DatistEQ之ggplot2两个变量绘图
2021-03-12 本文已影响0人
了无_数据科学
1、两个变量：x,y皆连续
使用数据集mtcars
mtcars数据集包含从1974年《美国汽车趋势》杂志中提取的数据，该数据描述了32辆汽车(1973–74年型号)的油耗以及汽车设计和性能的10个其他属性。
mtcars数据集
#先创建一个ggplot图层
library(ggplot2)
b <- ggplot(data = mtcars, aes(x=wt, y=mpg))
#DatistEQ回收数据，后续代码省略此行
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
可能添加的图层有：
- geom_point():散点图
- geom_smooth():平滑线
- geom_quantile():分位线
- geom_rug():边际地毯线
- geom_jitter():避免重叠
- geom_text():添加文本注释
x,y皆连续
1.1 散点图
b+geom_point()
散点图
将变量cyl映射给点的颜色和形状
b + geom_point(aes(color = factor(cyl), shape = factor(cyl)))
散点图
自定义颜色
b+geom_point(aes(color=factor(cyl), shape=factor(cyl)))+
scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+theme_classic()
自定义颜色的散点图
1.2 平滑线
可以添加回归曲线
b+geom_smooth()
平滑线
散点图+回归线
b+geom_point()+
geom_smooth(method = "lm", se=FALSE)#去掉置信区间
散点图+回归线
使用loess方法
b+geom_point()+
geom_smooth(method = "loess")
loess方法
将变量映射给颜色和形状
b+geom_point(aes(color=factor(cyl), shape=factor(cyl)))+
geom_smooth(aes(color=factor(cyl), shape=factor(cyl)), method = "lm", se=FALSE, fullrange=TRUE)
image.png
1.3 分位线
# Package `quantreg` required for `stat_quantile`.
if (!require("quantreg")) install.packages("quantreg")
ggplot(data = mpg, aes(cty, hwy))+
geom_point()+geom_quantile()+
theme_minimal()
image.png
1.4 边际地毯线
使用数据集faithful
ggplot(data = faithful, aes(x=eruptions, y=waiting))+
geom_point()+geom_rug()
image.png
避免重叠
实际上geom_jitter()是geom_point(position="jitter")的简称,下面使用数据集mpg
p <- ggplot(data = mpg, aes(displ, hwy))
p+geom_point()
image.png
1.5 增加抖动防止重叠
p+geom_jitter(width = 0.5, height = 0.5)
image.png
其中两个参数：
width：x轴方向的抖动幅度
height：y轴方向的抖动幅度
1.6 文本注释
参数label用来指定注释标签 (ggrepel可以避免标签重叠)
b+geom_text(aes(label=rownames(mtcars)))
image.png
完整代码如下：
header1("两个变量：x,y皆连续")
header2("散点图")
library(ggplot2)
b <- ggplot(data = mtcars, aes(x=wt, y=mpg))
b+geom_point()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将变量cyl映射给点的颜色和形状
b + geom_point(aes(color = factor(cyl), shape = factor(cyl)))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#自定义颜色
b+geom_point(aes(color=factor(cyl), shape=factor(cyl)))+
scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+theme_classic()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header2("平滑线")
b+geom_smooth()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#散点图+回归线
b+geom_point()+
geom_smooth(method = "lm", se=FALSE)#去掉置信区间
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#使用loess方法
b+geom_point()+
geom_smooth(method = "loess")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将变量映射给颜色和形状
b+geom_point(aes(color=factor(cyl), shape=factor(cyl)))+
geom_smooth(aes(color=factor(cyl), shape=factor(cyl)), method = "lm", se=FALSE, fullrange=TRUE)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#分位线
