Show difference between two matrices as lines through a 3d surface - r
I have a surface that I have represented as a matrix that has observed values. Some of these values are controls, and form a grid throughout my surface. I extracted JUST the controls, and interpolated/extrapolated in between to create a "control surface". Then, I can subtract the control surface from the observed surface to get the normalized values.
(sub)Question 1: The interpolation looks unnecessarily irregular... is my interpolation method correct? If not, is there a better method?
(main)Question 2: How can I visualize the result as lines poking through the control surface with the ids at the top? I would like something like this:
Code follows (sorry for ugly dput):
library(dplyr)
library(tidyr)
library(akima)
b <- structure(list(Entr = 1:931, Row = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L),
Col = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L,
37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L,
49L, 49L, 48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L,
38L, 37L, 36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L,
26L, 25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L,
14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L,
1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
49L, 48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L,
37L, 36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L,
25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L,
13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L,
48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L,
36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L,
24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L,
12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L,
47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L,
35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L,
23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L,
11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L,
46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L,
34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L,
22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L,
10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L,
45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L,
33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L,
21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L,
9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L, 45L,
44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L,
32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L,
20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L,
8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L,
44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L, 45L, 44L,
43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L, 32L,
31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L, 20L,
19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L,
7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L, 45L, 44L, 43L,
42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L, 32L, 31L,
30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L, 20L, 19L,
18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L,
6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L,
46L, 47L, 48L, 49L), Value = c(3597L, 3519L, 2974L, 3499L,
3437L, 3669L, 2972L, 2953L, 3088L, 3224L, 2739L, 2762L, 3238L,
