重新编码因子水平

Question

我有以下数据框:

forStack
  AGE  BMI time          A         B      ID
 1  59 23.8    0     (0,75]  (4,14.9] 9000099
 2  69 29.8    0 (96.4,100]  (-Inf,0] 9000296
 3  71 22.7    0  (75,89.3]  (4,14.9] 9000622
 4  56 32.4    0     (0,75] (14.9,68] 9000798
 5  72 30.7    0     (0,75] (14.9,68] 9001104
 6  75 23.5    0 (96.4,100]     (0,4] 9001400

dput (forStack)
structure(list(AGE = c(59, 69, 71, 56, 72, 75), BMI = c(23.8, 
29.8, 22.7, 32.4, 30.7, 23.5), time = c(0, 0, 0, 0, 0, 0), A = structure(c(2L, 
5L, 3L, 2L, 2L, 5L), .Label = c("(-Inf,0]", "(0,75]", "(75,89.3]", 
"(89.3,96.4]", "(96.4,100]", "(100, Inf]"), class = "factor"), 
B = structure(c(3L, 1L, 3L, 4L, 4L, 2L), .Label = c("(-Inf,0]", 
"(0,4]", "(4,14.9]", "(14.9,68]", "(68, Inf]"), class = "factor"), 
ID = c(9000099, 9000296, 9000622, 9000798, 9001104, 9001400
)), .Names = c("AGE", "BMI", "time", "A", "B", "ID"), row.names = c(NA, 
6L), class = "data.frame")

变量A和B是代表四分因素:

   forStack$A
   [1] (0,75]     (96.4,100] (75,89.3]  (0,75]     (0,75]     (96.4,100]
   Levels: (-Inf,0] (0,75] (75,89.3] (89.3,96.4] (96.4,100] (100, Inf]

   forStack$B
   [1] (4,14.9]  (-Inf,0]  (4,14.9]  (14.9,68] (14.9,68] (0,4]    
   Levels: (-Inf,0] (0,4] (4,14.9] (14.9,68] (68, Inf]

我想重新编码A并将B值归结为两级因子,如下所示:

为A,上部因子水平(96.4,100]和(100, Inf]应该被重新编码为0电平,其他级别-级别1

对于B最低因子水平(-Inf,0] ,(0,4]应记录为0水平,其他水平 - 为1水平

因此,数据框应如下所示:

 forStack
  AGE  BMI time          A         B      ID
 1  59 23.8    0         1         1   9000099
 2  69 29.8    0         0         0   9000296
 3  71 22.7    0         1         1   9000622
 4  56 32.4    0         1         1   9000798
 5  72 30.7    0         1         1   9001104
 6  75 23.5    0         0         0   9001400

最有效的方法是什么？非常感谢你提前

Answer 1

这是一种方法:

within(forStack, {
  A <- as.numeric(!A %in% tail(levels(A), 2))
  B <- as.numeric(!B %in% head(levels(B), 2))
})
#   AGE  BMI time A B      ID
# 1  59 23.8    0 1 1 9000099
# 2  69 29.8    0 0 0 9000296
# 3  71 22.7    0 1 1 9000622
# 4  56 32.4    0 1 1 9000798
# 5  72 30.7    0 1 1 9001104
# 6  75 23.5    0 0 0 9001400

这里的基本想法是,head和tail都有一个" n"的说法,让你指定你从想要多少价值的"头"和"你的载体或数据集的尾巴".这让我们很容易地抓住(96.4,100]和(100, Inf]矢量A和矢量B的相关值

within是一种动态替换你的值的便捷方式data.frame.