# Package `quantreg` required for `stat_quantile`.
if (!require("quantreg")) install.packages("quantreg")
ggplot(data = mpg, aes(cty, hwy))+ geom_point() + geom_quantile() + theme_minimal()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
ggplot(mtcars, aes(hp, disp)) +
geom_point() +
geom_quantile(quantiles = 0.5, aes(group = factor(gear), colour = factor(gear)),
xseq = min(mtcars$hp):max(mtcars$hp))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#边际地毯线
#使用数据集faithful
ggplot(data = faithful, aes(x=eruptions, y=waiting))+
geom_point()+geom_rug()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#避免重叠
#实际上geom_jitter()是geom_point(position="jitter")的简称,下面使用数据集mpg
p <- ggplot(data = mpg, aes(displ, hwy))
p+geom_point()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#增加抖动防止重叠
p+geom_jitter(width = 0.5, height = 0.5)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#文本注释
#参数label用来指定注释标签 (ggrepel可以避免标签重叠)
b <- ggplot(data = mtcars, aes(x=wt, y=mpg))
b+geom_text(aes(label=rownames(mtcars)))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
2、两个变量：连续二元分布
使用数据集diamonds，该数据集收集了约54000颗钻石的价格和质量的信息。每条记录由十个变量构成，其中有三个是名义变量，分别描述钻石的切工，颜色和净度；
注：数据集diamonds，源于ggplot2包（因为钻石的价格定价取决于重量，颜色，刀工等影响，价格该如何制定合理，为公司抢占市场制定价格提供依据。）
image.png
carat:克拉重量
cut:切工
color:颜色
clarity:净度
depth:深度
table:钻石宽度
以及X，Y，Z
image.png
创建ggplot图层,后面再逐步添加图层
c <- ggplot(data=diamonds, aes(carat, price))
可添加的图层有：
geom_bin2d(): 二维封箱热图
geom_hex(): 六边形封箱图
geom_density_2d(): 二维等高线密度图
image.png
2.1 二维封箱热图
geom_bin2d()将点的数量用矩形封装起来，通过颜色深浅来反映点密度
c+geom_bin2d()
二维封箱热图
设置bin的数量
c+geom_bin2d(bins=150)
二维封箱热图
2.2 六边形封箱图
geom_hex()依赖于另一个R包hexbin，所以没安装的先安装：
if (!require("hexbin")) install.packages("hexbin")
library(hexbin)
c+geom_hex()
六边形封箱图
修改bin的数目
c+geom_hex(bins=10)
六边形封箱图
2.3 二维等高线密度图
sp <- ggplot(faithful, aes(x=eruptions, y=waiting))
sp+geom_point()+ geom_density_2d()
等高线密度图
完整代码如下：
library(ggplot2)
#diamonds有54000颗钻石的数据，太大只显示头部的几行记录
output(head(diamonds))
#二维封箱热图
header1("二维封箱热图")
c <- ggplot(data=diamonds, aes(carat, price))
c+geom_bin2d()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#设置bin的数量
c+geom_bin2d(bins=150)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#六边形封箱图
header1("六边形封箱图")
if (!require("hexbin")) install.packages("hexbin")
library(hexbin)
c+geom_hex()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#修改bin的数目
c+geom_hex(bins=10)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header1("二维等高线密度图")
sp <- ggplot(faithful, aes(x=eruptions, y=waiting))
sp+geom_point()+ geom_density_2d()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
3、两个变量：连续函数
主要是如何通过线来连接两个变量，使用数据集economics。
head(economics)
economics数据集
先创建一个ggplot图层，后面逐步添加图层
d <- ggplot(data = economics, aes(x=date, y=unemploy))
可添加的图层有：
geom_area():面积图
geom_line()：折线图
geom_step(): 阶梯图
3.1 面积图
d+geom_area()
面积图
3.2 线图
d+geom_line()
线图
3.3 阶梯图
set.seed(1111)
ss <- economics[sample(1:nrow(economics), 20),]
ggplot(ss, aes(x=date, y=unemploy))+geom_step()
阶梯图
完整代码如下：
library(ggplot2)
output(head(economics))
#面积图
header1("面积图")
d <- ggplot(data = economics, aes(x=date, y=unemploy))
d+geom_area()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header1("线图")
d+geom_line()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header1("阶梯图")
set.seed(1111)
ss <- economics[sample(1:nrow(economics), 20),]
ggplot(ss, aes(x=date, y=unemploy))+ geom_step()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
4、 两个变量：x离散，y连续
使用数据集ToothGrowth,其中的变量len(Tooth length)是连续变量，dose是离散变量。
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
head(ToothGrowth)
创建图层
