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我有以下数据框：

> str(train)
'data.frame':   4619 obs. of  110 variables:
 $ UserID         : int  1 2 5 6 7 8 9 11 12 13 ...
 $ YOB            : int  1938 1985 1963 1997 1996 1991 1995 1983 1984 1997 ...
 $ Gender         : Factor w/ 3 levels "","Female","Male": 3 2 3 3 3 2 3 3 2 2 ...
 $ Income         : Factor w/ 7 levels "","$100,001 - $150,000",..: 1 3 6 5 4 7 5 2 4 6 ...
 $ HouseholdStatus: Factor w/ 7 levels "","Domestic Partners (no kids)",..: 5 6 5 6 6 6 6 5 5 6 ...
 $ EducationLevel : Factor w/ 8 levels "","Associate's Degree",..: 1 8 1 7 4 5 4 3 7 4 ...
 $ Party          : Factor w/ 6 levels "","Democrat",..: 3 2 1 6 1 1 6 3 6 2 ...
 $ Happy          : int  1 1 0 1 1 1 1 1 0 0 ...
 $ Q124742        : Factor w/ 3 levels "","No","Yes": 2 1 2 1 2 3 1 2 2 1 ...
 $ Q124122        : Factor w/ 3 levels "","No","Yes": 1 3 3 3 2 3 1 3 3 1 ...
 $ Q123464        : Factor w/ 3 levels "","No","Yes": 2 2 2 3 2 2 1 2 2 1 ...
 $ Q123621        : Factor w/ 3 levels "","No","Yes": 2 3 3 2 2 1 1 3 2 1 ...
 $ Q122769        : Factor w/ 3 levels "","No","Yes": 2 2 2 1 3 1 1 2 2 2 ...
 $ Q122770        : Factor w/ 3 levels "","No","Yes": 3 2 2 3 3 1 1 2 3 3 ...
 $ Q122771        : Factor w/ 3 levels "","Private","Public": 3 3 2 2 3 3 1 3 3 3 ...
 $ Q122120        : Factor w/ 3 levels "","No","Yes": 2 2 2 2 2 3 1 2 2 2 ...
 $ Q121699        : Factor w/ 3 levels "","No","Yes": 3 3 3 2 2 3 2 3 3 2 ...
 $ Q121700        : Factor w/ 3 levels "","No","Yes": 2 3 2 2 3 3 2 2 2 2 ...
 $ Q120978        : Factor w/ 3 levels "","No","Yes": 1 3 2 3 3 2 2 3 3 3 ...
 $ Q121011        : Factor w/ 3 levels "","No","Yes": 2 2 2 2 2 3 3 2 3 2 ...
 $ Q120379        : Factor w/ 3 levels "","No","Yes": 2 3 3 2 3 3 2 2 2 3 ...
 $ Q120650        : Factor w/ 3 levels "","No","Yes": 3 3 3 3 3 2 3 3 3 3 ...
 $ Q120472        : Factor w/ 3 levels "","Art","Science": 1 3 3 3 3 2 3 3 2 3 ...
 $ Q120194        : Factor w/ 3 levels "","Study first",..: 3 2 3 2 2 3 3 3 3 3 ...
 $ Q120012        : Factor w/ 3 levels "","No","Yes": 2 3 3 1 2 3 2 2 3 3 ...
 $ Q120014        : Factor w/ 3 levels "","No","Yes": 2 3 2 3 3 1 3 3 2 3 ...
 $ Q119334        : Factor w/ 3 levels "","No","Yes": 1 3 2 2 2 3 2 3 2 2 ...
 $ Q119851        : Factor w/ 3 levels "","No","Yes": 3 2 2 3 2 2 3 2 2 3 ...
 $ Q119650        : Factor w/ 3 levels "","Giving","Receiving": 1 2 2 3 2 1 2 2 2 3 ...
 $ Q118892        : Factor w/ 3 levels "","No","Yes": 3 3 3 2 3 2 1 3 2 2 ...
 $ Q118117        : Factor w/ 3 levels "","No","Yes": 3 2 2 3 3 3 1 2 2 2 ...
 $ Q118232        : Factor w/ 3 levels "","Idealist",..: 2 2 3 3 3 1 1 2 2 3 ...
 $ Q118233        : Factor w/ 3 levels "","No","Yes": 2 2 2 2 2 2 1 2 3 2 ...
 $ Q118237        : Factor w/ 3 levels "","No","Yes": 2 3 3 3 2 2 1 2 3 2 ...
 $ Q117186        : Factor w/ 3 levels "","Cool headed",..: 1 2 2 2 1 3 1 2 3 1 ...
 $ Q117193        : Factor w/ 3 levels "","Odd hours",..: 1 2 3 2 3 3 1 3 3 3 ...
 $ Q116797        : Factor w/ 3 levels "","No","Yes": 3 3 2 2 2 1 1 2 2 1 ...
 $ Q116881        : Factor w/ 3 levels "","Happy","Right": 2 2 3 3 2 2 1 2 2 1 ...
 $ Q116953        : Factor w/ 3 levels "","No","Yes": 3 3 3 3 1 3 3 3 3 1 ...
 $ Q116601        : Factor w/ 3 levels "","No","Yes": 3 3 3 2 3 3 1 3 3 1 ...
 $ Q116441        : Factor w/ 3 levels "","No","Yes": 2 2 2 2 2 2 1 2 2 1 ...
 $ Q116448        : Factor w/ 3 levels "","No","Yes": 2 3 3 3 2 2 1 2 3 1 ...
 $ Q116197        : Factor w/ 3 levels "","A.M.","P.M.": 3 2 2 2 2 3 1 2 3 1 ...
 $ Q115602        : Factor w/ 3 levels "","No","Yes": 3 3 3 3 3 2 1 3 2 1 ...
 $ Q115777        : Factor w/ 3 levels "","End","Start": 3 2 3 3 3 3 1 3 2 1 ...
 $ Q115610        : Factor w/ 3 levels "","No","Yes": 3 3 3 3 3 1 1 3 2 1 ...
 $ Q115611        : Factor w/ 3 levels "","No","Yes": 2 2 3 3 2 2 1 2 2 1 ...
 $ Q115899        : Factor w/ 3 levels "","Circumstances",..: 2 3 3 2 2 3 1 2 3 1 ...
 $ Q115390        : Factor w/ 3 levels "","No","Yes": 3 2 2 2 1 2 3 3 2 1 ...
 $ Q114961        : Factor w/ 3 levels "","No","Yes": 3 3 2 3 2 3 2 2 3 1 ...
 $ Q114748        : Factor w/ 3 levels "","No","Yes": 3 2 2 2 3 3 3 2 3 1 ...
 $ Q115195        : Factor w/ 3 levels "","No","Yes": 3 3 3 3 3 2 3 3 3 1 ...
 $ Q114517        : Factor w/ 3 levels "","No","Yes": 2 3 2 3 2 2 2 2 3 1 ...
 $ Q114386        : Factor w/ 3 levels "","Mysterious",..: 1 3 3 2 2 3 3 3 3 1 ...
 $ Q113992        : Factor w/ 3 levels "","No","Yes": 3 1 3 2 2 2 2 2 3 1 ...
 $ Q114152        : Factor w/ 3 levels "","No","Yes": 3 2 2 2 3 2 2 2 2 1 ...
 $ Q113583        : Factor w/ 3 levels "","Talk","Tunes": 2 3 2 3 3 3 3 2 3 1 ...
 $ Q113584        : Factor w/ 3 levels "","People","Technology": 3 2 2 3 2 1 3 2 2 1 ...
 $ Q113181        : Factor w/ 3 levels "","No","Yes": 2 3 3 3 2 3 3 2 2 1 ...

