Present Correlation Matrix with cor_plot - r

I have correlogram as below, using this code:
corMatrix <- myfiles %>% cor_mat(c("v1","v2","v3",
"v4","v5","v6",
"v7","v8","v9","v10",
"v11","v12","v13"),
method = "kendall")
cor_plot(corMatrix,method="color",
type="full",
p.mat = corMatrix_p, insig = "blank")
Below is the data structure for myfiles
myfiles <- structure(list(Date = structure(c(16075, 16082, 16089, 16096,
16103, 16110, 16117, 16124, 16131, 16138, 16145, 16152, 16159,
16166, 16173, 16180, 16187, 16194, 16201, 16208, 16215, 16222,
16229, 16236, 16243, 16250, 16257, 16264, 16271, 16278, 16285,
16292, 16299, 16306, 16313, 16320, 16327, 16334, 16341, 16348,
16355, 16362, 16369, 16376, 16383, 16390, 16397, 16404, 16411,
16418, 16425, 16432, 16439, 16446, 16453, 16460, 16467, 16474,
16481, 16488, 16495, 16502, 16509, 16516, 16523, 16530, 16537,
16544, 16551, 16558, 16565, 16572, 16579, 16586, 16593, 16600,
16607, 16614, 16621, 16628, 16635, 16642, 16649, 16656, 16663,
16670, 16677, 16684, 16691, 16698, 16705, 16712, 16719, 16726,
16733, 16740, 16747, 16754, 16761, 16768, 16775, 16782, 16789,
16796, 16803, 16810, 16817, 16824, 16831, 16838, 16845, 16852,
16859, 16866, 16873, 16880, 16887, 16894, 16901, 16908, 16915,
16922, 16929, 16936, 16943, 16950, 16957, 16964, 16971, 16978,
16985, 16992, 16999, 17006, 17013, 17020, 17027, 17034, 17041,
17048, 17055, 17062, 17069, 17076, 17083, 17090, 17097, 17104,
17111, 17118, 17125, 17132, 17139, 17146, 17153, 17160, 17167,
17174, 17181, 17188, 17195, 17202, 17209, 17216, 17223, 17230,
17237, 17244, 17251, 17258, 17265, 17272, 17279, 17286, 17293,
17300, 17307, 17314, 17321, 17328, 17335, 17342, 17349, 17356,
17363, 17370, 17377, 17384, 17391, 17398, 17405, 17412, 17419,
17426, 17433, 17440, 17447, 17454, 17461, 17468, 17475, 17482,
17489, 17496, 17503, 17510, 17517, 17524, 17531, 17538, 17545,
17552, 17559, 17566, 17573, 17580, 17587, 17594, 17601, 17608,
17615, 17622, 17629, 17636, 17643, 17650, 17657, 17664, 17671,
17678, 17685, 17692, 17699, 17706, 17713, 17720, 17727, 17734,
17741, 17748, 17755, 17762, 17769, 17776, 17783, 17790, 17797,
17804, 17811, 17818, 17825, 17832, 17839, 17846, 17853, 17860,
17867, 17874, 17881, 17888, 17895, 17902, 17909, 17916, 17923,
17930, 17937, 17944, 17951, 17958, 17965, 17972, 17979, 17986,
17993, 18000, 18007, 18014, 18021, 18028, 18035, 18042, 18049,
18056, 18063, 18070, 18077, 18084, 18091, 18098, 18105, 18112,
18119, 18126, 18133, 18140, 18147, 18154, 18161, 18168, 18175,
18182, 18189, 18196, 18203, 18210, 18217, 18224, 18231, 18238,
18245, 18252, 18259, 18266, 18273, 18280, 18287, 18294, 18301,
18308, 18315), class = "Date"), v1 = c(39L, 40L, 39L, 37L,
39L, 44L, 41L, 40L, 35L, 39L, 35L, 32L, 36L, 34L, 32L, 34L, 32L,
34L, 32L, 30L, 36L, 34L, 35L, 32L, 35L, 32L, 33L, 35L, 35L, 35L,
35L, 36L, 41L, 36L, 34L, 32L, 33L, 30L, 33L, 36L, 34L, 39L, 36L,
34L, 35L, 40L, 46L, 40L, 41L, 44L, 48L, 45L, 32L, 28L, 31L, 29L,
32L, 31L, 33L, 33L, 33L, 31L, 28L, 30L, 29L, 25L, 25L, 25L, 26L,
26L, 24L, 24L, 26L, 25L, 28L, 32L, 32L, 32L, 32L, 35L, 36L, 32L,
31L, 32L, 32L, 35L, 36L, 33L, 30L, 32L, 37L, 42L, 36L, 36L, 33L,
33L, 31L, 46L, 49L, 63L, 77L, 56L, 58L, 57L, 71L, 44L, 36L, 39L,
35L, 35L, 35L, 32L, 33L, 36L, 33L, 33L, 34L, 29L, 30L, 30L, 28L,
27L, 31L, 29L, 28L, 29L, 29L, 100L, 64L, 42L, 48L, 43L, 39L,
36L, 33L, 30L, 32L, 31L, 34L, 34L, 31L, 35L, 35L, 40L, 40L, 40L,
39L, 38L, 50L, 46L, 48L, 47L, 40L, 43L, 43L, 44L, 60L, 54L, 50L,
51L, 61L, 55L, 55L, 62L, 51L, 54L, 51L, 45L, 45L, 46L, 45L, 48L,
47L, 44L, 42L, 42L, 42L, 43L, 44L, 54L, 53L, 48L, 51L, 47L, 45L,
45L, 47L, 49L, 51L, 44L, 43L, 46L, 42L, 46L, 44L, 100L, 62L,
54L, 53L, 45L, 93L, 61L, 76L, 60L, 52L, 53L, 62L, 56L, 54L, 21L,
19L, 21L, 21L, 20L, 82L, 100L, 62L, 38L, 34L, 31L, 35L, 27L,
23L, 21L, 21L, 20L, 21L, 21L, 22L, 22L, 20L, 19L, 20L, 19L, 21L,
20L, 20L, 19L, 21L, 21L, 20L, 18L, 22L, 19L, 18L, 18L, 17L, 20L,
19L, 20L, 21L, 24L, 26L, 25L, 32L, 24L, 25L, 25L, 28L, 27L, 25L,
53L, 53L, 49L, 50L, 49L, 52L, 53L, 58L, 53L, 56L, 52L, 50L, 49L,
52L, 62L, 46L, 45L, 52L, 41L, 45L, 50L, 48L, 48L, 49L, 50L, 50L,
47L, 49L, 44L, 54L, 100L, 67L, 58L, 45L, 60L, 51L, 56L, 50L,
50L, 48L, 48L, 49L, 48L, 54L, 57L, 67L, 74L, 58L, 60L, 64L, 77L,
70L, 82L, 72L, 77L, 74L, 67L, 79L, 74L, 88L), v2 = c(4L,
6L, 5L, 5L, 5L, 6L, 5L, 5L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 5L, 5L,
5L, 5L, 4L, 5L, 5L, 4L, 6L, 5L, 4L, 5L, 5L, 6L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 4L, 5L, 6L, 5L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 5L, 6L,
4L, 7L, 6L, 5L, 5L, 7L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 5L, 4L,
4L, 6L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 3L, 5L, 4L, 4L, 4L, 5L, 4L,
4L, 4L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 4L, 6L,
5L, 7L, 7L, 5L, 7L, 9L, 7L, 6L, 6L, 5L, 5L, 5L, 4L, 6L, 5L, 6L,
4L, 5L, 5L, 5L, 4L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 5L, 3L, 5L, 4L,
5L, 4L, 5L, 4L, 4L, 5L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 4L, 4L, 7L, 6L, 5L, 4L, 5L, 7L, 8L, 8L, 8L, 8L, 7L,
7L, 8L, 8L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 7L, 6L, 5L, 7L, 8L, 6L,
6L, 6L, 6L, 6L, 6L, 8L, 7L, 7L, 8L, 7L, 8L, 8L, 6L, 7L, 6L, 6L,
8L, 7L, 7L, 7L, 6L, 7L, 8L, 8L, 8L, 10L, 8L, 5L, 7L, 7L, 9L,
8L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 2L, 3L, 3L, 2L, 3L, 4L, 4L, 4L, 4L, 3L,
3L, 4L, 4L, 5L, 4L, 8L, 7L, 8L, 7L, 5L, 7L, 8L, 8L, 7L, 7L, 7L,
9L, 6L, 9L, 8L, 6L, 6L, 8L, 7L, 7L, 8L, 7L, 7L, 7L, 8L, 7L, 7L,
6L, 7L, 7L, 8L, 6L, 7L, 6L, 8L, 6L, 5L, 9L, 6L, 8L, 7L, 6L, 6L,
7L, 6L, 7L, 8L, 8L, 8L, 7L, 10L, 10L, 10L, 11L, 11L, 10L, 9L,
10L, 10L, 9L), v3 = c(84L, 86L, 82L, 100L, 83L, 82L,
76L, 74L, 81L, 72L, 67L, 66L, 67L, 64L, 67L, 61L, 67L, 63L, 59L,
60L, 57L, 54L, 60L, 59L, 53L, 61L, 61L, 57L, 59L, 63L, 60L, 56L,
60L, 64L, 57L, 55L, 58L, 61L, 56L, 63L, 65L, 63L, 59L, 64L, 60L,
62L, 70L, 65L, 65L, 61L, 71L, 69L, 54L, 59L, 54L, 55L, 55L, 56L,
74L, 100L, 86L, 69L, 54L, 55L, 48L, 47L, 48L, 48L, 46L, 44L,
42L, 45L, 43L, 48L, 46L, 43L, 45L, 44L, 52L, 47L, 50L, 49L, 47L,
47L, 50L, 49L, 51L, 47L, 45L, 45L, 49L, 53L, 55L, 56L, 52L, 52L,
51L, 64L, 67L, 73L, 78L, 65L, 76L, 74L, 62L, 57L, 52L, 75L, 54L,
47L, 52L, 52L, 49L, 42L, 45L, 43L, 45L, 42L, 44L, 41L, 40L, 38L,
39L, 41L, 42L, 43L, 39L, 60L, 50L, 49L, 52L, 51L, 46L, 47L, 42L,
44L, 45L, 44L, 47L, 44L, 49L, 43L, 50L, 47L, 48L, 52L, 53L, 51L,
64L, 57L, 60L, 52L, 45L, 48L, 49L, 56L, 81L, 71L, 61L, 68L, 69L,
67L, 69L, 61L, 68L, 69L, 63L, 63L, 61L, 59L, 78L, 60L, 56L, 57L,
57L, 54L, 52L, 48L, 53L, 49L, 50L, 53L, 58L, 55L, 61L, 52L, 57L,
55L, 57L, 51L, 51L, 52L, 54L, 58L, 58L, 80L, 67L, 62L, 60L, 60L,
65L, 64L, 78L, 70L, 63L, 68L, 67L, 75L, 66L, 27L, 26L, 26L, 27L,
24L, 30L, 35L, 33L, 31L, 28L, 28L, 30L, 28L, 26L, 23L, 22L, 21L,
22L, 21L, 20L, 22L, 20L, 20L, 21L, 20L, 24L, 21L, 21L, 22L, 23L,
22L, 24L, 25L, 20L, 22L, 23L, 22L, 20L, 21L, 22L, 22L, 23L, 25L,
25L, 25L, 33L, 28L, 25L, 28L, 27L, 29L, 30L, 58L, 57L, 60L, 58L,
56L, 60L, 59L, 57L, 56L, 60L, 55L, 55L, 54L, 50L, 53L, 55L, 48L,
50L, 53L, 47L, 46L, 51L, 52L, 55L, 61L, 60L, 51L, 51L, 57L, 53L,
71L, 67L, 58L, 56L, 93L, 71L, 66L, 68L, 60L, 62L, 61L, 56L, 57L,
61L, 64L, 64L, 75L, 65L, 64L, 69L, 78L, 84L, 100L, 91L, 94L,
86L, 83L, 89L, 89L, 87L), v4 = c(75L, 77L, 73L, 71L, 70L,
78L, 76L, 72L, 71L, 72L, 75L, 75L, 70L, 74L, 72L, 74L, 74L, 73L,
69L, 74L, 72L, 71L, 74L, 72L, 72L, 82L, 74L, 83L, 78L, 73L, 73L,
80L, 88L, 88L, 74L, 68L, 70L, 76L, 72L, 76L, 75L, 76L, 71L, 77L,
96L, 85L, 100L, 90L, 81L, 80L, 87L, 86L, 81L, 77L, 81L, 74L,
73L, 74L, 76L, 71L, 84L, 79L, 74L, 74L, 72L, 80L, 72L, 73L, 70L,
69L, 69L, 77L, 72L, 77L, 72L, 77L, 77L, 85L, 77L, 74L, 77L, 77L,
76L, 77L, 75L, 77L, 79L, 73L, 71L, 73L, 78L, 78L, 76L, 74L, 74L,
75L, 81L, 86L, 95L, 91L, 85L, 83L, 90L, 92L, 72L, 67L, 72L, 77L,
68L, 64L, 68L, 73L, 75L, 71L, 71L, 70L, 69L, 72L, 68L, 67L, 65L,
65L, 63L, 64L, 64L, 67L, 64L, 80L, 73L, 70L, 100L, 73L, 78L,
62L, 63L, 66L, 60L, 61L, 61L, 62L, 61L, 73L, 71L, 70L, 69L, 67L,
67L, 68L, 64L, 73L, 75L, 70L, 67L, 64L, 68L, 76L, 71L, 73L, 75L,
71L, 74L, 68L, 68L, 72L, 71L, 70L, 69L, 69L, 69L, 71L, 73L, 73L,
68L, 71L, 68L, 64L, 65L, 73L, 66L, 67L, 69L, 72L, 80L, 66L, 69L,
68L, 66L, 72L, 67L, 75L, 75L, 69L, 70L, 68L, 69L, 83L, 70L, 70L,
71L, 73L, 76L, 77L, 82L, 74L, 71L, 70L, 71L, 77L, 71L, 66L, 65L,
74L, 68L, 66L, 79L, 82L, 79L, 71L, 73L, 75L, 79L, 80L, 76L, 71L,
70L, 74L, 70L, 72L, 75L, 71L, 71L, 70L, 74L, 72L, 83L, 68L, 71L,
82L, 79L, 72L, 70L, 67L, 66L, 66L, 65L, 68L, 68L, 65L, 63L, 65L,
68L, 73L, 69L, 74L, 77L, 68L, 67L, 65L, 67L, 72L, 74L, 75L, 74L,
76L, 73L, 72L, 73L, 77L, 75L, 71L, 73L, 73L, 71L, 72L, 74L, 70L,
66L, 72L, 72L, 70L, 67L, 69L, 69L, 75L, 73L, 75L, 83L, 71L, 69L,
66L, 66L, 79L, 74L, 67L, 64L, 68L, 70L, 67L, 68L, 73L, 70L, 73L,
72L, 69L, 77L, 77L, 76L, 82L, 77L, 73L, 71L, 79L, 84L, 84L, 74L,
76L, 72L, 73L, 76L, 75L, 73L), v5 = c(41L, 44L, 40L, 39L,
37L, 40L, 40L, 42L, 39L, 37L, 39L, 37L, 36L, 34L, 34L, 35L, 35L,
32L, 33L, 33L, 32L, 32L, 31L, 30L, 32L, 32L, 30L, 31L, 32L, 34L,
33L, 34L, 35L, 44L, 36L, 39L, 35L, 35L, 35L, 32L, 34L, 36L, 36L,
35L, 36L, 36L, 44L, 39L, 38L, 42L, 44L, 44L, 39L, 39L, 39L, 39L,
37L, 37L, 39L, 38L, 39L, 36L, 35L, 34L, 33L, 32L, 28L, 31L, 29L,
27L, 29L, 30L, 31L, 29L, 29L, 32L, 33L, 34L, 30L, 32L, 35L, 32L,
32L, 34L, 32L, 33L, 33L, 32L, 31L, 30L, 33L, 37L, 32L, 33L, 32L,
32L, 34L, 41L, 45L, 48L, 56L, 47L, 52L, 51L, 44L, 35L, 34L, 34L,
33L, 30L, 32L, 31L, 31L, 30L, 28L, 29L, 29L, 27L, 26L, 26L, 24L,
24L, 24L, 25L, 23L, 25L, 25L, 41L, 35L, 28L, 32L, 31L, 32L, 29L,
29L, 27L, 27L, 27L, 26L, 24L, 24L, 26L, 27L, 27L, 29L, 30L, 30L,
29L, 32L, 31L, 37L, 33L, 31L, 30L, 32L, 32L, 32L, 30L, 30L, 29L,
31L, 31L, 31L, 32L, 30L, 30L, 29L, 28L, 28L, 27L, 27L, 26L, 27L,
25L, 27L, 24L, 23L, 23L, 25L, 25L, 27L, 27L, 28L, 25L, 24L, 25L,
26L, 25L, 26L, 24L, 24L, 24L, 23L, 25L, 25L, 37L, 29L, 28L, 29L,
27L, 33L, 33L, 38L, 33L, 31L, 31L, 32L, 35L, 31L, 28L, 28L, 30L,
29L, 29L, 34L, 43L, 42L, 37L, 34L, 32L, 36L, 31L, 29L, 28L, 27L,
28L, 26L, 24L, 25L, 25L, 24L, 24L, 24L, 25L, 25L, 23L, 25L, 26L,
26L, 24L, 24L, 24L, 24L, 24L, 23L, 23L, 23L, 24L, 22L, 25L, 25L,
26L, 28L, 28L, 34L, 30L, 28L, 29L, 31L, 31L, 31L, 33L, 32L, 32L,
34L, 32L, 33L, 34L, 34L, 33L, 35L, 34L, 32L, 31L, 29L, 30L, 28L,
28L, 28L, 28L, 27L, 28L, 28L, 29L, 28L, 29L, 28L, 27L, 27L, 27L,
27L, 37L, 32L, 31L, 30L, 30L, 30L, 34L, 30L, 30L, 30L, 30L, 30L,
29L, 31L, 32L, 33L, 39L, 33L, 32L, 34L, 37L, 40L, 37L, 36L, 38L,
38L, 36L, 38L, 38L, 39L), v6 = c(6L, 6L, 6L, 6L, 5L,
6L, 7L, 6L, 6L, 5L, 6L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 7L, 6L, 6L, 8L, 7L, 7L, 7L, 7L, 7L, 5L,
5L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 5L, 5L,
5L, 6L, 6L, 6L, 5L, 6L, 7L, 6L, 7L, 6L, 6L, 6L, 7L, 9L, 7L, 7L,
8L, 8L, 8L, 6L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 5L,
6L, 9L, 7L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 7L, 6L, 7L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
6L, 7L, 7L, 6L, 7L, 10L, 7L, 7L, 7L, 7L, 8L, 7L, 6L, 6L, 5L,
5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 4L, 5L, 5L,
5L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 9L, 7L, 7L, 7L, 6L, 7L,
6L, 7L, 6L, 7L, 8L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 5L,
6L, 6L, 6L, 6L, 6L, 7L, 6L, 7L, 6L, 8L, 8L, 7L, 7L, 7L, 7L, 10L,
8L, 8L, 7L, 8L, 8L, 7L, 7L, 7L, 7L, 6L, 6L, 7L, 6L), v7 = c(83L,
84L, 81L, 90L, 79L, 78L, 78L, 81L, 78L, 75L, 76L, 77L, 75L, 77L,
79L, 82L, 85L, 81L, 80L, 81L, 81L, 82L, 85L, 80L, 77L, 82L, 83L,
76L, 73L, 74L, 78L, 73L, 77L, 74L, 72L, 70L, 72L, 73L, 70L, 70L,
72L, 75L, 74L, 73L, 73L, 77L, 82L, 81L, 79L, 82L, 86L, 86L, 85L,
79L, 79L, 77L, 76L, 75L, 75L, 78L, 78L, 77L, 74L, 72L, 68L, 69L,
72L, 69L, 72L, 71L, 71L, 72L, 69L, 69L, 72L, 71L, 70L, 72L, 75L,
73L, 74L, 72L, 74L, 75L, 71L, 71L, 73L, 72L, 71L, 70L, 72L, 73L,
72L, 75L, 76L, 76L, 75L, 80L, 83L, 100L, 95L, 84L, 84L, 89L,
76L, 69L, 68L, 67L, 66L, 64L, 67L, 69L, 64L, 63L, 63L, 67L, 66L,
65L, 69L, 64L, 62L, 62L, 63L, 63L, 60L, 63L, 66L, 69L, 64L, 67L,
68L, 63L, 64L, 63L, 61L, 61L, 57L, 64L, 61L, 68L, 65L, 74L, 67L,
66L, 67L, 73L, 69L, 68L, 64L, 68L, 72L, 73L, 69L, 72L, 75L, 80L,
94L, 83L, 81L, 79L, 76L, 72L, 73L, 74L, 74L, 72L, 69L, 70L, 78L,
78L, 81L, 76L, 75L, 76L, 75L, 73L, 74L, 73L, 73L, 72L, 75L, 72L,
76L, 70L, 71L, 70L, 71L, 70L, 69L, 66L, 66L, 63L, 70L, 68L, 68L,
79L, 72L, 75L, 78L, 75L, 75L, 77L, 79L, 82L, 85L, 82L, 83L, 87L,
100L, 89L, 86L, 81L, 84L, 78L, 83L, 92L, 100L, 90L, 87L, 81L,
82L, 79L, 79L, 79L, 81L, 79L, 79L, 76L, 78L, 74L, 73L, 68L, 73L,
71L, 73L, 71L, 72L, 69L, 73L, 70L, 71L, 69L, 73L, 70L, 70L, 73L,
73L, 73L, 69L, 73L, 74L, 76L, 75L, 76L, 77L, 79L, 81L, 78L, 82L,
81L, 94L, 100L, 93L, 91L, 88L, 86L, 90L, 83L, 82L, 82L, 81L,
81L, 82L, 84L, 82L, 81L, 80L, 82L, 81L, 81L, 80L, 78L, 77L, 75L,
72L, 75L, 72L, 73L, 75L, 73L, 75L, 83L, 78L, 77L, 77L, 77L, 75L,
78L, 80L, 77L, 73L, 79L, 79L, 76L, 88L, 91L, 90L, 80L, 82L, 82L,
82L, 85L, 99L, 100L, 97L, 91L, 89L, 82L, 85L, 82L, 83L), v8 = c(610L,
765L, 713L, 685L, 601L, 535L, 582L, 568L, 502L, 608L, 653L, 672L,
694L, 697L, 715L, 751L, 675L, 706L, 777L, 787L, 876L, 823L, 754L,
782L, 834L, 907L, 890L, 913L, 921L, 977L, 890L, 947L, 996L, 830L,
974L, 921L, 912L, 907L, 871L, 805L, 876L, 909L, 861L, 865L, 901L,
742L, 726L, 720L, 803L, 796L, 857L, 902L, 751L, 806L, 859L, 798L,
714L, 728L, 688L, 728L, 785L, 1166L, 1105L, 935L, 1037L, 1016L,
1037L, 932L, 1013L, 996L, 1016L, 1064L, 1104L, 1003L, 1051L,
913L, 944L, 1044L, 1018L, 1073L, 1109L, 1055L, 1076L, 1008L,
1016L, 996L, 1050L, 1030L, 969L, 1011L, 932L, 890L, 978L, 1008L,
928L, 1006L, 927L, 913L, 905L, 952L, 957L, 978L, 978L, 1044L,
1341L, 966L, 881L, 1052L, 981L, 864L, 927L, 887L, 943L, 1055L,
1010L, 1012L, 1059L, 913L, 1028L, 1060L, 1046L, 1061L, 1043L,
1027L, 1094L, 1065L, 1070L, 1000L, 1079L, 1114L, 1156L, 1069L,
1157L, 1234L, 1217L, 1216L, 1190L, 1208L, 1253L, 1182L, 1133L,
1046L, 1122L, 1013L, 1185L, 1208L, 1177L, 1227L, 1080L, 1197L,
1123L, 1260L, 1101L, 1139L, 1054L, 1222L, 1262L, 1158L, 1241L,
1190L, 1087L, 1155L, 1122L, 1159L, 1044L, 999L, 993L, 1193L,
1229L, 1217L, 1301L, 1239L, 1179L, 1092L, 1226L, 1211L, 1236L,
1327L, 1133L, 1149L, 1198L, 1158L, 1312L, 1183L, 1165L, 1163L,
1226L, 1136L, 1130L, 1129L, 1092L, 1039L, 1019L, 1196L, 1155L,
1169L, 1130L, 1185L, 1166L, 1174L, 1048L, 1083L, 1048L, 1161L,
997L, 1041L, 1123L, 895L, 1034L, 1095L, 1080L, 1223L, 1074L,
954L, 948L, 1011L, 982L, 1013L, 1078L, 1080L, 1055L, 1131L, 1145L,
999L, 1213L, 1192L, 1144L, 1082L, 1137L, 1150L, 1104L, 1059L,
1039L, 1099L, 1202L, 1092L, 1072L, 1126L, 1086L, 1098L, 1131L,
1071L, 1122L, 1061L, 988L, 1043L, 760L, 1073L, 950L, 1001L, 960L,
1034L, 919L, 922L, 944L, 996L, 970L, 996L, 996L, 1058L, 1235L,
964L, 1043L, 979L, 865L, 1012L, 906L, 987L, 925L, 847L, 1012L,
1011L, 1065L, 987L, 1078L, 1025L, 1010L, 1045L, 981L, 987L, 1125L,
1184L, 1070L, 995L, 1139L, 1205L, 1286L, 1180L, 1210L, 1147L,
1221L, 1112L, 1151L, 1117L, 1097L, 1066L, 1059L, 1050L, 1040L,
976L, 992L, 979L, 949L, 954L, 932L, 873L, 1015L, 982L, 982L,
1010L, 897L, 1056L, 1217L, 977L, 986L, 1004L, 906L, 890L, 877L,
894L, 672L)), row.names = c(NA, -321L), class = "data.frame")
Right now, using cor_plot, the end correlogram result is pictured as below.
I want to change the color, change the size of x and y axis title, and also add title for the correlogram matrix. What is the best way to do that? I already tried to use ggplot or corrplot, but it's not working as I hoped it would.

