R语言实验四(共48页)

1、精选优质文档-倾情为你奉上实验四 绘制常用统计图形、对图形进行参数设置【实验类型】验证性【实验学时】2 学时【实验目的】1、掌握基本统计图形的绘制方法和所表达的意义；2、掌握图形参数的设置与多图环境设置；3、了解 ggplot2 绘图包和其它绘图包的使用方法。【实验内容】1、绘制基本的统计图形，包括散点图、箱线图、Q-Q 图和协同图等；2、对图形进行参数设置，包括添加图题、标签、点、线和颜色等；3、使用 gglplot2 和其它绘图包绘制常见统计图形。【实验方法或步骤】第一部分、课件例题：4.1data(cars) #调取数据集，用data()可查看R所有数据集cars #车速与制动距离的数据

2、(R自带)attach(cars) #连接数据集par(mai=c(0.9, 0.9, 0.6, 0.3) #图形边缘空白(边距)的宽度for (i in c("p", "l", "b", "c", "o", "h", "s", "S", "n") #9种类型 plot(speed, dist, type=i, main = paste("type = "", i, "&quo

3、t;", sep = "") # "为双引号detach() #取消连接数据集4.2df<data.frame(Age=c(13,13,14,12,12,15,11,15,14,14,14,15,12,13,12,16,12,11,15), Height=c(144,166,163,143,152,169,130,159,160,175,161,170,146,159,150,183,165,146,169), Weight=c(38.1,44.5,40.8,34.9,38.3,50.8,22.9,51.0,46.5,51.0,46.5,60.3,

4、37.7,38.1,45.1,68.0,58.1,38.6,50.8) #数据框pairs(df) #多组图pairs( Age + Height + Weight, data=df) #与上述结果相4.3coplot(WeightHeight|Age, data=df) #年龄条件下的协同图4.4点图VADeaths #Virginia州在1940年的人口死亡率数据(R自带)me1<- apply(VADeaths, 1, mean) #矩阵的行向量的均值me2<- apply(VADeaths, 2, mean) #矩阵的列向量的均值dotchart(VADeaths, gda

5、ta=me2, gpch=19, #按类型分类 main = "Death Rates in Virginia - 1940")dotchart(t(VADeaths), gdata=me1, gpch=19, #按年龄分类 main = "Death Rates in Virginia - 1940")4.5饼图pie.sales<-c(39, 200, 42, 15, 67, 276, 27, 66);names(pie.sales)<-c("EUL","PES","EFA",&

6、quot;EDD","ELDR","EPP","UNE","other") #各候选人的得票结果# figure1，默认色彩，逆时针pie(pie.sales,radius = 0.9,main ="Ordinary chart")# figure2，彩虹色彩，顺时针pie(pie.sales,radius=0.9,col=rainbow(8),clockwise =TRUE,main="Rainbow colours")# figure3，灰度色彩，顺时针pie(

7、pie.sales,radius =0.9,clockwise =TRUE,col =gray(seq(0.4,1.0,length=8),main="Grey colours")# figure4，阴影色彩，逆时针pie(pie.sales,radius=0.9,density=10,angle=15+15*1:8,main="The density of shading lines")4.6条形图pie.sales<-c(39, 200, 42, 15, 67, 276, 27, 66);par(mai=c(0.9, 0.9, 0.3, 0.3

8、) #定义图像边距# figure1, 添加一条线r<barplot(pie.sales,space=1,col=rainbow(8);lines(r,pie.sales,type='h',col=1,lwd=2)# figure2,用text()添加平均值mp <- barplot(VADeaths);tot<-colMeans(VADeaths); text(mp, tot+ 3, format(tot), xpd = TRUE, col = "blue") # figure3, 添加条形的颜色barplot(VADeaths, spa

9、ce = 0.5, col = c("lightblue", "mistyrose", "lightcyan", "lavender", "cornsilk")# figure4, 条形平行排列barplot(VADeaths, beside = TRUE, col = c("lightblue", "mistyrose","lightcyan", "lavender", "cornsilk"),

