数据的可视化
描述连续型变量的分布
条形图（柱状图）
简单条形图
library(vcd) counts <- table(Arthritis$Improved) barplot(counts,main="Simple Bar Plot", + xlab="Improvement", ylab="Frequency")
barplot(counts,main="Horizontal Bar Plot", + ylab="Improvement", xlab="Frequency",horiz=TRUE)
堆砌条形图和分组条形图
counts<-table(Arthritis$Improved, Arthritis$Treatment) barplot(counts, main="Stacked Bar Plot", + xlab="Treatment",ylab="Frequency", + col=c("red","yellow","green"), + legend=rownames(counts) + )
barplot(counts, main="Grouped Bar Plot", + xlab="Treatment",ylab="Frequency", + col=c("red","yellow","green"), + legend=rownames(counts),beside=TRUE)
表示均值、中位数、标准差的条形图: 使用数据整合函数来生成条形图
barplot(means$x, names.arg=means$group.1) states <- data.frame(state.region, state.x77) means <- aggregate(states$Illiteracy, by=list(state.region),FUN=mean) means <- means[order(means$x),] barplot(means$x, names.arg=means$group.1) title("Mean Illiteracy Rate") lines(means$x, type="b", pch=17, lty=2, col="red")
饼图
pie(slices, labels=lbls, main="Simple Pie Chart") par(mfrow=c(2,2)) 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 Percentages")
mytable <- table(state.region) lbls3 <- paste(names(mytable),"\n",mytable, sep="") pie(mytable, labels=lbls,main="Pie Chart from a Table\n(with sample sizes)")
扇形图
library(plotrix) slices <- c(10,12,4,15,8) lbls <- c("US","UK","Australia","Germany","France") fan.plot(slices, labels=lbls, main="Fan Plot")
描述连续型变量的分布
直方图
par(mfrow=c(2,2)) hist(mtcars$mpg)
hist(mtcars$mpg, + breaks=12, + col="red", + xlab="Miles Per Gallon", + main="Colored histgram with 12 bins")
hist(mtcars$mpg, + freq=FALSE, + breaks=12, + col="red", + xlab="Miles Per Gallon", + main="Histgram,rug plot, density") rug(jitter(mtcars$mpg)) lines(density(mtcars$mpg),col="blue",lwd=2)
x <- mtcars$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$mids[1:2])*length(x) lines(xfit,yfit,col="blue",lwd=2) box()
核密度图
描述连续型随机变量概率密度的一种方法
par(mfrow=c(2,1)) d <- density(mtcars$mpg) plot(d)
plot(d,main="Kernel Density of Miles Per Gallon") polygon(d,col="red",border="blue") rug(mtcars$mpg,col="brown")
par(lwd=2) library(sm) Package 'sm', version 2.2-5.6: type help(sm) for summary information cyl.f <- factor(mtcars$cyl, levels=c(4,5,6), + labels=c("4 cylinder","6 cy;inder","8 cylinder")) sm.density.compare(mtcars$mpg,mtcars$cyl, xlab="Miles Per Gallon") title(main="Mpg Distribution by Car Clyinders") colfill <- c(2:(1+length(levels(cyl.f)))) legend(locator(1),levels(cyl.f),fill=colfill)
鼠标点击确定放置图例位置
箱线图
boxplot(mtcars$mpg,main="Box Plot", ylab="Miles per Gallon") boxplot.stats(mtcars$mpg)
boxplot(mpg~cyl,data=mtcars, + main="Car Mileage Data", + xlab="Number of Cylinders", + ylab="Miles Per Gallon")
如果凹槽不重叠，表明它们的中位数有显著差异
boxplot(mpg~cyl,data=mtcars, + notch=TRUE, + varwidth=TRUE, + col="red", + main="Car Mileage Data", + xlab="Number of Cylinders", + ylab="Miles Per Gallon")
小提琴图
x1<-mtcars$mpg[mtcars$cyl==4] x2<-mtcars$mpg[mtcars$cyl==6] x3<-mtcars$mpg[mtcars$cyl==8] library(vioplot) vioplot(x1,x2,x3, + names=c("4","6","8"), + col="gold")
点图
dotchart(mtcars$mpg, labels=row.names(mtcars),cex=.7)
mtc <- mtcars[order(mtcars$mpg),] mtc$cyl <- factor(mtc$cyl) mtc$color[mtc$cyl==4] <- "red" mtc$color[mtc$cyl==6] <- "blue" mtc$color[mtc$cyl==8] <- "darkgreen" dotchart(mtc$mpg, + labels=row.names(mtc), + cex=.7, + groups=mtc$cyl, + gcolor="black", + color=mtc$color, + pch=19, + main="Gas Mileage for Car Models\ngrounded by cylinder", + xlab="Miles Per Gallon")
