R语言基本图形设置

Posted 2021-04-18 一只数据狗的成长之路

在图形设计中特别要注意图形参数的设置，一个参数错误会导致脚本无法运行，报错后也不知道如何修改，这个时候是最尴尬的。

#R语言基本数据图形

#当显示为四分之一图形时可以用par(mfrow=c(1,1))实现显示整个图片，在脚本后面加入

barplot(height = 5)

library(vcd)

counts <- table(Arthritis$Improved)#读入Arthritis数据集

counts

barplot(counts,main = "Simple Bar Plot",xlab = "Improvement",ylab = "Frequency"

        ,horiz = T)#简单条形图,horiz = 表示把条形图转置过来

#绘制堆砌直方图和分组条形图

library(vcd)

counts <- table(Arthritis$Improved, Arthritis$Treatment)#读入Arthritis数据集

counts

barplot(counts,main = "Stacked Bar Plot",

        xlab = "Treatment",ylab = "Frequency",

        col=c("red","yellow","green"),

        legend=rownames(counts),beside = T)#不加beside为堆砌条形图，加上为分组条形图

states <- data.frame(state.region,state.x77)

means <- aggregate(states$Illiteracy,by=list(state.region),FUN=mean)#均值条形图

means

means <- means[order(means$x),]

means

barplot(means$x,names.arg = means$Group.1,col = rainbow(10))

title("Mean Illiteracy Rate")

par(mar=c(5,8,4,2))#增加边界的大小

par(las=2)#旋转条形的标签

counts <- table(Arthritis$Improved)

barplot(counts,

        main = "Treatment Outcome",

        horiz = T,

        cex.names = 0.8,#缩小字体大小

        names.arg = c("No Improvement","Some Improvement",

                      "Marked Improvement"),col = c("yellow","green","red"))#修改标签文本

#棘状图

library(vcd)

attach(Arthritis)

counts <- table(Treatment,Improved)

spine(counts, main = "Spinorgram Examole")#画棘状图

detach(Arthritis)

#饼图

par(mfrow=c(2,2))#图形显示为2乘2区域

slices <- c(10,12,4,16,8)

lbls <- c("US","UK","Australia","Germany","France")

pie(slices,labels = lbls,main = "Simple pie Chart")

pct <- round(slices/sum(slices)*100)

lbls2 <- paste(lbls," ",pct,"%",sep = " ")

pie(slices,labels = lbls2,col = rainbow(length(lbls2)),

    main = "pie chart with percengtager")

library(plotrix)

pie3D(slices,labels=lbls,explode=0.1,

      main="3D pie chart")

mytable <- table(state.region)

lbls3 <- paste(names(mytable),"\n",mytable,sep = " ")

pie(mytable,labels = lbls3,main = "pie Chart from a Table\n (with sample sizes")

#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")#下方填充为红色，边线为蓝色

rug(mtcars$mpg,col = "brown")#添加棕色的轴线

#箱线图

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

boxplot(mpg ~ cyl, data = mtcars,

        main="car Mileage Data",

        xlab = "Number of Cylinders",

        ylab = "Miles per Gallon",

        col="blue")

#小提琴图

library(vioplot)

par(mfrow=c(1,1))

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","8cyl"),

        col="gold")

title("Violin plots of Miles per Gallon",ylab='Miles per Gallon',

      xlab="Number of Cylinders")

#点图

dotchart(mtcars$mpg,labels = row.names(mtcars),cex = .8,

         main = "Gas Mileage for Car Models",

         xlab = "Miles Per Gallon")

#分组，排序，着色后的点图

x <- mtcars[order(mtcars$mpg),]#根据每加仑汽油行驶英里数升序对数据框mtcars进行排序，结果保存为x

x$cyl <- factor(x$cyl)#将数值向量转化为一个因子

x$color[x$cyl==4] <- "red"

x$color[x$cyl==6] <- "blue"

x$color[x$cyl==8] <- "darkgreen"

labels = row.names(x)#各数据点的标签取各自数据框的行名即（车辆型号）

dotchart(x$mpg,

         labels = row.names(x),

         cex = .9,

         groups = x$cyl,

         gcolor = "black",#数字4和6，8显示为黑色

         color = x$color,#点和标签的颜色来自向量color

         pch = 19,

         main = "Gas Mileage for Car Models\ngrouped by cylinder",

         xlab = "Miles per Gallon",bg="light blue")