基於R語言for循環的替換方案

Posted on by

R語言中，for循環運行比較慢

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

補充：R語言：for循環使用小結

基本結構展示：

vals =c(5,6,7)
for(v in vals){
  print(v)
}
#即把大括號裡的內容對vals裡的每一個值都循環run一遍

實例展示：

1. paste() 命令是把幾個字符連接起來

如paste(“A”,”B”,”C”,sep=” “)得到的就是“A B C”，在次基礎上寫如下for loop:

partnumber = c(1,2,5,78)
for(i in partnumber){
 print(paste("participant number",i, sep = " ")) 
}
#就可以得到一串參與者號碼，根據上面給定的幾個值， 從"participant number 1" 到"participant number 8"

2. 雙重loop

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 = " "))
}
}
# 先對j循環，後對i循環，得到如下結果
[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"
# 兩個loop的話，output得放最中心的loop裡面，如果隻要要第一層loop,就放在靠外一層括號裡面，第二層括號就保留最後的一個值

3. 數據庫實例演示

Titanic=read.csv("https://goo.gl/4Gqsnz")  #從網絡讀取數據<0.2, 0.2-0.6還是>0.6。

目的：看不同艙位（Pclass）和不同性別（Sex）的人的生存率是

A<- sort(unique(Pclass))   #sort可以把類別按大小順序排，unique()命令是把分類變量的種類提取出來
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"))} 
  }  
}

結果如下：

[1] “for class 1 sex female mean survival is more than 0.6”

[1] “for class 1 sex male mean survival is between 0.2 and 0.6”

[1] “for class 2 sex female mean survival is more than 0.6”

[1] “for class 2 sex male mean survival is less than 0.2”

[1] “for class 3 sex female mean survival is between 0.2 and 0.6”

[1] “for class 3 sex male mean survival is less than 0.2”

補充：R語言for循環批量生成變量，並且賦值

看代碼~

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])) #篩選特定的樣本
  mean_ <- as.data.frame(apply(tmp, 1, mean)) #行求平均值
  //assign()功能就是對變量進行賦值如i=1時，df1=mean_
  //把三次結果組合起來
  mean.data <- cbind.data.frame(mean.data,assign(paste("df", i, sep=""), mean_))
  //這裡沒有體現出變量，實際上生成瞭df1,df2,df3結果
}
colnames(mean.data) <- c("ID","H1299","Metf","OEMetf")
write.table(mean.data,file="MS_mean.xls",row.names = FALSE,sep = "\t",na="")

以上為個人經驗，希望能給大傢一個參考，也希望大傢多多支持WalkonNet。如有錯誤或未考慮完全的地方，望不吝賜教。

推薦閱讀：

    None Found