2838L, 2821L, 2765L, 3487L, 3696L, 3708L, 3369L, 3702L, 3362L,
3275L, 3073L, 3313L, 3316L, 3656L, 3898L, 3999L, 4074L, 3768L,
3846L, 3630L, 4130L, 3787L, 3418L, 3053L, 3764L, 3745L, 3187L,
3628L, 3147L, 3441L, 3465L, 3953L, 3288L, 3122L, 2208L, 3008L,
3487L, 3248L, 3411L, 3402L, 3627L, 3232L, 3713L, 3432L, 3657L,
3282L, 3859L, 3464L, 3161L, 3297L, 3308L, 3392L, 3334L, 3187L,
3396L, 3213L, 4019L, 3516L, 3578L, 3670L, 3484L, 3552L, 3365L,
3441L, 4022L, 3881L, 3343L, 3466L, 3128L, 3477L, 3398L, 3761L,
3540L, 3627L, 2864L, 3630L, 3320L, 3849L, 3939L, 3658L, 3424L,
3524L, 3626L, 3177L, 2923L, 3655L, 3750L, 4447L, 4426L, 4891L,
4705L, 4274L, 4398L, 4448L, 4148L, 4210L, 4141L, 4255L, 4083L,
4295L, 3190L, 3939L, 3258L, 3855L, 4181L, 3930L, 3871L, 3977L,
3594L, 4107L, 3416L, 4057L, 3692L, 3967L, 3943L, 4012L, 3852L,
4065L, 4019L, 3687L, 3663L, 4081L, 4015L, 3847L, 3690L, 3994L,
3447L, 3559L, 3636L, 3409L, 3121L, 2958L, 2899L, 3017L, 3158L,
3053L, 3572L, 2975L, 3431L, 3725L, 3702L, 3363L, 3487L, 3562L,
3190L, 3219L, 3923L, 3585L, 3989L, 4005L, 3658L, 3810L, 3983L,
3555L, 3712L, 3699L, 3774L, 3471L, 3428L, 3552L, 3468L, 3099L,
3069L, 3303L, 3470L, 3637L, 3624L, 3813L, 4344L, 3866L, 4044L,
3490L, 3809L, 3428L, 3839L, 2540L, 4349L, 3584L, 3627L, 3799L,
3800L, 2887L, 3523L, 3389L, 3411L, 3193L, 3111L, 3112L, 3222L,
3363L, 3551L, 3430L, 3483L, 3049L, 3340L, 4034L, 3447L, 3865L,
3626L, 3699L, 3758L, 4002L, 3500L, 3650L, 3354L, 3321L, 4088L,
3259L, 3520L, 3444L, 3191L, 3578L, 3369L, 2479L, 4070L, 4171L,
4093L, 4184L, 4295L, 2681L, 3597L, 3901L, 2720L, 2700L, 2717L,
3483L, 3311L, 3223L, 3046L, 3310L, 2531L, 3317L, 3233L, 2134L,
3020L, 3360L, 3679L, 2773L, 3665L, 3124L, 4042L, 3713L, 3862L,
3961L, 4109L, 3794L, 4062L, 4078L, 4181L, 3940L, 4602L, 4149L,
3849L, 3582L, 4035L, 3431L, 3954L, 4244L, 3353L, 3519L, 3496L,
3408L, 2988L, 3327L, 3086L, 3180L, 4583L, 3742L, 4580L, 4707L,
4247L, 4422L, 4426L, 4100L, 4042L, 4096L, 3703L, 4001L, 4002L,
4265L, 3249L, 4765L, 4280L, 4628L, 4905L, 4611L, 4010L, 4125L,
4452L, 5044L, 4932L, 4613L, 4768L, 5033L, 4199L, 3944L, 3951L,
4179L, 4192L, 4195L, 3889L, 3928L, 3301L, 3764L, 3537L, 3843L,
4342L, 3792L, 3973L, 4251L, 4169L, 4374L, 4172L, 4028L, 3050L,
4488L, 4068L, 4697L, 4824L, 4184L, 3930L, 4012L, 3219L, 3519L,
3663L, 3493L, 2939L, 3363L, 3383L, 3464L, 2789L, 2927L, 3059L,
2884L, 2782L, 3090L, 3158L, 3132L, 3644L, 3803L, 3895L, 3885L,
3265L, 3682L, 3464L, 3171L, 3539L, 3474L, 3265L, 3666L, 3549L,
3591L, 3249L, 3173L, 3088L, 2563L, 3530L, 3234L, 3453L, 3200L,
3405L, 3471L, 3750L, 2906L, 3241L, 3186L, 3789L, 3174L, 2977L,
3281L, 3479L, 3241L, 3783L, 3339L, 3503L, 3591L, 3379L, 3392L,
3399L, 3675L, 3624L, 3772L, 3873L, 3477L, 3950L, 3538L, 4347L,
3818L, 4332L, 3727L, 4028L, 3679L, 3737L, 3444L, 3258L, 3535L,
3555L, 3474L, 3447L, 3748L, 3423L, 3577L, 3725L, 3227L, 2903L,
3526L, 3670L, 3256L, 3282L, 3396L, 3719L, 3598L, 3608L, 3259L,
3610L, 3373L, 3432L, 3393L, 3001L, 2867L, 2982L, 3345L, 3311L,
2727L, 3106L, 3108L, 2950L, 2714L, 3520L, 3016L, 2939L, 3435L,
3020L, 3175L, 3805L, 2779L, 3895L, 3308L, 2995L, 3083L, 3080L,
3432L, 3318L, 4486L, 3876L, 3588L, 3742L, 3986L, 3765L, 3758L,
3523L, 3696L, 3040L, 3448L, 2687L, 3282L, 4166L, 4169L, 3742L,
4032L, 3986L, 4306L, 4371L, 4231L, 4260L, 3585L, 4342L, 4188L,
3220L, 3464L, 3536L, 3595L, 4045L, 3937L, 3886L, 4774L, 3696L,
4214L, 4250L, 4543L, 4550L, 4517L, 4691L, 5042L, 3956L, 3953L,
3986L, 4032L, 3643L, 3562L, 3833L, 3803L, 3634L, 3895L, 4299L,