e <- ggplot(data = ToothGrowth, aes(x=dose, y=len))
可添加的图层有：
geom_boxplot(): 箱线图
geom_violin()：小提琴图
geom_dotplot()：点图
geom_jitter(): 带状图
geom_line(): 线图
geom_bar(): 条形图
image.png
4.1 箱线图
e+geom_boxplot()
image.png
添加有缺口的箱线图
e+geom_boxplot(notch = TRUE)
image.png
将dose映射给填充颜色
e+geom_boxplot(aes(fill=dose))
image.png
按supp进行分类并映射给填充颜色
ggplot(ToothGrowth, aes(x=dose, y=len))+ geom_boxplot(aes(fill=supp))
image.png
4.2 小提琴图
e+geom_violin(trim = FALSE)
image.png
添加中值点
e+geom_violin(trim = FALSE)+
stat_summary(fun.data = mean_sdl, fun.args = list(mult=1),
geom="pointrange", color="red")
image.png
与箱线图结合
e+geom_violin(trim = FALSE)+
geom_boxplot(width=0.2)
image.png
将dose映射给颜色进行分组
e+geom_violin(aes(color=dose), trim = FALSE)
image.png
4.3 点图
e+geom_dotplot(binaxis = "y", stackdir = "center")
image.png
添加中值点
e + geom_dotplot(binaxis = "y", stackdir = "center") +
stat_summary(fun.data=mean_sdl, color = "red",geom = "pointrange",fun.args=list(mult=1))
image.png
与箱线图结合
e + geom_boxplot() +
geom_dotplot(binaxis = "y", stackdir = "center")
image.png
添加小提琴图
e + geom_violin(trim = FALSE) +
geom_dotplot(binaxis='y', stackdir='center')
image.png
将dose映射给颜色以及填充色
e + geom_dotplot(aes(color = dose, fill = dose),
binaxis = "y", stackdir = "center")
image.png
4.5 带状图
带状图是一种一维散点图，当样本量很小时，与箱线图相当
e + geom_jitter(position=position_jitter(0.2))
image.png
添加中值点
e + geom_jitter(position=position_jitter(0.2)) +
stat_summary(fun.data="mean_sdl", fun.args = list(mult=1),
geom="pointrange", color = "red")
image.png
与点图结合
e + geom_jitter(position=position_jitter(0.2)) +
geom_dotplot(binaxis = "y", stackdir = "center")
image.png
与小提琴图结合
e + geom_violin(trim = FALSE) +
geom_jitter(position=position_jitter(0.2))
image.png
将dose映射给颜色和形状
e + geom_jitter(aes(color = dose, shape = dose),
position=position_jitter(0.2))
image.png
4.6 线图
#构造数据集
df <- 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(df)
image.png
将supp映射线型
ggplot(df, aes(x=dose, y=len, group=supp)) +
geom_line(aes(linetype=supp))+
geom_point()
image.png
修改线型、点的形状以及颜色
ggplot(df, aes(x=dose, y=len, group=supp)) +
geom_line(aes(linetype=supp, color = supp))+
geom_point(aes(shape=supp, color = supp))
image.png
4.7 条形图
#构造数据集
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
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
创建图层
f <- ggplot(df, aes(x = dose, y = len))
f + geom_bar(stat = "identity")
image.png
修改填充色以及添加标签
f + geom_bar(stat="identity", fill="steelblue")+
geom_text(aes(label=len), vjust=-0.3, size=3.5)+
theme_minimal()
image.png
将dose映射给条形图颜色
f + geom_bar(aes(color = dose),
stat="identity", fill="white")
image.png
修改填充色
f + geom_bar(aes(fill = dose), stat="identity")
image.png
将变量supp映射给填充色，从而达到分组效果
g <- ggplot(data=df2, aes(x=dose, y=len, fill=supp))
g + geom_bar(stat = "identity")#position默认为stack
image.png
修改position为dodge
g + geom_bar(stat="identity", position=position_dodge())
image.png
完整代码如下：
library(ggplot2)
#output(head(ToothGrowth))
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
output(head(ToothGrowth))
#箱线图
header1("箱线图")
e <- ggplot(data = ToothGrowth, aes(x=dose, y=len))
e+geom_boxplot()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加有缺口的箱线图
e+geom_boxplot(notch = TRUE)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将dose映射给填充颜色
e+geom_boxplot(aes(fill=dose))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#按supp进行分类并映射给填充颜色
ggplot(ToothGrowth, aes(x=dose, y=len))+ geom_boxplot(aes(fill=supp))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#小提琴图
header1("小提琴图")
e+geom_violin(trim = FALSE)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加中值点
