Rearrange the rows as column variables

I have a large txt file with 41MB. It consists of 72 sensors measurements over the time. In case, I want to rearrange the table so that the 72 sensors are set as the column headings while the change of the "R" values of each of the sensors are shown under each column. For each row, the recording time of the #0 sensor will be put as each time step.

The resulting table would be like this:

As the file is too large and cannot be attached, below I show the first 3 sets of 72 sensor measurements. There are more than 10,000 sets in total. Thank you in advance.

# R Time

0 -2.1 03.11.2021 11:14:31

1 -7.04 03.11.2021 11:14:34

2 -11.85 03.11.2021 11:14:37

3 16 03.11.2021 11:14:41

4 12.13 03.11.2021 11:14:44

5 3.64 03.11.2021 11:14:47

6 -5.85 03.11.2021 11:14:50

7 37.43 03.11.2021 11:14:53

8 30.75 03.11.2021 11:14:57

9 23.03 03.11.2021 11:15:00

10 13.36 03.11.2021 11:15:03

11 60.76 03.11.2021 11:15:06

12 58.52 03.11.2021 11:15:09

13 56.85 03.11.2021 11:15:13

14 55.63 03.11.2021 11:15:16

15 87.26 03.11.2021 11:15:19

16 89.2 03.11.2021 11:15:22

17 104.1 03.11.2021 11:15:26

18 151.12 03.11.2021 11:15:29

19 113.1 03.11.2021 11:15:32

20 116.69 03.11.2021 11:15:35

21 140.12 03.11.2021 11:15:38

22 -5.56 03.11.2021 11:15:42

23 -17.22 03.11.2021 11:15:45

24 -27.46 03.11.2021 11:15:48

25 14.65 03.11.2021 11:15:51

26 3.89 03.11.2021 11:15:55

27 -15.5 03.11.2021 11:15:58

28 -53.9 03.11.2021 11:16:01

29 29.31 03.11.2021 11:16:04

30 19.07 03.11.2021 11:16:08

31 -5.17 03.11.2021 11:16:11

32 -60.37 03.11.2021 11:16:14

33 52.99 03.11.2021 11:16:17

34 41.11 03.11.2021 11:16:20

35 17.9 03.11.2021 11:16:24

36 -4.89 03.11.2021 11:16:27

37 80.25 03.11.2021 11:16:30

38 84.29 03.11.2021 11:16:33

39 81.63 03.11.2021 11:16:37

40 58.14 03.11.2021 11:16:40

41 110.88 03.11.2021 11:16:43

42 115.76 03.11.2021 11:16:46

43 110.73 03.11.2021 11:16:49

44 117.21 03.11.2021 11:16:53

45 -8.89 03.11.2021 11:16:56

46 -30.44 03.11.2021 11:16:59

47 -75.54 03.11.2021 11:17:02

48 9.1 03.11.2021 11:17:06

49 -0.3 03.11.2021 11:17:09

50 -40.23 03.11.2021 11:17:12

51 -141.76 03.11.2021 11:17:15

52 27.14 03.11.2021 11:17:18

53 14.53 03.11.2021 11:17:22

54 -31.13 03.11.2021 11:17:25

55 -291.18 03.11.2021 11:17:28

56 51.57 03.11.2021 11:17:31

57 40.84 03.11.2021 11:17:35

58 3.42 03.11.2021 11:17:38

59 -109.39 03.11.2021 11:17:41

60 72.22 03.11.2021 11:17:44

61 66.68 03.11.2021 11:17:48

62 43.86 03.11.2021 11:17:51

63 24.17 03.11.2021 11:17:54

64 99.99 03.11.2021 11:17:57

65 95.01 03.11.2021 11:18:00

66 83.49 03.11.2021 11:18:04

67 84.02 03.11.2021 11:18:07

68 -138.91 03.11.2021 11:18:10

69 -110.91 03.11.2021 11:18:13

70 -109.58 03.11.2021 11:18:16

71 -107.61 03.11.2021 11:18:20

0 -2.08 03.11.2021 14:15:03

1 -7.02 03.11.2021 14:15:07

2 -11.92 03.11.2021 14:15:10

3 16.04 03.11.2021 14:15:13

4 12.18 03.11.2021 14:15:16

5 3.72 03.11.2021 14:15:20

6 -5.92 03.11.2021 14:15:23

7 37.35 03.11.2021 14:15:26

8 30.59 03.11.2021 14:15:29

9 23.13 03.11.2021 14:15:33

10 13.46 03.11.2021 14:15:36

