R语言实战学习笔记-基本图形的绘制

 

本文主要介绍R语言中基本图形的绘制，包含以下几种图形：1.条形图 2.饼图 3.直方图 4.核密度图 5.箱线图 6.点图

1.直方图的绘制

 

#直方图绘制
barplot(height) #height是一个向量或者矩阵
a<-c(1,2,3,4,5,6) #垂直直方图
barplot(a,main="Simple Bar Plot",xlab="Improtved",ylab="Frequency") #水平直方图
barplot(a,main="Simple Bar Plot",xlab="Improtved",ylab="Frequency",horiz=TRUE)

 

 

 

#barplot(height) #height为矩阵时，绘制的是堆砌条形图或分组条形图
library(vcd) counts<-table(Arthritis$Improved,Arthritis$Treatment) #绘制堆叠图
barplot(counts,main="Stacked Bar Plot",xlab="Treatment",ylab="Frquency",col=c("red","yellow","green"),legend=rownames(counts)) #绘制分组图
barplot(counts,main="Grouped Bar Plot",xlab="Treatment",ylab="Grequency",col=c("red","yellow","green"),legend=rownames(counts),beside=TRUE)
#下面为counts的值

Placebo Treated
None 29 13
Some 7 7
Marked 7 21

#直方图的微调 #增加Y边界的大小
par(mar=c(5,8,4,2)) #旋转条形的标签
par(las=2) counts<-table(Arthritis$Improved) barplot(counts,main="Treatment Outcome",horiz=TRUE,cex.names=0.8,names.arg=c("No improved","some Improved","Marked Improved"))

#简单的棘状图
library(vcd) attach(Arthritis) counts<-table(Treatment,Improved) spine(counts,main="Spinogram Example") detach(Arthritis)

2.饼图的绘制

#饼图的绘制
pie(x,labels) #x为一个非负值向量,表示每个扇形的面积,labels为各个扇形标签的字符型向量
par(mfrow=c(2,2)) slices<-c(10,12,4,16,8) #基本的饼图
lbls<-c("UK","US","Australia","Germany","France") pie(slices,labels=lbls,main="Simple Pie Chart") #为饼形图添加比例数值
pct<-round(slices/sum(slices)*100) lbls2<-paste(lbls,"",pct,"%",sep="") pie(slices,labels=lbls2,col=rainbow(length(lbls2)),main="pie chart with Percentages") #3D饼形图
library(plotrix) pie3D(slices,labels=lbls,explode=0.1,main="3D Pie Chart") #从表格创建饼图
mytable<-table(state.region) lbls3<-paste(names(mytable),"\n",mytable,sep="") pie(mytable,labels=lbls3,main="Pie Chart form a Table")

#绘制容易看出大小的饼图
library(plotrix) slices<-c(10,12,4,16,8) lbls<-c("US","UK","Australia","Germany","France") fan.plot(slices,labels=lbls,main="Fan Plot")

 

 

 3.绘制直方图

直方图：通过在X轴上将值域分割为一定数量的组，在Y轴上显示相应的频数，展示连续变量的分析

#绘制直方图
par(mfrow=c(2,2)) #简单的直方图
hist(mtcars$mpg) #设置直方图的颜色，添加坐标轴标签
hist(mtcars$mpg,breaks=12,col="red",xlab="Miles Per Gallon", main="Colored histogram with 12 bins") #添加轴须图
hist(mtcars$mpg,freq=FALSE,breaks=12,col="red",xlab="Miles per Gallon",main="Histogram ,rug plot,density curve") rug(jitter(mtcars$mpg)) lines(density(mtcars$mpg),col="blue",lwd=2) #添加正态分布密度曲线和外框
x<-mtcars$mpg h<-hist(x,breaks=12,col="red",xlab="Miles Per Gallon",main="Histogram with normal curve and box") xfit<-seq(min(x),max(x),length=40) yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) yfit<-yfit*diff(h$mids[1:2]*length(x)) lines(xfit,yfit,col="blue",lwd=2) box()
#上述图形比较复杂，后续结合具体实例深入研究

4.绘制核密度图

核密度：核密度估计师用于估计随机变量概率密度函数的一种非参数方法

#绘制核密度图
par(mfrow=c(2,1)) d<-density(mtcars$mpg) plot(d) d<-density(mtcars$mpg) plot(d,main="kernel Density of Miles Per Gallon") polygon(d,col="red",border="blue") rug(mtcars$mpg,col="brown")

 

 

#一个图形中绘制多个核密度图
library(sm) attach(mtcars) cyl.f<-factor(cyl,levels=c(4,6,8),labels=c("4 cylinder","6 cylinder","8 cylinder")) sm.density.compare(mpg,cyl,xlab="Miles Per Gallon") title(main="MPG Distribution by Car Cylinders") detach(mtcars)

5.箱形图

箱形图：通过绘制连续性变量的五数总括，即最小值、下四分位、中位数、上四分位数、最大值

boxplot(mtcars$mpg,main="Box plot",ylab="Miles per Gallon")

#一个图中绘制多个箱形图
boxplot(formula,data=data.frame) boxplot(mpg~cyl,data=mtcars,main="Car Mileage Data",xlab="Number of Cylinders",ylab="Miles Per Gallon")

#凹槽型箱形图
boxplot(mpg~cyl,data=mtcars,notch=TRUE,col="red",main="Car Mileage Data",xlab="Number of Cylinders",ylab="Miles Per Gallon")

 

#绘制交叉因子箱形图
mtcars$cyl.f<-factor(mtcars$cyl,levels=c(4,6,8),labels=c("4","6","8")) mtcars$am.f<-factor(mtcars$am,levels=c(0,1),labels=c("auto","standard")) boxplot(mpg~am.f *cyl.f, data=mtcars, varwidth=TRUE, col=c("gold","darkgreen"), main="MPG Distribution by Auto Type", xlab="Auto Type", ylab="Miles Per Gallon")

#小提琴箱形图
library(vioplot) x1<-mtcars$mpg[mtcars$cyl==4] x2<-mtcars$mpg[mtcars$cyl==6] x3<-mtcars$mpg[mtcars$cyl==8] vioplot(x1,x2,x3,names=c("4 cyl","6 cyl","8 cyl"),col="gold")

6.点图

#水平刻度上绘制大量有标签值的方法
dotchart(x,labels) dotchart(mtcars$mpg,labels=row.names(mtcars),cex=0.7,main="Gas Mileafe for car Models",xlab="Miles Per Gallon")

#进行调整的点图
x<-mtcars[order(mtcars$mpg),] x$cyl<-factor(x$cyl) x$color[x$cyl==4]<-"red" x$color[x$cyl==6]<-"blue" x$color[x$cyl==8]<-"darkgreen" dotchart(x$mpg,labels=row.names(x),cex=0.7,groups=x$cyl,gcolor="black",color=x$color,pch=19,main="Gas Mileage for car Models",xlab="Miles Per Gallon")

以上简单探索了R语言中的集中常用图形，后续要根据具体业务需要灵活运用。