if (!require("Hmisc")) install.packages("Hmisc")
e+geom_violin(trim = FALSE)+
stat_summary(fun.data = mean_sdl, fun.args = list(mult=1),
geom="pointrange", color="red")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#与箱线图结合
e+geom_violin(trim = FALSE)+geom_boxplot(width=0.2)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将dose映射给颜色进行分组
e+geom_violin(aes(color=dose), trim = FALSE)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#点图
header1("点图")
e+geom_dotplot(binaxis = "y", stackdir = "center")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加中值点
e + geom_dotplot(binaxis = "y", stackdir = "center") +
stat_summary(fun.data=mean_sdl, color = "red",geom = "pointrange",fun.args=list(mult=1))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#与箱线图结合
e + geom_boxplot() +
geom_dotplot(binaxis = "y", stackdir = "center")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加小提琴图
e + geom_violin(trim = FALSE) +
geom_dotplot(binaxis='y', stackdir='center')
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将dose映射给颜色以及填充色
e + geom_dotplot(aes(color = dose, fill = dose),
binaxis = "y", stackdir = "center")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#带状图
header1("带状图")
e + geom_jitter(position=position_jitter(0.2))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加中值点
e + geom_jitter(position=position_jitter(0.2)) +
stat_summary(fun.data="mean_sdl", fun.args = list(mult=1),
geom="pointrange", color = "red")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#与点图结合
e + geom_jitter(position=position_jitter(0.2)) +
geom_dotplot(binaxis = "y", stackdir = "center")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#与小提琴图结合
e + geom_violin(trim = FALSE) +
geom_jitter(position=position_jitter(0.2))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将dose映射给颜色和形状
e + geom_jitter(aes(color = dose, shape = dose),
position=position_jitter(0.2))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#线图
header1("线图")
df <- 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))
output( head(df))
ggplot(df, aes(x=dose, y=len, group=supp)) +
geom_line(aes(linetype=supp))+
geom_point()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#修改线型、点的形状以及颜色
ggplot(df, aes(x=dose, y=len, group=supp)) +
geom_line(aes(linetype=supp, color = supp))+
geom_point(aes(shape=supp, color = supp))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header1("条形图")
df <- data.frame(dose=c("D0.5", "D1", "D2"),
len=c(4.2, 10, 29.5))
output(head(df))
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))
output(head(df2))
f <- ggplot(df, aes(x = dose, y = len))
f + geom_bar(stat = "identity")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#修改填充色以及添加标签
f + geom_bar(stat="identity", fill="steelblue")+
geom_text(aes(label=len), vjust=-0.3, size=3.5)+
theme_minimal()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将dose映射给条形图颜色
f + geom_bar(aes(color = dose),
stat="identity", fill="white")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#修改填充色
f + geom_bar(aes(fill = dose), stat="identity")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将变量supp映射给填充色，从而达到分组效果
g <- ggplot(data=df2, aes(x=dose, y=len, fill=supp))
g + geom_bar(stat = "identity")#position默认为stack
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#修改position为dodge
g + geom_bar(stat="identity", position=position_dodge())
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
5、 两个变量：x、y皆离散
使用数据集diamonds中的两个离散变量color以及cut
ggplot(diamonds, aes(cut, color)) +
geom_jitter(aes(color = cut), size = 0.5)