3862L, 3403L, 3272L, 3406L, 3253L, 3233L, 3344L, 3481L, 3363L,
2646L, 3631L, 3869L, 3246L, 3357L, 3696L, 3859L, 4296L, 3438L,
4000L, 3703L, 3960L, 3477L, 4247L, 3791L, 3853L, 3696L, 3835L,
3742L, 3588L, 3276L, 3093L, 3360L, 3207L, 3576L, 3025L, 3305L,
3295L, 3788L, 3963L, 3999L, 3294L, 3931L, 3448L, 2959L, 3304L,
3131L, 2685L, 3314L, 2887L, 3653L, 3141L, 3425L, 3542L, 3282L,
3478L, 3191L, 2639L, 3027L, 3504L, 3578L, 2806L, 4163L, 3735L,
3203L, 3419L, 3588L, 3545L, 3535L, 3333L, 3392L, 3806L, 3587L,
3134L, 2971L, 3069L, 3316L, 4520L, 3562L, 3665L, 3744L, 3207L,
3409L, 3744L, 3181L, 3096L, 3576L, 3572L, 3363L, 3386L, 3318L,
3791L, 3582L, 4032L, 4394L, 4087L, 4248L, 4342L, 4189L, 4656L,
3781L, 4335L, 3504L, 4267L, 4032L, 3824L, 3609L, 4012L, 3992L,
4417L, 4003L, 3846L, 4140L, 3683L, 3302L, 3859L, 4100L, 3847L,
3621L, 4176L, 4359L, 4081L, 3654L, 4062L, 3442L, 3560L, 3780L,
3295L, 3487L, 3409L, 3439L, 2362L, 3364L, 3412L, 3266L, 4051L,
3990L, 4068L, 3971L, 3197L, 3677L, 3765L, 3638L, 3439L, 4004L,
3648L, 3628L, 3475L, 3945L, 4167L, 3942L, 3929L, 4013L, 3906L,
3168L, 3606L, 4012L, 4317L, 4000L, 3781L, 4199L, 3997L, 4576L,
3997L, 4273L, 3891L, 3543L, 3294L, 3911L, 3715L, 4276L, 3660L,
4090L, 3921L, 3595L, 3513L, 3301L, 3470L, 3363L, 3989L, 3307L,
3565L, 3301L, 3738L, 3907L, 3653L, 3819L, 3232L, 3695L, 3435L,
2906L, 3620L, 3686L, 3284L, 4237L, 4100L, 4420L, 3654L, 2503L,
1680L, 3614L, 3314L, 4302L, 3114L, 2840L, 3036L, 1144L, 4153L,
3416L, 4484L, 3159L, 3839L, 3961L, 3373L, 3722L, 3605L, 3116L,
3818L, 3977L, 3527L, 3562L, 3794L, 4162L, 3800L, 3680L, 3578L,
3924L, 3484L, 3204L, 3200L, 3223L, 3536L, 3187L, 3171L, 3057L,
3268L, 3099L, 3517L, 3477L, 3751L, 3174L, 3569L, 3295L, 3229L,
3451L, 3200L, 3530L, 3798L, 3562L, 3484L, 2718L, 3980L, 3746L,
3576L, 3464L, 3302L, 4107L, 3452L, 3315L, 3680L, 3383L, 3462L,
3478L, 3888L, 3634L, 3445L, 3092L, 3445L, 2923L, 3040L, 2623L,
2874L, 3552L, 2336L, 3011L, 2671L, 2029L, 4002L, 3379L, 3779L,
3763L, 3496L, 3454L, 3613L, 3901L, 3727L, 3365L, 3836L, 2750L,
3763L, 3389L, 3542L, 3699L, 3904L, 3836L, 3399L, 3634L, 4162L,
3545L, 4182L, 3506L, 3849L, 3755L, 3770L, 2936L, 3670L, 3758L,
3487L, 3807L, 2868L, 3523L, 3148L, 3774L, 2851L, 2903L, 3181L,
3067L, 2695L, 3389L, 3670L, 2554L, 3494L, 4162L, 3533L, 2780L,
2822L, 2946L, 3324L, 1791L, 3530L, 3872L, 3676L, 3252L, 3395L,
3370L, 2662L, 2567L, 2786L, 2714L, 2479L, 1465L, 2000L, 3663L,
4375L, 3758L, 3742L, 3259L, 2985L, 3784L, 3373L, 2978L, 3487L,
3379L, 2953L, 3478L, 2890L, 2597L, 3001L, 2861L, 3988L, 3455L,
2950L, 3771L, 3550L, 2998L, 2991L, 3219L, 3073L, 3458L, 3585L,
3546L, 3637L, 4198L, 2903L, 3144L, 2825L, 2806L, 3409L, 1846L,
2564L, 3005L, 2675L, 2936L, 2124L, 2900L, 2388L, 2531L, 2916L,
778L, 2812L, 2577L, 2401L, 2868L, 3041L, 2039L, 2408L, 2104L,
3142L, 2610L, 3748L, 3370L, 2754L, 3546L, 2962L, 2453L, 3014L,
2626L, 2864L, 3399L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-931L), .Names = c("Entr", "Row", "Col", "Value"))
## Control every 10
b$Control[b$Entr%%10==0] <- 1
b$Control[is.na(b$Control)] <- 0
## Isolate controls
b$Yield2[b$Control==1] <- b$Value[b$Control==1]
b$Yield2[is.na(b$Yield2)] <- 0
## -------------------- GENERATE MATRIX FOR SURFACE CALCULATION----------------
## ALL DATA
MaxCol <- max(b$Col)
MaxRow <- max(b$Row)
RealSurf <- matrix(b$Value,MaxRow,MaxCol)
## Matrix of just controls, first emptyish
ControlSurf <- matrix(b$Yield2,MaxRow,MaxCol)