11 60.91 03.11.2021 14:15:39

12 58.53 03.11.2021 14:15:42

13 56.95 03.11.2021 14:15:45

14 55.87 03.11.2021 14:15:49

15 87.23 03.11.2021 14:15:52

16 89.47 03.11.2021 14:15:55

17 103.47 03.11.2021 14:15:58

18 151.15 03.11.2021 14:16:01

19 113.29 03.11.2021 14:16:05

20 116.45 03.11.2021 14:16:08

21 140.87 03.11.2021 14:16:11

22 -5.62 03.11.2021 14:16:14

23 -17.37 03.11.2021 14:16:18

24 -27.34 03.11.2021 14:16:21

25 14.6 03.11.2021 14:16:24

26 3.91 03.11.2021 14:16:27

27 -15.54 03.11.2021 14:16:30

28 -54.16 03.11.2021 14:16:34

29 29.26 03.11.2021 14:16:37

30 18.82 03.11.2021 14:16:40

31 -4.91 03.11.2021 14:16:43

32 -60.41 03.11.2021 14:16:46

33 52.75 03.11.2021 14:16:50

34 40.86 03.11.2021 14:16:53

35 17.73 03.11.2021 14:16:56

36 -4.76 03.11.2021 14:16:59

37 80.4 03.11.2021 14:17:03

38 84.38 03.11.2021 14:17:06

39 81.41 03.11.2021 14:17:09

40 58.67 03.11.2021 14:17:12

41 110.93 03.11.2021 14:17:15

42 115.56 03.11.2021 14:17:19

43 110.8 03.11.2021 14:17:22

44 116.95 03.11.2021 14:17:25

45 -8.28 03.11.2021 14:17:28

46 -30.01 03.11.2021 14:17:32

47 -74.32 03.11.2021 14:17:35

48 9.26 03.11.2021 14:17:38

49 -0.23 03.11.2021 14:17:41

50 -40.28 03.11.2021 14:17:44

51 -142.73 03.11.2021 14:17:48

52 27.11 03.11.2021 14:17:51

53 14.56 03.11.2021 14:17:54

54 -31.44 03.11.2021 14:17:57

55 -294.58 03.11.2021 14:18:00

56 51.62 03.11.2021 14:18:04

57 41.02 03.11.2021 14:18:07

58 3.26 03.11.2021 14:18:10

59 -108.46 03.11.2021 14:18:13

60 72.29 03.11.2021 14:18:17

61 66.25 03.11.2021 14:18:20

62 43.66 03.11.2021 14:18:23

63 24.59 03.11.2021 14:18:26

64 100.69 03.11.2021 14:18:29

65 95 03.11.2021 14:18:33

66 84.54 03.11.2021 14:18:36

67 84.67 03.11.2021 14:18:39

68 -103.68 03.11.2021 14:18:42

69 -4.82 03.11.2021 14:18:46

70 -9.55 03.11.2021 14:18:49

71 -16.63 03.11.2021 14:18:52

0 -2.08 03.11.2021 14:30:03

1 -6.93 03.11.2021 14:30:06

2 -11.77 03.11.2021 14:30:09

3 15.95 03.11.2021 14:30:12

4 12.1 03.11.2021 14:30:16

5 3.61 03.11.2021 14:30:19

6 -5.83 03.11.2021 14:30:22

7 37.47 03.11.2021 14:30:25

8 30.61 03.11.2021 14:30:28

9 23.19 03.11.2021 14:30:32

10 13.46 03.11.2021 14:30:35

11 60.85 03.11.2021 14:30:38

12 58.32 03.11.2021 14:30:41

13 57.15 03.11.2021 14:30:45

14 55.74 03.11.2021 14:30:48

15 87.05 03.11.2021 14:30:51

16 89.12 03.11.2021 14:30:54

17 103.73 03.11.2021 14:30:57

18 151.31 03.11.2021 14:31:01

19 113.31 03.11.2021 14:31:04

20 116.4 03.11.2021 14:31:07

21 140.44 03.11.2021 14:31:10

22 -5.69 03.11.2021 14:31:13

23 -17.52 03.11.2021 14:31:17

24 -27.84 03.11.2021 14:31:20

25 14.29 03.11.2021 14:31:23

26 4.04 03.11.2021 14:31:26

27 -15.75 03.11.2021 14:31:30

28 -53.91 03.11.2021 14:31:33

29 29 03.11.2021 14:31:36

30 18.93 03.11.2021 14:31:39

31 -5.1 03.11.2021 14:31:42

32 -60.53 03.11.2021 14:31:46

33 52.97 03.11.2021 14:31:49

34 40.82 03.11.2021 14:31:52

35 17.52 03.11.2021 14:31:55

36 -4.83 03.11.2021 14:31:59

37 80.38 03.11.2021 14:32:02

38 84.13 03.11.2021 14:32:05

39 81.79 03.11.2021 14:32:08

40 58.01 03.11.2021 14:32:11