image.png
6、 两个变量：绘制误差图
df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df)
## 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
绘制误差图需要知道均值以及标准误，下面这个函数用来计算每组的均值以及标准误。
data_summary <- function(data, varname, grps){
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, grps, .fun=summary_func, varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
计算均值以及标准误
df2 <- data_summary(df, varname="len", grps= "dose")
# Convert dose to a factor variable
df2$dose=as.factor(df2$dose)
head(df2)
## dose len sd
## 1 0.5 10.605 4.499763
## 2 1 19.735 4.415436
## 3 2 26.100 3.774150
创建图层
f <- ggplot(df2, aes(x = dose, y = len,
ymin = len-sd, ymax = len+sd))
可添加的图层有：
geom_crossbar(): 空心柱，上中下三线分别代表ymax、mean、ymin
geom_errorbar(): 误差棒
geom_errorbarh(): 水平误差棒
geom_linerange()：竖直误差线
geom_pointrange()：中间为一点的误差线
image.png
6.1 空心柱
geom_crossbar()
f+geom_crossbar()
image.png
将dose映射给颜色
f+geom_crossbar(aes(color=dose))
image.png
自定义颜色
f+geom_crossbar(aes(color=dose))+
scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9"))+theme_classic()
image.png
修改填充色
f+geom_crossbar(aes(fill=dose))+
scale_fill_manual(values = c("#999999", "#E69F00", "#56B4E9"))+
theme_classic()
image.png
通过将supp映射给颜色实现分组，可以利用函数stat_summary()来计算mean和sd
f <- ggplot(df, aes(x=dose, y=len, color=supp))
f+stat_summary(fun.data = mean_sdl, fun.args = list(mult=1), geom="crossbar", width=0.6, position = position_dodge(0.8))
image.png
6.2 误差棒
f <- ggplot(df2, aes(x=dose, y=len, ymin=len-sd, ymax=len+sd))
将dose映射给颜色
f+geom_errorbar(aes(color=dose), width=0.2)
image.png
与线图结合
f+geom_line(aes(group=1))+
geom_errorbar(width=0.15)
image.png
与条形图结合，并将变量dose映射给颜色
f+geom_bar(aes(color=dose), stat = "identity", fill="white")+
geom_errorbar(aes(color=dose), width=0.1)
image.png
6.3 水平误差棒
#构造数据集
df2 <- data_summary(ToothGrowth, varname="len", grps = "dose")
df2$dose <- as.factor(df2$dose)
head(df2)
## dose len sd
## 1 0.5 10.605 4.499763
## 2 1 19.735 4.415436
## 3 2 26.100 3.774150
创建图层
f <- ggplot(data = df2, aes(x=len, y=dose,xmin=len-sd, xmax=len+sd))
参数xmin与xmax用来设置水平误差棒
f+geom_errorbarh()
image.png
通过映射实现分组
f+geom_errorbarh(aes(color=dose))
image.png
6.4 竖直误差线
geom_linerange()与geom_pointrange()
f <- ggplot(df2, aes(x=dose, y=len, ymin=len-sd, ymax=len+sd))
line range
f+geom_linerange()
image.png
6.5 中间为一点的误差线
point range
f+geom_pointrange()
image.png
6.6 点图+误差棒
g <- ggplot(df, aes(x=dose, y=len))+
geom_dotplot(binaxis = "y", stackdir = "center")
添加geom_crossbar()
g+stat_summary(fun.data = mean_sdl, fun.args = list(mult=1), geom="crossbar", color="red", width=0.1)
image.png
添加geom_errorbar()
g + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
geom="errorbar", color="red", width=0.2) +
stat_summary(fun.y=mean, geom="point", color="red")
image.png
添加geom_pointrange()
g + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
geom="pointrange", color="red")
image.png
完整代码如下：
library(ggplot2)
df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df)
data_summary <- function(data, varname, grps){
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, grps, .fun=summary_func, varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
df2 <- data_summary(df, varname="len", grps= "dose")
# Convert dose to a factor variable
df2$dose=as.factor(df2$dose)
head(df2)
f <- ggplot(df2, aes(x = dose, y = len,
ymin = len-sd, ymax = len+sd))
f+geom_crossbar()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#将dose映射给颜色
f+geom_crossbar(aes(color=dose))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#自定义颜色
f+geom_crossbar(aes(color=dose))+
scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9"))+theme_classic()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#修改填充色
f+geom_crossbar(aes(fill=dose))+
scale_fill_manual(values = c("#999999", "#E69F00", "#56B4E9"))+
theme_classic()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
f <- ggplot(df, aes(x=dose, y=len, color=supp))
f+stat_summary(fun.data = mean_sdl, fun.args = list(mult=1), geom="crossbar", width=0.6, position = position_dodge(0.8))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header1("误差棒")
f <- ggplot(df2, aes(x=dose, y=len, ymin=len-sd, ymax=len+sd))
#将dose映射给颜色
f+geom_errorbar(aes(color=dose), width=0.2)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#与线图结合
f+geom_line(aes(group=1))+
geom_errorbar(width=0.15)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
f+geom_bar(aes(color=dose), stat = "identity", fill="white")+
geom_errorbar(aes(color=dose), width=0.1)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
header1("水平误差棒")
df2 <- data_summary(ToothGrowth, varname="len", grps = "dose")
df2$dose <- as.factor(df2$dose)
output(head(df2))
#创建图层
f <- ggplot(data = df2, aes(x=len, y=dose,xmin=len-sd, xmax=len+sd))
f+geom_errorbarh()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
f+geom_errorbarh(aes(color=dose))
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#line range
f <- ggplot(df2, aes(x=dose, y=len, ymin=len-sd, ymax=len+sd))
f+geom_linerange()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#point range
f+geom_pointrange()
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#点图+误差棒
g <- ggplot(df, aes(x=dose, y=len))+
geom_dotplot(binaxis = "y", stackdir = "center")
#添加geom_crossbar()
g+stat_summary(fun.data = mean_sdl, fun.args = list(mult=1), geom="crossbar", color="red", width=0.1)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加geom_errorbar()
g + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
geom="errorbar", color="red", width=0.2) +
stat_summary(fun.y=mean, geom="point", color="red")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
#添加geom_pointrange()
g + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
geom="pointrange", color="red")
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
7、 两个变量：地图绘制
ggplot2提供了绘制地图的函数geom_map()，依赖于包maps提供地理信息。
安装map
if (!require("maps")) install.packages("maps")
下面将绘制美国地图，数据集采用USArrests
library(maps)
head(USArrests)
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
对数据进行整理一下,添加一列state
crimes <- data.frame(state=tolower(rownames(USArrests)), USArrests)
head(crimes)
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
#数据重铸
library(reshape2)
crimesm <- melt(crimes, id=1)
head(crimesm)
## state variable value
## 1 alabama Murder 13.2
## 2 alaska Murder 10.0
## 3 arizona Murder 8.1
## 4 arkansas Murder 8.8
## 5 california Murder 9.0
## 6 colorado Murder 7.9
map_data <- map_data("state")
#绘制地图，使用Murder进行着色
ggplot(crimes, aes(map_id=state))+
geom_map(aes(fill=Murder), map=map_data)+
expand_limits(x=map_data$long, y=map_data$lat)
image.png
完整代码如下：
library(ggplot2)
if (!require("maps")) install.packages("maps")
library(maps)
output(head(USArrests))
crimes <- data.frame(state=tolower(rownames(USArrests)), USArrests)
output(head(crimes))
if (!require("reshape2")) install.packages("reshape2")
library(reshape2)
crimesm <- melt(crimes, id=1)
output(head(crimesm))
map_data <- map_data("state")
ggplot(crimes, aes(map_id=state))+
geom_map(aes(fill=Murder), map=map_data)+
expand_limits(x=map_data$long, y=map_data$lat)
ggsave(gettempfile(), width = 15, height = 10, dpi=300,units = "cm")
