'eliminating drops in data in R based on a sequence of 2 variables
I have very limited knowledge of R and data science in general. I have a dataset based on a test I conducted on concrete samples where a load was applied until failure was observed.
Due to noise in the signals, drops in the data is an issue that is commonly observed with those tests. Is there an easy was to eliminated those noise notes.
For example, if the Bottom column is in sequence/order the load.Cell values are not and I was to eliminate the dips in the Load.Cell column.
ï..Time
Bottom
Load.Cell
Top.Left
Top.right
X
1
03:00.4
0.017
110.8
0.894
0.001
0.448
2
03:00.4
0.017
111.1
0.896
0.004
0.450
3
03:00.4
0.018
111.3
0.899
0.006
0.452
4
03:00.5
0.017
111.5
0.901
0.007
0.454
5
03:00.5
0.017
111.7
0.902
0.008
0.455
6
03:00.5
0.018
111.9
0.905
0.010
0.458
7
03:00.5
0.017
112.1
0.906
0.012
0.459
8
03:00.5
0.017
112.3
0.908
0.014
0.461
9
03:00.5
0.018
112.5
0.910
0.016
0.463
10
03:00.6
0.018
112.7
0.911
0.017
0.464
11
03:00.6
0.018
112.8
0.913
0.018
0.466
12
03:00.6
0.018
112.9
0.914
0.019
0.466
13
03:00.6
0.018
113.0
0.915
0.019
0.467
14
03:00.6
0.018
113.0
0.915
0.019
0.467
15
03:00.6
0.018
112.9
0.915
0.020
0.468
16
03:00.7
0.018
112.7
0.916
0.020
0.468
17
03:00.7
0.018
112.5
0.915
0.019
0.467
18
03:00.7
0.018
112.4
0.915
0.019
0.467
19
03:00.7
0.018
112.3
0.916
0.019
0.468
20
03:00.7
0.018
112.2
0.915
0.019
0.467
21
03:00.7
0.018
112.1
0.916
0.019
0.468
22
03:00.8
0.018
112.0
0.915
0.019
0.467
23
03:00.8
0.018
111.9
0.915
0.019
0.467
24
03:00.8
0.018
111.9
0.916
0.019
0.468
25
03:00.8
0.018
111.8
0.915
0.019
0.467
26
03:00.8
0.018
111.7
0.916
0.019
0.468
27
03:00.8
0.018
111.6
0.915
0.019
0.467
28
03:00.9
0.018
111.6
0.915
0.019
0.467
29
03:00.9
0.018
111.5
0.916
0.019
0.468
30
03:00.9
0.018
111.4
0.915
0.019
0.467
31
03:00.9
0.018
111.4
0.916
0.019
0.468
32
03:00.9
0.018
111.3
0.915
0.019
0.467
33
03:00.9
0.018
111.2
0.915
0.019
0.467
34
03:01.0
0.018
111.2
0.916
0.019
0.468
35
03:01.0
0.018
111.1
0.915
0.019
0.467
36
03:01.0
0.018
111.1
0.915
0.019
0.467
37
03:01.0
0.018
111.0
0.915
0.018
0.466
38
03:01.0
0.018
111.0
0.915
0.018
0.466
39
03:01.0
0.018
110.9
0.916
0.018
0.467
40
03:01.1
0.018
110.8
0.915
0.018
0.466
41
03:01.1
0.018
110.8
0.915
0.018
0.466
42
03:01.1
0.018
110.8
0.915
0.018
0.466
43
03:01.1
0.018
110.7
0.915
0.017
0.466
44
03:01.1
0.018
110.7
0.915
0.017
0.466
45
03:01.1
0.018
110.6
0.915
0.017
0.466
46
03:01.2
0.018
110.6
0.915
0.017
0.466
47
03:01.2
0.018
110.5
0.915
0.017
0.466
48
03:01.2
0.018
110.5
0.915
0.016
0.466
49
03:01.2
0.018
110.5
0.915
0.016
0.466
50
03:01.2
0.018
110.4
0.915
0.016
0.466
51
03:01.2
0.018
110.4
0.915
0.016
0.466
52
03:01.3
0.018
110.3
0.915
0.015
0.465
53
03:01.3
0.018
110.3
0.915
0.015
0.465
54
03:01.3
0.018
110.3
0.915
0.015
0.465
55
03:01.3
0.018
110.2
0.915
0.014
0.464
56
03:01.3
0.018
110.2
0.915
0.014
0.464
57
03:01.3
0.018
110.2
0.915
0.014
0.464
58
03:01.4