## Interpolate empty data...
idx <- which(ControlSurf > 0, arr.ind=TRUE)
RealSurf.nz <- ControlSurf[idx]
## ... into a fullish matrix
InterpolatedControls <- interp.new(idx[,1], idx[,2], RealSurf.nz, xo=1:MaxRow, yo=1:MaxCol, extrap=TRUE)
ControlSurf <- matrix(InterpolatedControls$z,MaxRow,MaxCol)
##################### ???????????? IS THIS EVEN CORRECT??????? ##################
## Plot surfaces
par(mfrow=c(1,2))
persp( z=ControlSurf, theta = 50, phi = 30, expand = 0.1, col = "lightblue")
persp( z=RealSurf, theta = 50, phi = 30, expand = 0.1, col = "red")
## How to make the real surface "poke" through the Control Surface?
Related
How to use transpose function (t) to transpose rows by hours of day (0 to 23)?
I want to transpose hours of day (0 to 23) on each rows of each subjects( i have 21 subjects)
I use to create two data frame: test and teste. Then i use two loops to transpose all (0,1,2,3 to 23) of each subject. But my code is so bad that i can't find expect result.
test=data.frame(1:24,t(0:23))
teste=data.frame(1:21,t(0:23))
for(i in 1:21){
for(j in 1:24){
test[j,]=t(data_temps_All1[j,])
j=j+1
}
teste[i,]= test[j,]
i=i+1
}
i want to have:
first row: 0 ,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23
second row: 0 ,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23
---
21 row: 0 ,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23
This is my data set:
structure(list(hour = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 16L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 6L,
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L)), row.names = c(NA,
-504L), class = c("data.table", "data.frame"))
Since you have only one column you could unlist the data convert it into matrix with required dimension and change it to data frame / data.table data.frame(matrix(unlist(df), ncol = 24, byrow = TRUE))
Don't know what is your application, but isn't this the data.frame you wish to have data.frame(1:21, t(0:23)[rep(1, 21), ])
Arranging time stamped data in chronological order
An output table of one of my codes looks like this:
> head(act.byHour_corr)
hour date activity
1: 0 Activity on 6/20/2018 59
2: 1 Activity on 6/20/2018 74
3: 2 Activity on 6/20/2018 2683
4: 3 Activity on 6/20/2018 4341
5: 4 Activity on 6/20/2018 3676
6: 5 Activity on 6/20/2018 2143
The column hour represents the hours of the day from 0 to 23 and the data in date is chronologically organized. Unfortunately, when the data comes to the point where the next month 7/dd/2018 is reached, date is not chronologically organized anymore:
> head(act.byHour_corr[287:293])
hour date activity
1: 22 Activity on 7/1/2018 400
2: 23 Activity on 7/1/2018 201
3: 0 Activity on 7/10/2018 705
4: 1 Activity on 7/10/2018 47
5: 2 Activity on 7/10/2018 605
6: 3 Activity on 7/10/2018 257
You can see that 7/10/2018 and its associated values come after 7/1/2018 instead of that being 7/2/2018.
If that helps I can provide my dataset below:
> dput(act.byHour_corr)
structure(list(hour = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L), date = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
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"data.frame"), .internal.selfref = <pointer: 0x0000000002641ef0>)
Hope I can get some help to organize the data chronologically for the full dataset. Any input is appreciated.