Related

Convert pixel values stored in text file to image

I've been trying to find a way to convert text files with pixels values into images (no matter the format) in R but I couldn't find a way to do it.
I found solutions for MatLab and Python, for example.
I have a file with 520 x 640 pixels with values from 0 to 255.
This is a small piece of it.
mid1al <- read.table("C:/Users/u015/Mid1_R_Al.txt", header = FALSE, sep = ";")
mid1al <- mid1al[1:20,1:20]
dput(mid1al)
structure(list(V1 = c(84L, 79L, 97L, 67L, 98L, 113L, 77L, 46L,
41L, 37L, 42L, 46L, 23L, 28L, 24L, 34L, 45L, 51L, 24L, 24L),
V2 = c(118L, 107L, 105L, 82L, 87L, 108L, 100L, 40L, 71L,
74L, 81L, 55L, 41L, 25L, 22L, 58L, 53L, 38L, 26L, 36L), V3 = c(103L,
116L, 128L, 82L, 77L, 104L, 97L, 50L, 65L, 78L, 98L, 111L,
86L, 59L, 35L, 51L, 43L, 46L, 33L, 47L), V4 = c(114L, 91L,
90L, 96L, 103L, 98L, 86L, 36L, 50L, 65L, 98L, 125L, 86L,
32L, 24L, 36L, 36L, 44L, 34L, 43L), V5 = c(68L, 70L, 85L,
85L, 100L, 111L, 61L, 12L, 42L, 70L, 103L, 103L, 45L, 27L,
18L, 27L, 32L, 43L, 51L, 41L), V6 = c(43L, 87L, 85L, 89L,
130L, 123L, 78L, 43L, 15L, 39L, 62L, 44L, 27L, 14L, 19L,
61L, 83L, 90L, 88L, 88L), V7 = c(20L, 72L, 116L, 124L, 133L,
133L, 103L, 56L, 21L, 9L, 19L, 26L, 18L, 32L, 67L, 92L, 100L,
105L, 94L, 79L), V8 = c(69L, 96L, 120L, 144L, 142L, 101L,
96L, 46L, 14L, 4L, 8L, 2L, 24L, 73L, 96L, 106L, 103L, 116L,
109L, 74L), V9 = c(118L, 122L, 134L, 135L, 133L, 98L, 57L,
20L, 5L, 5L, 2L, 14L, 51L, 89L, 117L, 95L, 103L, 93L, 104L,
77L), V10 = c(122L, 107L, 127L, 147L, 128L, 88L, 24L, 11L,
10L, 4L, 10L, 31L, 74L, 104L, 113L, 107L, 109L, 99L, 103L,
45L), V11 = c(105L, 120L, 114L, 132L, 125L, 112L, 51L, 6L,
3L, 9L, 18L, 49L, 82L, 111L, 111L, 96L, 92L, 81L, 75L, 18L
), V12 = c(98L, 104L, 103L, 126L, 147L, 128L, 61L, 26L, 2L,
9L, 18L, 50L, 105L, 103L, 101L, 98L, 74L, 53L, 18L, 1L),
V13 = c(107L, 91L, 108L, 109L, 138L, 114L, 88L, 33L, 2L,
4L, 9L, 61L, 71L, 77L, 78L, 83L, 43L, 38L, 8L, 5L), V14 = c(53L,
60L, 43L, 49L, 104L, 128L, 72L, 44L, 6L, 8L, 10L, 24L, 35L,
27L, 33L, 37L, 31L, 24L, 10L, 5L), V15 = c(13L, 16L, 11L,
27L, 62L, 78L, 73L, 30L, 8L, 7L, 31L, 66L, 66L, 33L, 13L,
27L, 16L, 18L, 12L, 7L), V16 = c(11L, 12L, 7L, 3L, 16L, 35L,
45L, 13L, 5L, 7L, 22L, 74L, 73L, 31L, 16L, 43L, 35L, 14L,
15L, 8L), V17 = c(15L, 16L, 7L, 8L, 1L, 5L, 15L, 13L, 31L,
33L, 22L, 34L, 38L, 17L, 18L, 41L, 39L, 26L, 19L, 12L), V18 = c(9L,
15L, 7L, 2L, 2L, 5L, 5L, 25L, 50L, 55L, 35L, 25L, 14L, 8L,
18L, 44L, 36L, 36L, 19L, 0L), V19 = c(15L, 16L, 4L, 6L, 4L,
6L, 22L, 45L, 59L, 48L, 56L, 58L, 52L, 30L, 22L, 46L, 41L,
50L, 23L, 7L), V20 = c(20L, 7L, 4L, 2L, 6L, 14L, 40L, 55L,
74L, 60L, 69L, 74L, 60L, 56L, 38L, 45L, 67L, 39L, 25L, 11L
)), row.names = c(NA, 20L), class = "data.frame")
Is there a way to create this image in Rstudio?

