注：R语言的再复习之路    

1. 散点图

# 添加最佳拟合曲线的散点图 attach(mtcars) plot(wt, mpg) abline(lm(mpg ~ wt), col = 'red', lwd = 2, lty = 1) lines(lowess(wt, mpg), col = 'blue', lwd = 2, lty = 2) # scatterplot()函数 library(car) scatterplot(mpg ~ wt | cyl, data = mtcars, lwd = 2, boxplots = 'xy')

1.1 散点图矩阵

# pairs()函数 pairs(~ mpg + disp + drat + wt, data = mtcars) # scatterplotMatrix()函数 library(car) scatterplotMatrix(~ mpg + disp + drat + wt, data = mtcars) # pairs2()函数 library(TeachingDemos) pairs2(mtcars[, c(1, 3, 4)], mtcars[, c(5, 6, 7)], panel = panel.smooth) # xysplom()函数 library(HH) xysplom(mpg + disp ~ wt + drat | cyl, data = mtcars, layout = c(3, 4)) # kepairs()函数 library(ResourceSelection) kdepairs(mtcars[, c(1, 3, 5)])

1.2 高密度散点图

# 生成数据 set.seed(1234) n <- 10000 c1 <- matrix(rnorm(n, mean = 0, sd = .5), ncol = 2) c2 <- matrix(rnorm(n, mean = 3, sd = 2), ncol = 2) mydata <- rbind(c1, c2) mydata <- as.data.frame(mydata) names(mydata) <- c('x', 'y') # smoothScatter()函数 with(mydata, smoothScatter(x, y, main = 'Scatter Plot Colored by Smoothed Densities')) # hexbin()函数 library(hexbin) with(mydata, {bin <- hexbin(x, y, xbins = 50) plot(bin) })

1.3 三维散点图

library(scatterplot3d) attach(mtcars) s3d <- scatterplot3d(wt, disp, mpg, pch = 16, highlight.3d = TRUE, type = 'h', main = '3D Scatter Plot with Vertical Lines and Regression Plane') fit <- lm(mpg ~ wt + disp) s3d$plane3d(fit)

1.4 旋转三维散点图

# 方法1 library(rgl) attach(mtcars) plot3d(wt, disp, mpg, col = 'red', size = 5) # 方法2 library(car) with(mtcars, scatter3d(wt, disp, mpg))

1.5 气泡图

attach(mtcars) r <- sqrt(disp/pi) symbols(wt, mpg, circle = r, inches = .30, fg = 'white', bg = 'lightblue') text(wt, mpg, rownames(mtcars), cex = .6) attach(mtcars)

   

2. 折线图

类型图形外观p只有点l只有线o实心点和线（点覆盖在线上）b, c线连接点（c时不绘制点）s, S阶梯线h直方图式的垂直线n不生成任何点和线

如果对图形有要求，你可以先通过plot()函数中的type = 'n'选项来创建坐标轴、标题和其他图形特征，然后再使用lines()函数添加各种需要绘制的曲线

Orange$Tree <- as.numeric(Orange$Tree) ntrees <- max(Orange$Tree) xrange <- range(Orange$age) yrange <- range(Orange$age) plot(xrange, yrange, type = 'n', xlab = 'Age (days)', ylab = 'Circumference (mm)') colors <- rainbow(ntrees) linetype <- c(1:ntrees) plotchar <- seq(18, 18 + ntrees, 1) for (i in 1:ntrees){ tree <- subset(Orange, Tree == i) lines(tree$age, tree$circumference, type = 'b', lwd = 2, lty = linetype[i], col = colors[i], pch = plotchar[i])} title('Tree Growth', 'example of line plot') legend(xrange[1], yrange[2], 1:ntrees, cex = .8, col = colors, pch = plotchar, lty =linetype, title = 'Tree')

   

3. 相关图

library(corrgram) corrgram(mtcars, order = TRUE, lower.panel = panel.shade, upper.panel = panel.pie, text.panel = panel.txt, main = 'Corrgram of mtcars intercorrelations') 位置面板选项描述非对角线panel.pie用饼图的填充比例表示相关性大小非对角线panel.shade用阴影的深度来表示相关性大小非对角线panel.ellipse画一个置信椭圆和平滑曲线非对角线panel.pts画一个散点图非对角线panel.conf画出相关性及置信区间主对角线panel.txt输出变量名主对角线panel.minmax输出变量的最大值最小值和变量名主对角线panel.ednsity输出核密度曲线和变量名

   

4. 马赛克图

# 格式 mosaic(formula, data = ) # 例子 library(vcd) mosaic(Titanic, shade = TRUE, legend = TRUE) # 或者 mosaic(~ Class + Sex + Age + Survived, data = Titanic, shade = TRUE, legend = TRUE)