This should help!
act.byHour_corr$date <- as.Date(gsub('Activity on ', '', act.byHour_corr$date),
format = '%m/%d/%Y')
act.byHour_corr <- act.byHour_corr[order(act.byHour_corr$date),]
It removes the 'Activity on' portion of the column. Does that work, or do you need to keep the 'Activity on' part?
Add hour to you data: library(data.table) library(lubridate) library(stringr) act.byHour_corr[, data_hour:=(paste0(date," ", str_pad(hour, 2, "left",0),":00"))] act.byHour_corr[, data_hour:=mdy_hm(data_hour)] act.byHour_corr[order(data_hour)]
Predict values for each group in gamm4
I have a data set like this:
dat <- structure(list(Y = c(152.75, 167.7, 169.7, 173.2, 174.4, 177.1,
196, 200.45, 206.1, 206.65, 203, 186.65, 208.9, 192.95, 201.05,
203.45, 200.3, 197.55, 205.1, 198.1, 205.15, 189.35, 201.25,
194.55, 204.15, 200.95, 166.6, 165.1, 175.2, 168.4, 153, 168.4,
161, 170.1, 168.15, 167.3, 169.2, 169.25, 185.35, 185.9, 178.55,
193.2, 210.25, 203.75, 203.25, 203.7, 200.15, 204, 204, 206.3,
197.7, 190.5, 185.95, 199, 185.1, 194.35, 186.2, 190.95, 191.55,
177.8, 182.95, 186.3, 177.25, 186.35, 177.1, 183.9, 188.55, 184.05,
188.55, 187.25, 185.25, 174.8, 180.9, 171.4, 169.6, 176.7, 178.35,
191.3, 180.45, 187.5, 183.85, 187.7, 176.45, 188.7, 179.15, 183.25,
180.1, 184.35, 185.35, 184.25, 182.55, 185.15, 181.2, 184.6,
183.05, 182.35, 177.55, 179.85, 176.1, 175.9, 173.7, 180.7, 194.55,
190.3, 200.5, 193.05, 191.55, 190.65, 194.9, 192.8, 202.65, 200.35,
181.95, 194.85, 198.3, 199.7, 185.7, 195.9, 195.15, 191.85, 198.65,
188.9, 192.25, 197.8, 185.75, 193.5, 178.2, 170.15, 175.4, 176.25,
176.6, 179.8, 182, 173.35, 181.75, 188.05, 198.05, 204.75, 190.75,
196.15, 193.15, 195.4, 192.35, 165.55, 187.15, 191.35, 200.4,
200.4, 204.85, 211.3, 206.45, 205.95, 201, 198.6, 202.45, 192.95,
198.25, 190.85, 182.9, 184.5, 175.75, 174.95, 178.8, 173.2, 174,
176.75, 167.2, 161.1, 155.6, 178.6, 187.8, 194.05), X1 = c(4L,
6L, 7L, 8L, 9L, 10L, 4L, 6L, 7L, 8L, 9L, 10L, 11L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 12L, 13L, 14L, 15L, 4L, 5L, 6L, 7L, 8L, 4L, 5L,
6L, 7L, 11L, 14L, 15L, 16L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 4L, 5L, 9L, 13L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 26L, 27L, 28L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 4L, 5L, 8L, 9L, 10L, 11L, 4L, 5L, 6L, 7L, 8L,
10L, 11L, 12L, 13L, 4L, 6L, 7L, 8L, 9L, 12L, 13L, 14L, 15L, 16L,
17L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 12L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 4L, 6L, 7L, 4L, 5L, 7L, 9L, 11L, 12L, 15L, 16L, 17L,
20L, 21L, 22L, 4L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 16L,
18L, 4L, 5L, 6L), X2 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 18L, 18L, 18L), .Label = c("bec", "bi", "ebk", "ele",
"eli", "ian", "isy", "ith", "lda", "lli", "na", "nja", "ra",
"rda", "ria", "rik", "tje", "tri"), class = "factor")), .Names = c("Y",
"X1", "X2"), row.names = c(142L, 143L, 144L, 145L, 146L, 147L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 160L, 161L, 162L, 163L, 164L,
165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 82L, 83L, 84L, 85L, 86L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
112L, 113L, 114L, 115L, 116L, 117L, 118L, 133L, 134L, 135L, 136L,
137L, 138L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 119L,
120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 66L,
67L, 68L, 69L, 70L, 71L, 72L, 73L, 94L, 95L, 96L, 97L, 98L, 99L,
100L, 101L, 130L, 131L, 132L, 148L, 149L, 150L, 151L, 152L, 153L,
154L, 155L, 156L, 157L, 158L, 159L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 139L, 140L, 141L), class = "data.frame")
and I applied a gamm4-model from gamm4-package on it:
library(gamm4)
gamm.1 <- gamm4(Y ~ s(X1),random = ~(1+X1|X2),data = dat)
I also predicted and plotted the smoothed values using:
newDat <- data.frame(X1 = min(dat$X1):max(dat$X1))
p0 <- predict(gamm.1$gam,newDat,se=T)
plot(dat$X1,dat$Y)
lines(newDat$X1,p0$fit,lwd=3)
My question is: how can I predict the smoothed lines for each of the groups (X2)?
