> 

nx <- c(rnorm(10))

随机生成一个包含10个正态分布数据的向量（一维表格）

> 

nx

查看向量

[1]  0.436219296 -0.003864687  1.666923704 -0.755768282  1.070840200

 [6]  0.943247037  0.861156081 -1.083567875 -1.137469924  0.303574238

查看结果

> 

write.csv(nx, file="D:/bear.csv")

将向量保存到表格bear.csv中

结果

> 

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

读入表格，存在data向量中

> 

data

查看向量

     F1.Hz.   F2.Hz.    F3.Hz.    F4.Hz.   F5.Hz.    F6.Hz.

1  3431.654 4596.179  9642.441 12348.066 3838.552  7498.416

2  3461.062 4524.386 10666.409 11647.196 4754.872  7498.416

3  3411.314 4415.518 10666.409 11166.294 4773.052  7498.416

4  3605.767 3838.552 10666.409 10008.806 4676.657  7498.416

5  3445.936 4754.872 10666.409  9935.201 4813.661  7498.416

6  3500.930 4773.052 10666.409 10051.817 4686.464  7498.416

7  3518.311 4676.657 10666.409 10441.247 4792.689  9506.292

8  3536.907 4813.661  7736.680 11894.051 4739.644  8709.927

9  3545.723 4686.464  8599.928 12063.672 4861.783  7913.562

10 3553.186 4792.689 10824.886 11938.194 4813.760  7117.197

11 3552.418 4739.644 10015.512 11809.212 4819.385  8208.955

12 3553.105 4861.783 11534.382 12513.075 4688.018  8278.993

13 3554.377 4813.760  9760.246 11382.993 4666.107  8349.031

14 3539.068 4819.385  7407.902 11420.825 4788.276  8419.069

15 3514.625 4688.018  7117.997 11035.042 4794.959  8489.106

16 3534.030 4666.107  8525.595 11722.272 4794.926  8559.144

17 3521.814 4788.276  8525.595 10939.196 4819.187  8629.182

18 3588.896 4794.959  8525.595 10921.920 4789.445  8699.220

19 3615.696 4794.926  8525.595 11512.921 4786.146  8769.257

20 3595.440 4819.187  7539.253 11756.123 4787.794  8839.295

21 3595.121 4789.445  6696.898 12120.326 4810.097  8909.333

22 3596.052 4786.146  6662.958 12316.042 4784.533  8979.371

23 3603.943 4787.794  7415.122 12355.483 4882.977  9049.409

24 3614.603 4810.097  6935.103 11976.211 4896.201  9119.446

25 3605.269 4784.533  7498.416 11756.809 4909.424  9189.484

26 3594.244 4801.659  7498.416 11148.864 4922.647  9259.522

27 3557.889 4777.278  7498.416 11530.219 4935.870  9329.560

28 3533.653 4769.104  7498.416 11038.150 4949.094  9399.597

29 3524.953 4801.545  7498.416 10680.637 4962.317  9469.635

30 3583.689 4746.466  7498.416 10477.168 4975.540  9539.673

31 3601.696 4735.630  7498.416 10999.146 4988.764  9609.711

32 3583.889 4735.707  7498.416 10841.006 5001.987  9679.749

33 3531.654 4684.883  7498.416 10726.529 5015.210  9749.786

34 3524.498 4719.818  7498.416 11226.458 5028.433  9819.824

35 3590.700 4794.351  9506.292 11493.798 5041.657  9889.862

36 3533.200 4741.003  8709.927 12484.489 5054.880  9959.900

37 3508.570 4735.610  7913.562 11674.365 5068.103 10029.938

38 3544.007 4699.263  7117.197 11521.220 5081.327 10099.975

39 3652.210 4622.555  6320.832 12220.909 5094.550 10170.013

40 3657.347 4356.328  9531.433 13190.689 5107.773 10240.051

41 3707.543 4240.136 10560.521 13190.689 5120.996 10310.089

42 3553.006 4480.197 11589.609 10394.072 5134.220 10380.126

> 

二维向量的查看结果，6列、42行

data <- data/100+50

对表格中所有的单元格数据都除以100之后加上50

> 

data

查看运算后data向量

> 

     F1.Hz.   F2.Hz.   F3.Hz.   F4.Hz.    F5.Hz.   F6.Hz.

