Dear helpers,
I'm using plyr to process a large matrix for the first time. My code is set
up to work with matrixes, since I learned the hard way that dataframes
are considerably slower to process.
I started using aaply(), but the data was rearranged from a flat matrix to
a [, , 4] array for larger input matrixes. I'm sure something clever is
happening that I'm just not seeing - anyone have any insight? I can provide
the code for index.frames() if you like, but it's pretty turgid stuff.
For now I'm just using adply(), since it gives an output that I'd
expect.
Best
toy.mat <- all.combinations[10:30,]> aaply(toy.mat,1, index.frames)
Var1 1 2 3 4
46 3599.848 3665.454 12946.41 12946.41
51 3600.020 3666.424 12946.41 12946.41
56 3600.167 3667.301 12946.41 12946.41
61 3600.291 3668.058 12946.41 12946.41
66 3600.404 3668.766 12946.41 12946.41
71 3600.563 3669.779 12946.41 12946.41
76 3600.563 3669.779 12946.41 12946.41
81 3600.563 3669.779 12946.41 12946.41
86 3600.563 3669.779 12946.41 12946.41
91 3600.563 3669.779 12946.41 12946.41
96 3600.563 3669.779 12946.41 12946.41
101 3600.563 3669.779 12946.41 12946.41
106 3600.563 3669.779 12946.41 12946.41
111 3600.563 3669.779 12946.41 12946.41
116 3600.563 3669.779 12946.41 12946.41
121 3600.563 3669.779 12946.41 12946.41
126 3600.563 3669.779 12946.41 12946.41
131 3600.563 3669.779 12946.41 12946.41
136 3600.563 3669.779 12946.41 12946.41
141 3600.563 3669.779 12946.41 12946.41
146 3600.563 3669.779 12946.41 12946.41
> toy.mat <- all.combinations[10:100,]
> aaply(toy.mat,1, index.frames, .progress="win")
, , = 1
Var2
Var1 2 7 12 17
1 NA 3632.275 3630.730 3627.652
6 NA 3638.913 3638.271 3635.214
11 NA 3592.933 3593.322 3595.973
16 NA 3588.024 3588.232 3589.256
21 NA 3593.917 3594.088 3594.834
26 NA 3596.888 3597.051 3597.752
31 NA 3597.896 3598.056 3598.741
36 NA 3598.994 3599.153 3599.837
41 NA 3599.571 3599.729 3600.413
46 3599.848 3599.848 3600.006 3600.689
51 3600.020 3600.020 3600.178 NA
56 3600.167 3600.167 3600.325 NA
61 3600.291 3600.291 3600.448 NA
66 3600.404 3600.404 3600.561 NA
71 3600.563 3600.563 3600.721 NA
76 3600.563 3600.563 3600.721 NA
81 3600.563 3600.563 3600.721 NA
86 3600.563 3600.563 3600.721 NA
91 3600.563 3600.563 3600.721 NA
96 3600.563 3600.563 3600.721 NA
101 3600.563 3600.563 3600.721 NA
106 3600.563 3600.563 3600.721 NA
111 3600.563 3600.563 3600.721 NA
116 3600.563 3600.563 3600.721 NA
121 3600.563 3600.563 3600.721 NA
126 3600.563 3600.563 3600.721 NA
131 3600.563 3600.563 3600.721 NA
136 3600.563 3600.563 3600.721 NA
141 3600.563 3600.563 3600.721 NA
146 3600.563 3600.563 3600.721 NA
, , = 2
Var2
Var1 2 7 12 17
1 NA 3681.001 3698.490 3688.247
6 NA 3664.453 3676.527 3666.970
11 NA 3662.162 3662.919 3668.211
16 NA 3661.484 3661.476 3661.975
21 NA 3648.731 3647.986 3650.290
26 NA 3649.497 3648.367 3653.130
31 NA 3652.778 3651.586 3656.638
36 NA 3660.082 3659.050 3662.755
41 NA 3663.944 3663.025 3665.933
46 3665.454 3665.454 3664.572 3667.226
51 3666.424 3666.424 3665.571 NA
56 3667.301 3667.301 3666.477 NA
61 3668.058 3668.058 3667.261 NA
66 3668.766 3668.766 3667.998 NA
71 3669.779 3669.779 3669.056 NA
76 3669.779 3669.779 3669.056 NA
81 3669.779 3669.779 3669.056 NA
86 3669.779 3669.779 3669.056 NA
91 3669.779 3669.779 3669.056 NA
96 3669.779 3669.779 3669.056 NA
101 3669.779 3669.779 3669.056 NA
106 3669.779 3669.779 3669.056 NA
111 3669.779 3669.779 3669.056 NA
116 3669.779 3669.779 3669.056 NA
121 3669.779 3669.779 3669.056 NA
126 3669.779 3669.779 3669.056 NA
131 3669.779 3669.779 3669.056 NA
136 3669.779 3669.779 3669.056 NA