10、 legend = rownames(VADeaths),ylim = c(0, 100)4.7直方图df<data.frame(Age=c(13,13,14,12,12,15,11,15,14,14,14,15,12,13,12,16,12,11,15), Height=c(144,166,163,143,152,169,130,159,160,175,161,170,146,159,150,183,165,146,169), Weight=c(38.1,44.5,40.8,34.9,38.3,50.8,22.9,51.0,46.5,51.0,46.5,60.3,37.7,38.1,4

11、5.1,68.0,58.1,38.6,50.8) #数据框par(mai=c(0.9, 0.9, 0.6, 0.3) #图形边距attach(df) #连接数据框# figure1，增加直方图和外框的颜色，以及相应的频数hist(Height, col="lightblue", border="red", labels = TRUE, ylim=c(0, 7.2)# figure2，使用线条阴影并利用text()标出频数，用lines()绘出数据的密度曲线(蓝色)和正态分布密度曲线(红色)r<-hist(Height,breaks=12,freq=

12、FALSE,density=10,angle = 15+30*1:6);text(r$mids, 0, r$counts, adj=c(.5, -.5),cex=1.2 );lines(density(Height),col="blue",lwd=2);x<-seq(from=130, to=190, by=0.5);lines(x, dnorm(x,mean(Height), sd(Height), col="red", lwd=2)detach() #取消连接数据框4.8箱线图（1）x<c(25,45,50,54,55,61,64,68,

13、72,75,75,78,79,81,83,84,84,84,85,86,86,86,87,89,89,89,90,91,91,92,100)fivenum(x) #上、下四分位数,中位数, 最大和最小值boxplot(x) #绘制箱线图（2）InsectSprays #数据框，其中count为昆虫数目，spray为杀虫剂的类型boxplot(countspray,data =InsectSprays,col="lightgray")#矩形箱线图boxplot(countspray,data=InsectSprays,notch=TRUE,col=2:7,add=TRUE)4

14、.9 QQ图df<-data.frame(Age=c(13,13,14,12,12,15,11,15,14,14,14,15,12,13,12,16,12,11,15),Height=c(144,166,163,143,152,169,130,159,160,175,161,170,146,159,150,183,165,146,169),Weight=c(38.1,44.5,40.8,34.9,38.3,50.8,22.9,51.0,46.5,51.0,46.5,60.3,37.7,38.1,45.1,68.0,58.1,38.6,50.8) #数据框par(mai=c(0.9, 0.

15、9, 0.6, 0.3)attach(df)qqnorm(Weight) #数据的正态Q-Q图qqline(Weight) #在Q-Q图上增加一条理论直线y =x +qqnorm(Height)qqline(Height)detach()4.10 三维透视图perspy <- x <- seq(-7.5, 7.5, by = 0.5) #定义域f<-function(x,y)r<-sqrt(x2+y2) + 2-52 #加上一个很小的量2-52是为了避免在下一行运算时分母为零z<-sin(r)/r;z<-outer(x,y,f) #对f作外积运算形成网格pa

16、r(mai=c(0.0,0.2,0.0,0.1) #图像边距persp(x,y,z,theta=30,phi=15,expand=.7,col="lightblue",xlab="X",ylab="Y",zlab="Z") #绘制三维图4.11 等值线contoury<-x <- seq(-3, 3, by = 0.125) #定义域f<-function(x,y)z<-3*(1-x)2*exp(-x2-(y+1)2)-10*(x/5-x3-y5)*exp(-x2-y2)-1/3*exp(-

17、(x+1)2 -y2);z <- outer(x, y, f) #对函数f作外积运算形成网格par(mai=c(0.8, 0.8, 0.2, 0.2) #图像边距contour(x,y,z,levels=seq(-6.5,8,by=0.75),xlab="X",ylab="Y",col="blue") #绘制等值线4.12 添加点、线、文字或符号data(iris) #调用数据op<-par(mai=c(1,1,0.3,0.3),cex=1.1) #定义图形参数x<-iris$Petal.Length;y<-i

