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I use to see the videos from:
http://www.twotorials.com/
and the video:
How to order and sort stuff in R
is really useful to apply this concept to organize and understand better our sample sets before to proceed to develop a calibration.
The idea of this post is after watching the video to create a new dataframe sorted by the “Moisture” constituent in an ascending order.
After that we can subtract the spectra with the highest moisture value from the spectra with the lowest moisture value. This way we can study he difference spectrum in order to get some conclusions about the band positions for moisture, and other constituents.
As we can see in the video, we can use the functions:
sort:
> sort(demoNIR_msc\$Moisture)
[1] 3.98 5.05 5.20 5.32 5.34 5.41 5.51 5.53 5.57 5.63 5.64 5.67 5.71 5.73 5.77
[16] 5.82 5.85 5.85 5.86 5.87 5.89 5.90 5.91 5.99 5.99 6.04 6.05 6.09 6.10 6.14
[31] 6.16 6.20 6.22 6.23 6.31 6.33 6.33 6.34 6.36 6.38 6.40 6.43 6.47 6.56 6.56
[46] 6.57 6.59 6.61 6.66 6.66 6.69 6.73 6.73 6.84 6.88 6.95 7.07 7.10 7.12 7.30
[61] 7.42 7.43 7.48 8.00 8.12 8.17
order:
> order(demoNIR_msc\$Moisture)
[1]  4 12 15 18 29 14 17 20 36 38 66 37 39 30 63 13 16 33 65  7  3 28  5 10 25
[26] 32  6 56 64 11 21 31 42  8  2 48 49 62 41 58 26 24 59 27 55  9 34 22 54 61
[51]  1 23 46 35 19 47 50 45 51 40 52 53 60 57 44 43
Now we prepare a new dataframe, sorted by moisture values in ascending order:
> moiNIR_msc<-demoNIR_msc[order(demoNIR_msc\$Moisture),]
> moiNIR_msc[,1:5]
Protein   Fat   Ash    DM Moisture
4    74.51 10.51 14.88 96.02     3.98
12   70.24 10.73 18.61 94.95     5.05
15   71.17 12.14 16.26  94.8      5.2
18   71.29 12.08 15.78 94.68     5.32
29   71.71 10.94 16.72 94.66     5.34
14    72.2 11.73 15.64 94.59     5.41
17   70.97 12.82  16.1 94.49     5.51
20   68.95 12.53 16.42 94.47     5.53
36      76 11.37 11.68 94.43     5.57
38   64.56  9.46 25.55 94.37     5.63
66    73.4  9.15 16.82 94.36     5.64
37   78.06  11.5 10.86 94.33     5.67
39   64.46  9.36  26.5 94.29     5.71
30   71.99 10.68 17.12 94.27     5.73
63   72.65 10.14 17.32 94.23     5.77
13   72.43 11.98 14.85 94.18     5.82
16   71.73 12.33  15.3 94.15     5.85
33   76.14 12.67 11.09 94.15     5.85
65   73.95  9.07 16.35 94.14     5.86
7    72.64 10.39 16.44 94.13     5.87
3    73.14 10.51 15.29 94.11     5.89
28   72.46 11.44 15.57  94.1      5.9
5    72.29 10.08 15.39 94.09     5.91
10   73.51 10.53 15.64 94.01     5.99
25   75.05  9.83  14.9 94.01     5.99
32   73.76 10.37 15.45 93.96     6.04
6    70.21 11.06 17.87 93.95     6.05
56   79.07 11.52  8.88 93.91     6.09
64   69.76 10.01 19.81  93.9      6.1
11   71.57 10.65 17.36 93.86     6.14
21   70.66 11.65 17.16 93.84     6.16
31   73.04 11.04  15.5  93.8      6.2
42   71.17  9.57 19.36 93.78     6.22
8    75.19 10.33 14.27 93.77     6.23
2     72.9 14.56 10.95 93.69     6.31
48   67.63   6.5  24.6 93.67     6.33
49   65.18  6.39 28.22 93.67     6.33
62   72.48  10.2 17.43 93.66     6.34
41   73.09 10.74 16.28 93.64     6.36
58      68     0     0 93.62     6.38
26   75.35 11.53 12.91  93.6      6.4
24    70.9 10.66 18.34 93.57     6.43
59   70.64 10.53 17.77 93.53     6.47
27   70.15 10.77 17.16 93.44     6.56
55      77  9.16 13.63 93.44     6.56
9    73.71 10.52 15.24 93.43     6.57
34   66.88  9.37 22.79 93.41     6.59
22   70.58 11.51 17.48 93.39     6.61
54   74.15     9 16.42 93.34     6.66
61   72.04  9.71 17.61 93.34     6.66
1    72.19 15.08 11.76 93.31     6.69
23   70.94 12.07 15.81 93.27     6.73
46    69.2  8.98  21.6 93.27     6.73
35   68.53 11.28 20.09 93.16     6.84
19   70.22 12.15 16.02 93.12     6.88
47   66.63  9.55 24.25 93.05     6.95
50   74.52  8.28 16.67 92.93     7.07
45   64.41  8.06  27.1  92.9      7.1
51   73.47  10.2 16.33 92.88     7.12
40    64.8 10.44 24.54  92.7      7.3
52   71.04  9.47 19.16 92.58     7.42
53   70.75  7.74 20.97 92.57     7.43
60   69.33 10.15 18.68 92.52     7.48
57   68.89 10.73 19.82    92        8
44   64.17  7.27 28.23 91.88     8.12
43   68.81  8.83 21.26 91.83     8.17
We can use the same procedure for any of the other constituents