1  84.31654 95.96179 146.4244 173.4807  88.38552 124.9842

2  84.61062 95.24386 156.6641 166.4720  97.54872 124.9842

3  84.11314 94.15518 156.6641 161.6629  97.73052 124.9842

4  86.05767 88.38552 156.6641 150.0881  96.76657 124.9842

5  84.45936 97.54872 156.6641 149.3520  98.13661 124.9842

6  85.00930 97.73052 156.6641 150.5182  96.86464 124.9842

7  85.18311 96.76657 156.6641 154.4125  97.92689 145.0629

8  85.36907 98.13661 127.3668 168.9405  97.39644 137.0993

9  85.45723 96.86464 135.9993 170.6367  98.61783 129.1356

10 85.53186 97.92689 158.2489 169.3819  98.13760 121.1720

11 85.52418 97.39644 150.1551 168.0921  98.19385 132.0896

12 85.53105 98.61783 165.3438 175.1308  96.88018 132.7899

13 85.54377 98.13760 147.6025 163.8299  96.66107 133.4903

14 85.39068 98.19385 124.0790 164.2083  97.88276 134.1907

15 85.14625 96.88018 121.1800 160.3504  97.94959 134.8911

16 85.34030 96.66107 135.2559 167.2227  97.94926 135.5914

17 85.21814 97.88276 135.2559 159.3920  98.19187 136.2918

18 85.88896 97.94959 135.2559 159.2192  97.89445 136.9922

19 86.15696 97.94926 135.2559 165.1292  97.86146 137.6926

20 85.95440 98.19187 125.3925 167.5612  97.87794 138.3930

21 85.95121 97.89445 116.9690 171.2033  98.10097 139.0933

22 85.96052 97.86146 116.6296 173.1604  97.84533 139.7937

23 86.03943 97.87794 124.1512 173.5548  98.82977 140.4941

24 86.14603 98.10097 119.3510 169.7621  98.96201 141.1945

25 86.05269 97.84533 124.9842 167.5681  99.09424 141.8948

26 85.94244 98.01659 124.9842 161.4886  99.22647 142.5952

27 85.57889 97.77278 124.9842 165.3022  99.35870 143.2956

28 85.33653 97.69104 124.9842 160.3815  99.49094 143.9960

29 85.24953 98.01545 124.9842 156.8064  99.62317 144.6964

30 85.83689 97.46466 124.9842 154.7717  99.75540 145.3967

31 86.01696 97.35630 124.9842 159.9915  99.88764 146.0971

32 85.83889 97.35707 124.9842 158.4101 100.01987 146.7975

33 85.31654 96.84883 124.9842 157.2653 100.15210 147.4979

34 85.24498 97.19818 124.9842 162.2646 100.28433 148.1982

35 85.90700 97.94351 145.0629 164.9380 100.41657 148.8986

36 85.33200 97.41003 137.0993 174.8449 100.54880 149.5990

37 85.08570 97.35610 129.1356 166.7437 100.68103 150.2994

38 85.44007 96.99263 121.1720 165.2122 100.81327 150.9998

39 86.52210 96.22555 113.2083 172.2091 100.94550 151.7001

40 86.57347 93.56328 145.3143 181.9069 101.07773 152.4005

41 87.07543 92.40136 155.6052 181.9069 101.20996 153.1009

42 85.53006 94.80197 165.8961 153.9407 101.34220 153.8013

看见了吧，这比在Excel表格中的繁琐的“公式输入”、“拖曳”、新建表格等操作容易多了

> 

write.csv(data, file="D:/bearf2.csv")

将向量保存到表格bearf2.csv中（下图）