141 3669.779 3669.779 3669.056 NA
146 3669.779 3669.779 3669.056 NA
, , = 3
Var2
Var1 2 7 12 17
1 NA 12946.41 12946.41 12946.41
6 NA 12946.41 12946.41 12946.41
11 NA 12946.41 12946.41 12946.41
16 NA 12946.41 12946.41 12946.41
21 NA 12946.41 12946.41 12946.41
26 NA 12946.41 12946.41 12946.41
31 NA 12946.41 12946.41 12946.41
36 NA 12946.41 12946.41 12946.41
41 NA 12946.41 12946.41 12946.41
46 12946.41 12946.41 12946.41 12946.41
51 12946.41 12946.41 12946.41 NA
56 12946.41 12946.41 12946.41 NA
61 12946.41 12946.41 12946.41 NA
66 12946.41 12946.41 12946.41 NA
71 12946.41 12946.41 12946.41 NA
76 12946.41 12946.41 12946.41 NA
81 12946.41 12946.41 12946.41 NA
86 12946.41 12946.41 12946.41 NA
91 12946.41 12946.41 12946.41 NA
96 12946.41 12946.41 12946.41 NA
101 12946.41 12946.41 12946.41 NA
106 12946.41 12946.41 12946.41 NA
111 12946.41 12946.41 12946.41 NA
116 12946.41 12946.41 12946.41 NA
121 12946.41 12946.41 12946.41 NA
126 12946.41 12946.41 12946.41 NA
131 12946.41 12946.41 12946.41 NA
136 12946.41 12946.41 12946.41 NA
141 12946.41 12946.41 12946.41 NA
146 12946.41 12946.41 12946.41 NA
, , = 4
Var2
Var1 2 7 12 17
1 NA 12946.41 12946.41 12946.41
6 NA 12946.41 12946.41 12946.41
11 NA 12946.41 12946.41 12946.41
16 NA 12946.41 12946.41 12946.41
21 NA 12946.41 12946.41 12946.41
26 NA 12946.41 12946.41 12946.41
31 NA 12946.41 12946.41 12946.41
36 NA 12946.41 12946.41 12946.41
41 NA 12946.41 12946.41 12946.41
46 12946.41 12946.41 12946.41 12946.41
51 12946.41 12946.41 12946.41 NA
56 12946.41 12946.41 12946.41 NA
61 12946.41 12946.41 12946.41 NA
66 12946.41 12946.41 12946.41 NA
71 12946.41 12946.41 12946.41 NA
76 12946.41 12946.41 12946.41 NA
81 12946.41 12946.41 12946.41 NA
86 12946.41 12946.41 12946.41 NA
91 12946.41 12946.41 12946.41 NA
96 12946.41 12946.41 12946.41 NA
101 12946.41 12946.41 12946.41 NA
106 12946.41 12946.41 12946.41 NA
111 12946.41 12946.41 12946.41 NA
116 12946.41 12946.41 12946.41 NA
121 12946.41 12946.41 12946.41 NA
126 12946.41 12946.41 12946.41 NA
131 12946.41 12946.41 12946.41 NA
136 12946.41 12946.41 12946.41 NA
141 12946.41 12946.41 12946.41 NA
146 12946.41 12946.41 12946.41 NA
> toy.mat
Var1 Var2 Var3 Var4 Var5
10 46 2 3 4 5
11 51 2 3 4 5
12 56 2 3 4 5
13 61 2 3 4 5
14 66 2 3 4 5
15 71 2 3 4 5
16 76 2 3 4 5
17 81 2 3 4 5
18 86 2 3 4 5
19 91 2 3 4 5
20 96 2 3 4 5
21 101 2 3 4 5
22 106 2 3 4 5
23 111 2 3 4 5
24 116 2 3 4 5
25 121 2 3 4 5
26 126 2 3 4 5
27 131 2 3 4 5
28 136 2 3 4 5
29 141 2 3 4 5
30 146 2 3 4 5
31 1 7 3 4 5
32 6 7 3 4 5
33 11 7 3 4 5
34 16 7 3 4 5
35 21 7 3 4 5
36 26 7 3 4 5
37 31 7 3 4 5
38 36 7 3 4 5
39 41 7 3 4 5
40 46 7 3 4 5
41 51 7 3 4 5
42 56 7 3 4 5
43 61 7 3 4 5
44 66 7 3 4 5
45 71 7 3 4 5
46 76 7 3 4 5
47 81 7 3 4 5
48 86 7 3 4 5
49 91 7 3 4 5
50 96 7 3 4 5
51 101 7 3 4 5
52 106 7 3 4 5
53 111 7 3 4 5
54 116 7 3 4 5
55 121 7 3 4 5
56 126 7 3 4 5
57 131 7 3 4 5
58 136 7 3 4 5
59 141 7 3 4 5
60 146 7 3 4 5
61 1 12 3 4 5
62 6 12 3 4 5
63 11 12 3 4 5
64 16 12 3 4 5
65 21 12 3 4 5
66 26 12 3 4 5
67 31 12 3 4 5
68 36 12 3 4 5
69 41 12 3 4 5
70 46 12 3 4 5
71 51 12 3 4 5
72 56 12 3 4 5
73 61 12 3 4 5
74 66 12 3 4 5
75 71 12 3 4 5
76 76 12 3 4 5
77 81 12 3 4 5
78 86 12 3 4 5
79 91 12 3 4 5
80 96 12 3 4 5
81 101 12 3 4 5
82 106 12 3 4 5
83 111 12 3 4 5
84 116 12 3 4 5
85 121 12 3 4 5
86 126 12 3 4 5
87 131 12 3 4 5
88 136 12 3 4 5
89 141 12 3 4 5
90 146 12 3 4 5
91 1 17 3 4 5
92 6 17 3 4 5
93 11 17 3 4 5
94 16 17 3 4 5
95 21 17 3 4 5
96 26 17 3 4 5
97 31 17 3 4 5
98 36 17 3 4 5
99 41 17 3 4 5
100 46 17 3 4 5>
Ben Caldwell
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