【数据挖掘】实验6：初级绘图

实验6：初级绘图

一：实验目的与要求

1：了解R语言中各种图形元素的添加方法，并能够灵活应用这些元素。

2：了解R语言中的各种图形函数，掌握常见图形的绘制方法。

二：实验内容

【直方图】

Eg.1：画出cars数据集中speed的直方图

|------------------|
| hist(cars$speed) |

【条形图】

Eg.1：VADeaths数据集的条形图------beside=T，horiz=F

|-------------------------------|
| barplot(VADeaths, beside = T) |

Eg.2：VADeaths数据集的条形图------beside=F，horiz=T

|-------------------------------|
| barplot(VADeaths, beside = T) |

Eg.3：VADeaths数据集的条形图------beside=T，horiz=T

|------------------------------------------|
| barplot(VADeaths, beside = T, horiz = T) |

【饼图】

Eg.1：数据集VADeaths展示不同人群死亡率的占比情况

|-------------------------------------------------------------------------------------------------------------------------------|
| percent <- colSums(VADeaths)*100/sum(VADeaths) pie(percent, labels = paste0(colnames(VADeaths),'\n',round(percent,2),'%')) |

【散点图】

Eg.1：cars数据集的速度与刹车距离的散点图，绘制方法1

|-------------------------------|
| plot(cars[, 1], cars[,2]) |

Eg.2：cars数据集的速度与刹车距离的散点图，绘制方法2

|------------|
| plot(cars) |

【散点矩阵图】

Eg.1：iris数据集为例，用pairs函数绘制散点矩阵图

|-------------------------------------------------------------------------------------------------------------|
| # 方法1 pairs(iris[,1:4]) # 方法2 pairs(~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris) |

【多变量相关矩阵图】

Eg.1：mtcars数据集绘图

[1] 相关图，主对角线上方绘制置信椭圆和平滑拟合曲线，主对角线下方绘制阴影

[2] 相关图，主对角线上方绘制散点图，主对角线下方绘制饼图

[3] 相关图，主对角线上方绘制置信区间，主对角线下方绘制相关系数

【核密度图】

Eg.1：

|--------------------------------------------------------------------------------------|
| install.packages("sm") library(sm) sm.density.compare(mtcars $wt, factor(mtcars$ cyl)) |

【小提琴图】

Eg.1：mtcars数据集wt的小提琴图

【QQ图】

Eg.1：mtcars数据集wt的QQ图

|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| qqnorm(wt) qqline(wt) qqplot(qt(ppoints(length(wt)), df = 5), wt,xlab = "Theoretical Quantiles", ylab = "Sample Quantiles", main = "Q-Q plot for t dsn") qqline(wt) |

【星状图】

Eg.1：

|---------------------------------|
| stars(mtcars,draw.segments = T) |

【等高图】

Eg.1：

|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| library(KernSmooth) mtcars1 = data.frame(wt, mpg) est = bkde2D(mtcars1, apply(mtcars1, 2, dpik)) contour(est $x1, est$ x2, est$fhat, nlevels = 15, col = "darkgreen", xlab = "wt",ylab = "mpg") points(mtcars1) |

【固定颜色选择函数】

Eg.1：查看前20种颜色

|------------------|
| colors()[1:20] |

Eg.2：打印657种颜色

Eg.3：固定调色板

|------------------------------------------------------------------|
| palette() palette(colors()[1:10]) palette() palette('default') |

Eg.4：在不同Species使用不同的颜色绘制散点，以便区分种类

|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| # 方法1 plot(iris $Sepal.Length, iris$ Sepal.Width, col = iris $Species) # 方法2 plot(iris$ Sepal.Length, iris$Sepal.Width, col = rep(palette()[1:3], each = 50)) |

Eg.5：渐变色生成函数调色板的颜色样式

【RColorBrewer依赖包】

Eg.1：颜色展示

|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| par(mfrow = c(1,3)) library(RColorBrewer) par(mar=c(0.1,3,0.1,0.1)) display.brewer.all(type="seq") display.brewer.all(type="div") display.brewer.all(type="qual") |

Eg.2：绘制颜色散点图

【axis函数绘图】

Eg.1：

【legend函数绘制图例】

Eg.1：

【par函数】

Eg.1：

|-----------------------------------------------------------------------------------|
| mfrow1=par(mfrow=c(2,3)) for(i in 1:6){ plot(c(1:i),main=paste("I'm image:",i)) } |