41 110.93 03.11.2021 14:32:15

42 115.43 03.11.2021 14:32:18

43 110.78 03.11.2021 14:32:21

44 117.2 03.11.2021 14:32:24

45 -9.37 03.11.2021 14:32:27

46 -31.7 03.11.2021 14:32:31

47 -78.47 03.11.2021 14:32:34

48 8.42 03.11.2021 14:32:37

49 -0.78 03.11.2021 14:32:40

50 -41.45 03.11.2021 14:32:44

51 -142.92 03.11.2021 14:32:47

52 26.08 03.11.2021 14:32:50

53 13.67 03.11.2021 14:32:53

54 -32.11 03.11.2021 14:32:56

55 -296.35 03.11.2021 14:33:00

56 51.13 03.11.2021 14:33:03

57 40.15 03.11.2021 14:33:06

58 2.87 03.11.2021 14:33:09

59 -109.49 03.11.2021 14:33:13

60 71.65 03.11.2021 14:33:16

61 65.62 03.11.2021 14:33:19

62 42.51 03.11.2021 14:33:22

63 23.27 03.11.2021 14:33:25

64 99.31 03.11.2021 14:33:29

65 94.27 03.11.2021 14:33:32

66 82.23 03.11.2021 14:33:35

67 83.96 03.11.2021 14:33:38

68 -3.89 03.11.2021 14:33:41

69 -5.14 03.11.2021 14:33:45

70 -9.38 03.11.2021 14:33:48

71 -16.86 03.11.2021 14:33:51

[Stephen23]

Keeping the data in three columns would probably make it easier to work with.

For example, three columns means you can use the standard methods for analyzing table data:

https://www.mathworks.com/help/matlab/matlab_prog/grouping-variables-for-splitting-data.html

Answer 1

[Voss]

If I understand correctly, you only need the times corresponding to sensor #0, and the other times can be discarded/ignored. If that's the case, maybe something along these lines will work for you:

t = readtable('table.txt','PreserveVariableNames',true);

N_sensors = 72;
zero_times = t{t{:,'#'} == 0,'Time'};
R = num2cell(reshape(t.R,N_sensors,[]).',1);

t_new = table(zero_times,R{:},'VariableNames',['Time',sprintfc('%d',0:N_sensors-1)])

t_new = 3×73 table
           Time              0        1        2         3        4       5        6        7        8        9       10       11       12       13       14       15       16        17        18        19        20        21       22        23        24       25       26       27        28       29       30       31        32       33       34       35       36       37       38       39       40        41        42        43        44       45        46        47       48      49        50        51        52       53        54        55        56       57       58       59        60       61       62       63        64       65       66       67        68         69         70         71   
    ___________________    _____    _____    ______    _____    _____    ____    _____    _____    _____    _____    _____    _____    _____    _____    _____    _____    _____    ______    ______    ______    ______    ______    _____    ______    ______    _____    ____    ______    ______    _____    _____    _____    ______    _____    _____    _____    _____    _____    _____    _____    _____    ______    ______    ______    ______    _____    ______    ______    ____    _____    ______    _______    _____    _____    ______    _______    _____    _____    ____    _______    _____    _____    _____    _____    ______    _____    _____    _____    _______    _______    _______    _______