Creating scatter plot class or group wise

Im using ggstatsplot's ggscatterstats function to calculate correlation between various clinical parameters and then plotting them. For example
here my variables are age and WBC. This is taking all the data points irrespective of the class they belong. I would like to do the same with each FAB classification that is present in my data.
dat <- merge_clinical_class_TMB %>% select(FAB,AGE,Wbc,Platelet,HB,PB_Blasts,BM_Blasts,TMB_NONSYNONYMOUS)
df2 <- dat
library(ggstatsplot)
ggscatterstats(
df2,
x = AGE,
y = Wbc,
type = "np" # try the "robust" correlation too! It might be even better here
#, marginal.type = "boxplot"
)
My dataframe looks like this
head(df2)
FAB AGE Wbc Platelet HB PB_Blasts BM_Blasts TMB_NONSYNONYMOUS
1 M4 50 17 231 10 88 52 0.3000000
2 M3 61 1 90 10 44 0 0.4333333
3 M3 30 6 114 11 82 6 0.2333333
4 M0 77 92 105 9 67 56 0.4000000
5 M1 46 29 90 9 90 81 0.5666667
6 M1 68 3 63 8 91 55 0.9000000
My data
dput(df2)
structure(list(FAB = structure(c(5L, 4L, 4L, 1L, 2L, 2L, 3L,
3L, 3L, 5L, 3L, 5L, 1L, 5L, 5L, 3L, 3L, 3L, 1L, 2L, 1L, 4L, 6L,
6L, 5L, 3L, 5L, 7L, 5L, 1L, 6L, 5L, 5L, 6L, 5L, 6L, 3L, 3L, 4L,
4L, 5L, 7L, 3L, 3L, 5L, 2L, 5L, 1L, 3L, 6L, 2L, 5L, 2L, 5L, 7L,
3L, 3L, 8L, 6L, 4L, 2L, 2L, 2L, 2L, 3L, 8L, 3L, 2L, 2L, 4L, 6L,
3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 6L, 2L, 1L, 3L, 2L, 5L, 5L,
1L, 2L, 5L, 6L, 6L, 2L, 6L, 4L, 2L, 5L, 2L, 2L, 2L, 1L, 4L, 4L,
1L, 3L, 9L, 6L, 5L, 5L, 1L, 3L, 3L, 5L, 1L, 2L, 2L, 3L, 5L, 1L,
5L, 5L, 6L, 2L, 2L, 2L, 1L, 3L, 3L, 6L, 5L, 2L, 5L, 1L, 2L, 8L,
2L, 3L, 9L, 5L, 2L, 1L, 5L, 3L, 5L, 5L, 1L, 3L, 2L, 5L, 3L, 6L,
5L, 1L, 2L, 2L, 5L, 3L, 5L, 5L, 6L, 5L, 5L, 3L, 5L, 6L, 3L, 2L,
3L, 3L, 2L, 4L, 6L, 4L, 1L, 2L, 6L, 3L, 6L, 2L, 3L, 2L, 4L, 2L,
2L, 4L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 3L, 6L, 2L, 4L, 2L, 5L, 2L,
4L), .Label = c("M0", "M1", "M2", "M3", "M4", "M5", "M6", "M7",
"nc"), class = "factor"), AGE = c(50L, 61L, 30L, 77L, 46L, 68L,
23L, 64L, 76L, 81L, 25L, 78L, 39L, 49L, 57L, 63L, 62L, 52L, 76L,
64L, 65L, 61L, 44L, 31L, 64L, 33L, 55L, 50L, 64L, 59L, 59L, 77L,
33L, 48L, 35L, 66L, 67L, 51L, 74L, 51L, 64L, 77L, 63L, 37L, 57L,
53L, 62L, 39L, 72L, 66L, 51L, 51L, 18L, 63L, 54L, 75L, 40L, 60L,
76L, 33L, 63L, 53L, 75L, 67L, 66L, 77L, 64L, 76L, 51L, 42L, 51L,
59L, 43L, 45L, 60L, 47L, 68L, 24L, 48L, 73L, 60L, 44L, 71L, 25L,
60L, 57L, 55L, 69L, 42L, 42L, 45L, 50L, 41L, 21L, 50L, 69L, 76L,
70L, 27L, 76L, 65L, 48L, 59L, 69L, 81L, 22L, 61L, 51L, 63L, 61L,
22L, 73L, 49L, 41L, 47L, 54L, 44L, 55L, 83L, 78L, 59L, 57L, 57L,
88L, 43L, 71L, 62L, 75L, 62L, 58L, 65L, 66L, 60L, 35L, 76L, 72L,
35L, 73L, 67L, 70L, 48L, 65L, 41L, 52L, 67L, 58L, 34L, 60L, 55L,
56L, 61L, 31L, 71L, 56L, 57L, 60L, 57L, 58L, 79L, 55L, 34L, 76L,
82L, 67L, 67L, 54L, 53L, 71L, 61L, 30L, 50L, 35L, 29L, 45L, 38L,
81L, 31L, 75L, 67L, 29L, 51L, 40L, 32L, 57L, 25L, 63L, 75L, 25L,
68L, 62L, 25L, 31L, 68L, 45L, 61L, 35L, 22L, 23L, 21L, 53L),
Wbc = c(17L, 1L, 6L, 92L, 29L, 3L, 32L, 117L, 62L, 91L, 34L,
10L, 2L, 57L, 88L, 77L, 75L, 4L, 15L, 1L, 3L, 86L, 9L, 137L,
132L, 3L, 22L, 6L, 3L, 1L, 12L, 40L, 26L, 116L, 53L, 112L,
2L, 42L, 32L, 4L, 2L, 3L, 17L, 19L, 14L, 3L, 119L, 5L, 3L,
79L, 104L, 3L, 35L, 77L, 2L, 8L, 8L, 1L, 4L, 1L, 46L, 2L,
6L, 31L, 3L, 2L, 3L, 34L, 2L, 2L, 15L, 12L, 4L, 29L, 12L,
12L, 60L, 224L, 33L, 2L, 7L, 14L, 5L, 11L, 47L, 5L, 31L,
6L, 11L, 38L, 5L, 7L, 134L, 93L, 3L, 10L, 3L, 48L, 90L, 297L,
1L, 1L, 1L, 2L, 2L, 115L, 35L, 50L, 18L, 62L, 52L, 15L, 12L,
48L, 81L, 13L, 35L, 28L, 78L, 17L, 30L, 99L, 20L, 3L, 172L,
6L, 28L, 98L, 59L, 101L, 68L, 2L, 2L, 43L, 4L, 38L, 34L,
59L, 37L, 1L, 111L, 49L, 43L, 298L, 26L, 47L, 14L, 16L, 114L,
203L, 8L, 133L, 1L, 31L, 3L, 68L, 3L, 20L, 19L, 73L, 20L,
5L, 1L, 15L, 45L, 68L, 88L, 36L, 10L, 23L, 1L, 72L, 1L, 2L,
40L, 12L, 13L, 7L, 46L, 2L, 64L, NA, 5L, 103L, 8L, 1L, 3L,
16L, 29L, 1L, 99L, 2L, 6L, 2L, 3L, 2L, 115L, 27L, 8L, 1L),
Platelet = c(231L, 90L, 114L, 105L, 90L, 63L, 38L, 100L,
32L, 32L, 23L, 98L, 215L, 14L, 56L, 19L, 110L, 22L, 85L,
42L, 16L, 22L, 50L, 42L, 15L, 61L, 65L, 50L, 134L, 102L,
57L, 29L, 111L, 50L, 44L, 34L, 28L, 232L, 42L, 58L, 27L,
86L, 23L, 38L, 76L, 108L, 52L, 175L, 52L, 132L, 23L, 143L,
30L, 41L, 9L, 21L, 95L, 59L, 79L, 38L, 11L, 68L, 22L, 141L,
168L, 70L, 41L, 21L, 25L, 35L, 14L, 20L, 67L, 116L, 45L,
57L, 8L, 34L, 32L, 60L, 93L, 145L, 48L, 33L, 50L, 129L, 9L,
61L, 176L, 12L, 53L, 136L, 40L, 73L, 27L, 12L, 166L, 30L,
87L, 40L, 94L, 52L, 23L, 127L, 39L, 57L, 35L, 21L, 148L,
25L, 149L, 64L, 351L, 71L, 53L, 22L, 35L, 31L, 46L, 85L,
18L, 80L, 62L, 156L, 32L, 50L, 69L, 31L, 20L, 57L, 142L,
37L, 79L, 66L, 21L, 31L, 88L, 11L, 15L, 82L, 53L, 76L, 51L,
68L, 64L, 55L, 40L, 90L, 37L, 45L, 36L, 52L, 86L, 88L, 35L,
174L, 28L, 121L, 131L, 17L, 152L, 52L, 30L, 79L, 79L, 87L,
30L, 44L, 140L, 59L, 58L, 19L, 29L, 156L, 19L, 61L, 36L,
11L, 71L, 13L, 45L, 34L, 39L, 82L, 18L, 43L, 118L, 32L, 73L,
15L, 60L, 208L, 96L, 257L, 61L, 12L, 32L, 23L, 52L, 46L),
HB = c(10L, 10L, 11L, 9L, 9L, 8L, 7L, 10L, 10L, 11L, 11L,
10L, 10L, 8L, 10L, 13L, 11L, 9L, 9L, 8L, 9L, 12L, 8L, 6L,
10L, 7L, 8L, 9L, 11L, 12L, 11L, 10L, 10L, 9L, 8L, 10L, 9L,
13L, 9L, 8L, 12L, 9L, 12L, 9L, 9L, 9L, 11L, 10L, 11L, 12L,
12L, 11L, 9L, 10L, 9L, 9L, 10L, 9L, 10L, 9L, 8L, 9L, 9L,
10L, 12L, 10L, 10L, 8L, 10L, 9L, 11L, 11L, 11L, 8L, 9L, 9L,
9L, 6L, 10L, 10L, 9L, 9L, 8L, 9L, 9L, 7L, 9L, 11L, 12L, 10L,
9L, 10L, 12L, NA, 10L, 7L, 11L, 10L, 9L, 11L, 10L, 9L, 8L,
8L, 10L, 9L, 12L, 11L, 8L, 13L, 11L, 9L, 9L, 12L, 10L, 9L,
10L, 8L, 9L, 9L, 9L, 10L, 9L, 10L, 10L, 9L, 10L, 8L, 7L,
9L, 9L, 8L, 9L, 9L, 8L, 10L, 8L, 9L, 9L, 8L, 9L, 9L, 9L,
9L, 9L, 10L, 9L, 8L, 9L, 10L, 7L, 11L, 11L, 10L, 6L, 8L,
9L, 9L, 10L, 8L, 11L, 10L, 11L, 8L, 9L, 8L, 9L, 8L, 10L,
10L, 10L, 9L, 9L, 12L, 9L, 9L, 11L, 9L, 13L, 9L, 10L, 8L,
9L, 10L, 10L, 11L, 9L, 9L, 10L, 9L, 9L, 11L, 7L, 13L, 14L,
12L, 8L, 12L, 8L, 9L), PB_Blasts = c(88L, 44L, 82L, 67L,
90L, 91L, 59L, 60L, 48L, 98L, 53L, 40L, 75L, 81L, 90L, 57L,
46L, 67L, 74L, 61L, 99L, 73L, 74L, 83L, 72L, 33L, 35L, 70L,
85L, 61L, 95L, 80L, 71L, 83L, 90L, 90L, 50L, 64L, 51L, 93L,
95L, 75L, 80L, 52L, 61L, 72L, 65L, 83L, 45L, 32L, 85L, 73L,
86L, 82L, 30L, 48L, 47L, 58L, 78L, 100L, 81L, 82L, 40L, 89L,
70L, 47L, 80L, 73L, 62L, 88L, 57L, 70L, 40L, 56L, 86L, 37L,
90L, 77L, 75L, 37L, 94L, 86L, 97L, 72L, 87L, 40L, 52L, 60L,
68L, 40L, 95L, 81L, 92L, 90L, 90L, 42L, 37L, 84L, 77L, 99L,
83L, 65L, 79L, 82L, 46L, 94L, 71L, 39L, 62L, 95L, 55L, 11L,
51L, 42L, 77L, 72L, 39L, 69L, 75L, 70L, 75L, 52L, 91L, 33L,
87L, 55L, 72L, 76L, 85L, 79L, 79L, 81L, 50L, 81L, 33L, 88L,
34L, 90L, 69L, 32L, 92L, 90L, 47L, 75L, 30L, 59L, 57L, 62L,
54L, 60L, 89L, 82L, 90L, 90L, 64L, 89L, 43L, 58L, 58L, 97L,
71L, 91L, 53L, 75L, 85L, 67L, 86L, 70L, 43L, 86L, 74L, 87L,
0L, 0L, 86L, 53L, 63L, 41L, 76L, 45L, 85L, 0L, 94L, 6L, 91L,
0L, 2L, 93L, 85L, 82L, 56L, 40L, 48L, 0L, 14L, 90L, 71L,
51L, 91L, 42L), BM_Blasts = c(52L, 0L, 6L, 56L, 81L, 55L,
0L, 0L, 88L, 37L, 87L, 6L, 4L, 48L, 84L, 70L, 53L, 18L, 82L,
5L, 34L, 68L, 5L, 6L, 90L, 0L, 67L, 0L, 22L, 12L, 0L, 2L,
14L, 3L, 18L, 7L, 17L, 79L, 0L, 40L, 0L, 8L, 71L, 33L, 17L,
41L, 65L, 53L, 0L, 11L, 85L, 2L, 90L, 39L, 0L, 54L, 23L,
0L, 0L, 0L, 97L, 42L, 48L, 61L, 6L, 0L, 46L, 55L, 10L, 2L,
0L, 48L, 39L, 37L, 43L, 0L, 91L, 76L, 41L, 16L, 30L, 17L,
54L, 50L, 65L, 0L, 59L, 22L, 51L, 16L, 6L, 10L, 90L, 72L,
0L, 32L, 0L, 49L, 88L, 98L, 0L, 0L, 15L, 0L, 0L, 94L, 55L,
39L, 9L, 86L, 70L, 11L, 5L, 74L, 79L, 90L, 83L, 57L, 74L,
28L, 17L, 4L, 91L, 0L, 91L, 50L, 49L, 80L, 22L, 64L, 84L,
12L, 14L, 86L, 6L, 18L, 40L, 0L, 61L, 6L, 87L, 0L, 62L, 51L,
6L, 72L, 59L, 29L, 24L, 96L, 0L, 53L, 13L, 45L, 61L, 56L,
35L, 10L, 0L, 8L, 58L, 16L, 25L, 10L, 3L, 71L, 52L, 67L,
32L, 88L, 10L, 8L, 0L, 0L, 97L, 7L, 45L, 0L, 49L, 9L, 85L,
0L, 70L, 91L, 7L, 0L, 2L, 0L, 32L, 11L, 71L, 0L, 48L, 0L,
14L, 7L, 90L, 63L, 83L, 29L), TMB_NONSYNONYMOUS = c(0.3,
0.433333333333, 0.233333333333, 0.4, 0.566666666667, 0.9,
0.3, 0.133333333333, 0.4, 0.3, 0.233333333333, 0.5, 0.266666666667,
0, 0.2, 0.4, 0.266666666667, 0.333333333333, 0.4, 0.4, 0.566666666667,
0.0333333333333, 0.166666666667, 0.1, 0.166666666667, 0.266666666667,
0.3, 0.3, 0.466666666667, 0.0666666666667, 0.266666666667,
0.266666666667, 0.0333333333333, 0.1, 0.133333333333, 0.0333333333333,
0.5, 0.6, 0.0333333333333, 0.1, 0.0333333333333, 0.333333333333,
0.433333333333, 0.2, 0.466666666667, 0.2, 0.0333333333333,
0.733333333333, 0.2, 0.233333333333, 0.233333333333, 0.3,
0.133333333333, 0, 0.3, 0.333333333333, 0.333333333333, 0.266666666667,
0.533333333333, 0.2, 0.533333333333, 0.466666666667, 0.533333333333,
0.0333333333333, 0.3, 0.5, 0.333333333333, 0.266666666667,
0.5, 0.333333333333, 0.0666666666667, 0.466666666667, 0.333333333333,
0.266666666667, 0.7, 0.433333333333, 0.166666666667, 0.0666666666667,
0.233333333333, 0.5, 0.0333333333333, 0.2, 0.433333333333,
0.433333333333, 0.4, 0.233333333333, 0.0666666666667, 0.233333333333,
0.466666666667, 0.0666666666667, 0, 0.1, 0.4, 0.1, 0.2, 0.4,
0.433333333333, 0.566666666667, 0.2, 0.0333333333333, 0.533333333333,
0.566666666667, 0.3, 0.466666666667, 0.566666666667, 0.0333333333333,
0.4, 0.0666666666667, 0.633333333333, 0.4, 0.466666666667,
0.466666666667, 0.3, 0.5, 0.0333333333333, 0.333333333333,
0.333333333333, 0.266666666667, 0.366666666667, 0.666666666667,
0.333333333333, 0.533333333333, 0.466666666667, 0.6, 0.333333333333,
0.4, 0.266666666667, 0.366666666667, 0.2, 0.0333333333333,
0.266666666667, 0.3, 0.166666666667, 0.4, 0.566666666667,
0.4, 0.1, 0.1, 0.0666666666667, 0.366666666667, 0, 0.4, 0.0333333333333,
0.1, 0.0666666666667, 0.5, 0.3, 0.466666666667, 0.0333333333333,
0.4, 0.1, 0.0666666666667, 0.766666666667, 0.5, 0.466666666667,
0.333333333333, 0.4, 0.333333333333, 0.4, 0.266666666667,
0.2, 0.3, 0.7, 0.166666666667, 0.2, 0, 0.5, 0.166666666667,
0.533333333333, 0.233333333333, 0.166666666667, 0.133333333333,
0.0666666666667, 0.4, 0.333333333333, 0.133333333333, 0.4,
0.233333333333, 0.466666666667, 0.366666666667, 0.266666666667,
0.266666666667, 0.266666666667, 0.4, 0.2, 0.166666666667,
0.4, 0.333333333333, 0.166666666667, 0.266666666667, 0.1,
0.333333333333, 0.733333333333, 0.466666666667, 0.466666666667,
0.2, 0.1, 1.13333333333, 0.2, 0.3)), class = "data.frame", row.names = c(NA,
-200L))
Objective I would like to do the same with various FABI have FAB label from M0 to M7 I would like to ignore nc
So for each FAB label I would like to see the correlation for example if I have to take the M0 class then I would like to see their Age vs Wbc correlation and similarly for other FAB class as well. Is it possible to do these in ggstataplot as I don't see for correlation any such functionality there .
Simple way is I can subset them and do the same like M0 ,M1, M2 etc etc but that is a long process can I split the FAB column and pass it to the library?
I would like to know other ways to do the above and plot the same
Any help or suggestion would be appreciated