I know that I can get the random effects via ranef(gamm.1$mer) but I don't know how to use them correctly.
How to convert adjacency list to adjacency matrix in R?
I have adjacency list in the form of: 1. 3,4 2. 4 3. 1,4 4. 1,2,3 and I want to transform into adjacency matrix using R. I have tried various commands like transformation of adjacency list to igraph object and then retransformation of igraph to adjacency matrix, but the obtained adjacency matrix is S4 class. I want simple commands to transform adjacency list to adjacency matrix in R. data list(c(1L, 3L, 4L, 8L, 14L, 31L, 2L, 29L, 33L, 7L, 11L, 17L, 5L, 6L, 34L), c(2L, 3L, 4L, 8L, 9L, 12L, 13L, 14L, 18L, 22L, 1L, 10L, 33L, 34L), c(2L, 3L, 4L, 8L, 9L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 31L, 34L, 24L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 1L, 31L, 10L, 28L, 29L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 17L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 17L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 17L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 31L, 10L, 28L, 29L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 10L, 28L, 29L, 33L, 34L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 27L), c(2L, 4L, 8L, 9L, 10L, 14L, 28L, 29L, 33L, 15L, 16L, 19L, 20L, 21L, 23L, 24L, 27L, 30L, 31L, 32L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 17L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 31L, 10L, 28L, 29L, 33L, 15L, 16L, 19L, 21L, 23L, 24L, 27L, 30L), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L), c(1L, 7L, 11L, 17L, 5L, 6L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 31L), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L ), c(3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 31L, 10L, 15L, 16L, 19L, 21L, 23L, 24L, 27L, 28L, 29L, 30L), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 31L ), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L), c(24L, 25L, 32L, 3L, 34L, 27L, 33L, 9L, 15L, 16L, 19L, 21L, 23L, 30L, 31L, 10L, 14L, 20L, 28L, 29L), c(24L, 25L, 32L, 34L, 26L, 29L), c(26L, 28L, 30L, 33L, 34L, 32L, 25L, 29L), c(24L, 27L, 33L, 9L, 10L, 14L, 15L, 16L, 19L, 20L, 21L, 23L, 28L, 29L, 30L, 31L, 32L), c(4L, 8L, 9L, 10L, 14L, 28L, 29L, 33L, 26L, 30L, 32L, 15L, 16L, 19L, 20L, 21L, 23L, 24L, 27L, 31L), c(1L, 4L, 8L, 9L, 10L, 14L, 28L, 29L, 33L, 25L, 26L, 15L, 16L, 19L, 20L, 21L, 23L, 24L, 27L, 30L, 31L, 32L), c(26L, 28L, 30L, 33L, 34L, 9L, 15L, 16L, 19L, 21L, 23L, 24L, 31L, 32L, 10L, 14L, 20L, 27L, 29L), c(1L, 3L, 4L, 8L, 14L, 18L, 20L, 22L, 31L, 33L, 34L, 9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 32L, 10L, 27L, 28L, 29L), c(3L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 26L, 28L, 24L, 25L, 15L, 16L, 19L, 21L, 23L, 30L, 31L, 10L, 27L, 29L), c(1L, 2L, 9L, 10L, 14L, 28L, 29L, 33L, 31L, 34L, 26L, 30L, 24L, 27L), c(1L, 3L, 31L, 33L, 34L, 2L, 26L, 30L, 24L, 25L, 9L))
