我有一个我想要读入R的DAT文件，但是当我导入我的数据时，它继续显示我有10列/变量（来自第一行），实际上，它实际上应该是29列/变量。我该如何解决这个问题？
记事本上的DAT文件示例：
smsa66 smsa76 nearc2 nearc4 nearc4a nearc4b ed76 ed66 age76 daded
nodaded momed nomomed momdad14 sinmom14 step14 south66 south76
lwage76 famed black wage76 enroll76 kww iqscore mar76 libcrd14
exp76 exp762
1 1 0 0 0 0 7
5 29 9.94 1 10.25 1 1
0 0 0 0 6.306275 9 1
548 0 15 . 1 0 16
256
1 1 0 0 0 0 12
11 27 8 0 8 0 1
0 0 0 0 6.175867 8 0
481 0 35 93 1 1 9
81
1 1 0 0 0 0 12
12 34 14 0 12 0 1
0 0 0 0 6.580639 2 0
721 0 42 103 1 1 16
256
1 1 1 1 1 0 11
11 27 11 0 12 0 1
0 0 0 0 5.521461 6 0
250 0 25 88 1 1 10
100
1 1 1 1 1 0 12
12 34 8 0 7 0 1
0 0 0 0 6.591674 8 0
729 0 34 108 1 0 16
256
1 1 1 1 1 0 12
11 26 9 0 12 0 1
0 0 0 0 6.214608 6 0
500 0 38 85 1 1 8
64
1 1 1 1 1 0 18
16 33 14 0 14 0 1
0 0 0 0 6.336826 1 0
565 0 41 119 1 1 9
81
1 1 1 1 1 0 14
13 29 14 0 14 0 1
0 0 0 0 6.410175 1 0
608 0 46 108 1 1 9
81
答案 0 :(得分：0)
txt1<-" smsa66 smsa76 nearc2 nearc4 nearc4a nearc4b ed76 ed66 age76 daded
nodaded momed nomomed momdad14 sinmom14 step14 south66 south76
lwage76 famed black wage76 enroll76 kww iqscore mar76 libcrd14
exp76 exp762"
txt2 <-
" 1 1 0 0 0 0 7
5 29 9.94 1 10.25 1 1
0 0 0 0 6.306275 9 1
548 0 15 NA 1 0 16
256
1 1 0 0 0 0 12
11 27 8 0 8 0 1
0 0 0 0 6.175867 8 0
481 0 35 93 1 1 9
81
1 1 0 0 0 0 12
12 34 14 0 12 0 1
0 0 0 0 6.580639 2 0
721 0 42 103 1 1 16
256
1 1 1 1 1 0 11
11 27 11 0 12 0 1
0 0 0 0 5.521461 6 0
250 0 25 88 1 1 10
100
1 1 1 1 1 0 12
12 34 8 0 7 0 1
0 0 0 0 6.591674 8 0
729 0 34 108 1 0 16
256
1 1 1 1 1 0 12
11 26 9 0 12 0 1
0 0 0 0 6.214608 6 0
500 0 38 85 1 1 8
64
1 1 1 1 1 0 18
16 33 14 0 14 0 1
0 0 0 0 6.336826 1 0
565 0 41 119 1 1 9
81
1 1 1 1 1 0 14
13 29 14 0 14 0 1
0 0 0 0 6.410175 1 0
608 0 46 108 1 1 9
81"
现在代码：
inp <- scan(text=txt2, what="numeric")
inmat <- matrix( as.numeric(inp), ncol=29, byrow=TRUE)
dfrm <- as.data.frame(inmat)
scan(text=txt1, what="")
Read 29 items
[1] "smsa66" "smsa76" "nearc2" "nearc4" "nearc4a" "nearc4b" "ed76"
[8] "ed66" "age76" "daded" "nodaded" "momed" "nomomed" "momdad14"
[15] "sinmom14" "step14" "south66" "south76" "lwage76" "famed" "black"
[22] "wage76" "enroll76" "kww" "iqscore" "mar76" "libcrd14" "exp76"
[29] "exp762"
names(dfrm) <- scan(text=txt1, what="")
#Read 29 items
dfrm
#-----------------------
smsa66 smsa76 nearc2 nearc4 nearc4a nearc4b ed76 ed66 age76 daded nodaded momed nomomed
1 1 1 0 0 0 0 7 5 29 9.94 1 10.25 1
2 1 1 0 0 0 0 12 11 27 8 0 8 0
3 1 1 0 0 0 0 12 12
snipped remainder of output
最终结果：
str(dfrm)
'data.frame': 8 obs. of 29 variables:
$ smsa66 : num 1 1 1 1 1 1 1 1
$ smsa76 : num 1 1 1 1 1 1 1 1
$ nearc2 : num 0 0 0 1 1 1 1 1
$ nearc4 : num 0 0 0 1 1 1 1 1
$ nearc4a : num 0 0 0 1 1 1 1 1
$ nearc4b : num 0 0 0 0 0 0 0 0
$ ed76 : num 7 12 12 11 12 12 18 14
$ ed66 : num 5 11 12 11 12 11 16 13
$ age76 : num 29 27 34 27 34 26 33 29
$ daded : num 9.94 8 14 11 8 9 14 14
$ nodaded : num 1 0 0 0 0 0 0 0
$ momed : num 10.2 8 12 12 7 ...
$ nomomed : num 1 0 0 0 0 0 0 0
$ momdad14: num 1 1 1 1 1 1 1 1
$ sinmom14: num 0 0 0 0 0 0 0 0
$ step14 : num 0 0 0 0 0 0 0 0
$ south66 : num 0 0 0 0 0 0 0 0
$ south76 : num 0 0 0 0 0 0 0 0
$ lwage76 : num 6.31 6.18 6.58 5.52 6.59 ...
$ famed : num 9 8 2 6 8 6 1 1
$ black : num 1 0 0 0 0 0 0 0
$ wage76 : num 548 481 721 250 729 500 565 608
$ enroll76: num 0 0 0 0 0 0 0 0
$ kww : num 15 35 42 25 34 38 41 46
$ iqscore : num NA 93 103 88 108 85 119 108
$ mar76 : num 1 1 1 1 1 1 1 1
$ libcrd14: num 0 1 1 1 0 1 1 1
$ exp76 : num 16 9 16 10 16 8 9 9
$ exp762 : num 256 81 256 100 256 64 81 81