解决方案二， 待更新

> 

data<-read.csv("D:\ziliao\zhuanye\R bear\harmo.csv")

导入表格harmo.csv数据，存在矩阵data中

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data

查看data空间

结果

          F3        F4        F5

1   5446.635  5858.746  6843.617

2   6654.305  7005.958  6924.623

3   7172.323  7169.585  6940.106

4   7311.956  7525.422  6977.832

5   8058.262 11214.798  7580.775

6   8116.698 11505.268  7915.856

7   8312.447 11544.472  8472.229

8   8667.151 11810.009  9492.205

9   9093.771 11881.491  9934.308

10  9103.865 11908.340 10787.881

11  9215.153 11909.480 10882.602

12  9461.068 12028.218 11161.032

13  9625.699 12112.602 11307.906

14  9896.211 12115.175 11497.302

15  9912.590 12174.058 11696.347

16  9942.585 12524.577 11800.720

17 10049.769 12533.796 11944.833

18 10120.347 12778.348 12439.379

19 10153.751 12862.208 13929.031

20 10160.672 12894.624 14691.925

21 10365.499 13010.052 14794.472

22 10393.181 13101.747 15368.400

23 10482.846 13188.517 15461.178

24 10583.587 13189.188 15558.937

25 10591.209 13243.095 15559.645

26 10640.676 13300.190 15623.761

27 10675.679 13664.360 15657.390

28 10723.670 14070.819 15714.827

29 10729.272 14155.309 15746.066

30 10900.465 14284.890 15756.113

31 10945.887 14287.759 15777.929

32 10989.468 14297.971 15793.488

33 11042.734 14318.933 15815.089

34 11095.552 14354.718 15817.700

35 11199.642 14530.077 15818.295

36 11200.800 14681.983 15860.937

37 11211.056 14685.578 16003.928

38 11243.513 14766.872 16013.536

39 11324.318 14817.897 16015.512

40 11394.809 14821.364 16052.696

41 11429.242 15015.235 16059.643

42 11514.232 15043.056 16067.201

43 11515.162 15236.188 16069.255

44 11547.771 15280.614 16079.015

45 11576.145 15549.818 16079.814

46 11671.397 15640.638 16081.685

47 11747.088 15746.188 16120.205

48 11775.793 15808.372 16134.122

49 12207.104 16510.259 16140.042

50 12921.229        NA 16151.293

51 12925.543        NA 16152.269

52        NA        NA 16184.463

53        NA        NA 16230.646

54        NA        NA 16243.203

55        NA        NA 16251.307

56        NA        NA 16337.266

57        NA        NA 16360.895

58        NA        NA 16367.404

59        NA        NA 16406.798

60        NA        NA 16709.307

61        NA        NA 16744.672

62        NA        NA 16860.826

63        NA        NA 16957.370

64        NA        NA 16975.136

65        NA        NA 17054.482

66        NA        NA 17193.373

67        NA        NA 18040.702

68        NA        NA 19188.307

三列，由于每列数据量的不同，所以出现缺省值

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tem <- data[,1]

data数据第一列赋值给临时向量tem

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x <- tem[!is.na(tem)]

tem中非缺省值赋值给x

> 

x

查看x向量空间

[1]  5446.635  6654.305  7172.323  7311.956  8058.262  8116.698  8312.447

 [8]  8667.151  9093.771  9103.865  9215.153  9461.068  9625.699  9896.211

[15]  9912.590  9942.585 10049.769 10120.347 10153.751 10160.672 10365.499

[22] 10393.181 10482.846 10583.587 10591.209 10640.676 10675.679 10723.670

[29] 10729.272 10900.465 10945.887 10989.468 11042.734 11095.552 11199.642

[36] 11200.800 11211.056 11243.513 11324.318 11394.809 11429.242 11514.232

[43] 11515.162 11547.771 11576.145 11671.397 11747.088 11775.793 12207.104

[50] 12921.229 12925.543

> 

tem <- data[,2]

data数据第2列赋值给临时向量tem

> 

y <- tem[!is.na(tem)]

tem中非缺省值赋值给y

> 

y

查看y向量空间

[1]  5858.746  7005.958  7169.585  7525.422 11214.798 11505.268 11544.472

 [8] 11810.009 11881.491 11908.340 11909.480 12028.218 12112.602 12115.175

[15] 12174.058 12524.577 12533.796 12778.348 12862.208 12894.624 13010.052

[22] 13101.747 13188.517 13189.188 13243.095 13300.190 13664.360 14070.819

[29] 14155.309 14284.890 14287.759 14297.971 14318.933 14354.718 14530.077

[36] 14681.983 14685.578 14766.872 14817.897 14821.364 15015.235 15043.056

[43] 15236.188 15280.614 15549.818 15640.638 15746.188 15808.372 16510.259

查询结果

> 

tem <- data[,3]

data数据第3列赋值给临时向量tem

> 

z <- tem[!is.na(tem)]

tem中非缺省值赋值给z

> 

z

查看z向量空间

[1]  6843.617  6924.623  6940.106  6977.832  7580.775  7915.856  8472.229

 [8]  9492.205  9934.308 10787.881 10882.602 11161.032 11307.906 11497.302

[15] 11696.347 11800.720 11944.833 12439.379 13929.031 14691.925 14794.472

[22] 15368.400 15461.178 15558.937 15559.645 15623.761 15657.390 15714.827

[29] 15746.066 15756.113 15777.929 15793.488 15815.089 15817.700 15818.295

[36] 15860.937 16003.928 16013.536 16015.512 16052.696 16059.643 16067.201

[43] 16069.255 16079.015 16079.814 16081.685 16120.205 16134.122 16140.042

[50] 16151.293 16152.269 16184.463 16230.646 16243.203 16251.307 16337.266

[57] 16360.895 16367.404 16406.798 16709.307 16744.672 16860.826 16957.370

[64] 16975.136 17054.482 17193.373 18040.702 19188.307

查询结果