R基础绘图学习笔记（一）

了解绘图

1.散点图

plot(cars$dist~cars$speed, # y~x     main="Relationship between car distance &speedr", #散点图标题     xlab="Speed (miles per hour)", #X轴标题     ylab="Distance travelled (miles)", #Y轴标题     xlim=c(0,30), #坐标轴表示范围     ylim=c(0,140),     yaxs="r", #     col="red", #设置颜色红色     pch=19) #设置符号

plot(cars$dist~cars$speed)

2.画线图

代码实现：

plot(sales$units~as.Date(sales$date,"%d/%m/%y"),     type="l", #Specify type of plot as l for line     main="Unit Sales in the month of January 2010",     xlab="Date",     ylab="Number of units sold",     col="blue")lines(sales$units~as.Date(sales$date,"%d/%m/%y"),      col="red")

3.条形图

代码如下：

#Vertical barsbarplot(sales$ProductA,        names.arg= sales$City,        col="black")#Horizontal barsbarplot(sales$ProductA,        names.arg= sales$City,        horiz=TRUE,        col="black")#Grouped bars with legendbarplot(as.matrix(sales[,2:4]), beside= TRUE,        legend=sales$City,        col=heat.colors(5),        border="white")#Horizontal grouped bars with legendbarplot(as.matrix(sales[,2:4]), beside=TRUE,        legend=sales$City,        col=heat.colors(5),        border="white",        horiz=TRUE)

直方图和密度图

代码如下：

    hist(rnorm(1000))    hist(islands)    plot(density(rnorm(1000)))

箱线图

代码如下

boxplot(copper$Cu~copper$Source,         xlab="Measurement Site",        ylab="Atmospheric Concentration of Copper in ng per cubic metre",        main="Atmospheric Copper Concentrations in London")boxplot(metals,        xlab="Metals",        ylab="Atmospheric Concentration in ng per cubic metre",        main="Atmospheric Metal Concentrations in London")

热图，地图

代码如下：

heatmap(as.matrix(mtcars),         Rowv=NA,         Colv=NA,         col = heat.colors(256),         scale="column",        margins=c(2,8),        main = "Car characteristics by Model")image(x=1:ncol( genes),      y=1:nrow(genes),      z=t(as.matrix(genes)),      axes=FALSE,      xlab="",      ylab="" ,      main="Gene Correlation Matrix")axis(1,at=1:ncol(genes),labels=colnames(genes),col="white",las=2,cex.axis=0.8)           axis(2,at=1:nrow(genes),labels=rownames(genes),col="white",las=1,cex.axis=0.8)map('world', fill = TRUE,col=heat.colors(10))

emmm

pairs(iris[,1:4])plot(iris[,1:4],main="Relationships between characteristics of iris flowers",pch=19,col="blue",cex=0.9)