0.018
110.1
0.915
0.013
0.464
59
03:01.4
0.018
110.1
0.915
0.012
0.464
60
03:01.4
0.019
110.1
0.915
0.012
0.464
61
03:01.4
0.019
110.0
0.915
0.011
0.463
62
03:01.4
0.019
110.0
0.915
0.011
0.463
63
03:01.4
0.019
110.0
0.915
0.011
0.463
64
03:01.5
0.019
110.0
0.915
0.011
0.463
65
03:01.5
0.019
110.0
0.915
0.011
0.463
66
03:01.5
0.019
110.1
0.915
0.011
0.463
67
03:01.5
0.019
110.2
0.915
0.011
0.463
68
03:01.5
0.019
110.3
0.915
0.011
0.463
69
03:01.5
0.019
110.4
0.915
0.011
0.463
70
03:01.6
0.019
110.6
0.915
0.011
0.463
71
03:01.6
0.019
110.8
0.915
0.011
0.463
72
03:01.6
0.019
111.0
0.915
0.011
0.463
73
03:01.6
0.019
111.2
0.915
0.012
0.464
74
03:01.6
0.019
111.4
0.915
0.012
0.464
75
03:01.6
0.019
111.7
0.915
0.013
0.464
76
03:01.7
0.019
111.9
0.915
0.016
0.466
77
03:01.7
0.019
112.2
0.916
0.017
0.466
78
03:01.7
0.019
112.5
0.915
0.019
0.467
79
03:01.7
0.019
112.7
0.916
0.021
0.469
80
03:01.7
0.019
113.0
0.917
0.024
0.471
81
03:01.7
0.019
113.3
0.918
0.026
0.472
82
03:01.8
0.019
113.6
0.920
0.029
0.475
83
03:01.8
0.019
113.8
0.922
0.030
0.476
84
03:01.8
0.019
114.1
0.925
0.032
0.479
85
03:01.8
0.019
114.3
0.928
0.034
0.481
86
03:01.8
0.019
114.5
0.929
0.036
0.483
87
03:01.8
0.019
114.7
0.931
0.037
0.484
88
03:01.9
0.019
114.8
0.932
0.037
0.485
89
03:01.9
0.019
114.9
0.933
0.038
0.486
90
03:01.9
0.019
114.9
0.934
0.039
0.487
91
03:01.9
0.019
114.9
0.934
0.039
0.487
92
03:01.9
0.019
114.7
0.935
0.039
0.487
93
03:01.9
0.019
114.6
0.934
0.039
0.487
94
03:02.0
0.019
114.4
0.934
0.039
0.487
95
03:02.0
0.019
114.3
0.935
0.039
0.487
96
03:02.0
0.019
114.2
0.934
0.039
0.487
97
03:02.0
0.019
114.1
0.935
0.039
0.487
98
03:02.0
0.020
114.0
0.934
0.039
0.487
99
03:02.0
0.019
114.0
0.934
0.039
0.487
100
03:02.1
0.019
113.9
0.935
0.039
0.487
101
03:02.1
0.019
113.8
0.934
0.039
0.487
102
03:02.1
0.020
113.7
0.935
0.039
0.487
103
03:02.1
0.020
113.7
0.935
0.039
0.487
104
03:02.1
0.020
113.6
0.934
0.039
0.487
105
03:02.1
0.020
113.5
0.935
0.039
0.487
106
03:02.2
0.020
113.5
0.934
0.039
0.487
107
03:02.2
0.020
113.4
0.934
0.039
0.487
108
03:02.2
0.020
113.3
0.934
0.039
0.487
109
03:02.2
0.020
113.3
0.934
0.039
0.487
110
03:02.2
0.020
113.2
0.935
0.039
0.487
111
03:02.2
0.020
113.2
0.934
0.038
0.486
112
03:02.3
0.020
113.1
0.934
0.038
0.486
113
03:02.3
0.020
113.1
0.934
0.038
0.486
114
03:02.3
0.020
113.0
0.934
0.038
0.486
115
03:02.3
0.020
113.0
0.934
0.038
0.486
116
03:02.3
0.020
112.9
0.934
0.038
0.486
117
03:02.3
0.020
112.8
0.934
0.037
0.486
118
03:02.4
0.020
112.8
0.934
0.037
0.486
119
03:02.4
0.020
112.8
0.934
0.037
0.486
120
03:02.4
0.020
112.7
0.934
0.037
0.486
121
03:02.4
0.020
112.7
0.934
0.036
0.485
122
03:02.4
0.020
112.6
0.934
0.036
0.485
123
03:02.4
0.020
112.6
0.934
0.036
0.485
124
03:02.5
0.020
112.5
0.934
0.035
0.485
125
03:02.5
0.020
112.5
0.934
0.035
0.485
126
03:02.5
0.020
112.4
0.934
0.035
0.485
Help is very much appreciated. Sami.
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