【layout函数】

Eg.1：

|-----------------------------------------------------------------------------------------------------------------------------------|
| mat<-matrix(c(1,1,2,3,3,4,4,5,5,6), nrow = 2, byrow = TRUE) layout(mat) for(i in 1:6){ plot(c(1:i),main=paste("I'm image:",i)) } |

【保存图形】

Eg.1：

|---------------------------------------------------------------------------------|
| jpeg(filename = "C:/Users/86158/Desktop/iris.jpg") plot(iris[,1:4]) dev.off() |

Eg.2：657种颜色的打印并保存为PDF文件格式

三：课堂练习

【练习1】PPT-08，第18页，iris箱线图

Eg.1：合并显示

|----------------------------------------------------------------------------------------------------------------------------|
| par(mfrow = c(1,2)) boxplot(iris[1:4], main = '单独的箱线图') boxplot(Sepal.Length ~ Species, data = iris, main = '组间比较的箱线图') |

Eg.2：单独显示

|----------------------------------------------------------------------------------------------------------------------------|
| par(mfrow = c(1,1)) boxplot(iris[1:4], main = '单独的箱线图') boxplot(Sepal.Length ~ Species, data = iris, main = '组间比较的箱线图') |

【练习2】绘图散点图

Eg.1：

|------------------------------------------------------------------------------------------|
| attach(mtcars) # 绑定数据框mtcars plot(wt, mpg) # 打开图形窗口，绘制散点图 detach(mtcars) # 解除绑定数据框mtcars |

【练习3】palette函数的应用

Eg.1：

【练习4】渐变色生成函数

Eg.1：

Eg.2：

【练习5】点的样式

Eg.1：

【练习6】改变点的样式

Eg.1：

|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| attach(mtcars) # 绑定数据框mtcars cyl <- as.factor(cyl) plot(wt, mpg, col = "white") points(wt, mpg, pch = as.integer(cyl) + 1, col = as.integer(cyl) + 1) legend(5, 35, c(4, 6, 8), pch = 2:4, col = 2:4, bty = "n") |

Eg.2：

|---------------------------------------------------------------------------------------------------------------------------------------------------------------|
| plot(wt, mpg, pch = as.integer(cyl) + 1, col = as.integer(cyl) + 1) legend(5, 35, c(4, 6, 8), pch = 2:4, col = 2:4, bty = "n") detach(mtcars) # 解除绑定数据框mtcars |

运行结果：同上图。

【练习7】使用title()展示标题位置

Eg.1：

|----------------------------------------------------------------------------------------------------------------------------------------------------|
| plot(c(0:5), col = "white", xlab = "", ylab = "") title(main = list("主标题", cex = 1.5), sub = list("副标题", cex = 1.2), xlab = "x轴标题", ylab = "y轴标题") |

【练习8】使用text()展示字体样式、字体大小

Eg.1：

【练习9】mtext()展示文本位置

Eg.1：

|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| plot(c(0:5), col = "white") mtext("side = 1:下边", side = 1, line = 2); mtext("side = 2:左边" , side = 2, line = 2) mtext("side = 3:上边", side = 3); mtext("side = 4:右边" , side = 4) |

【练习10】以mtcars数据集为例，将散点图变为文本字符

Eg.1：

Eg.2：

|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| plot(wt, mpg, pch = as.character(cyl), col = as.integer(cyl) + 1, xlab = "Weight", ylab = "Miles per Gallon ", main = "Miles per Gallon vs. Weight by Cylinder", cex.main = 1.5) |

运行结果：同上图。

【练习11】线的样式和线的宽度

Eg.1：线的样式

Eg.2：线的宽度

【练习12】添加参考线

Eg.1：

|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| # 绘制空白画布 plot(c(0:10), col = "white") # 添加水平线 abline(h = c(2, 6, 8)) # 添加垂直线 abline(v = seq(2, 10, 2), lty = 2, col = "blue") # 添加直线y = 2+x abline(a = 2, b = 1) |

【练习13】添加线段和箭头

Eg.1：

|-----------------------------------------------------------------------------------------------------------------|
| plot(c(0:10), col = "white") segments(2, 1, 4, 8) arrows(4, 0, 7, 3, angle = 30) arrows(4, 2, 7, 5, angle = 60) |

