• 設定常用工作目錄：

• 匯入資料，先檢視工作目錄，再把資料檔案放到工作目錄內

> getwd() 

> setwd("/Users/yuki/desktop/machine learning"

> credit=read.table("german.data.txt",header=F,sep=" ",stringsAsFactors=F)

> credit=read.table("german.data.txt",nrows=3,header=F,sep=" ",stringsAsFactors=F)
> credit
 

• 檢視資料型別

  str(credit)
  dim(credit)

  
  
• 更改資料標題

  colnames(credit) = c(‘a’,'b'....)

  
  
• 從縮寫到真實意義的對映變數

mapping = list('A11'='... < 0 DM',
 'A12'='0 <= ... < 200 DM',
 'A13'='... >= 200 DM / salary assignments for at least 1 year',
 'A14'='no checking account',
   ...
)

for(i in 1:(dim(credit))[2]) {
  if(class(credit[,i])=='character') {
      credit[,i] = as.factor(as.character(mapping[credit[,i]]))
  }
}
 

• 將處理的變數0，1factor型別附上名字

> str(credit$V21)

$ V21: int  1 2 1 1 2 1 1 1 1 2 ...

> credit$V21 = ifelse(credit$Good.Loan==1,'GoodLoan','BadLoan')  ## (1=GOOD, 2=BAD)

> str(credit$V21)
 chr [1:1000] "Goodloan" "Badloan" "Goodloan" "Goodloan" ...     ##  這裡是字元型，方便處理要改成factor型
 

> credit$V21=as.factor(credit$V21)
> str(credit$V21)
 Factor w/ 2 levels "Badloan","Goodloan": 2 1 2 2 1 2 2 2 2 1 ...