Question

我有一个数据集中的磷浓度为17天（浓度是累积的，因此从第1天到第102天在所有情况下都增加）。有22种不同的处理方式（列= Trmt）。每个Trmt有3个等级（等级= X，Y，Z）。每个级别2次测量，每个Trmt共6次。

我的目标是使用ggplot2通过浓度（y轴）绘制3天的日线图（x轴；数字）。数据应按Trmt，级别和天分组，总共进行51次测量（3行x 17天）。

我的数据如下：

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))

所需的库： tidyr，plyr，ggplot2

到目前为止，我采取的步骤是：

将数据转换为长格式（df =数据集名称）：

    Fig1 <- gather(df, day, phosphorus, Day1:Day102, factor_key=TRUE)

将因子日更改为数字

    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"))

和

df$day3 <- as.numeric(as.character(df$day2))

按Trmt，级别和第3天分组

 GroupedDF <- df %>% group_by(Trmt, Level, day3)
 GroupedCO2M <- GroupedDF %>% summarise(disp = mean(phosphorus))

我现在想通过考虑Trmt和Level来减去值，从而将行数从102减少到51。我想从相应的'nF'格中减去'yT'Trmt格，对于每个Level（ X，Y和Z）。例如，从A01nf_X减去A01yT_X，从A01nf_Y减去A01yT_Y，从A01nf_Z减去A01yT_Z，依此类推。这应该总共得到51分，每个等级17分。

以下是我的想法：

非常感谢您的任何建议。

Answer 1

感谢您分享数据。您发布的数据有点长，因此可能无法完全复制和粘贴

您的数据具有广泛的格式，您需要查找相似组之间的每个测量值的平均值（按天，水平，治疗定义）。因此，我们可以采用多种格式进行处理：

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

这将为您提供每个Trmt，Level的平均值，并且每一列（天）分别是平均值。下一步是在Trmt下定义2个子组（对于A01，A02 ..为nF和yT），为此，我们可以引入一个名为“站点”的子组，该子组是没有nF，yT的Trmt。一旦使用此“站点”和级别对data.frame进行分组，第一行将始终为nF，第二行将始终为yT，因此，以该分组中所有“天”列的差异为准。所以我们这样做：

    # 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)

现在您有了每种治疗，每种水平和每天的nF-yT值

> 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

最后一部分是绘图。我们将其转换为long，然后也将“ Day”（天的数字形式）制成。

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"))

上图显示了每个站点的差异。对于差异是所有网站的平均值：

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"))

示例数据集，为Day1，Day2和Day4子集

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))

基于多个分组因子减去值

1 个答案: