的类型

# Set line types manually
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point()+
  scale_linetype_manual(values=c("twodash", "dotted"))

不同分组，绘制不同颜色的线

p<-ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(color=supp))+
  geom_point(aes(color=supp))
p

也可以通过 change manually line colors 手动设置线的颜色

scale_color_manual() : to use custom colors
scale_color_brewer() : to use color palettes from RColorBrewer package
scale_color_grey() : to use grey color palettes

# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey() + theme_classic()

改变图例（legend）位置

p <- p + scale_color_brewer(palette="Paired")+
  theme_minimal()
p + theme(legend.position="top")
p + theme(legend.position="bottom")
# Remove legend
p + theme(legend.position="none")

legend.position 可选的选项有 : “left”,“top”, “right”, “bottom”.

绘制X轴为数值型的线图

# Create some data
df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("0.5", "1", "2"),2),
                len=c(6.8, 15, 33, 4.2, 10, 29.5))
head(df2)

##   supp dose  len
## 1   VC  0.5  6.8
## 2   VC    1 15.0
## 3   VC    2 33.0
## 4   OJ  0.5  4.2
## 5   OJ    1 10.0
## 6   OJ    2 29.5

# x axis treated as continuous variable
df2$dose <- as.numeric(as.vector(df2$dose))
ggplot(data=df2, aes(x=dose, y=len, group=supp, color=supp)) +
  geom_line() + geom_point()+
  scale_color_brewer(palette="Paired")+
  theme_minimal()
# Axis treated as discrete variable
df2$dose<-as.factor(df2$dose)
ggplot(data=df2, aes(x=dose, y=len, group=supp, color=supp)) +
  geom_line() + geom_point()+
  scale_color_brewer(palette="Paired")+
  theme_minimal()

!](https://upload-images.jianshu.io/upload_images/9218360-42ec840eec7b60de.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240 "ggplot2 line plot - R software and data visualization")

当X轴为时间类型数据

head(economics)

##         date   pce    pop psavert uempmed unemploy
## 1 1967-06-30 507.8 198712     9.8     4.5     2944
## 2 1967-07-31 510.9 198911     9.8     4.7     2945
## 3 1967-08-31 516.7 199113     9.0     4.6     2958
## 4 1967-09-30 513.3 199311     9.8     4.9     3143
## 5 1967-10-31 518.5 199498     9.7     4.7     3066
## 6 1967-11-30 526.2 199657     9.4     4.8     3018

# Basic line plot
ggplot(data=economics, aes(x=date, y=pop))+
  geom_line()
# Plot a subset of the data
ggplot(data=subset(economics, date > as.Date("2006-1-1")), 
       aes(x=date, y=pop))+geom_line()

修改线的大小

# Change line size
ggplot(data=economics, aes(x=date, y=pop, size=unemploy/pop))+
  geom_line()

绘制带有误差棒的线图

#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of a column containing the variable
  #to be summariezed
# groupnames : vector of column names to be used as
  # grouping variables
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}

df3 <- data_summary(ToothGrowth, varname="len", 
                    groupnames=c("supp", "dose"))
head(df3)

##   supp dose   len       sd
## 1   OJ  0.5 13.23 4.459709
## 2   OJ  1.0 22.70 3.910953
## 3   OJ  2.0 26.06 2.655058
## 4   VC  0.5  7.98 2.746634
## 5   VC  1.0 16.77 2.515309
## 6   VC  2.0 26.14 4.797731

函数 geom_errorbar()可以用来绘制带有误差棒的线图

# Standard deviation of the mean
ggplot(df3, aes(x=dose, y=len, group=supp, color=supp)) + 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1) +
    geom_line() + geom_point()+
   scale_color_brewer(palette="Paired")+theme_minimal()
# Use position_dodge to move overlapped errorbars horizontally
ggplot(df3, aes(x=dose, y=len, group=supp, color=supp)) + 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1, 
    position=position_dodge(0.05)) +
    geom_line() + geom_point()+
   scale_color_brewer(palette="Paired")+theme_minimal()

自定义线图的一些例子

# Simple line plot
# Change point shapes and line types by groups
ggplot(df3, aes(x=dose, y=len, group = supp, shape=supp, linetype=supp))+ 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1, 
    position=position_dodge(0.05)) +
    geom_line() +
    geom_point()+
    labs(title="Plot of lengthby dose",x="Dose (mg)", y = "Length")+
    theme_classic()
# Change color by groups
# Add error bars
p <- ggplot(df3, aes(x=dose, y=len, group = supp, color=supp))+ 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1, 
    position=position_dodge(0.05)) +
    geom_line(aes(linetype=supp)) + 
    geom_point(aes(shape=supp))+
    labs(title="Plot of lengthby dose",x="Dose (mg)", y = "Length")+
    theme_classic()
p + theme_classic() + scale_color_manual(values=c('#999999','#E69F00'))

p + scale_color_brewer(palette="Paired") + theme_minimal()
# Greens
p + scale_color_brewer(palette="Greens") + theme_minimal()
# Reds
p + scale_color_brewer(palette="Reds") + theme_minimal()

R语言之可视化（27）ggplot2绘制线图

目录

R语言之可视化（27）ggplot2绘制线图

基本线图

数据

带点线图

多分组线图

数据

不同分组使用不同的类型的线

不同分组，绘制不同颜色的线

改变图例（legend）位置

绘制X轴为数值型的线图

当X轴为时间类型数据

绘制带有误差棒的线图

自定义线图的一些例子

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