我只是想将我的 CLD 值添加到箱形图中。箱形图根据第三个变量的降序重新排序，并具有自定义 x 轴标签。
我有这样的数据；
df<-structure(list(Cross = c("CBG X CBG", "CBG X CBG", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ",
"SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "SDZ X SDZ", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2", "USBG2 X USBG2",
"USBG2 X USBG2", "USBG2 X USBG2", "USBG1 X USBG1", "USBG1 X USBG1",
"USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1",
"USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1",
"USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1",
"USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1",
"USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1", "USBG1 X USBG1",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2", "USBG1 X USBG2",
"USBG1 X USBG2", "USBG1 X USBG2", "NTBG X CBG", "NTBG X CBG",
"NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG",
"NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG",
"NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG",
"NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG",
"NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG",
"NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X CBG", "NTBG X USBG2",
"NTBG X USBG2", "NTBG X USBG2", "NTBG X USBG2", "NTBG X USBG2",
"NTBG X USBG2", "NTBG X USBG2", "CBG X USBG2", "CBG X USBG2",
"CBG X USBG2", "CBG X USBG2", "CBG X USBG2", "CBG X USBG2", "CBG X USBG2",
"CBG X USBG2", "CBG X USBG2", "CBG X USBG2", "CBG X USBG2", "CBG X USBG2",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1", "NTBG X USBG1",
"CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1",
"CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1",
"CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1",
"CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1",
"CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "CBG X USBG1",
"CBG X USBG1", "CBG X USBG1", "CBG X USBG1", "USBG1 X UCBG",
"USBG1 X UCBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG",
"SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG",
"SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG",
"SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG",
"SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG", "SDZ X CBG",
"SDZ X CBG", "SDZ X CBG", "NTBG X SDZ", "NTBG X SDZ", "NTBG X SDZ",
"NTBG X SDZ", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG",
"SDZ X UCBG", "SDZ X UCBG", "SDZ X UCBG", "SDZ X USBG1", "SDZ X USBG1",
"SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1",
"SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1",
"SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1",
"SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1", "SDZ X USBG1",
"SDZ X USBG1", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2", "SDZ X USBG2",
"SDZ X USBG2", "CBG X UCBG", "CBG X UCBG", "CBG X UCBG", "CBG X UCBG",
"CBG X UCBG", "CBG X UCBG", "CBG X UCBG", "CBG X UCBG", "CBG X UCBG",
"CBG X UCBG"), Relatedness = c(0.42, 0.42, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38,
0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38,
0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.3,
0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,
0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, -0.01, -0.01, -0.01,
