按 Space 拆分列

Split Column By Space

DATA <- data.frame("V1" = c("SCORE : 9.931 5.092",
                            "SCORE : 6.00007 15.1248",
                            "SCORE : 1.0002 12.987532",
                            "SCORE : 3.1 3.98532"))
WANT <- data.frame("VAR1" = c(9.931, 6.00007, 1.0002, 3.1),
                   'VAR2' = c(5.092, 15.1248, 12.987532, 3.98532))

我拥有的是学生考试成绩数据,其输入方式如 'DATA' 所示,但我希望将其拆分,以便我拥有的就像 'WANT' 框架中显示的那样第一个数字在 'VAR1' 中,第二个数字在 'VAR2' 中,忽略空格

我的尝试:

DATA[, c("VAR1", "VAR2") := trimws(V1, whitespace = ".*:\s+")]

生产:

我们可以去掉带trimws的前缀子串,使用read.table读取列,默认为sepspace到return两列data.framebase R

read.table(text = trimws(DATA$V1, whitespace = ".*:\s+"), 
   header = FALSE, col.names = c("VAR1", "VAR2"))
     VAR1     VAR2
1 9.93100  5.09200
2 6.00007 15.12480
3 1.00020 12.98753
4 3.10000  3.98532

或者可以使用 extract 来自 tidyr

library(tidyr)
extract(DATA, V1, into = c("VAR1", "VAR2"),
    ".*:\s+([0-9.]+)\s+([0-9.]+)", convert = TRUE)
     VAR1     VAR2
1 9.93100  5.09200
2 6.00007 15.12480
3 1.00020 12.98753
4 3.10000  3.98532

如果我们要data.table,用同样的方法可以在去掉前缀子串后用fread读取

library(data.table)
fread(text = setDT(DATA)[, trimws(V1, whitespace = ".*:\s+")], 
   col.names = c("VAR1", "VAR2"))
      VAR1     VAR2
     <num>    <num>
1: 9.93100  5.09200
2: 6.00007 15.12480
3: 1.00020 12.98753
4: 3.10000  3.98532

或使用 fread

中的 select 选项
fread(text = DATA$V1, select = c(3, 4), col.names = c("VAR1", "VAR2"))
     VAR1     VAR2
     <num>    <num>
1: 9.93100  5.09200
2: 6.00007 15.12480
3: 1.00020 12.98753
4: 3.10000  3.98532

或读作四列,并子集

fread(text = DATA$V1)[, .(VAR1 = V3, VAR2 = V4)]
     VAR1     VAR2
     <num>    <num>
1: 9.93100  5.09200
2: 6.00007 15.12480
3: 1.00020 12.98753
4: 3.10000  3.98532

或者可以使用tstrsplit

setDT(DATA)[, c("VAR1", "VAR2") := tstrsplit(trimws(V1, 
       whitespace = ".*:\s+"), " ")]
DATA <- type.convert(DATA, as.is = TRUE)
DATA
                         V1    VAR1     VAR2
                     <char>   <num>    <num>
1:      SCORE : 9.931 5.092 9.93100  5.09200
2:  SCORE : 6.00007 15.1248 6.00007 15.12480
3: SCORE : 1.0002 12.987532 1.00020 12.98753
4:      SCORE : 3.1 3.98532 3.10000  3.98532