[列表输出被截断]

如您所见，我有111 个变量，我正在尝试建立一个预测模型来使用这些变量预测幸福感。如果我将它们保留为因子形式（CART 模型、randomForest 等），那么我正在尝试将它们转换为矢量化或数字类型（以使算法的工作更轻松）......目前我正在一一做，例如

> table(train_new$Q117193)

                    Odd hours Standard hours 
          1410           1299           1910 
> train_new$Q117193 = as.integer(train_new$Q117193)
> table(train_new$Q117193)

   1    2    3 
1410 1299 1910

但这非常乏味和累人:(有没有办法可以轻松转换它？？

任何帮助将不胜感激

干杯

4

1 回答 1

0

如果train_new是 data.frame 并且您想更改以“Q1 ...”开头的列，请尝试以下操作：

train_new[,grep(pattern="^Q1",colnames(train_new))] = lapply(train_new[,grep(pattern="^Q1",colnames(train_new))],as.integer)

或者，如果不是您要转换的每一列都是一个因素（可以是字符或数字）：

train_new[,grep(pattern="^Q1",colnames(train_new))] = lapply(train_new[,grep(pattern="^Q1",colnames(train_new))], function(x) { as.integer(as.factor(x)) } )

应用于一般 data.frame df：

df = data.frame(A=sample(LETTERS,200,replace=T),Q11=as.factor(sample(letters,200,replace=T)),Q12=as.factor(sample(letters,200,replace=T)),stringsAsFactors=T)
df[,grep(pattern="^Q1",colnames(df))] = lapply(df[,grep(pattern="^Q1",colnames(df))],as.integer)
于 2014-05-04T15:33:35.510 回答