@Macux
2015-12-01T06:46:50.000000Z
字数 864
阅读 1004
R语言_学习笔记
> library(corrplot)
> mcor <- cor(mtcars)
> col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
> corrplot(mcor,method="shade",shade.col=NA,tl.col="black",tl.srt=45,addCoef.col="black",
col=col(200),order="AOE")
> library(igraph)
> m <- madmen[1:nrow(madmen)%%2==1,]
> g <- graph.data.frame(m,directed=FALSE)
> plot(g, layout=layout.fruchterman.reingold,
vertex.size = 4,
vertex.label = V(g)$name,
vertex.label.cex = 0.8,
vertex.label.dist = 0.3,
vertex.label.color = "darkorange")
> c2 <- subset(countries,Year==2009)
> c2 <- c2[complete.cases(c2),] #去除含有NA的行
> set.seed(201)
> c2 <- c2[sample(1:nrow(c2),25),] #随机抽取25个样本
> rownames(c2) <- c2$Name #由于调用sample()函数后,第一列会自动变为随机数的号码。现将其改为"Name"
> c2 <- c2[,4:7] #我们只需要第4至第7列的数据
> c3 <- scale(c2) #对数据进行标准化
> hc <- hclust(dist(c3)) #计算"距离",并进行聚类
> par(mar=c(5,4,6,2)) #增加边界的作图面积
> plot(hc,hang=-1) #绘制谱系图,并且对齐文本