18、ris$Petal.Widthplot(x,y,type="n",xlab="Petal Length",ylab="Petal Width",cex.lab=1.3)Species<-c("setosa","versicolor","virginica")pch<-c(24,22,25) #图中点的形状for(i in 1:3)index<-iris$Species=Speciesi;points(xindex,yindex,pc=pchi,col=i+1

19、,bg=i+1) #添加点par(op) #访问当前图形参数设置text(c(3, 2.5, 4),c(0.25, 1.5, 2.25),labels=Species,font=2,col=c(2,3,4),cex=1.5) #添加文字说明4.13 添加直线、线段和图例data(cars)Q1<-function(beta,data) sum(abs(data,2-beta1-beta2*data,1) #偏差的绝对值之和Qinf<-function(beta,data) max(abs(data,2-beta1-beta2*data,1)z1<-optim(c(-17,4)

20、,Q1,data=cars);zinf<-optim(c(-17, 4),Qinf,data =cars);lm.sol<-lm(distspeed,data=cars) #线性回归op<-par(mai=c(.9,.9,.5,0.1),cex=1.1) #绘图参数plot(cars,main="Stopping Distance versus Speed",ylim=c(0,140),xlab="Speed (mph)",ylab="Distance (ft)",pch=19,col="magenta&q

21、uot;,cex.lab=1.2)abline(lm.sol,lwd=2,col="blue") #加线abline(a = z1$par1, b = z1$par2, lty = 4, lwd=2, col="red")abline(a = zinf$par1, b = zinf$par2, lty=5, lwd=2, col="green")pre<-predict(lm.sol); x0 <- cars$speed23; y0 <- cars$dist23segments(x0, y0, x1 = x0, y1

22、 = pre23, col= 1, lwd=2) #加线段和符号expr<-expression(paste("(", xi,",", yi, ")"); text(x0+1.5, y0, expr);expr1<-expression(min=sum(beta0+beta1*xi-yi)2,i=1,n);expr2<-expression(min=sum(abs(beta0+beta1*xi-yi),i=1,n);expr3<-expression(min=max(abs(beta0+beta1*xi-yi)

23、,i)legend(4, 140, legend=c(expr1, expr2, expr3),col=c("blue", "red", "green"),lty=c(1,4,5),lwd=2);par(op) #加图例4.14 添加图题、坐标轴与边框plot(cars,main ="", axes = F) # 散点图，不含图题、坐标轴title(main = " 制动距离与车速 ") # 添加图题axis(side = 1); axis(side = 2) # 添加坐标轴box(lty =

24、2, lwd = 2, col = 2) # 添加边框4.15绘制多边形和阴影区域。#绘制多边形op <- par(mai=c(0.9, 0.9, 0.6, 0.3)x <- c(1, 15, 20, 30, 15); y <- c(10, 1, 20, 15, 30)plot(x, y, type="n", main = "Polygon")polygon(x,y,density=5,angle=15,lwd=2,border="red",lty=2,col="yellow2")#绘制正态分布的

25、上侧分位数x<-seq(-4,4,by=0.1);plot(x,dnorm(x),type="l",lwd=2,col=4,xlim=c(-3,3),ylim=c(-0.01,0.4),ylab="Normal Density",main="Shadow");abline(h=0,v=0)z<-qnorm(1-0.05);xx<-seq(z,4,by=0.1)polygon(c(xx,z),c(dnorm(xx),dnorm(4),col="yellow1")text(z,-0.015,expre