    03.11.2021 11:14:31     -2.1    -7.04    -11.85       16    12.13    3.64    -5.85    37.43    30.75    23.03    13.36    60.76    58.52    56.85    55.63    87.26     89.2     104.1    151.12     113.1    116.69    140.12    -5.56    -17.22    -27.46    14.65    3.89     -15.5     -53.9    29.31    19.07    -5.17    -60.37    52.99    41.11     17.9    -4.89    80.25    84.29    81.63    58.14    110.88    115.76    110.73    117.21    -8.89    -30.44    -75.54     9.1     -0.3    -40.23    -141.76    27.14    14.53    -31.13    -291.18    51.57    40.84    3.42    -109.39    72.22    66.68    43.86    24.17     99.99    95.01    83.49    84.02    -138.91    -110.91    -109.58    -107.61
    03.11.2021 14:15:03    -2.08    -7.02    -11.92    16.04    12.18    3.72    -5.92    37.35    30.59    23.13    13.46    60.91    58.53    56.95    55.87    87.23    89.47    103.47    151.15    113.29    116.45    140.87    -5.62    -17.37    -27.34     14.6    3.91    -15.54    -54.16    29.26    18.82    -4.91    -60.41    52.75    40.86    17.73    -4.76     80.4    84.38    81.41    58.67    110.93    115.56     110.8    116.95    -8.28    -30.01    -74.32    9.26    -0.23    -40.28    -142.73    27.11    14.56    -31.44    -294.58    51.62    41.02    3.26    -108.46    72.29    66.25    43.66    24.59    100.69       95    84.54    84.67    -103.68      -4.82      -9.55     -16.63
    03.11.2021 14:30:03    -2.08    -6.93    -11.77    15.95     12.1    3.61    -5.83    37.47    30.61    23.19    13.46    60.85    58.32    57.15    55.74    87.05    89.12    103.73    151.31    113.31     116.4    140.44    -5.69    -17.52    -27.84    14.29    4.04    -15.75    -53.91       29    18.93     -5.1    -60.53    52.97    40.82    17.52    -4.83    80.38    84.13    81.79    58.01    110.93    115.43    110.78     117.2    -9.37     -31.7    -78.47    8.42    -0.78    -41.45    -142.92    26.08    13.67    -32.11    -296.35    51.13    40.15    2.87    -109.49    71.65    65.62    42.51    23.27     99.31    94.27    82.23    83.96      -3.89      -5.14      -9.38     -16.86

[Man Lap Fung]

Yes. That's what I would like to create. Thank you!

[Voss]

You're welcome!

Answer 2

[Image Analyst]

Did you try readtable

t = readtable('dataTable.txt')

t = 216×3 table
    Number      R           DateAndTime    
    ______    ______    ___________________

       0        -2.1    03.11.2021 11:14:31
       1       -7.04    03.11.2021 11:14:34
       2      -11.85    03.11.2021 11:14:37
       3          16    03.11.2021 11:14:41
       4       12.13    03.11.2021 11:14:44
       5        3.64    03.11.2021 11:14:47
       6       -5.85    03.11.2021 11:14:50
       7       37.43    03.11.2021 11:14:53
       8       30.75    03.11.2021 11:14:57
       9       23.03    03.11.2021 11:15:00
      10       13.36    03.11.2021 11:15:03
      11       60.76    03.11.2021 11:15:06
      12       58.52    03.11.2021 11:15:09
      13       56.85    03.11.2021 11:15:13
      14       55.63    03.11.2021 11:15:16
      15       87.26    03.11.2021 11:15:19

It doesn't look like for each unique time there are 72 readings, so what do you plan on doing about that?