【练习14】添加网格线

Eg.1：

|--------------------------------------------------------------------------------------------------|
| plot(c(0:10), col = "white") # 空白画布 grid(nx = 4, ny = 8, lwd = 1, lty = 2, col = "blue") # 添加网格线 |

【练习15】添加坐标轴须

Eg.1：

|-----------------------------------------------------------------------------------------------------------------|
| set.seed(123) # 种子 x <- rnorm(500) # 生成500个标准正态分布的数据 plot(density(x)) # 绘制核密度曲线 rug(x , col = "blue") # 添加坐标轴须 |

【练习16】以mtcars数据集为例查看不同的线元素函数的用法

Eg.1：

|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| smpg <- (mpg - min(mpg)) / (max(mpg) - min(mpg)) plot(wt, smpg, ylab = "standardized mpg") # 添加核密度曲线图 lines(density(wt), col = "red") # 指向密度曲线的箭头 arrows(1.8, 0.05, 1.5, 0.1, angle = 10, cex = 0.5) text(2, 0.05, "核密度曲线", cex = 0.6) # 添加回归线 abline(lm(smpg ~ wt), lty = 2, col = "green") # 指向回归直线的箭头 arrows(2, 0.5, 2, 0.7, angle = 10, cex = 0.5) text(2, 0.45, "回归线", cex = 0.6) # wt与mpg反向线性相关，添加最大最小值线段表现这种关系 segments(min(wt), max(smpg), max(wt), min(smpg), lty = 3, col = "blue") # 指向最大最小值线段的箭头 arrows(3, 0.8, 2.5, 0.76, angle = 10, cex = 0.5) text(3.3, 0.8, "最大最小值线段", cex = 0.6) # 添加网格线作为背景 grid(nx = 4, ny = 5, lty = 2, col = "grey") |

Eg.2：

【练习17】输出到屏幕

Eg.1：

|----------------------------------------------------------------------------------|
| windows() # 打开图形设备界面 attach(mtcars) plot(wt, mpg) X11() # 打开图形设备界面 plot(wt, mpg) |

【练习18】以mtcars为例，生成的直方图

Eg.1：

|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| op <- par(mfrow = c(2, 3), mar = c(4, 4, 2, 0.5), mgp = c(2, 0.5, 0)) hist(wt, main = "freq = TRUE") # 默认的频数直方图，左下，中上，中下，右上，右下 hist(wt, breaks = 5, main = "breaks = 5") # 减小区间段数的直方图 hist(wt, col = "light blue", main = "colored") # 给直方图的柱形添加颜色 hist(wt, freq = FALSE, main = "freq = FALSE") # 概率密度直方图 hist(wt, breaks = 40, main = "breaks = 40") # 增大区间段数的直方图 # 在直方图上添加密度曲线和正态分布概率密度曲线 hist(wt, freq = FALSE, main = "with density curve and normal curve") lines(density(wt), col = "blue") lines(density(rnorm(1e+6, mean(wt), sd(wt))), lty = 2, col = "red") par(op) |

【练习19】绘图箱线图

Eg.1：

|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| set.seed(1234) normal <- rnorm(100, mean(wt), sd(wt)) # 生成100个正态分布数据 op <- par(mfrow = c(1, 3)) boxplot(list(wt, normal), xaxt = "n") # 绘制箱线图 axis(1, at = 1:2, labels = c("wt", "normal")) # 添加坐标轴 rug(wt, side = 2, col = 2); rug(normal, side = 4, col = 3) # 添加坐标轴须 legend("bottomleft", c("wt", "normal"), lty = 1, col = 2:3, bty = "n") # 添加图例 boxplot(list(wt, normal), xaxt = "n", varwidth = TRUE) rug(wt, side = 2, col = 2); rug(normal, side = 4, col = 3) axis(1, at = 1:2, labels = c("wt", "normal")) legend("bottomleft", c("wt", "normal"), lty = 1, col = 2:3, bty = "n") boxplot(wt ~ cyl) rug(wt[cyl == 4], side = 2, col = 2); rug(wt[cyl == 6], side = 4, col = 3) rug(wt[cyl == 8], side = 2, col = 4) legend("topleft", c("4", "6", "8"), lty = 1, col = 2:4, bty = "n") par(op) |

