今天画的图都是简单图形，大部分EXCEL就能实现，但是当做复习记录，还是再来重述一遍。
1：饼状图（用于描述量，频率或者百分比之间的相对关系）
> countries <- c("Brazil", "Russia", "India", "China", "South Africa") > GDP <- c(23920, 20790, 18618, 94906, 3066) > pie(GDP, labels = countries, mian = "GDP of BRICS countries (2013)")
> pie(GDP, labels = countries, main = "GDP of BRICS countries (2013)",clockwise = T) # 显示方向次序
> pie(GDP, labels = countries, main = "GDP of BRICS countries (2013)",clockwise = T, col = gray(seq(0.4,1.0, length = 5))) # 按灰度来显示颜色
> per <- round(GDP/sum(GDP)*100, 2) > index <- paste(countries, "", per, "%", sep = "") > pie(GDP, labels = index, col = rainbow(length(index)), main = "Pie Chart with Percentages")
round()#四舍五入，保留两位小数点，使用rainbow（）函数来定义各个扇形的颜色。
> library(plotrix) > pie3D(GDP, labels = countries, explode = 0.1, main = "3D Pie Chart")
> fan.plot(GDP, labels = countries, main = "Fan Plot")
2：直方图
> attach(mtcars) > mpg [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 32.4 30.4 33.9 21.5 [22] 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4 > hist(mpg) > hist(mpg,breaks = 12, col = "lightblue", border = "pink", xlab = "Miles/Gallon", main = "Colored Histogram Example.1") > hist(mpg,breaks = 12, col = "blue1", xlim = c(10, 35), xlab = "Miles/Gallon", main = "Colored Histogram Example.2")参数breaks的赋值为12后，矩形条同时被等分为12个档次。
正常情况下绘制归一化直方图应该是通过将参数freq的值置为FALSE来实现。
3：核密图（用于估计随时变量概率密度的一种非参数方法，也可以用于组间差异）
> d <- density(mpg) > plot(d)
还可以用于组间差异（箱线图也是常用的图形软件）
> plot(density(mtcars[mtcars$cyl == 4,]$mpg), col = "red", lty = 1, xlim = c(5,40), ylim = c(0,0.25),xlab = "", main = "") > par(new = T) > plot(density(mtcars[mtcars$cyl == 6,]$mpg), col = "blue", lty = 1, xlim = c(5,40), ylim = c(0,0.25),xlab = "", main = "") > par(new = T) > plot(density(mtcars[mtcars$cyl == 8,]$mpg), col = "green", lty = 1, xlim = c(5,40), ylim = c(0,0.25),xlab = "", main = "MPG Distribution by Cylinders") > par(new = T) > legend("topright", legend = text.legend, lty = c(1,2,3), col = c("red","blue", "green"))