Update: We could also use the built in function see comments:
Many thanks to #Indrajeet Patil: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html#grouped-analysis-with-grouped_ggscatterstats
To subset FAB we use filter:
## for reproducibility
set.seed(123)
## plot
grouped_ggscatterstats(
## arguments relevant for ggscatterstats
data = df2 %>% filter(as.integer(FAB)<5),
x = AGE,
y = Wbc,
grouping.var = FAB,
type = "r",
# ggtheme = ggthemes::theme_tufte(),
## arguments relevant for combine_plots
annotation.args = list(
title = "Relationship between Wbc and Age",
caption = "Source: stackoverflow"
),
plotgrid.args = list(nrow = 2, ncol = 2)
)
First answer:
We could do something like this:
write a function and pass the data frame + the column FAB value:
library(ggstatsplot)
my_function <- function(df, x){
ggscatterstats(
df %>% filter(FAB == x),
x = AGE,
y = Wbc,
type = "np" # try the "robust" correlation too! It might be even better here
#, marginal.type = "boxplot"
)
}
M0 <- my_function(df2, "M0")
M1 <- my_function(df2, "M1")
M2 <- my_function(df2, "M2")
M3 <- my_function(df2, "M3")
.
.
.
library(patchwork)
(M0 / M1 | M2 / M3)

