我就废话不多说了，大家还是直接看代码吧~
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ts1<-ts(test_data$tot_num,frequency = 365,start=c(2017,11,21))plot(ts1,col='blue',lty='dotted',ylim=c(50,550))par(new=TRUE)ts2<-ts(test_data$pre_result,frequency = 365,start=c(2017,11,21))plot(ts2,col='red',ylim=c(50,550)) |
好用，需要注意控制相同的xlim和ylim
补充：用R语言在同一副图里面画两个折线图的注意事项
如下所示：
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setwd("C:/Users/11565/Desktop/合并二")shuju <- read.csv("数据.csv",header = T)library(psych)head(shuju)attach(shuju)head(mtcars)aggregate(shuju,by= list(state),FUN = mean,na.rm = T)tapply(shuju[,3],shuju$year,mean)#每次只能求一列aggregate(shuju[,3:4],list(shuju[,2]),mean)#每次可以求多列mean_1 <- aggregate(shuju[,3:6],list(shuju[,2]),mean)detach(shuju)attach(mean_1)head(mean_1)mean_1$pcap <- mean_1$pcap/10mean_1$Group.1<- as.numeric(mean_1$Group.1)plot(Group.1,pcap,lty= 3,pch = 21,ylim = c(1000,3000))#画不出两幅图的原因很可能是两个变量数量级存在问题lines(Group.1,hwy/10,lty = 2,pch = 22)#画折现图要先看一下x对应的y和y的数量级如何str(mean_1) |
补充：在R中把多条曲线放置在一张图中
如下：
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library(xlsx)myield<-read.xlsx("myield.xlsx",header=T,sheetIndex=1)head(myield) time X3m X6m X1y X2y X3y X4y1 2002.010.019889 0.020353 0.021264 0.023014 0.024670 0.0262322 2002.020.020781 0.021131 0.021819 0.023155 0.024437 0.0256633 2002.030.018449 0.018829 0.019574 0.021015 0.022389 0.0236954 2002.040.019096 0.019325 0.019780 0.020668 0.021529 0.0223625 2002.050.018228 0.018425 0.018815 0.019578 0.020319 0.0210376 2002.060.017224 0.017479 0.017980 0.018952 0.019882 0.020771tm<-myield[,2]sm<-myield[,3]oy<-myield[,4]ty<-myield[,5]time<-myield[,1]thy<-myield[,6]fy<-myield[,7]plot(tm~time,type="l",lty=1)lines(sm~time,col="red",lty=2)lines(oy~time,col="blue",lty=3)lines(ty~time,col="yellow",lty=4)lines(thy~time,col="green",lty=5)lines(fy~time,col="grey",lty=6)title("YIELD(m)",lwd=3)legend("topleft",cex=.6,c("tm","sm","oy","ty","thy","fy"),col=c("black","red","blue","yellow","green","grey"),lty=1:6) |
也可以应用matplot函数，但使用之前注意把数值型数据转换为数据框或者向量，根据本例，即把tm,sm,oy等数值型数据转化为数据框，
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z<-data.frame(cbind(tm,sm,oy,ty,thy,fy)) |
然后
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matplot(time,z,col=1:6,type="l",lwd=2,xlab="",ylab="",lty=1:5) |
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。如有错误或未考虑完全的地方，望不吝赐教。
原文链接：https://blog.csdn.net/Jinpeijie217/article/details/88991235