26、ssion(Zalpha),adj=0.4,cex=1.1)text(2,0.02,expression(alpha),adj=0.5,cex=1.5)legend(-3,0.4,expression(alpha=0.05),adj=0.2)par(op)4.16par(omi=c(.5,.5,.5,.5);par(mfrow=c(3, 2)par(mar=c(3,2,2,1) # figrue 1plot(c(0,10),c(0,10),type="n",axes=F,xlab="",ylab="")text(5,5,labels=

27、"图1",cex=1.5);box(which="figure",lwd=2);box(lwd=2,lty=2)par(mar=c(3,3,2,1) # figrue 2boxplot(countspray,data=InsectSprays,col="lightgray")boxplot(countspray,data=InsectSprays,notch=TRUE,col=2:7,add=TRUE);box(which ="figure",lwd=2)Height<-c(144,166,163,143,1

28、52,169,130,159,160,175,161,170,146,159,150,183,165,146,169)par(mar=c(4.5, 4.5, 2, 1) # figrue 3hist(Height, col="lightblue", border="red", labels = TRUE, ylim=c(0, 7.2);box(which = "figure", lwd=2)plot(c(0,10),c(0,10), type="n", axes=F, xlab="", ylab

29、="")text(5,5, labels="图4", cex=1.5)par(mar=c(3, 2, 2, 1) # figrue 4box(lwd=2, lty=2); box(which = "figure",lwd=2)par(mar=c(3, 3, 2, 1) # figrue 5plot(cars); box(which = "figure",lwd=2)par(mar=c(2, 2, 1, 1) # figrue 6plot(c(0,10),c(0,10), type="n", ax

30、es=F, xlab="", ylab="")box(); text(5,5, labels="mfg=c(3,2,3,2)", cex=1.5)box(which = "figure", lwd=2); box(which = "outer", lwd=2)mtext("总图题", line=1, outer=T, cex=1.5)4.17op<-par(lwd=2, omi = c(.1, .1, .1, .1) # alayout(matrix(1:4, 2, 2

31、)layout.show(4)layout(matrix(1:6, 3, 2, byrow=TRUE) # blayout.show(6)layout(matrix(c(1,2,3,3), 2, 2, byrow=TRUE) # clayout.show(3)layout(matrix(1:4, 2, 2, byrow=TRUE), widths=c(3,1), heights=c(1,3) # dlayout.show(4)layout(matrix(c(1,1,2,1), 2, 2), widths=c(2,1), heights=c(1,2)layout.show(2) # elayou

32、t(matrix(c(0,1,2,3), 2, 2), widths=c(1,3), heights=c(1,3)layout.show(3) # fpar(op) #恢复原来的图形参数par(mfrow = c(1,1) #取消一页多图4.18library(ggplot2)library(gridExtra) # 加载包 ( 需先安装 )# 散点图p1<-ggplot(iris,aes(x=Petal.Length,y=Petal.Width)+(base_family="STKaiti",base_size=9)+geom_point(aes(colour=Sp

33、ecies)+labs(title="散点图");p1# 箱线图p2<-ggplot(iris,aes(x = Species,y = Sepal.Length)+theme_gray(base_family = "STKaiti",base_size = 9)+geom_violin(aes(fill = Species),show.legend = F)+labs(title = " 箱线图 ")+theme(plot.title = element_text(hjust = 0.5);p2#19 # 融合汽缸数 (cyl)

34、 和档位数 (gear) 这两个变量library(reshape2)mtcars.m<-melt(mtcars, id = c("mpg", "disp", "hp", "drat","wt", "qsec", "am" ,"vs", "carb") #id 中不含 cyl 和 gearhead(mtcars)mtcars.m#20p1<-ggplot(data = mtcars); summary(p

35、1)p2 <- ggplot(data = mtcars, mapping = aes(x = wt, y = hp, color =gear); summary(p2) #aes() 指定了横纵坐标分别为 wt 和 hp, 颜色为gear 这三种图形属性p <- ggplot(mtcars, aes(x = mpg, y = wt, color = factor(gear)#设定默认的映射关系p + geom_point()#沿用默认的映射关系来绘制散点图p + geom_point(aes(shape = factor(carb)#添加图层中的shape的映射关系p + geo