Kendall Correlation P-Value

I want to test correlation for 9 different columns using kendall and extract p-value
for correlation between v9 and 7 other columns (v2 until v8).
Date v1 v2 v3 v4 v5 v6 v7 v8
1 2014-01-05 39 4 84 75 41 6 83 610
2 2014-01-12 40 6 86 77 44 6 84 765
3 2014-01-19 39 5 82 73 40 6 81 713
4 2014-01-26 37 5 100 71 39 6 90 685
5 2014-02-02 39 5 83 70 37 5 79 601
6 2014-02-09 44 6 82 78 40 6 78 535
AllData <- structure(list(Date = structure(c(16075, 16082, 16089, 16096,
16103, 16110, 16117, 16124, 16131, 16138, 16145, 16152, 16159,
16166, 16173, 16180, 16187, 16194, 16201, 16208, 16215, 16222,
16229, 16236, 16243, 16250, 16257, 16264, 16271, 16278, 16285,
16292, 16299, 16306, 16313, 16320, 16327, 16334, 16341, 16348,
16355, 16362, 16369, 16376, 16383, 16390, 16397, 16404, 16411,
16418, 16425, 16432, 16439, 16446, 16453, 16460, 16467, 16474,
16481, 16488, 16495, 16502, 16509, 16516, 16523, 16530, 16537,
16544, 16551, 16558, 16565, 16572, 16579, 16586, 16593, 16600,
16607, 16614, 16621, 16628, 16635, 16642, 16649, 16656, 16663,
16670, 16677, 16684, 16691, 16698, 16705, 16712, 16719, 16726,
16733, 16740, 16747, 16754, 16761, 16768, 16775, 16782, 16789,
16796, 16803, 16810, 16817, 16824, 16831, 16838, 16845, 16852,
16859, 16866, 16873, 16880, 16887, 16894, 16901, 16908, 16915,
16922, 16929, 16936, 16943, 16950, 16957, 16964, 16971, 16978,
16985, 16992, 16999, 17006, 17013, 17020, 17027, 17034, 17041,
17048, 17055, 17062, 17069, 17076, 17083, 17090, 17097, 17104,
17111, 17118, 17125, 17132, 17139, 17146, 17153, 17160, 17167,
17174, 17181, 17188, 17195, 17202, 17209, 17216, 17223, 17230,
17237, 17244, 17251, 17258, 17265, 17272, 17279, 17286, 17293,
17300, 17307, 17314, 17321, 17328, 17335, 17342, 17349, 17356,
17363, 17370, 17377, 17384, 17391, 17398, 17405, 17412, 17419,
17426, 17433, 17440, 17447, 17454, 17461, 17468, 17475, 17482,
17489, 17496, 17503, 17510, 17517, 17524, 17531, 17538, 17545,
17552, 17559, 17566, 17573, 17580, 17587, 17594, 17601, 17608,
17615, 17622, 17629, 17636, 17643, 17650, 17657, 17664, 17671,
17678, 17685, 17692, 17699, 17706, 17713, 17720, 17727, 17734,
17741, 17748, 17755, 17762, 17769, 17776, 17783, 17790, 17797,
17804, 17811, 17818, 17825, 17832, 17839, 17846, 17853, 17860,
17867, 17874, 17881, 17888, 17895, 17902, 17909, 17916, 17923,
17930, 17937, 17944, 17951, 17958, 17965, 17972, 17979, 17986,
17993, 18000, 18007, 18014, 18021, 18028, 18035, 18042, 18049,
18056, 18063, 18070, 18077, 18084, 18091, 18098, 18105, 18112,
18119, 18126, 18133, 18140, 18147, 18154, 18161, 18168, 18175,
18182, 18189, 18196, 18203, 18210, 18217, 18224, 18231, 18238,
18245, 18252, 18259, 18266, 18273, 18280, 18287, 18294, 18301,
18308, 18315), class = "Date"), v1 = c(39L, 40L, 39L, 37L,
39L, 44L, 41L, 40L, 35L, 39L, 35L, 32L, 36L, 34L, 32L, 34L, 32L,
34L, 32L, 30L, 36L, 34L, 35L, 32L, 35L, 32L, 33L, 35L, 35L, 35L,
35L, 36L, 41L, 36L, 34L, 32L, 33L, 30L, 33L, 36L, 34L, 39L, 36L,
34L, 35L, 40L, 46L, 40L, 41L, 44L, 48L, 45L, 32L, 28L, 31L, 29L,
32L, 31L, 33L, 33L, 33L, 31L, 28L, 30L, 29L, 25L, 25L, 25L, 26L,
26L, 24L, 24L, 26L, 25L, 28L, 32L, 32L, 32L, 32L, 35L, 36L, 32L,
31L, 32L, 32L, 35L, 36L, 33L, 30L, 32L, 37L, 42L, 36L, 36L, 33L,
33L, 31L, 46L, 49L, 63L, 77L, 56L, 58L, 57L, 71L, 44L, 36L, 39L,
35L, 35L, 35L, 32L, 33L, 36L, 33L, 33L, 34L, 29L, 30L, 30L, 28L,
27L, 31L, 29L, 28L, 29L, 29L, 100L, 64L, 42L, 48L, 43L, 39L,
36L, 33L, 30L, 32L, 31L, 34L, 34L, 31L, 35L, 35L, 40L, 40L, 40L,
39L, 38L, 50L, 46L, 48L, 47L, 40L, 43L, 43L, 44L, 60L, 54L, 50L,
51L, 61L, 55L, 55L, 62L, 51L, 54L, 51L, 45L, 45L, 46L, 45L, 48L,
47L, 44L, 42L, 42L, 42L, 43L, 44L, 54L, 53L, 48L, 51L, 47L, 45L,
45L, 47L, 49L, 51L, 44L, 43L, 46L, 42L, 46L, 44L, 100L, 62L,
54L, 53L, 45L, 93L, 61L, 76L, 60L, 52L, 53L, 62L, 56L, 54L, 21L,
19L, 21L, 21L, 20L, 82L, 100L, 62L, 38L, 34L, 31L, 35L, 27L,
23L, 21L, 21L, 20L, 21L, 21L, 22L, 22L, 20L, 19L, 20L, 19L, 21L,
20L, 20L, 19L, 21L, 21L, 20L, 18L, 22L, 19L, 18L, 18L, 17L, 20L,
19L, 20L, 21L, 24L, 26L, 25L, 32L, 24L, 25L, 25L, 28L, 27L, 25L,
53L, 53L, 49L, 50L, 49L, 52L, 53L, 58L, 53L, 56L, 52L, 50L, 49L,
52L, 62L, 46L, 45L, 52L, 41L, 45L, 50L, 48L, 48L, 49L, 50L, 50L,
47L, 49L, 44L, 54L, 100L, 67L, 58L, 45L, 60L, 51L, 56L, 50L,
50L, 48L, 48L, 49L, 48L, 54L, 57L, 67L, 74L, 58L, 60L, 64L, 77L,
70L, 82L, 72L, 77L, 74L, 67L, 79L, 74L, 88L), v2 = c(4L,
6L, 5L, 5L, 5L, 6L, 5L, 5L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 5L, 5L,
5L, 5L, 4L, 5L, 5L, 4L, 6L, 5L, 4L, 5L, 5L, 6L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 4L, 5L, 6L, 5L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 5L, 6L,
4L, 7L, 6L, 5L, 5L, 7L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 5L, 4L,
4L, 6L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 3L, 5L, 4L, 4L, 4L, 5L, 4L,
4L, 4L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 4L, 6L,
5L, 7L, 7L, 5L, 7L, 9L, 7L, 6L, 6L, 5L, 5L, 5L, 4L, 6L, 5L, 6L,
4L, 5L, 5L, 5L, 4L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 5L, 3L, 5L, 4L,
5L, 4L, 5L, 4L, 4L, 5L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 4L, 4L, 7L, 6L, 5L, 4L, 5L, 7L, 8L, 8L, 8L, 8L, 7L,
7L, 8L, 8L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 7L, 6L, 5L, 7L, 8L, 6L,
6L, 6L, 6L, 6L, 6L, 8L, 7L, 7L, 8L, 7L, 8L, 8L, 6L, 7L, 6L, 6L,
8L, 7L, 7L, 7L, 6L, 7L, 8L, 8L, 8L, 10L, 8L, 5L, 7L, 7L, 9L,
8L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 2L, 3L, 3L, 2L, 3L, 4L, 4L, 4L, 4L, 3L,
3L, 4L, 4L, 5L, 4L, 8L, 7L, 8L, 7L, 5L, 7L, 8L, 8L, 7L, 7L, 7L,
9L, 6L, 9L, 8L, 6L, 6L, 8L, 7L, 7L, 8L, 7L, 7L, 7L, 8L, 7L, 7L,
6L, 7L, 7L, 8L, 6L, 7L, 6L, 8L, 6L, 5L, 9L, 6L, 8L, 7L, 6L, 6L,
7L, 6L, 7L, 8L, 8L, 8L, 7L, 10L, 10L, 10L, 11L, 11L, 10L, 9L,
10L, 10L, 9L), v3 = c(84L, 86L, 82L, 100L, 83L, 82L,
76L, 74L, 81L, 72L, 67L, 66L, 67L, 64L, 67L, 61L, 67L, 63L, 59L,
60L, 57L, 54L, 60L, 59L, 53L, 61L, 61L, 57L, 59L, 63L, 60L, 56L,
60L, 64L, 57L, 55L, 58L, 61L, 56L, 63L, 65L, 63L, 59L, 64L, 60L,
62L, 70L, 65L, 65L, 61L, 71L, 69L, 54L, 59L, 54L, 55L, 55L, 56L,
74L, 100L, 86L, 69L, 54L, 55L, 48L, 47L, 48L, 48L, 46L, 44L,
42L, 45L, 43L, 48L, 46L, 43L, 45L, 44L, 52L, 47L, 50L, 49L, 47L,
47L, 50L, 49L, 51L, 47L, 45L, 45L, 49L, 53L, 55L, 56L, 52L, 52L,
51L, 64L, 67L, 73L, 78L, 65L, 76L, 74L, 62L, 57L, 52L, 75L, 54L,
47L, 52L, 52L, 49L, 42L, 45L, 43L, 45L, 42L, 44L, 41L, 40L, 38L,
39L, 41L, 42L, 43L, 39L, 60L, 50L, 49L, 52L, 51L, 46L, 47L, 42L,
44L, 45L, 44L, 47L, 44L, 49L, 43L, 50L, 47L, 48L, 52L, 53L, 51L,
64L, 57L, 60L, 52L, 45L, 48L, 49L, 56L, 81L, 71L, 61L, 68L, 69L,
67L, 69L, 61L, 68L, 69L, 63L, 63L, 61L, 59L, 78L, 60L, 56L, 57L,
57L, 54L, 52L, 48L, 53L, 49L, 50L, 53L, 58L, 55L, 61L, 52L, 57L,
55L, 57L, 51L, 51L, 52L, 54L, 58L, 58L, 80L, 67L, 62L, 60L, 60L,
65L, 64L, 78L, 70L, 63L, 68L, 67L, 75L, 66L, 27L, 26L, 26L, 27L,
24L, 30L, 35L, 33L, 31L, 28L, 28L, 30L, 28L, 26L, 23L, 22L, 21L,
22L, 21L, 20L, 22L, 20L, 20L, 21L, 20L, 24L, 21L, 21L, 22L, 23L,
22L, 24L, 25L, 20L, 22L, 23L, 22L, 20L, 21L, 22L, 22L, 23L, 25L,
25L, 25L, 33L, 28L, 25L, 28L, 27L, 29L, 30L, 58L, 57L, 60L, 58L,
56L, 60L, 59L, 57L, 56L, 60L, 55L, 55L, 54L, 50L, 53L, 55L, 48L,
50L, 53L, 47L, 46L, 51L, 52L, 55L, 61L, 60L, 51L, 51L, 57L, 53L,
71L, 67L, 58L, 56L, 93L, 71L, 66L, 68L, 60L, 62L, 61L, 56L, 57L,
61L, 64L, 64L, 75L, 65L, 64L, 69L, 78L, 84L, 100L, 91L, 94L,
86L, 83L, 89L, 89L, 87L), v4 = c(75L, 77L, 73L, 71L, 70L,
78L, 76L, 72L, 71L, 72L, 75L, 75L, 70L, 74L, 72L, 74L, 74L, 73L,
69L, 74L, 72L, 71L, 74L, 72L, 72L, 82L, 74L, 83L, 78L, 73L, 73L,
80L, 88L, 88L, 74L, 68L, 70L, 76L, 72L, 76L, 75L, 76L, 71L, 77L,
96L, 85L, 100L, 90L, 81L, 80L, 87L, 86L, 81L, 77L, 81L, 74L,
73L, 74L, 76L, 71L, 84L, 79L, 74L, 74L, 72L, 80L, 72L, 73L, 70L,
69L, 69L, 77L, 72L, 77L, 72L, 77L, 77L, 85L, 77L, 74L, 77L, 77L,
76L, 77L, 75L, 77L, 79L, 73L, 71L, 73L, 78L, 78L, 76L, 74L, 74L,
75L, 81L, 86L, 95L, 91L, 85L, 83L, 90L, 92L, 72L, 67L, 72L, 77L,
68L, 64L, 68L, 73L, 75L, 71L, 71L, 70L, 69L, 72L, 68L, 67L, 65L,
65L, 63L, 64L, 64L, 67L, 64L, 80L, 73L, 70L, 100L, 73L, 78L,
62L, 63L, 66L, 60L, 61L, 61L, 62L, 61L, 73L, 71L, 70L, 69L, 67L,
67L, 68L, 64L, 73L, 75L, 70L, 67L, 64L, 68L, 76L, 71L, 73L, 75L,
71L, 74L, 68L, 68L, 72L, 71L, 70L, 69L, 69L, 69L, 71L, 73L, 73L,
68L, 71L, 68L, 64L, 65L, 73L, 66L, 67L, 69L, 72L, 80L, 66L, 69L,
68L, 66L, 72L, 67L, 75L, 75L, 69L, 70L, 68L, 69L, 83L, 70L, 70L,
71L, 73L, 76L, 77L, 82L, 74L, 71L, 70L, 71L, 77L, 71L, 66L, 65L,
74L, 68L, 66L, 79L, 82L, 79L, 71L, 73L, 75L, 79L, 80L, 76L, 71L,
70L, 74L, 70L, 72L, 75L, 71L, 71L, 70L, 74L, 72L, 83L, 68L, 71L,
82L, 79L, 72L, 70L, 67L, 66L, 66L, 65L, 68L, 68L, 65L, 63L, 65L,
68L, 73L, 69L, 74L, 77L, 68L, 67L, 65L, 67L, 72L, 74L, 75L, 74L,
76L, 73L, 72L, 73L, 77L, 75L, 71L, 73L, 73L, 71L, 72L, 74L, 70L,
66L, 72L, 72L, 70L, 67L, 69L, 69L, 75L, 73L, 75L, 83L, 71L, 69L,
66L, 66L, 79L, 74L, 67L, 64L, 68L, 70L, 67L, 68L, 73L, 70L, 73L,
72L, 69L, 77L, 77L, 76L, 82L, 77L, 73L, 71L, 79L, 84L, 84L, 74L,
76L, 72L, 73L, 76L, 75L, 73L), v5 = c(41L, 44L, 40L, 39L,
37L, 40L, 40L, 42L, 39L, 37L, 39L, 37L, 36L, 34L, 34L, 35L, 35L,
32L, 33L, 33L, 32L, 32L, 31L, 30L, 32L, 32L, 30L, 31L, 32L, 34L,
33L, 34L, 35L, 44L, 36L, 39L, 35L, 35L, 35L, 32L, 34L, 36L, 36L,
35L, 36L, 36L, 44L, 39L, 38L, 42L, 44L, 44L, 39L, 39L, 39L, 39L,
37L, 37L, 39L, 38L, 39L, 36L, 35L, 34L, 33L, 32L, 28L, 31L, 29L,
27L, 29L, 30L, 31L, 29L, 29L, 32L, 33L, 34L, 30L, 32L, 35L, 32L,
32L, 34L, 32L, 33L, 33L, 32L, 31L, 30L, 33L, 37L, 32L, 33L, 32L,
32L, 34L, 41L, 45L, 48L, 56L, 47L, 52L, 51L, 44L, 35L, 34L, 34L,
33L, 30L, 32L, 31L, 31L, 30L, 28L, 29L, 29L, 27L, 26L, 26L, 24L,
24L, 24L, 25L, 23L, 25L, 25L, 41L, 35L, 28L, 32L, 31L, 32L, 29L,
29L, 27L, 27L, 27L, 26L, 24L, 24L, 26L, 27L, 27L, 29L, 30L, 30L,
29L, 32L, 31L, 37L, 33L, 31L, 30L, 32L, 32L, 32L, 30L, 30L, 29L,
31L, 31L, 31L, 32L, 30L, 30L, 29L, 28L, 28L, 27L, 27L, 26L, 27L,
25L, 27L, 24L, 23L, 23L, 25L, 25L, 27L, 27L, 28L, 25L, 24L, 25L,
26L, 25L, 26L, 24L, 24L, 24L, 23L, 25L, 25L, 37L, 29L, 28L, 29L,
27L, 33L, 33L, 38L, 33L, 31L, 31L, 32L, 35L, 31L, 28L, 28L, 30L,
29L, 29L, 34L, 43L, 42L, 37L, 34L, 32L, 36L, 31L, 29L, 28L, 27L,
28L, 26L, 24L, 25L, 25L, 24L, 24L, 24L, 25L, 25L, 23L, 25L, 26L,
26L, 24L, 24L, 24L, 24L, 24L, 23L, 23L, 23L, 24L, 22L, 25L, 25L,
26L, 28L, 28L, 34L, 30L, 28L, 29L, 31L, 31L, 31L, 33L, 32L, 32L,
34L, 32L, 33L, 34L, 34L, 33L, 35L, 34L, 32L, 31L, 29L, 30L, 28L,
28L, 28L, 28L, 27L, 28L, 28L, 29L, 28L, 29L, 28L, 27L, 27L, 27L,
27L, 37L, 32L, 31L, 30L, 30L, 30L, 34L, 30L, 30L, 30L, 30L, 30L,
29L, 31L, 32L, 33L, 39L, 33L, 32L, 34L, 37L, 40L, 37L, 36L, 38L,
38L, 36L, 38L, 38L, 39L), v6 = c(6L, 6L, 6L, 6L, 5L,
6L, 7L, 6L, 6L, 5L, 6L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 7L, 6L, 6L, 8L, 7L, 7L, 7L, 7L, 7L, 5L,
5L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 5L, 5L,
5L, 6L, 6L, 6L, 5L, 6L, 7L, 6L, 7L, 6L, 6L, 6L, 7L, 9L, 7L, 7L,
8L, 8L, 8L, 6L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 5L,
6L, 9L, 7L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 7L, 6L, 7L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
6L, 7L, 7L, 6L, 7L, 10L, 7L, 7L, 7L, 7L, 8L, 7L, 6L, 6L, 5L,
5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 4L, 5L, 5L,
5L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 9L, 7L, 7L, 7L, 6L, 7L,
6L, 7L, 6L, 7L, 8L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 5L,
6L, 6L, 6L, 6L, 6L, 7L, 6L, 7L, 6L, 8L, 8L, 7L, 7L, 7L, 7L, 10L,
8L, 8L, 7L, 8L, 8L, 7L, 7L, 7L, 7L, 6L, 6L, 7L, 6L), v7 = c(83L,
84L, 81L, 90L, 79L, 78L, 78L, 81L, 78L, 75L, 76L, 77L, 75L, 77L,
79L, 82L, 85L, 81L, 80L, 81L, 81L, 82L, 85L, 80L, 77L, 82L, 83L,
76L, 73L, 74L, 78L, 73L, 77L, 74L, 72L, 70L, 72L, 73L, 70L, 70L,
72L, 75L, 74L, 73L, 73L, 77L, 82L, 81L, 79L, 82L, 86L, 86L, 85L,
79L, 79L, 77L, 76L, 75L, 75L, 78L, 78L, 77L, 74L, 72L, 68L, 69L,
72L, 69L, 72L, 71L, 71L, 72L, 69L, 69L, 72L, 71L, 70L, 72L, 75L,
73L, 74L, 72L, 74L, 75L, 71L, 71L, 73L, 72L, 71L, 70L, 72L, 73L,
72L, 75L, 76L, 76L, 75L, 80L, 83L, 100L, 95L, 84L, 84L, 89L,
76L, 69L, 68L, 67L, 66L, 64L, 67L, 69L, 64L, 63L, 63L, 67L, 66L,
65L, 69L, 64L, 62L, 62L, 63L, 63L, 60L, 63L, 66L, 69L, 64L, 67L,
68L, 63L, 64L, 63L, 61L, 61L, 57L, 64L, 61L, 68L, 65L, 74L, 67L,
66L, 67L, 73L, 69L, 68L, 64L, 68L, 72L, 73L, 69L, 72L, 75L, 80L,
94L, 83L, 81L, 79L, 76L, 72L, 73L, 74L, 74L, 72L, 69L, 70L, 78L,
78L, 81L, 76L, 75L, 76L, 75L, 73L, 74L, 73L, 73L, 72L, 75L, 72L,
76L, 70L, 71L, 70L, 71L, 70L, 69L, 66L, 66L, 63L, 70L, 68L, 68L,
79L, 72L, 75L, 78L, 75L, 75L, 77L, 79L, 82L, 85L, 82L, 83L, 87L,
100L, 89L, 86L, 81L, 84L, 78L, 83L, 92L, 100L, 90L, 87L, 81L,
82L, 79L, 79L, 79L, 81L, 79L, 79L, 76L, 78L, 74L, 73L, 68L, 73L,
71L, 73L, 71L, 72L, 69L, 73L, 70L, 71L, 69L, 73L, 70L, 70L, 73L,
73L, 73L, 69L, 73L, 74L, 76L, 75L, 76L, 77L, 79L, 81L, 78L, 82L,
81L, 94L, 100L, 93L, 91L, 88L, 86L, 90L, 83L, 82L, 82L, 81L,
81L, 82L, 84L, 82L, 81L, 80L, 82L, 81L, 81L, 80L, 78L, 77L, 75L,
72L, 75L, 72L, 73L, 75L, 73L, 75L, 83L, 78L, 77L, 77L, 77L, 75L,
78L, 80L, 77L, 73L, 79L, 79L, 76L, 88L, 91L, 90L, 80L, 82L, 82L,
82L, 85L, 99L, 100L, 97L, 91L, 89L, 82L, 85L, 82L, 83L), v8 = c(610L,
765L, 713L, 685L, 601L, 535L, 582L, 568L, 502L, 608L, 653L, 672L,
694L, 697L, 715L, 751L, 675L, 706L, 777L, 787L, 876L, 823L, 754L,
782L, 834L, 907L, 890L, 913L, 921L, 977L, 890L, 947L, 996L, 830L,
974L, 921L, 912L, 907L, 871L, 805L, 876L, 909L, 861L, 865L, 901L,
742L, 726L, 720L, 803L, 796L, 857L, 902L, 751L, 806L, 859L, 798L,
714L, 728L, 688L, 728L, 785L, 1166L, 1105L, 935L, 1037L, 1016L,
1037L, 932L, 1013L, 996L, 1016L, 1064L, 1104L, 1003L, 1051L,
913L, 944L, 1044L, 1018L, 1073L, 1109L, 1055L, 1076L, 1008L,
1016L, 996L, 1050L, 1030L, 969L, 1011L, 932L, 890L, 978L, 1008L,
928L, 1006L, 927L, 913L, 905L, 952L, 957L, 978L, 978L, 1044L,
1341L, 966L, 881L, 1052L, 981L, 864L, 927L, 887L, 943L, 1055L,
1010L, 1012L, 1059L, 913L, 1028L, 1060L, 1046L, 1061L, 1043L,
1027L, 1094L, 1065L, 1070L, 1000L, 1079L, 1114L, 1156L, 1069L,
1157L, 1234L, 1217L, 1216L, 1190L, 1208L, 1253L, 1182L, 1133L,
1046L, 1122L, 1013L, 1185L, 1208L, 1177L, 1227L, 1080L, 1197L,
1123L, 1260L, 1101L, 1139L, 1054L, 1222L, 1262L, 1158L, 1241L,
1190L, 1087L, 1155L, 1122L, 1159L, 1044L, 999L, 993L, 1193L,
1229L, 1217L, 1301L, 1239L, 1179L, 1092L, 1226L, 1211L, 1236L,
1327L, 1133L, 1149L, 1198L, 1158L, 1312L, 1183L, 1165L, 1163L,
1226L, 1136L, 1130L, 1129L, 1092L, 1039L, 1019L, 1196L, 1155L,
1169L, 1130L, 1185L, 1166L, 1174L, 1048L, 1083L, 1048L, 1161L,
997L, 1041L, 1123L, 895L, 1034L, 1095L, 1080L, 1223L, 1074L,
954L, 948L, 1011L, 982L, 1013L, 1078L, 1080L, 1055L, 1131L, 1145L,
999L, 1213L, 1192L, 1144L, 1082L, 1137L, 1150L, 1104L, 1059L,
1039L, 1099L, 1202L, 1092L, 1072L, 1126L, 1086L, 1098L, 1131L,
1071L, 1122L, 1061L, 988L, 1043L, 760L, 1073L, 950L, 1001L, 960L,
1034L, 919L, 922L, 944L, 996L, 970L, 996L, 996L, 1058L, 1235L,
964L, 1043L, 979L, 865L, 1012L, 906L, 987L, 925L, 847L, 1012L,
1011L, 1065L, 987L, 1078L, 1025L, 1010L, 1045L, 981L, 987L, 1125L,
1184L, 1070L, 995L, 1139L, 1205L, 1286L, 1180L, 1210L, 1147L,
1221L, 1112L, 1151L, 1117L, 1097L, 1066L, 1059L, 1050L, 1040L,
976L, 992L, 979L, 949L, 954L, 932L, 873L, 1015L, 982L, 982L,
1010L, 897L, 1056L, 1217L, 977L, 986L, 1004L, 906L, 890L, 877L,
894L, 672L)), row.names = c(NA, -321L), class = "data.frame")
x <- AllData[2:9]
y <- AllData[2:9]
correlationcoef <- data.frame(cor(x,y,method="kendall"))
I used the above code to run the data but it only gives me the correlation coefficient,
not the p-value that I needed. I also need to store this value into one data frame so
that I will be able to evaluate all correlations in one go.

