hist(cars$speed)

barplot(VADeaths, beside = T)

barplot(VADeaths, beside = T)

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

percent <- colSums(VADeaths)*100/sum(VADeaths)

pie(percent, labels = paste0(colnames(VADeaths),'\n',round(percent,2),'%'))

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

plot(cars)

# 方法1

pairs(iris[,1:4])

# 方法2

pairs(~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)

install.packages("corrgram")

library(corrgram)

corrgram(mtcars, order=TRUE, upper.panel=panel.ellipse, main="Correlogram of mtcars intercorrelations")

corrgram(mtcars, order=TRUE, upper.panel=panel.pts, lower.panel=panel.pie,main="Correlogram of mtcars intercorrelations")

corrgram(mtcars, order=TRUE, upper.panel=panel.conf, lower.panel=panel.cor,main="Correlogram of mtcars intercorrelations")

install.packages("sm")

library(sm)

sm.density.compare(mtcars$wt, factor(mtcars$cyl))

install.packages("vioplot")

library(vioplot)

attach(mtcars)

par(mfrow=c(1,2))

vioplot(wt[cyl == 4], wt[cyl == 6], wt[cyl == 8], border = "black", col = "lightgreen", rectCol = "blue", horizontal = TRUE)

title(main = '小提琴图')

boxplot(wt~cyl, main = '箱线图', horizontal=TRUE, pars=list(boxwex=0.1),border="blue")

par(mfrow = c(1, 1))

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)

stars(mtcars,draw.segments = T)

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)

colors()[1:20]

par(mfrow = c(length(colors())%/%60 + 1, 1))

par(mar=c(0.1,0.1,0.1,0.1), xaxs = "i", yaxs = "i")

for(i in 1:(length(colors())%/%60 + 1)){

  barplot(rep(1,60),col=colors()[((i-1)*60+1):(i*60)],border=colors()[((i-1)*60+1):(i*60)],axes=F)

  box()

}

palette()

palette(colors()[1:10])

palette()

palette('default')

# 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))

rgb<-rgb(red=255,green=1:255,blue=0,max=255)

par(mfrow=c(6,1))

par(mar=c(0.1,0.1,2,0.1), xaxs="i", yaxs="i")

barplot(rep(1,255),col= rgb,border=rgb,main="rgb")

barplot(rep(1,100),col=rainbow(100),border=rainbow(100),main="rainbow(100))")

barplot(rep(1,100),col=heat.colors(100),border=heat.colors(100),main="heat.colors(100))")

barplot(rep(1,100),col=terrain.colors(100),border=terrain.colors(100),main="terrain.colors(100))")

barplot(rep(1,100),col=topo.colors(100),border=topo.colors(100),main="topo.colors(100))")

barplot(rep(1,100),col=cm.colors(100),border=cm.colors(100),main="cm.colors(100))")

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")

library(RColorBrewer)

par(mfrow = c(1,2))

my_col <- brewer.pal(3,'RdYlGn')

plot(iris$Sepal.Length, iris$Sepal.Width, col = rep(my_col, each =50))

plot(iris$Sepal.Length, iris$Sepal.Width, col = rep(rainbow(3), each = 50))

plot(c(1:12), col="white", xaxt="n", yaxt="n", ann = FALSE)

axis(1, at=1:12, col.axis="red", labels=month.abb)

axis(2, at=seq(1,12,length=10), col.axis="red", labels=1:10, las=2)

axis(3, at=seq(1,12,length=7), col.axis="blue", cex.axis=0.7, tck=-0.01, labels = c("Mon", "Tues", "Wed", "Thu", "Fri", "Sat", "Sun"))

axis(4, at=seq(1,12,length=11), col.axis="blue", cex.axis=0.7, tck=-0.01, labels=seq(0, 1, 0.1),las=2)

local=c("bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", "center")

par(mar = c(4,2,4,2), pty='m')

plot(c(0:10), col = "white")

legend(3, 8, "图例在(3,8)")

legend(1, 13, "图例在(11,11)", xpd=T)

for(i in 1:9){

  legend(local[i],paste("图例在",local[i]))

}

mfrow1=par(mfrow=c(2,3))

for(i in 1:6){

  plot(c(1:i),main=paste("I'm image:",i))

}

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))

}

jpeg(filename = "C:/Users/86158/Desktop/iris.jpg")

plot(iris[,1:4])

dev.off()

pdf("colors-bar.pdf", height=120)

par(mar = c(0,10,3,0)+0.1,yaxs="i")

barplot(rep(1, length(colors())), col = rev(colors()), names.arg=rev(colors()),horiz = T, las = 1, xaxt="n", main = expression("Bars of colors in"~ italic(colors())))

dev.off()

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')

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')

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

data(mtcars)

par(mfrow = c(1, 1))

plot(mtcars$wt, mtcars$mpg, col = "blue")  

plot(mtcars$wt, mtcars$mpg, col = 4) 

attach(mtcars)   

str(mtcars)

plot(wt, mpg, col = "red", xlim = c(1.3, 5.6), ylim = c(8, 35))

points(wt[cyl == 6], mpg[cyl == 6], col = "green")

points(wt[cyl == 8], mpg[cyl == 8], col = "blue")

legend(5, 35, c(4, 6, 8), pch = 1, col = c("red", "green", "blue"), bty = "n")

library(RColorBrewer)

attach(mtcars)

cl <- brewer.pal(3, "Dark2")  # 左图代码,RColorBrewer包配色方案的使用

par(mfrow = c(1, 1))

plot(wt, mpg, col = cl[1])

points(wt[cyl == 6], mpg[cyl == 6], col = cl[2])

points(wt[cyl == 8], mpg[cyl == 8], col = cl[3])

legend(5, 35, c(4, 6, 8), pch = 1, col = cl, bty = "n")

cl <- rainbow(3)  # 右图代码，rainbow函数的使用

plot(wt, mpg, col = cl[1])

points(wt[cyl == 6], mpg[cyl == 6], col = cl[2])

points(wt[cyl == 8], mpg[cyl == 8], col = cl[3])

legend(5, 35, c(4, 6, 8), pch = 1, col = cl, bty = "n")

plot(1, col = "white", xlim = c(1, 8), ylim = c(1, 7))

symbol <- c("*", "、", ".", "o", "O", "0", " + ", " - ", "|")