Suppose el is a list of edge list:
el = list(c(3,4),
c(2,4),
c(1,4),
c(1,2,3))
#Get the matrix dimension
dim <- length(el)
m <- sapply(el, function(x) { r<-rep(0,dim); r[unlist(x)]<-1;r})
[,1] [,2] [,3] [,4]
[1,] 0 0 1 1
[2,] 0 1 0 1
[3,] 1 0 0 1
[4,] 1 1 1 0
Create and output multiple plots from list
I am attempting to create and output as pdfs a list of 64 items. My data takes the form: QQJAN List of 64 file1: List of 2 ..$x: num [1:161] 96.7 96.8 97.5 ... ..$y: num [1:161] 9.3 10.3 17.3 ... .................................................................. file64: List of 2 ..$x: num [1:161] 42.6 59.9 70.4 ... ..$y: num [1:161] 9.3 10.3 17.3 ... I can do this for any single item in the list using: plot(QQJAN$file1) and can then output these files to my working directory as pdfs, but how can all 64 files in the list be plotted and outputted with their names, i.e. file1.pdf, file 2.pdf etc. Can the lapply function be used here? A reproducible example: QQJAN$file1$x=c(1,2,3,4) QQJAN$file1$y=c(2,4,5,6) QQJAN$file2$x=c(2,2,3,5) QQJAN$file2$y=c(2,4,5,6)
Not tested due to lack of a reproducible example:
for (i in seq_along(QQJAN)) {
pdf(sprintf("plot%i.pdf", i)) #or pdf(paste0(names(QQJAN)[i], ".pdf"))
plot(QQJAN[[i]])
dev.off()
}
If you are only interested in side effects, such as plotting, a for loop is usually appropriate. You should use lapply if you need a return value.
We can use lapply to loop over the names of the list elements, create the pdf file by pasteing the individual names with .pdf, subset the list (QQJAN[[x]]), plot.
invisible(lapply(names(QQJAN), function(x) {
pdf(paste0(x, '.pdf'))
plot(QQJAN[[x]])
dev.off()}))
data
QQJAN <- structure(list(file1 = structure(list(x = c(6L, 5L, 15L, 11L,
14L, 19L, 6L, 16L, 17L, 6L, 13L, 8L, 14L, 14L, 7L, 19L, 4L, 1L,
11L, 3L, 2L, 12L, 15L, 3L, 5L, 14L, 2L, 12L, 13L, 1L, 7L, 5L,
8L, 3L, 19L, 5L, 15L, 13L, 14L, 20L), y = c(29L, 23L, 17L, 14L,
3L, 5L, 24L, 22L, 16L, 21L, 28L, 52L, 28L, 43L, 33L, 60L, 28L,
18L, 11L, 9L, 30L, 15L, 17L, 8L, 44L, 19L, 57L, 59L, 45L, 30L,
9L, 13L, 1L, 60L, 39L, 21L, 35L, 50L, 3L, 44L)), .Names = c("x",
"y")), file2 = structure(list(x = c(11L, 3L, 11L, 5L, 8L, 7L,
6L, 18L, 8L, 17L, 7L, 15L, 19L, 3L, 10L, 12L, 13L, 2L, 9L, 10L,
15L, 13L, 3L, 6L, 16L, 1L, 20L, 5L, 9L, 4L, 12L, 1L, 6L, 13L,
18L, 7L, 18L, 19L, 15L, 13L), y = c(56L, 31L, 40L, 43L, 20L,
45L, 55L, 8L, 43L, 26L, 7L, 52L, 7L, 31L, 11L, 14L, 55L, 26L,
4L, 42L, 34L, 44L, 12L, 4L, 30L, 60L, 23L, 44L, 29L, 55L, 6L,
37L, 11L, 14L, 36L, 52L, 28L, 22L, 31L, 33L)), .Names = c("x",
"y"))), .Names = c("file1", "file2"))