One could use a loop, but another approach to getting the p-values of the kendall correlation test is to use the rstatix package to create a correlation matrix and a corresponding p-value matrix:
library(rstatix)
# sample data
AllData <- data.frame(
Date = c("2014-01-05", "2014-01-12","2014-01-19", "2014-01-26","2014-02-02", "2014-02-09"),
v1 = c(39,40,39,37,39,44),
v2 = c(4,6,5,5,5,6),
v3 = c(84,86,82,100,83,82),
v4 = c(75,77,73,71,70,78),
v5 = c(41,44,40,39,37,40),
v6 = c(6,6,6,6,5,6),
v7 = c(83,84,81,90,79,78),
v8 = c(610,765,713,685,601,535)
)
# get the correlation matrix
corMatrix <- AllData %>% cor_mat(v1:v8, method = "kendall")
corMatrix
# get the p.values
corMatrix_p <- corMatrix %>% cor_get_pval()
corMatrix_p
And you can specify the variables you want to include in the matrix with the varsargument:
cor_mat(data, ..., vars = NULL, method = "pearson", alternative =
"two.sided", conf.level = 0.95)
Just set vars equal to a character vector of the variable names. In other words, you could also do this:
corMatrix <- AllData %>% cor_mat(c("v1","v2","v3","v4","v5","v6","v7","v8"), method = "kendall")
corMatrix
# get the p.values
corMatrix_p <- corMatrix %>% cor_get_pval()
corMatrix_p

