基于R语言for循环的替换方案

for(i in 1:1000){
print(i^2)
}

vals =c(5,6,7)
for(v in vals){
  print(v)
}
#即把大括号里的内容对vals里的每一个值都循环run一遍

partnumber = c(1,2,5,78)
for(i in partnumber){
 print(paste("participant number",i, sep = " ")) 
}
#就可以得到一串参与者号码，根据上面给定的几个值， 从"participant number 1" 到"participant number 8" 

partnumber = c(1,2,5,78)
institution =c("cancer center", "RMH", "Florey")
for(i in partnumber){
  for(j in institution){
  print(paste("participant number",i,", institution",j,sep = " "))
}
}
# 先对j循环，后对i循环，得到如下结果
[1] "participant number 1 , institution cancer center"
[1] "participant number 1 , institution RMH"
[1] "participant number 1 , institution Florey"
[1] "participant number 2 , institution cancer center"
[1] "participant number 2 , institution RMH"
[1] "participant number 2 , institution Florey"
[1] "participant number 5 , institution cancer center"
[1] "participant number 5 , institution RMH"
[1] "participant number 5 , institution Florey"
[1] "participant number 78 , institution cancer center"
[1] "participant number 78 , institution RMH"
[1] "participant number 78 , institution Florey"
# 两个loop的话，output得放最中心的loop里面，如果只要要第一层loop,就放在靠外一层括号里面，第二层括号就保留最后的一个值

Titanic=read.csv("https://Goo.gl/4Gqsnz")  #从网络读取数据<0.2, 0.2-0.6还是>0.6。

A<- sort(unique(Pclass))   #sort可以把类别按大小顺序排，unique()命令是把分类变量的种类提取出来
B<- sort(unique(Sex))
for(i in A){ 
  for(j in B){
   if(mean(Survived[Pclass==i&Sex==j])<0.2){
    print(paste("for class",i,"sex",j,"mean survival is less than 0.2"))
  } else if (mean(Survived[Pclass==i&Sex==j])>0.6){
    print(paste("for class",i,"sex",j,"mean survival is more than 0.6"))
  } else {
    print(paste("for class",i,"sex",j,"mean survival is between 0.2 and 0.6"))} 
  }  
}

rm(list=ls())
data <- read.table("MS_identified_infORMation.txt",header = T,sep = "\t",quote="",na.strings = "",row.names = 1,comment.char = "")
name1 <- paste("H1299",sep = "_",c(1:3))
name2 <- paste("Metf",sep = "_",c(1:3))
name3 <- paste("OEMetf",sep = "_",c(1:3))
name <- data.frame(name1,name2,name3)
mean.data=data.frame(row.names(data))
for (i in 1:3){
  tmp <- subset(data,select = as.vector.factor(name[,i])) #筛选特定的样本
  mean_ <- as.data.frame(apply(tmp, 1, mean)) #行求平均值
  //assign()功能就是对变量进行赋值如i=1时，df1=mean_
  //把三次结果组合起来
  mean.data <- cbind.data.frame(mean.data,assign(paste("df", i, sep=""), mean_))
  //这里没有体现出变量，实际上生成了df1,df2,df3结果
}
colnames(mean.data) <- c("ID","H1299","Metf","OEMetf")
write.table(mean.data,file="MS_mean.xls",row.names = FALSE,sep = "\t",na="")