# 创建循环添加点

for (i in c(0:34)) {

  x <- (i %/% 5) * 1 + 1

  y <- 6 - (i %% 5)

  if (i > 25) {

    points(x, y, pch = symbol[i - 25], cex = 1.3)

    text(x + 0.5, y + 0.1, labels = paste("pch = ", symbol[i - 25]), cex = 0.8)

  } else {

    if (sum(c(21:25) == i) > 0) {

      points(x, y, pch = i, bg = "red", cex = 1.3)

    } else {

      points(x, y, pch = i, cex = 1.3)

    }

    text(x + 0.5, y + 0.1, labels = paste("pch = ", i), cex = 0.8)

  }

}

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")

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

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")

plot(c(0:5), col = "white")

text(2, 4, labels = "font = 1:正常字体（默认）", font = 1)

text(3, 3, labels = "font = 2:粗体字体", font = 2)

text(4, 2, labels = "font = 3:斜体字体", font = 3)

text(5, 1, labels = "font = 4:粗斜体字体", font = 4)

# 大小

plot(c(0:6), col = "white", xlim = c(1, 8))

text(2, 5, labels = "cex = 0.5:放大0.5倍", cex = 0.5)

text(3, 4, labels = "cex = 0.8:放大0.8倍", cex = 0.8)

text(4, 3, labels = "cex = 1(默认):正常大小", cex = 1)

text(5, 2, labels = "cex = 1.2:放大1.2倍", cex = 1.2)

text(6, 1, labels = "cex = 1.5:放大1.5倍", cex = 1.5)

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)

cyl <- as.factor(cyl)

plot(wt, mpg, col = "white", xlab = "", ylab = "")

text(wt, mpg, cyl, col = as.integer(cyl) + 1)

title(main = list("Miles per Gallon vs. Weight by Cylinder", cex = 1.5),

      xlab = "Weight", ylab = "Miles per Gallon")

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)

data <- matrix(rep(rep(1:7), 10), ncol = 10, nrow = 7)

plot(data[1, ], type = "l", lty = 0, ylim = c(1, 8), xlim = c(-1, 10), axes = F)

text(0, 1, labels = "lty = 0")

for (i in c(2:7)) {

  lines(data[i, ], lty = i - 1)

  text(0, i, labels = paste("lty = ", i - 1))

}

data <- matrix(rep(rep(1:6), 10), ncol = 10, nrow = 6)

plot(data[1, ], type = "l", lwd = 0.5, ylim = c(1, 8), xlim = c(-1, 10), axes = F); text(0, 1, labels = "lwd = 0.5")

lines(data[2, ], type = "l", lwd = 0.8);text(0, 2, labels = "lwd = 0.8")

lines(data[3, ], type = "l", lwd = 1);text(0, 3, labels = "lwd = 1")

lines(data[4, ], type = "l", lwd = 1.5);text(0, 4, labels = "lwd = 1.5")

lines(data[5, ], type = "l", lwd = 2);text(0, 5, labels = "lwd = 2")

lines(data[6, ], type = "l", lwd = 4);text(0, 6, labels = "lwd = 4")

# 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)

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)

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

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

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")

par(mfrow = c(1, 3))

plot(density(wt), col = "red")  # 绘制核密度曲线

plot(wt, fitted(lm(mpg ~ wt)), type = "l", lty = 2, col = "green")  # 绘制回归线

plot(seq(min(wt), max(wt), length = 100), seq(max(mpg), min(mpg), length = 100),

     type = "l", lty = 3, col = "blue")  # 绘制最大、最小值线

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)

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)

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)

# 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")

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)

dotchart(bardata, bg = pal)

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

methods("plot")

plot(density(wt), type = "l")

class(density(wt))  # 第一个参数density类，画核密度曲线

plot(table(cyl, vs)); class(table(cyl, vs))  # 第一个参数table类，画马赛克图

opr <- par(mfrow = c(2, 3), mar = c(4, 4, 2, 4))   

for (i in 1:6) {

  plot(lm(mpg ~ wt), i)  # 第一个参数lm类，画回归诊断图

}

par(opr); class(lm(mpg ~ wt))

plot(mtcars[, c(1, 3:7)])

class(mtcars[, c(1, 3:7)])  # 第一个参数data.frame类，画散点图矩阵

x <- seq(from = 0, to = 2*pi, length = 10)  # 取10个x值

y <- sin(x)  # 计算相对应的y值

type <- c("p", "l", "b", "o", "c", "h", "s", "S", "n" )  # 图形类型向量

op <- par(mfrow = c(3, 3), mar = c(4, 4, 1, 1))

for (i in 1:9) {

  plot(x, y, type = type[i] , main = paste("type:", type[i]))

}

par(op) 

pairs( ~ mpg + disp + drat + wt, data = mtcars, col = as.integer(factor(cyl)) + 1,

       main = "Scatter Plot Matrix")

pairs(mtcars[, c(1, 3, 5, 6)], col = as.integer(factor(cyl)) + 1,

      main = "Scatter Plot Matrix")

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

sunflowerplot(mpg, cyl, col = "green", seg.col = "light green")

heatmap(as.matrix(mtcars), col = pal, scale = "column")