如何在R中将多个数值变量转换为因子变量？
有时，变量的数据类型不正确，并且将因子变量读取为数字变量是很常见的，尤其是在因子水平由数字表示的情况下。如果我们不更改因子变量的数据类型，则分析结果将不正确。因此，如果因子变量的数据类型与因子不同，则必须将其转换为因子数据类型。要将多个变量转换为因子类型，我们可以创建一个具有所有因子变量名称的向量，然后使用lapply将其转换为因子。
示例
请看以下数据帧-
> set.seed(123) > x1<-rep(c(1,2,3,4,5),times=4) > x2<-rep(c(5,10,15,20),each=5) > x3<-sample(1:10,20,replace=TRUE) > x4<-sample(1:100,20) > x5<-rep(c(LETTERS[1:5]),times=4) > x6<-rnorm(20,1) > x7<-runif(20,2,10) > df<-data.frame(x1,x2,x3,x4,x5,x6,x7) > df x1 x2 x3 x4 x5 x6 x7 1 1 5 3 9 A 0.18148428 3.501529 2 2 5 3 83 B 1.68493608 8.258354 3 3 5 10 36 C 0.67994358 2.748760 4 4 5 2 78 D -0.31152241 5.734232 5 5 5 6 81 E 0.40039167 6.092044 6 1 10 5 43 A 0.87058931 6.799912 7 2 10 4 76 B 1.88673615 4.662588 8 3 10 6 15 C 0.84860404 5.908904 9 4 10 9 32 D 1.32979120 9.635791 10 5 10 10 7 E -2.22732283 5.863219 11 1 15 5 100 A 0.22820823 9.122802 12 2 15 3 41 B 1.28654857 9.315505 13 3 15 9 74 C -0.22051198 6.869880 14 4 15 9 23 D 1.43455038 5.285518 15 5 15 9 27 E 1.80017687 3.176758 16 1 20 3 60 A 0.83606903 9.482398 17 2 20 8 53 B 2.24291877 4.409831 18 3 20 10 91 C 0.06561494 2.485765 19 4 20 7 84 D 1.39370865 9.581816 20 5 20 10 86 E 1.40363146 7.764770 > str(df) 'data.frame': 20 obs. of 7 variables: $ x1: num 1 2 3 4 5 1 2 3 4 5 ... $ x2: num 5 5 5 5 5 10 10 10 10 10 ... $ x3: int 3 3 10 2 6 5 4 6 9 10 ... $ x4: int 9 83 36 78 81 43 76 15 32 7 ... $ x5: Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5 1 2 3 4 5 ... $ x6: num 0.181 1.685 0.68 -0.312 0.4 ... $ x7: num 3.5 8.26 2.75 5.73 6.09 ...
在这里，我们有一个因子变量。现在假设我们要将x1，x2和x3转换为因子变量，则可以按以下步骤完成-
> Factors<-c("x1","x2","x3")
> df[Factors]<-lapply(df[Factors],factor)检查x1，x2和x3是否为因子变量-
> str(df) 'data.frame': 20 obs. of 7 variables: $ x1: Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ... $ x2: Factor w/ 4 levels "5","10","15",..: 1 1 1 1 1 2 2 2 2 2 ... $ x3: Factor w/ 9 levels "2","3","4","5",..: 2 2 9 1 5 4 3 5 8 9 ... $ x4: int 9 83 36 78 81 43 76 15 32 7 ... $ x5: Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5 1 2 3 4 5 ... $ x6: num 0.181 1.685 0.68 -0.312 0.4 ... $ x7: num 3.5 8.26 2.75 5.73 6.09 ...