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 ...
[list output truncated]
正如您所看到的，我有 111变量我正在尝试构建一个预测模型来使用这些变量来预测幸福。如果我把它们留在因子形式（CART模型，randomForest等斗争）所以我试图将它们转换为矢量化或数字类型（使算法的生活更容易一些）...目前我正在做一个接一个，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
但这是非常乏味和累人的:( 有没有办法可以轻松转换它？
任何帮助都将受到高度赞赏
干杯
答案 0 :(得分：0)
如果train_new是data.frame并且您想要更改以＆＃34; Q1 ...＆＃34;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（可能是字符或数字）：
train_new[,grep(pattern="^Q1",colnames(train_new))] = lapply(train_new[,grep(pattern="^Q1",colnames(train_new))], function(x) { as.integer(as.factor(x)) } )
应用于一般数据框架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)