【练习20】绘图小提琴图

Eg.1：

|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| # 页面分割掉1/2，为与箱线图和核密度图对比而作，小提琴图只需要第二个语句即可 par(fig = c(0, 1, 0.5, 1), mfrow = c(2, 1)) # 绘制小提琴图 library(vioplot) vioplot(wt[cyl == 4], wt[cyl == 6], wt[cyl == 8], border = "black", col = "light green", rectCol = "blue", horizontal = TRUE) # 分割另外1/2页面 par(fig = c(0, 1, 0, .5), mar = c(0, 2, 0, 0.5) , new = TRUE) # 绘制箱线图 boxplot(wt ~ cyl, horizontal = TRUE, pars = list(boxwex = 0.1), border = "blue") # 在箱线图上叠加核密度图 par(fig = c(0, 0.53, 0.1, 0.2), new = TRUE) plot(density(wt[cyl == 4], bw = 0.3), xaxt = "n", yaxt = "n", ann = FALSE, bty = "n") par(fig = c(0.26, 0.56, 0.25, 0.35), new = TRUE) plot(density(wt[cyl == 6], bw = 0.3), xaxt = "n", yaxt = "n", ann = FALSE, bty = "n") par(fig = c(0.33, 1, 0.4, 0.5), new = TRUE) plot(density(wt[cyl == 8], bw = 0.5), xaxt = "n", yaxt = "n", ann = FALSE, bty = "n") |

【练习21】绘图条形图

Eg.1：

|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| bardata <- table(cyl, carb) # 得到表格数据 pal <- RColorBrewer::brewer.pal(3, "Set1") # 颜色调配 op <- par(mfrow = c(2, 2), mar = c(3, 3, 3, 2), mgp = c(1.5, 0.5, 0)) barplot(bardata, col = pal, beside = TRUE, xlab = "carb") # 分组条形图 legend("topright", c("4", "6", "8"), pch = 15, col = pal, bty = "n") barplot(bardata, col = pal, xlab = "carb") # 默认堆砌条形图 legend("topright", c("4", "6", "8"), pch = 15, col = pal, bty = "n") barplot(bardata, col = pal, beside = TRUE, horiz = TRUE, ylab = "carb") # 水平放置的条形图 legend(5.3, 26, c("4", "6", "8"), pch = 15, col = pal, bty = "n", cex = 0.6) barplot(bardata, col = pal, beside = TRUE, ylim = c(0, 7), xlab = "carb") legend("topright", c("4", "6", "8"), pch = 15, col = pal, bty = "n") # 显示数值 text(labels = as.vector(bardata), cex = 0.7, x = c(1.5:23.5)[1:23 %% 4 > 0], y = as.vector(bardata) + 0.5) par(op) |

【练习22】绘图点图

Eg.1：

|-----------------------------|
| dotchart(bardata, bg = pal) |

【练习23】绘图饼图

Eg.1：

|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| percent <- as.vector(table(cyl)) / sum(as.vector(table(cyl))) * 100 # 计算百分比 pie(table(cyl), labels = paste(c("4", "6", "8"), "cylinders:", percent, "%")) # 画饼图 |

【练习24】用methods()查plot()的作图方法

Eg.1：

|-----------------|
| methods("plot") |

【练习25】plot()函数的应用

Eg.1：

Eg.2：

【练习26】绘图散点图矩阵

Eg.1：绘图对象为公式

|-------------------------------------------------------------------------------------------------------------------|
| pairs( ~ mpg + disp + drat + wt, data = mtcars, col = as.integer(factor(cyl)) + 1, main = "Scatter Plot Matrix") |

Eg.2：绘图对象为数据框

|---------------------------------------------------------------------------------------------------|
| pairs(mtcars[, c(1, 3, 5, 6)], col = as.integer(factor(cyl)) + 1, main = "Scatter Plot Matrix") |

运行结果：同上图。

Eg.1：

|----------------------------------------------------------------------------------------------------------------------|
| mosaicdata <- ftable(cyl, vs) # 二维列联表 par(mfrow = c(1, 1)) mosaicplot(mosaicdata, shade = TRUE, main = "") # 绘制马赛克图 |