Subtract Values based on Multiple Grouping Factors

I have a dataset with phosphorus concentrations for 17 separate days (concentrations are cumulative, so increase from Day1 to Day102 in all cases). There are 22 different treatments (column = Trmt). Each Trmt has 3 Levels (Level = X, Y, Z). 2 measurements per Level for a total of 6 per Trmt.
My goal is to plot a 3-line graph of Days (x-axis; numeric) by Concentration (y-axis) using ggplot2. Data should be grouped by Trmt, Level and day for a total of 51 measurements (3 lines x 17 days).
My data looks as follows:
structure(list(Trmt = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 7L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L, 12L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 11L, 11L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 13L, 13L, 13L, 13L, 13L, 16L, 16L, 16L, 16L, 16L, 16L, 15L, 15L, 15L, 15L, 15L, 15L, 18L, 18L, 18L, 18L, 18L, 18L, 17L, 17L, 17L, 17L, 17L, 17L, 20L, 20L, 20L, 20L, 20L, 20L, 19L, 19L, 19L, 19L, 19L, 19L, 22L, 22L, 22L, 22L, 22L, 22L, 21L, 21L, 21L, 21L, 21L, 21L), .Label = c("A01nF", "A01yT", "A02nF", "A02yT", "A03nF", "A03yT", "A04nF", "A04yT", "A05nF", "A05yT", "A06nF", "A06yT", "A07nF", "A07yT", "A08nF", "A08yT", "A10nF", "A10yT", "A11nF", "A11yT", "A13nF", "A13yT"), class = "factor"), Level = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("X", "Y", "Z"), class = "factor"), Day1 = c(3L, 1L, 4L, 2L, 4L, 2L, 5L, 4L, 1L, 2L, 5L, 1L, 5L, 2L, 5L, 5L, 3L, 5L, 3L, 3L, 1L, 4L, 1L, 1L, 5L, 4L, 1L, 5L, 4L, 5L, 3L, 5L, 3L, 5L, 3L, 4L, 2L, 4L, 2L, 4L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 5L, 2L, 4L, 4L, 3L, 1L, 4L, 4L, 1L, 4L, 1L, 2L, 5L, 1L, 5L, 1L, 2L, 4L, 4L, 4L, 4L, 2L, 4L, 5L, 5L, 4L, 1L, 3L, 2L, 3L, 5L, 4L, 3L, 2L, 3L, 5L, 4L, 1L, 3L, 4L, 3L, 3L, 5L, 3L, 1L, 1L, 4L, 4L, 5L, 1L, 4L, 4L, 4L, 1L, 4L, 5L, 5L, 1L, 5L, 3L, 1L, 4L, 1L, 4L, 5L, 5L, 3L, 3L, 2L, 4L, 5L, 3L, 2L, 1L, 5L, 5L, 2L, 2L, 3L, 4L, 3L, 4L, 2L, 2L, 4L), Day2 = c(10L, 9L, 7L, 7L, 6L, 7L, 10L, 9L, 10L, 6L, 10L, 7L, 8L, 9L, 8L, 9L, 7L, 10L, 7L, 10L, 6L, 8L, 6L, 8L, 8L, 8L, 10L, 6L, 8L, 8L, 6L, 10L, 7L, 10L, 7L, 10L, 6L, 6L, 7L, 9L, 8L, 10L, 8L, 7L, 9L, 8L, 6L, 9L, 7L, 9L, 8L, 6L, 6L, 8L, 10L, 7L, 8L, 6L, 8L, 8L, 6L, 9L, 10L, 6L, 8L, 7L, 9L, 7L, 8L, 10L, 10L, 6L, 7L, 10L, 9L, 9L, 8L, 9L, 6L, 8L, 6L, 8L, 6L, 9L, 10L, 7L, 7L, 7L, 8L, 7L, 8L, 10L, 7L, 8L, 9L, 6L, 8L, 9L, 8L, 9L, 6L, 7L, 10L, 9L, 10L, 7L, 6L, 9L, 9L, 9L, 6L, 10L, 9L, 8L, 9L, 7L, 10L, 7L, 10L, 9L, 6L, 8L, 9L, 8L, 9L, 6L, 6L, 10L, 9L, 8L, 8L, 7L), Day4 = c(11L, 12L, 14L, 11L, 15L, 15L, 12L, 11L, 15L, 12L, 15L, 12L, 12L, 11L, 15L, 15L, 13L, 11L, 13L, 14L, 12L, 11L, 13L, 12L, 15L, 15L, 14L, 11L, 15L, 11L, 12L, 11L, 13L, 11L, 12L, 13L, 13L, 14L, 13L, 15L, 14L, 15L, 12L, 14L, 11L, 13L, 15L, 11L, 12L, 13L, 11L, 15L, 11L, 13L, 11L, 11L, 14L, 12L, 14L, 15L, 11L, 12L, 15L, 12L, 13L, 12L, 14L, 12L, 11L, 13L, 12L, 12L, 11L, 15L, 13L, 12L, 11L, 12L, 13L, 14L, 14L, 14L, 13L, 12L, 15L, 12L, 15L, 15L, 12L, 13L, 12L, 12L, 12L, 14L, 13L, 13L, 14L, 11L, 12L, 11L, 15L, 11L, 11L, 11L, 14L, 11L, 12L, 15L, 15L, 11L, 12L, 14L, 15L, 14L, 14L, 12L, 14L, 13L, 15L, 15L, 14L, 13L, 12L, 15L, 15L, 11L, 13L, 12L, 11L, 13L, 12L, 14L), Day7 = c(19L, 17L, 17L, 20L, 17L, 19L, 18L, 19L, 17L, 20L, 16L, 20L, 19L, 18L, 20L, 19L, 17L, 16L, 18L, 18L, 17L, 18L, 19L, 18L, 17L, 19L, 17L, 20L, 19L, 20L, 19L, 20L, 17L, 18L, 20L, 19L, 20L, 18L, 18L, 20L, 18L, 20L, 17L, 19L, 17L, 19L, 17L, 17L, 20L, 18L, 18L, 17L, 16L, 18L, 20L, 16L, 17L, 19L, 16L, 19L, 16L, 17L, 16L, 20L, 16L, 19L, 19L, 17L, 17L, 17L, 20L, 19L, 18L, 16L, 20L, 17L, 19L, 16L, 18L, 19L, 16L, 19L, 20L, 20L, 16L, 16L, 18L, 17L, 16L, 18L, 16L, 17L, 16L, 18L, 20L, 16L, 16L, 20L, 20L, 16L, 20L, 18L, 17L, 19L, 18L, 18L, 19L, 19L, 16L, 18L, 19L, 19L, 17L, 17L, 18L, 18L, 20L, 18L, 20L, 20L, 18L, 19L, 19L, 16L, 16L, 17L, 20L, 16L, 17L, 18L, 16L, 20L), Day10 = c(24L, 23L, 23L, 21L, 21L, 23L, 21L, 21L, 22L, 25L, 21L, 23L, 21L, 25L, 25L, 25L, 24L, 22L, 25L, 24L, 21L, 23L, 24L, 23L, 23L, 22L, 23L, 22L, 22L, 25L, 25L, 22L, 21L, 24L, 25L, 23L, 23L, 23L, 24L, 23L, 25L, 23L, 21L, 23L, 22L, 24L, 22L, 23L, 24L, 22L, 25L, 23L, 23L, 21L, 25L, 24L, 24L, 25L, 25L, 25L, 22L, 23L, 21L, 22L, 24L, 22L, 23L, 22L, 24L, 22L, 21L, 22L, 23L, 21L, 25L, 25L, 22L, 21L, 25L, 24L, 22L, 21L, 25L, 24L, 21L, 24L, 25L, 22L, 23L, 22L, 24L, 23L, 25L, 25L, 23L, 25L, 22L, 23L, 23L, 23L, 22L, 25L, 22L, 23L, 24L, 25L, 22L, 21L, 21L, 22L, 23L, 24L, 21L, 24L, 23L, 23L, 25L, 24L, 25L, 23L, 22L, 25L, 25L, 25L, 21L, 22L, 23L, 21L, 24L, 24L, 25L, 21L), Day13 = c(29L, 29L, 26L, 27L, 30L, 30L, 30L, 26L, 30L, 29L, 30L, 27L, 26L, 29L, 28L, 26L, 30L, 28L, 29L, 27L, 28L, 26L, 29L, 28L, 30L, 26L, 27L, 30L, 26L, 29L, 26L, 28L, 29L, 28L, 29L, 28L, 27L, 27L, 28L, 26L, 26L, 27L, 27L, 29L, 27L, 29L, 27L, 30L, 26L, 27L, 30L, 26L, 29L, 29L, 27L, 29L, 26L, 29L, 28L, 28L, 29L, 30L, 28L, 30L, 30L, 30L, 28L, 29L, 28L, 27L, 28L, 27L, 27L, 28L, 27L, 30L, 27L, 30L, 27L, 28L, 29L, 27L, 30L, 29L, 30L, 30L, 26L, 30L, 29L, 30L, 27L, 26L, 27L, 27L, 28L, 26L, 30L, 28L, 30L, 30L, 30L, 30L, 26L, 28L, 27L, 26L, 29L, 26L, 29L, 26L, 30L, 29L, 30L, 26L, 27L, 30L, 29L, 30L, 27L, 30L, 28L, 26L, 30L, 27L, 30L, 26L, 28L, 29L, 26L, 28L, 28L, 26L), Day18 = c(32L, 31L, 32L, 31L, 31L, 34L, 32L, 34L, 32L, 33L, 31L, 34L, 35L, 34L, 34L, 32L, 33L, 35L, 32L, 35L, 31L, 31L, 33L, 33L, 32L, 31L, 32L, 31L, 32L, 34L, 33L, 33L, 34L, 31L, 35L, 35L, 31L, 34L, 32L, 32L, 34L, 33L, 34L, 33L, 33L, 35L, 35L, 31L, 35L, 31L, 33L, 34L, 31L, 33L, 34L, 32L, 32L, 33L, 31L, 32L, 35L, 34L, 31L, 32L, 34L, 35L, 34L, 31L, 34L, 33L, 35L, 35L, 31L, 32L, 35L, 34L, 31L, 32L, 32L, 33L, 32L, 35L, 32L, 32L, 35L, 33L, 34L, 32L, 34L, 35L, 34L, 33L, 33L, 31L, 31L, 31L, 35L, 34L, 33L, 32L, 33L, 33L, 33L, 35L, 34L, 33L, 31L, 34L, 34L, 34L, 34L, 33L, 33L, 31L, 31L, 31L, 33L, 33L, 35L, 32L, 32L, 31L, 31L, 32L, 33L, 32L, 34L, 34L, 31L, 35L, 31L, 35L), Day23 = c(39L, 40L, 38L, 37L, 37L, 38L, 37L, 36L, 37L, 36L, 36L, 38L, 40L, 38L, 37L, 36L, 36L, 40L, 40L, 40L, 40L, 39L, 40L, 36L, 38L, 36L, 36L, 37L, 38L, 37L, 36L, 37L, 39L, 39L, 38L, 38L, 37L, 40L, 36L, 38L, 37L, 40L, 36L, 37L, 39L, 38L, 38L, 38L, 40L, 38L, 37L, 36L, 38L, 36L, 36L, 36L, 39L, 40L, 39L, 37L, 39L, 39L, 37L, 36L, 37L, 39L, 39L, 37L, 36L, 37L, 40L, 36L, 39L, 40L, 39L, 40L, 39L, 38L, 39L, 40L, 37L, 40L, 38L, 38L, 38L, 40L, 40L, 36L, 39L, 39L, 39L, 39L, 38L, 37L, 37L, 36L, 37L, 39L, 37L, 40L, 40L, 40L, 38L, 38L, 39L, 38L, 36L, 37L, 36L, 36L, 40L, 39L, 39L, 39L, 36L, 39L, 38L, 40L, 36L, 37L, 38L, 38L, 36L, 37L, 39L, 36L, 40L, 40L, 39L, 38L, 37L, 38L), Day28 = c(42L, 43L, 43L, 44L, 44L, 44L, 42L, 42L, 43L, 42L, 45L, 43L, 43L, 43L, 42L, 44L, 42L, 44L, 45L, 44L, 44L, 45L, 44L, 41L, 41L, 42L, 44L, 44L, 44L, 45L, 43L, 42L, 43L, 42L, 41L, 44L, 43L, 43L, 42L, 42L, 44L, 42L, 42L, 42L, 45L, 44L, 45L, 42L, 43L, 45L, 45L, 44L, 41L, 42L, 42L, 41L, 44L, 44L, 44L, 44L, 42L, 45L, 41L, 42L, 45L, 43L, 44L, 45L, 44L, 42L, 41L, 43L, 41L, 44L, 43L, 41L, 45L, 42L, 45L, 41L, 45L, 41L, 45L, 42L, 45L, 42L, 45L, 45L, 41L, 41L, 43L, 41L, 41L, 42L, 43L, 41L, 42L, 44L, 43L, 45L, 41L, 41L, 44L, 41L, 44L, 43L, 43L, 45L, 44L, 41L, 44L, 43L, 42L, 45L, 45L, 41L, 45L, 42L, 41L, 44L, 41L, 41L, 41L, 43L, 41L, 41L, 45L, 41L, 42L, 45L, 41L, 44L), Day35 = c(50L, 50L, 50L, 50L, 48L, 46L, 50L, 46L, 48L, 50L, 50L, 50L, 46L, 49L, 46L, 47L, 49L, 49L, 48L, 49L, 46L, 47L, 49L, 46L, 49L, 50L, 49L, 46L, 49L, 50L, 46L, 48L, 50L, 46L, 50L, 48L, 46L, 48L, 50L, 50L, 47L, 47L, 47L, 47L, 47L, 49L, 48L, 46L, 46L, 48L, 50L, 46L, 49L, 48L, 46L, 49L, 50L, 49L, 48L, 48L, 48L, 50L, 49L, 47L, 48L, 50L, 50L, 46L, 47L, 46L, 48L, 48L, 48L, 47L, 49L, 48L, 49L, 46L, 47L, 50L, 47L, 50L, 47L, 47L, 46L, 46L, 47L, 50L, 49L, 49L, 48L, 47L, 46L, 50L, 46L, 50L, 50L, 46L, 47L, 47L, 49L, 50L, 50L, 46L, 47L, 50L, 47L, 48L, 46L, 50L, 49L, 46L, 46L, 50L, 50L, 49L, 46L, 49L, 46L, 46L, 46L, 48L, 47L, 47L, 50L, 47L, 46L, 48L, 50L, 48L, 46L, 46L), Day42 = c(52L, 51L, 53L, 53L, 54L, 55L, 55L, 54L, 52L, 51L, 55L, 51L, 54L, 53L, 53L, 55L, 54L, 55L, 51L, 51L, 55L, 54L, 54L, 53L, 55L, 53L, 52L, 53L, 53L, 51L, 54L, 54L, 55L, 53L, 54L, 55L, 51L, 51L, 54L, 52L, 51L, 51L, 55L, 54L, 54L, 52L, 52L, 55L, 55L, 51L, 55L, 52L, 55L, 51L, 53L, 52L, 53L, 54L, 51L, 54L, 54L, 55L, 52L, 54L, 52L, 52L, 51L, 52L, 55L, 52L, 54L, 51L, 52L, 55L, 51L, 52L, 55L, 54L, 52L, 53L, 53L, 52L, 55L, 51L, 51L, 55L, 52L, 55L, 55L, 55L, 53L, 52L, 53L, 54L, 52L, 52L, 52L, 52L, 53L, 51L, 54L, 54L, 51L, 53L, 55L, 51L, 54L, 54L, 54L, 53L, 53L, 54L, 54L, 55L, 52L, 52L, 54L, 51L, 52L, 51L, 51L, 55L, 52L, 51L, 51L, 53L, 54L, 51L, 51L, 54L, 55L, 52L), Day52 = c(59L, 57L, 56L, 58L, 59L, 59L, 57L, 59L, 57L, 56L, 58L, 58L, 60L, 59L, 56L, 56L, 60L, 57L, 60L, 57L, 59L, 56L, 60L, 59L, 59L, 56L, 60L, 58L, 60L, 57L, 57L, 60L, 56L, 57L, 59L, 60L, 56L, 58L, 57L, 57L, 58L, 58L, 59L, 56L, 58L, 56L, 57L, 60L, 58L, 59L, 58L, 56L, 56L, 57L, 60L, 59L, 60L, 58L, 59L, 60L, 57L, 60L, 59L, 57L, 60L, 56L, 57L, 56L, 58L, 60L, 56L, 58L, 56L, 60L, 57L, 57L, 57L, 60L, 58L, 59L, 58L, 60L, 59L, 58L, 56L, 56L, 58L, 57L, 60L, 56L, 58L, 56L, 57L, 58L, 58L, 60L, 59L, 60L, 59L, 59L, 59L, 57L, 57L, 60L, 59L, 57L, 57L, 58L, 59L, 57L, 59L, 58L, 60L, 59L, 56L, 57L, 57L, 56L, 57L, 60L, 58L, 57L, 56L, 59L, 59L, 59L, 57L, 57L, 58L, 56L, 58L, 60L), Day62 = c(67L, 65L, 68L, 65L, 69L, 70L, 69L, 66L, 65L, 70L, 70L, 65L, 67L, 68L, 65L, 67L, 65L, 66L, 66L, 68L, 68L, 66L, 65L, 67L, 66L, 69L, 69L, 69L, 68L, 67L, 66L, 69L, 65L, 65L, 69L, 66L, 69L, 68L, 69L, 67L, 65L, 69L, 69L, 69L, 70L, 67L, 65L, 65L, 65L, 66L, 66L, 69L, 68L, 66L, 67L, 66L, 70L, 70L, 70L, 69L, 70L, 70L, 67L, 66L, 65L, 69L, 67L, 66L, 70L, 70L, 70L, 65L, 66L, 67L, 66L, 66L, 67L, 68L, 70L, 67L, 69L, 66L, 67L, 65L, 70L, 65L, 70L, 66L, 66L, 69L, 68L, 65L, 65L, 67L, 68L, 67L, 69L, 68L, 69L, 66L, 68L, 70L, 69L, 68L, 70L, 66L, 69L, 66L, 66L, 67L, 65L, 69L, 69L, 67L, 70L, 65L, 70L, 69L, 66L, 68L, 67L, 68L, 66L, 65L, 67L, 70L, 66L, 67L, 66L, 67L, 67L, 70L), Day72 = c(74L, 74L, 71L, 75L, 74L, 71L, 75L, 71L, 75L, 71L, 72L, 72L, 75L, 73L, 75L, 74L, 74L, 74L, 71L, 74L, 72L, 71L, 71L, 74L, 74L, 73L, 72L, 73L, 71L, 71L, 75L, 72L, 73L, 74L, 75L, 73L, 71L, 71L, 74L, 71L, 73L, 75L, 75L, 74L, 71L, 75L, 74L, 72L, 72L, 71L, 72L, 75L, 73L, 74L, 71L, 75L, 75L, 73L, 72L, 73L, 73L, 72L, 75L, 72L, 71L, 72L, 73L, 72L, 72L, 74L, 72L, 72L, 73L, 75L, 74L, 75L, 73L, 74L, 75L, 72L, 75L, 73L, 71L, 71L, 72L, 74L, 72L, 75L, 71L, 71L, 71L, 73L, 72L, 71L, 75L, 75L, 74L, 73L, 71L, 71L, 72L, 71L, 71L, 74L, 72L, 73L, 71L, 75L, 74L, 75L, 74L, 73L, 73L, 73L, 72L, 75L, 73L, 71L, 71L, 72L, 72L, 71L, 71L, 71L, 72L, 73L, 75L, 75L, 72L, 73L, 75L, 75L), Day82 = c(76L, 78L, 78L, 78L, 79L, 77L, 78L, 77L, 80L, 79L, 80L, 76L, 76L, 80L, 80L, 80L, 78L, 78L, 78L, 78L, 80L, 78L, 76L, 79L, 76L, 77L, 76L, 79L, 78L, 76L, 76L, 79L, 79L, 77L, 77L, 77L, 78L, 78L, 80L, 77L, 77L, 76L, 77L, 79L, 78L, 78L, 78L, 80L, 79L, 76L, 79L, 77L, 76L, 80L, 78L, 77L, 79L, 80L, 77L, 80L, 78L, 79L, 78L, 76L, 76L, 79L, 77L, 77L, 78L, 78L, 79L, 78L, 78L, 78L, 80L, 79L, 78L, 77L, 78L, 78L, 78L, 79L, 80L, 77L, 77L, 80L, 77L, 80L, 77L, 76L, 77L, 76L, 77L, 77L, 80L, 79L, 77L, 78L, 80L, 80L, 79L, 80L, 79L, 79L, 78L, 76L, 76L, 79L, 79L, 80L, 79L, 78L, 76L, 79L, 77L, 77L, 76L, 76L, 78L, 78L, 79L, 78L, 76L, 78L, 79L, 76L, 77L, 78L, 76L, 79L, 78L, 77L), Day92 = c(85L, 84L, 85L, 85L, 83L, 82L, 83L, 82L, 85L, 85L, 82L, 85L, 85L, 85L, 81L, 81L, 84L, 81L, 85L, 82L, 85L, 84L, 81L, 82L, 83L, 82L, 84L, 84L, 81L, 85L, 83L, 85L, 82L, 81L, 83L, 83L, 85L, 83L, 81L, 83L, 82L, 84L, 83L, 83L, 82L, 85L, 85L, 82L, 82L, 82L, 85L, 81L, 81L, 82L, 82L, 84L, 81L, 85L, 81L, 82L, 81L, 81L, 85L, 83L, 81L, 83L, 83L, 84L, 83L, 85L, 85L, 83L, 81L, 85L, 81L, 84L, 83L, 83L, 85L, 83L, 82L, 82L, 82L, 83L, 82L, 83L, 81L, 84L, 83L, 84L, 82L, 83L, 81L, 83L, 81L, 82L, 82L, 82L, 85L, 85L, 84L, 81L, 81L, 81L, 84L, 81L, 84L, 81L, 81L, 84L, 84L, 83L, 83L, 82L, 82L, 81L, 85L, 85L, 82L, 83L, 81L, 83L, 82L, 84L, 83L, 82L, 84L, 81L, 83L, 82L, 84L, 85L), Day102 = c(89L, 88L, 88L, 90L, 88L, 90L, 87L, 88L, 89L, 87L, 90L, 86L, 86L, 89L, 86L, 89L, 90L, 88L, 87L, 88L, 88L, 87L, 90L, 86L, 90L, 87L, 88L, 89L, 88L, 90L, 88L, 87L, 89L, 90L, 88L, 87L, 89L, 88L, 87L, 86L, 90L, 86L, 89L, 89L, 90L, 88L, 90L, 86L, 88L, 88L, 90L, 89L, 88L, 88L, 90L, 87L, 88L, 88L, 87L, 90L, 89L, 87L, 90L, 90L, 86L, 87L, 86L, 90L, 88L, 87L, 86L, 88L, 90L, 86L, 89L, 90L, 87L, 87L, 88L, 86L, 86L, 89L, 89L, 86L, 87L, 86L, 86L, 88L, 88L, 88L, 89L, 90L, 88L, 86L, 88L, 88L, 87L, 88L, 90L, 89L, 89L, 86L, 90L, 89L, 89L, 88L, 90L, 88L, 86L, 90L, 90L, 87L, 89L, 90L, 90L, 88L, 88L, 89L, 90L, 88L, 90L, 90L, 87L, 89L, 90L, 90L, 90L, 89L, 86L, 88L, 89L, 88L)), class = "data.frame", row.names = c(NA, -132L))
Required libraries:
tidyr, plyr, ggplot2
The steps that I have taken so far are to:
Convert the data to long format (df = name of dataset):
Fig1 <- gather(df, day, phosphorus, Day1:Day102, factor_key=TRUE)
Change the factor day to numeric
df$day2 <-revalue(df$day, c("Day1"="1", "Day2"="2", "Day4"="4", "Day7"="7", "Day10"="10", "Day13"="13", "Day18"="18", "Day23" = "23","Day28" = "28", "Day35" = "35", "Day42" = "42", "Day52" = "52", "Day62" = "62", "Day72" = "72", "Day82" = "82", Day92" = "92", "Day102" = "102"))
and
df$day3 <- as.numeric(as.character(df$day2))
Group by Trmt, Level and day3
GroupedDF <- df %>% group_by(Trmt, Level, day3)
GroupedCO2M <- GroupedDF %>% summarise(disp = mean(phosphorus))
I would now like to subtract values by accounting for Trmt and Level, thus reducing the number of rows from 102 to 51. I would like to subtract 'yT' Trmt cases from respective 'nF' cases, uniquely for each Level (X, Y and Z). For example, subtract A01yT_X from A01nf_X, A01yT_Y from A01nf_Y, A01yT_Z from A01nf_Z etc. This should give a total of 51 points, 17 for each Level.
Here is a figure of what I have in mind:
Many thanks for any advice.

