R studio 导入数据与处理

• 设置常用工作目录：

 

• 导入数据，先查看工作目录，再把数据文件放到工作目录内

> getwd() 

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

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

＃＃＃如何知道header设置 －－先查看数据前几行

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

• 查看数据类型

  str(credit)
  dim(credit)

  
  
• 更改数据标题

  colnames(credit) = c(‘a’,'b'....)

  
  
• 从缩写到真实意义的映射变量

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]]))
  }
}

看起来有点复杂，首先我们建立了一个从缩写到真实意义的映射变量`mapping`，接下来对应每个为字符的列（这里你就明白了为什么前面读入数据我们用了stringsAsFactors=F），我们对列的值进行一个映射。映射的规则就是mapping。

• 将处理的变量0，1factor类型附上名字

> 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" ...     ##  这里是字符型，方便处理要改成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 ...