-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01,
-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01,
-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01,
-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.04, -0.04, -0.04,
-0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04,
-0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04,
-0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.06, -0.06,
-0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09,
-0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09,
-0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.12,
-0.12, -0.12, -0.12, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17, -0.17,
-0.17, -0.17, -0.19, -0.19, -0.19, -0.19, -0.19, -0.19, -0.19,
-0.19, -0.19, -0.19, -0.19, -0.19, -0.19, -0.19, -0.19, -0.19,
-0.19, -0.19, -0.19, -0.19, -0.19, -0.19, -0.19, -0.24, -0.24,
-0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24,
-0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24,
-0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24,
-0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24,
-0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.28, -0.28,
-0.28, -0.28, -0.28, -0.28, -0.28, -0.28, -0.28, -0.28), Rel.factor = structure(c(17L,
17L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("-0.28", "-0.24", "-0.19", "-0.17", "-0.12",
"-0.09", "-0.06", "-0.04", "-0.01", "0.02", "0.11", "0.24", "0.25",
"0.3", "0.38", "0.4", "0.42"), class = "factor"), Height = c(17,
48.09, 4.2, 5.24, 12.36, 3.94, 10.68, 29.01, 28.81, 16.27, 20.88,
43.36, 20.75, 64.02, 60.88, 37.18, 98.18, 32.64, 71.19, 48.24,
67.24, 55.97, 60.15, 92.95, 116.2, 51.51, 50.46, 117.01, 91.83,
73.32, 115.73, 100.92, 117.61, 93.29, 123.53, 121.25, 83.48,
7.49, 6.38, 72.12, 54.65, 53.05, 58.83, 24.59, 31.4, 31.72, 46.81,
80.44, 65.76, 39.52, 69.2, 37.55, 42.55, 85.97, 77.42, 44.86,
54.91, 71.82, 52.13, 84.49, 105.67, 80.6, 101.15, 138.11, 15.64,
5.9, 41.9, 59.64, 52.54, 63.8, 48.63, 40.89, 71.46, 20.96, 63.27,
40.46, 69.35, 91.03, 63.03, 95.8, 67.81, 67.35, 82.9, 64.46,
58.12, 58.68, 17.08, 17.9, 8.87, 35.18, 15.67, 27.46, 15.66,
15.81, 30.17, 33.84, 32.81, 30.47, 24.46, 43.44, 57.03, 66.62,
68.14, 79.81, 47.23, 60.01, 79.93, 63.76, 43.45, 38.83, 43.8,
63.29, 49.6, 90.85, 71.58, 74.56, 54.28, 97.1, 123.8, 93.69,
88.88, 76.03, 116.95, 107.46, 8.32, 9.35, 12.73, 33.51, 4, 21.05,
7.83, 27.48, 30.07, 14.47, 51.6, 20.59, 21.64, 16.81, 32.58,
50.26, 54.03, 54.89, 54.39, 44.43, 69.19, 45.41, 55.77, 76.78,
26.28, 41.25, 71.18, 40.6, 99.85, 124.26, 67.4, 17.86, 16.99,
29.21, 20.64, 34.36, 23.91, 22.42, 4.19, 5.71, 8.48, 43.72, 33.61,
67.1, 56.19, 44.59, 65.32, 82.4, 82.69, 62.88, 43.76, 51.84,
52.29, 10.1, 19.04, 20.54, 29.11, 8.92, 5.89, 16.26, 15.81, 11.5,
54.22, 40.45, 42.05, 18.36, 49.19, 47.04, 79.76, 41.2, 24.4,
29.55, 49.56, 24.2, 92.97, 66.3, 64.77, 37.32, 64.4, 36.44, 76.31,
40.5, 80.8, 70.6, 91.09, 66.11, 3.92, 17.52, 2.54, 22, 32.42,
12.85, 26.2, 50.37, 43.12, 70.67, 70.97, 73.68, 37.17, 47.92,
67.44, 31.11, 61.12, 86.2, 67.47, 60.34, 82.8, 99.28, 52.27,
73.8, 88.83, 80.55, 75.01, 127.78, 8.57, 4.27, 56.35, 21.25,
35.82, 83.99, 40.43, 39.2, 22.02, 19.06, 57.7, 55.14, 43, 45.65,
43.79, 89.78, 111.48, 76.47, 79.78, 54.49, 88.26, 77.43, 76.36,
91.32, 67.6, 93.98, 91.5, 123.62, 15.26, 106.06, 73.14, 72.41,