thanks for sharing the data. The data you have posted is a bit long, hence might not be able to totally copy and paste
Your data is in the wide format, and you need to find the average for each measurement between similar groups (defined by Day, Level, Treatment). So we can work on this in the wide format:
tmp <- Data %>% group_by(Trmt,Level) %>% summarise_all(mean)
> head(tmp)
# A tibble: 6 x 19
# Groups: Trmt [2]
Trmt Level Day1 Day2 Day4 Day7 Day10 Day13 Day18 Day23 Day28 Day35 Day42
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A01nF X 3.5 8 12 19 23 29.5 32.5 36.5 42 50 53
2 A01nF Y 4.5 9.5 13 17.5 21 28 32.5 36 43.5 48 54.5
3 A01nF Z 1 8.5 13.5 18.5 22.5 28.5 33 37.5 43 49 51.5
4 A01yT X 2.5 8.5 11 19.5 22.5 28 31.5 38 43 50 52.5
5 A01yT Y 2.5 7.5 13.5 17 22 29.5 31 38.5 43.5 49 52.5
6 A01yT Z 3 7 14.5 18 23 28 33 38 43.5 48 54
This gives you the average for each Trmt,Level, and each column (Day) is average separately. Next step is to define the 2 subgroups under Trmt (nF and yT for A01,A02..), and for this we can introduce a subgroup called "site", which is Trmt without the nF,yT. Once you group your data.frame with this "site" and level, the first row will always be nF, and 2nd row yT, so taking the diff for all your Day columns within this grouping, will give you the difference. So we do it like this:
# need to ungroup Trmt to remove it later
tmp <- tmp%>% ungroup(Trmt) %>%
mutate(site = sub("[yn][TF]","",Trmt)) %>%
select(-Trmt) %>%
group_by(site,Level) %>%
summarize_all(diff)
Now you have the nF - yT values for each treatment, each level and each day
> head(tmp)
# A tibble: 6 x 19
# Groups: site [2]
site Level Day1 Day2 Day4 Day7 Day10 Day13 Day18 Day23 Day28 Day35 Day42
<chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A01 X -1 0.5 -1 0.5 -0.5 -1.5 -1 1.5 1 0 -0.5
2 A01 Y -2 -2 0.5 -0.5 1 1.5 -1.5 2.5 0 1 -2
3 A01 Z 2 -1.5 1 -0.5 0.5 -0.5 0 0.5 0.5 -1 2.5
4 A02 X 1.5 1 1.5 1 -1 -1.5 2 -1.5 -1.5 -1 2
5 A02 Y 0.5 0 -1.5 -1 0.5 1.5 -0.5 -3 -1.5 0 1
6 A02 Z 4 2 1 0.5 1.5 0 2.5 0.5 0.5 1.5 0
Come the last part, which is to plot. We convert it to long and also make "Day", a numeric form of day.
plotdf <- gather(tmp, day, Diff, Day1:Day102, factor_key=TRUE) %>%
mutate(Day=as.numeric(sub("Day","",day)))
# and plot
ggplot(plotdf,aes(x=Day,y=Diff,col=Level,shape=Level)) + geom_line() + geom_point() + facet_wrap(~site) + scale_color_manual(values=c("grey10","grey40","grey80"))
Plot above shows the difference for each site. For diff that is the average across all sites:
meandf <- plotdf %>% group_by(Level,Day) %>% summarize(Diff=mean(Diff))
ggplot(meandf,aes(x=Day,y=Diff,col=Level,shape=Level)) + geom_line() + geom_point() + scale_color_manual(values=c("grey10","grey40","grey80"))
example dataset, subsetted for Day1, Day2 and Day4
Data <- structure(list(Trmt = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L,
8L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 7L, 10L, 10L, 10L, 10L, 10L,
10L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L, 12L, 12L, 12L, 12L, 11L,
11L, 11L, 11L, 11L, 11L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 13L,
13L, 13L, 13L, 13L, 16L, 16L, 16L, 16L, 16L, 16L, 15L, 15L, 15L,
15L, 15L, 15L, 18L, 18L, 18L, 18L, 18L, 18L, 17L, 17L, 17L, 17L,
17L, 17L, 20L, 20L, 20L, 20L, 20L, 20L, 19L, 19L, 19L, 19L, 19L,
19L, 22L, 22L, 22L, 22L, 22L, 22L, 21L, 21L, 21L, 21L, 21L, 21L
), .Label = c("A01nF", "A01yT", "A02nF", "A02yT", "A03nF", "A03yT",
"A04nF", "A04yT", "A05nF", "A05yT", "A06nF", "A06yT", "A07nF",
"A07yT", "A08nF", "A08yT", "A10nF", "A10yT", "A11nF", "A11yT",
"A13nF", "A13yT"), class = "factor"), Level = structure(c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L), .Label = c("X", "Y", "Z"), class = "factor"), Day1 = c(3L,
1L, 4L, 2L, 4L, 2L, 5L, 4L, 1L, 2L, 5L, 1L, 5L, 2L, 5L, 5L, 3L,
5L, 3L, 3L, 1L, 4L, 1L, 1L, 5L, 4L, 1L, 5L, 4L, 5L, 3L, 5L, 3L,
5L, 3L, 4L, 2L, 4L, 2L, 4L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 5L, 2L,
4L, 4L, 3L, 1L, 4L, 4L, 1L, 4L, 1L, 2L, 5L, 1L, 5L, 1L, 2L, 4L,
4L, 4L, 4L, 2L, 4L, 5L, 5L, 4L, 1L, 3L, 2L, 3L, 5L, 4L, 3L, 2L,
3L, 5L, 4L, 1L, 3L, 4L, 3L, 3L, 5L, 3L, 1L, 1L, 4L, 4L, 5L, 1L,
4L, 4L, 4L, 1L, 4L, 5L, 5L, 1L, 5L, 3L, 1L, 4L, 1L, 4L, 5L, 5L,
3L, 3L, 2L, 4L, 5L, 3L, 2L, 1L, 5L, 5L, 2L, 2L, 3L, 4L, 3L, 4L,
2L, 2L, 4L), Day2 = c(10L, 9L, 7L, 7L, 6L, 7L, 10L, 9L, 10L,
6L, 10L, 7L, 8L, 9L, 8L, 9L, 7L, 10L, 7L, 10L, 6L, 8L, 6L, 8L,
8L, 8L, 10L, 6L, 8L, 8L, 6L, 10L, 7L, 10L, 7L, 10L, 6L, 6L, 7L,
9L, 8L, 10L, 8L, 7L, 9L, 8L, 6L, 9L, 7L, 9L, 8L, 6L, 6L, 8L,
10L, 7L, 8L, 6L, 8L, 8L, 6L, 9L, 10L, 6L, 8L, 7L, 9L, 7L, 8L,
10L, 10L, 6L, 7L, 10L, 9L, 9L, 8L, 9L, 6L, 8L, 6L, 8L, 6L, 9L,
10L, 7L, 7L, 7L, 8L, 7L, 8L, 10L, 7L, 8L, 9L, 6L, 8L, 9L, 8L,
9L, 6L, 7L, 10L, 9L, 10L, 7L, 6L, 9L, 9L, 9L, 6L, 10L, 9L, 8L,
9L, 7L, 10L, 7L, 10L, 9L, 6L, 8L, 9L, 8L, 9L, 6L, 6L, 10L, 9L,
8L, 8L, 7L), Day4 = c(11L, 12L, 14L, 11L, 15L, 15L, 12L, 11L,
15L, 12L, 15L, 12L, 12L, 11L, 15L, 15L, 13L, 11L, 13L, 14L, 12L,
11L, 13L, 12L, 15L, 15L, 14L, 11L, 15L, 11L, 12L, 11L, 13L, 11L,
12L, 13L, 13L, 14L, 13L, 15L, 14L, 15L, 12L, 14L, 11L, 13L, 15L,
11L, 12L, 13L, 11L, 15L, 11L, 13L, 11L, 11L, 14L, 12L, 14L, 15L,
11L, 12L, 15L, 12L, 13L, 12L, 14L, 12L, 11L, 13L, 12L, 12L, 11L,
15L, 13L, 12L, 11L, 12L, 13L, 14L, 14L, 14L, 13L, 12L, 15L, 12L,
15L, 15L, 12L, 13L, 12L, 12L, 12L, 14L, 13L, 13L, 14L, 11L, 12L,
11L, 15L, 11L, 11L, 11L, 14L, 11L, 12L, 15L, 15L, 11L, 12L, 14L,
15L, 14L, 14L, 12L, 14L, 13L, 15L, 15L, 14L, 13L, 12L, 15L, 15L,
11L, 13L, 12L, 11L, 13L, 12L, 14L)), class = "data.frame", row.names = c(NA,
-132L))

Plotting a dendrogram with only a subset of observations in R

From an hclust object, how can I extract only selected observations (to_plot below) and plot a dendrogram from these selected observations? This subset of observations I want to plot as a dendrogram, will not correspond to the tree structure of the hclust object, so I can't extract branches from the dendrogram.
NB. I do not wish to cluster or calculate the distance matrix using the subset of selected observations
Data
1/ hclust object
structure(list(merge = structure(c(-31L, -62L, -46L, -37L, -55L,
-47L, -75L, -57L, -6L, -2L, -45L, -99L, -51L, -12L, -30L, -4L,
3L, -53L, -61L, -27L, -56L, -83L, -38L, -101L, -69L, -11L, -14L,
-21L, -34L, -48L, -82L, -92L, -15L, -7L, -35L, -65L, -105L, -52L,
-40L, -64L, -23L, -94L, -98L, -1L, -25L, -8L, 8L, -41L, -3L,
-33L, -108L, 23L, -58L, -20L, -5L, -93L, 30L, -68L, -49L, -28L,
-17L, 9L, -32L, 35L, -95L, -67L, 26L, -107L, 17L, -19L, -74L,
-63L, 37L, 20L, -84L, 50L, -10L, -13L, 49L, 34L, 39L, 60L, -16L,
63L, 44L, 29L, 10L, -24L, 75L, 73L, 47L, 61L, 57L, 18L, 66L,
43L, 80L, 83L, -78L, -71L, 90L, 93L, 84L, 94L, 102L, 98L, 100L,
87L, 106L, 108L, -97L, 1L, -100L, -43L, -59L, -106L, 4L, -90L,
5L, 2L, -87L, -103L, -86L, -54L, -89L, -42L, 11L, 13L, 12L, -77L,
7L, 14L, 6L, -110L, 22L, -60L, -44L, -91L, -111L, -102L, -88L,
-104L, -50L, -22L, -36L, -79L, 28L, 24L, -66L, 15L, -29L, 25L,
32L, -109L, -39L, 45L, 42L, -96L, 16L, 33L, 19L, 40L, 27L, 31L,
-9L, 41L, 46L, -80L, -81L, -70L, -26L, 21L, -73L, 48L, 38L, 36L,
53L, 56L, 51L, -72L, -85L, -76L, 52L, 58L, 71L, 59L, 64L, -18L,
68L, 54L, 55L, 65L, 70L, 79L, 72L, 74L, 69L, 78L, 77L, 76L, 62L,
81L, 82L, 67L, 86L, 85L, 95L, 89L, 92L, 88L, 91L, 97L, 96L, 99L,
103L, 104L, 105L, 101L, 107L, 109L), .Dim = c(110L, 2L)), height = c(0,
0.188350217744365, 0.247401000321179, 0.249231910045009, 0.261866742195707,
0.377720124194474, 0.378461142310176, 0.527418629683044, 0.636480697844057,
0.70489556723743, 0.799857388088743, 0.895267189098051, 0.940604516439695,
1, 1, 1.25645841742159, 1.47637080579504, 1.49661353166068, 1.60280854934758,
1.64538982117314, 1.65011076915935, 1.66666666666667, 1.8661900064933,
1.91530600787293, 1.95979930296005, 2, 2, 2, 2, 2, 2, 2, 2.06532735656427,
2.32083831336158, 2.44558763136158, 2.48004395957454, 2.65074432837975,
2.69489799737569, 2.71536352494182, 2.75337988132381, 2.87695888696678,
2.89093184314013, 2.91669905927746, 3, 3.03504556878056, 3.42442760079317,
3.50924315636259, 3.54456009196554, 3.58118052752614, 3.80716728885077,
4.26149878117642, 4.63502500606874, 4.66666666666667, 4.66666666666667,
4.76912295317528, 4.90702353976517, 4.92512811564295, 5, 5.15887380396718,
5.20227981903921, 5.39890417564938, 5.71781232947912, 5.94961450567626,
6.17569787723772, 6.21000141305934, 6.47150288200403, 6.48552894195153,
6.61209720286382, 7.27379923250834, 7.65301130607984, 7.74920607244712,
7.8800745368487, 8.17570945188961, 8.75305138718179, 8.87870428752716,
9.36365055557565, 9.68439736325147, 10, 10.121604958431, 10.2845151775143,
10.7517404855684, 10.8165382868783, 11.4489962313067, 11.5939995243571,
12.8179231278111, 12.3055509866599, 14.1589468158871, 14.6988252554622,
14.7792803434488, 15.276874084329, 16.0150635281041, 17.9467649484583,
21.2687065983256, 21.3844895922187, 24.196270007066, 25.3163200486723,
34.1772731084418, 37.4454933955768, 42.6291683810462, 45.1916356921658,
52.531016897072, 55.6590891226214, 61.0699226448619, 73.7706208334886,
98.5310119994231, 148.608243702477, 150.474954574704, 187.419419688973,
241.610436881262, 487.90491231433), order = c(2L, 62L, 31L, 97L,
46L, 100L, 45L, 87L, 108L, 61L, 99L, 103L, 105L, 21L, 91L, 38L,
47L, 106L, 64L, 30L, 89L, 33L, 15L, 50L, 49L, 81L, 57L, 90L,
94L, 69L, 83L, 12L, 54L, 6L, 55L, 59L, 56L, 75L, 37L, 43L, 16L,
19L, 72L, 84L, 74L, 85L, 10L, 35L, 36L, 41L, 96L, 53L, 51L, 86L,
11L, 60L, 58L, 14L, 44L, 78L, 17L, 26L, 40L, 66L, 5L, 9L, 71L,
24L, 13L, 18L, 48L, 102L, 8L, 25L, 39L, 28L, 70L, 95L, 52L, 101L,
110L, 7L, 22L, 20L, 82L, 88L, 67L, 65L, 79L, 34L, 111L, 27L,
77L, 68L, 80L, 32L, 73L, 3L, 4L, 42L, 107L, 93L, 23L, 29L, 98L,
92L, 104L, 1L, 109L, 63L, 76L), labels = c("DX_100203", "DX_100208",
"DX_30528", "DX_100159", "DX_100211", "DX_100215", "DX_100246", "DX_100253",
"DX_100271", "DX_100212", "DX_100035", "DX_100164", "DX_100249", "DX_100036",
"DX_100165", "DX_100221", "DX_100254", "DX_100262", "DX_100274", "DX_100046",
"DX_100171", "DX_100230", "DX_100255", "DX_100275", "DX_100180", "DX_100269",
"DX_100278", "DX_100161", "DX_100229", "DX_100238", "DX_100093", "DX_100191",
"DX_100241", "DX_100237", "DX_100268", "DX_30515", "DX_90862", "DX_30529",
"DX_100073", "DX_90264", "DX_90221", "DX_30550", "DX_90885", "DX_100028",
"DX_100049", "DX_90257", "DX_90215", "DX_30527", "DX_30526", "DX_90892",
"DX_100051", "DX_90333", "DX_90286", "DX_90217", "DX_90252", "DX_90232",
"DX_30573", "DX_100214", "DX_90769", "DX_90907", "DX_100037", "DX_100054",
"DX_30568", "DX_90230", "DX_90280", "DX_90779", "DX_90959", "DX_100187",
"DX_100081", "DX_90310", "DX_90782", "DX_100023", "DX_90994", "DX_100042",
"DX_90304", "DX_100152", "DX_90272", "DX_90861", "DX_100043", "DX_100068",
"DX_30571", "DX_100085", "DX_90312", "DX_30590", "DX_90413", "DX_30561",
"DX_30548", "DX_90296", "DX_30558", "DX_90243", "DX_90293", "DX_90365",
"DX_30584", "DX_90274", "DX_90332", "DX_30583", "DX_30575", "DX_30523",
"DX_30578", "DX_90377", "DX_90297", "DX_30593", "DX_30555", "DX_30549",
"DX_90292", "DX_30565", "DX_30512", "DX_90285", "DX_90231", "DX_90209",
"DX_30570"), method = "ward", call = hclust(d = distance, method = method.hclust),
dist.method = "maximum"), .Names = c("merge", "height", "order",
"labels", "method", "call", "dist.method"), class = "hclust")
2/ subset of observations to extract for plotting as a dendrogram
to_plot <- c("DX_90264", "DX_90221", "DX_30550", "DX_90885", "DX_100028", "DX_100159",
"DX_100049", "DX_90257", "DX_90215", "DX_30527", "DX_30526", "DX_90892",
"DX_100051", "DX_90333", "DX_90286", "DX_90217", "DX_90252", "DX_90232",
"DX_30573", "DX_100214", "DX_90769", "DX_90907", "DX_100037", "DX_100054", "DX_30565")

Based on the comment of #RomanLuštrik I would suggest something like this:
hc <- hclust(dist(USArrests), "ave")
## select some observations to plot
set.seed(1)
toPlot <- sample(rownames(USArrests), size=20)
## use rownames as labels
labels <- rownames(USArrests)
## clear labels not present in toPlot
labels[ !(labels %in% toPlot) ] <- ""
plot(hc, labels=labels)

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