36、m_point(aes(y = carb)#修改默认的y的映射关系, 注意图中y轴名称仍然是默认的wtp + geom_point(aes(color = NULL)#删除默认的color映射关系#21#矩阵散点图和平行坐标图分析 iris 中变量间的关系#GGally包中的ggscatmat()可绘制矩阵散点图library(GGally)ggscatmat(data = iris,1:5,columns = 1:4,color = "Species" , alpha = 0.8)+theme_bw(base_family = "STKaiti" ,

37、base_size = 10)+theme(plot.title = element_text(hjust = 0.5)+ggtitle("矩阵散点图") #columns表示绘制矩阵散点图的变量, color 为指定数据中的分组变量#使用平行坐标图分析每个样本在各个特征上的变化情况ggparcoord(data = iris,1:5,columns = 1:4,groupColumn = "Species",scale = "center")+theme_bw(base_family = "STKaiti",ba

38、se_size = 10)+theme(plot.title = element_text(hjust = 0.5),legend.position = "bottom")+ggtitle("平行坐标图")+labs(x = "")#22 例4.6： ：直方图探索 120 年来奥运会运动员数据集的信息# 读取数据，数据融合library(readr); library(dplyr)athlete_events <- read_csv("F:/文档/大学课程/R语言/ch04/athlete_events.csv&quo

39、t;)noc_regions <- read_csv("F:/文档/大学课程/R语言/ch04/noc_regions.csv")athletedata <- inner_join(athlete_events,noc_regions,1:2,by=c("NOC"="NOC")summary(athletedata); head(athletedata); str(athletedata) # 查看数据# 查看每个国家参与奥运会运动员人数plotdata <- athletedata%>%group_by(re

40、gion)%>%summarise(number=n()%>% arrange(desc(number)# 可视化前40个人数多的国家的参与人数ggplot(plotdata1:30,aes(x=reorder(region,number),y=number)+ theme_bw(base_family = "STKaiti")+ geom_bar(aes(fill=number),stat = "identity",show.legend = F)+ coord_flip()+scale_fill_gradient(low = "

41、#56B1F7", high = "#132B43")+ labs(x="地区",y="运动员人数",title="不同地区奥运会运动员人数")+ theme(axis.text.x = element_text(vjust = 0.5), plot.title = element_text(hjust = 0.5)#23 例4.7： ：热力图探索 120 年来奥运会数据集男女运动员变化# 可视化数据，分析参赛运动员男女人数的变化library(RColorBrewer)# 人数最多的30个地区，不同年份

42、运动员人数变化region30 <- athletedata%>%group_by(region)%>% summarise(number=n()%>% arrange(desc(number)region30 <- region30$region1:30# 不同性别下的，可视化人数最多的15个地区，不同年份运动员人数变化plotdata <- athletedataathletedata$region %in%region301:15,%>% group_by(region,Year,Sex)%>% summarise(number=n()#绘

43、热力图ggplot(data=plotdata, aes(x=Year,y=region) + theme_bw(base_family = "STKaiti") + geom_tile(aes(fill = number),colour = "white")+ scale_fill_gradientn(colours=rev(brewer.pal(10,"RdYlGn")+ scale_x_continuous(breaks=unique( plotdata$Year) + theme(axis.text.x = element_t

44、ext(angle = 90,vjust = 0.5)+ facet_wrap(Sex,nrow = 2)#24 例4.8： ：表情图探索奥运会数据集各地区奖牌数量# “USA”,“Germany”,“France” ,“UK”,“Russia”,“China”6个地区获奖情况library(ggChernoff)# 查看不同季节举办的的奥运会运动员人数变化region6 <- c("USA","Germany","France" ,"UK","Russia","China&qu

45、ot;)index <- (athletedata$region %in% region6)&(!is.na(athletedata$Medal)&(athletedata$Season="Summer")plotdata <- athletedataindex,plotdata2 <- plotdata%>%group_by(Year,region)%>% summarise(Medalnum=n()# 绘制表情图ggplot(plotdata2,aes(x=Year,y=Medalnum)+ theme_bw(base_fa

46、mily = "STKaiti")+ geom_line()+ geom_chernoff(fill = 'goldenrod1')+ facet_wrap(region,ncol = 2)+ labs(x="举办时间",y="奖牌数")#26 例4.10： ：使用韦恩图分析集合之间的关系#分析几个向量之间的交集library(VennDiagram) #VennDiagram包最多可以绘制5个集合的韦恩图library(grid)library(futile.logger)#绘制4个数组的韦恩图vcol <-

47、c("red","blue","green","DeepPink")T<-venn.diagram(list(First =c(1:30), Second=seq(1,50,by = 2), Third =seq(2,50,by = 2), Four = c(20,70), filename = NULL,lwd = 0.5, fill = vcol,alpha = 0.5,margin = 0.1)grid.draw(T)#27 例4.11： ：使用奥运会 120 年的运动员数据集树形图可视化# 树图可视化

48、数据library(treemap); library(readr); library(dplyr)athlete_events <- read_csv("F:/文档/大学课程/R语言/ch04/athlete_events.csv")noc_regions <- read_csv("F:/文档/大学课程/R语言/ch04/noc_regions.csv")athletedata <- inner_join(athlete_events,noc_regions,1:2,by=c("NOC"="NOC&quo

49、t;)plotdata <- athletedata%>% group_by(region,Sex)%>% summarise(number=n()# 计算奖牌数量plotdata2 <- athletedata!is.na(athletedata$Medal),%>% group_by(region,Sex)%>% summarise(Medalnum=n()# 合并数据plotdata3 <- inner_join(plotdata2,plotdata,by=c("region", "Sex")# 使用tre

50、emap 可视化数据treemap(plotdata3,index = c("Sex","region"),vSize = "number", vColor = "Medalnum",type="value",palette="RdYlGn", title = "不同性别下每个国家的运动员人数",fontfamily.title = "STKaiti", title.legend = "奖牌数量",fontfamil

51、y.legend="STKaiti")#28 例4.12： ：可视化美国的各个飞机场之间的航班联系#使用飞机航线数据可视化library(maps); library(geosphere)# 读取飞机航线的数据usaairline <- read.csv("F:/文档/大学课程/R语言/ch04/usaairline.csv")airportusa <- read.csv("F:/文档/大学课程/R语言/ch04/airportusa.csv")map("state",col="palegre

52、en", fill=TRUE, bg="black", lwd=0.1)# 添加起点的位置points(x=airportusa$Longitude, y=airportusa$Latitude, pch=19, cex=0.4,col="tomato")col.1 <- adjustcolor("orange", alpha=0.4)# 添加边for(i in 1:nrow(usaairline) node1 <- usaairlinei,c("Latitude.x","Longi

53、tude.x") node2 <- usaairlinei,c("Latitude.y","Longitude.y") arc <- gcIntermediate( c(node1$Longitude.x, node1$Latitude.x), c(node2$Longitude.y, node2$Latitude.y), n=1000, addStartEnd=TRUE ) lines(arc, col=col.1, lwd=0.2) #29 例4.13： ：利用航班数量展示国家之间联系的频繁程度#利用igraph库可视化社交网络

54、图library(igraph)# 读取节点和边的数据vertexdata <- read.csv("F:/文档/大学课程/R语言/ch04/vertex.csv")edgedata <- read.csv("F:/文档/大学课程/R语言/ch04/edge.csv")# Country:国家; airportnumber:机场数量; vtype:节点的类型; etype:边的类型;# Country.x, Country.y:连线的两个点; connectnumber:连接的数量# 定义网络图g <- graph_from_data_

55、frame(edgedata,vertices = vertexdata,directed = TRUE)# 添加边的宽度E(g)$width <- log10(E(g)$connectnumber)#生成节点和边的颜色colrs <- c("gray50", "tomato", "gold")V(g)$color <- colrsV(g)$vtypeE(g)$color <- colrsE(g)$etype# plot 4个图 - 2 rows, 2 columns，每个图使用不同的图像样式par(mfrow

56、=c(2,2), mar=c(0,0,0,0)plot(g, layout = layout_in_circle(g), edge.arrow.size=0.4, vertex.size = 10*log10(V(g)$airportnumber), vertex.label.cex = 0.6)plot(g, layout = layout_with_fr(g), edge.arrow.size=0.4, vertex.size = 10*log10(V(g)$airportnumber), vertex.label.cex = 0.6)plot(g, layout = layout_on_

57、sphere(g), edge.arrow.size=0.4, vertex.size = 10*log10(V(g)$airportnumber), vertex.label.cex = 0.6)plot(g, layout = layout_randomly(g), edge.arrow.size=0.4, vertex.size = 10*log10(V(g)$airportnumber), vertex.label.cex = 0.6)#30 例4.14：构造函数绘制 3D 可视化图# 使用plot3D 包绘制3D 图像library(plot3D)library(ggplot2)x

58、<- y <- seq(0,10,by = 0.5)# 生成网格数据并计算Zxy <- mesh(x,y)z <- sin(xy$x) + cos(xy$y) + sin(xy$x) * cos(xy$y)par(mfrow = c(1,2)hist3D(x,y,z,phi = 45, theta = 45,space = 0.1,colkey = F,bty = "g")surf3D(xy$x,xy$y,z,colkey = F,border = "black",bty = "b2")# 使用plotly 包

59、绘制3D 图像library(plotly)plot_ly(x = xy$x, y = xy$y, z = z,showscale = FALSE)%>% add_surface()第二部分、教材例题：1.#二项分布n<-20p<-0.2k<-seq(0,n)plot(k,dbinom(k,n,p),type='h',main='Binomial distribution,n=20,p=0.2',xlab='k')#泊松分布lambda<-4.0k<-seq(0,20)plot(k,dpois(k,lambda

60、),type='h', main='Poisson distribution, lambda=5.5',xlab='k')#几何分布p<-0.5k<-seq(0,10)plot(k,dgeom(k,p),type='h', main='Geometric distribution,p=0.5',xlab='k')#几何分布p<-0.5k<-seq(0,10)plot(k,dgeom(k,p),type='h', main='Geometric dist

61、ribution,p=0.5',xlab='k')#超几何分布N<-30M<-10n<-10k<-seq(0,10)plot(k,dhyper(k,N,M,n),type='h',main='Hypergeometric distribution,N=30,M=10,n=10',xlab='k')#负二项分布n<-10p<-0.5k<-seq(0,40)plot(k,dnbinom(k,n,p),type='h',main='Negative Binomia

62、l distribution,n=10,p=0.5',xlab='k')#正态分布curve(dnorm(x,0,1), xlim=c(-5,5), ylim=c(0,.8), col= ' red ' , lwd=2, lty=3)curve(dnorm(x,0,2), add=T, col= ' blue ' , lwd=2, lty=2)curve(dnorm(x,0,1/2), add=T, lwd=2, lty=1)title(main="Gaussian distributions")legend(par(

63、'usr')2, par('usr')4, xjust=1, c('sigma=1','sigma=2','sigma=1/2'), lwd=c(2,2,2), lty=c(3,2,1), col=c('red','blue',par("fg")#t分布curve(dt(x,1), xlim=c(-3,3), ylim=c(0,.4),col= ' red ' , lwd=2, lty=1)curve(dt(x,2), add=T, col= ' green ' , lwd=2, lty=2)curve(dt(x,10), add=T, col= ' orange ' , lwd=2, lty=3)curve(dnor