49.26, 95.98, 91.77, 47.41, 24.82, 59.59, 27.32, 33.52, 63.33,
48.97, 92.02, 106.54, 89.93, 92.77, 86.63, 61.51, 89.31, 68.08,
104.8, 67.29, 51.68, 83.43, 44.06, 112.81, 104.65, 106.47, 104.01,
107.48, 64.05, 70.5, 69.56, 106.18, 79.87, 79.82, 75.62, 120,
115.47, 115.57, 147.35, 87.44, 97.78, 108.06, 92.71, 141.6, 101.56,
121.34, 124.83, 115.3, 121.46, 128.89, 88.37, 94.98, 133.51,
125.07, 114.61, 59.12, 143.01, 110.53, 60.85, 138.78, 159.24,
155.5, 1.45, 25.91, 81.29, 64.51, 63.45, 55.5, 65.56, 94.1, 32.73,
56.79, 86.98, 86.35, 65.08, 59, 76.96, 54.03, 57, 53.37, 101.52,
94.3, 107.33, 76.22, 72.85, 32.29, 12.6, 10.49, 12.24, 12.48,
32.94, 7.55, 25.19, 47.2, 52.52, 45.41, 21.96, 48.65, 57.03,
42.43, 52.89, 23.13, 31.22, 47.38, 63.77, 59.79, 67, 42.02, 26.09,
31.02, 92.87, 89.03, 77.3, 68.24, 63.16, 75.35, 75.06, 94.89,
85, 65.16, 89.48, 90.43, 68.74, 55.3, 86.71, 68.55, 75.56, 44.98,
84.63, 94.75, 16.46, 41.14, 7.6, 40.72, 48.8, 81.26, 96.59, 67.74,
85.94, 97.11)), row.names = c(NA, -410L), class = c("tbl_df",
"tbl", "data.frame"))
我的摘要和 CLD 值如下所示；
df.sum<-structure(list(Cross = c("CBG X CBG", "CBG X UCBG", "CBG X USBG1",
"CBG X USBG2", "NTBG X CBG", "NTBG X SDZ", "NTBG X USBG1", "NTBG X USBG2",
"SDZ X CBG", "SDZ X SDZ", "SDZ X UCBG", "SDZ X USBG1", "SDZ X USBG2",
"USBG1 X UCBG", "USBG1 X USBG1", "USBG1 X USBG2", "USBG2 X USBG2"
), Relatedness = c(0.42, -0.28, -0.04, 0.02, 0.24, -0.12, -0.01,
0.11, -0.09, 0.4, -0.17, -0.19, -0.24, -0.06, 0.3, 0.25, 0.38
), Rel.factor = structure(c(17L, 1L, 8L, 10L, 12L, 5L, 9L, 11L,
6L, 16L, 4L, 3L, 2L, 7L, 14L, 13L, 15L), .Label = c("-0.28",
"-0.24", "-0.19", "-0.17", "-0.12", "-0.09", "-0.06", "-0.04",
"-0.01", "0.02", "0.11", "0.24", "0.25", "0.3", "0.38", "0.4",
"0.42"), class = "factor"), n = c(2L, 10L, 28L, 12L, 31L, 4L,
36L, 7L, 26L, 35L, 62L, 23L, 45L, 2L, 22L, 38L, 27L), Mean.height = c(32.545,
58.336, 55.9053571428571, 46.4066666666667, 41.5483870967742,
66.7175, 43.6847222222222, 23.6271428571429, 64.8257692307692,
61.1508571428571, 93.2898387096774, 66.6208695652174, 54.4551111111111,
6.42, 56.5281818181818, 55.4078947368421, 59.97), sd.height = c(21.9839498270898,
32.2618103094734, 30.329989496053, 28.3102674382324, 28.0006307187028,
37.7242613137311, 24.3547219811226, 6.24679041397984, 28.0167527273347,
38.5679274993697, 31.7287500330865, 24.7068915511154, 26.1159727876728,
3.04055915910216, 22.4408230551809, 30.4903842314789, 29.7461798452274
), height.3q = c(`75%` = 40.3175, `75%` = 84.77, `75%` = 74.1025,
`75%` = 65.765, `75%` = 54.64, `75%` = 81.37, `75%` = 64.4925,
`75%` = 26.56, `75%` = 87.1925, `75%` = 93.12, `75%` = 115.1275,
`75%` = 83.82, `75%` = 75.35, `75%` = 7.495, `75%` = 67.695,
`75%` = 75.6625, `75%` = 78.93), Mean.width = c(7.58, 11.058,
10.8089285714286, 9.8325, 9.59709677419355, 12.3275, 8.93805555555556,
6.91714285714286, 12.1138461538462, 10.8597142857143, 12.0629032258065,
12.2504347826087, 10.2477777777778, 3.525, 10.7818181818182,
10.1423684210526, 11.5074074074074), sd.width = c(3.88908729652601,
5.52200406454839, 5.24339255408085, 5.10901542729748, 4.82402887881653,
5.31163738094636, 4.26402547529541, 1.61882379405248, 4.20342058511691,
5.57155986486111, 4.29947933832053, 4.92780836243832, 4.45136643417649,
0.530330085889911, 3.72253707182108, 4.00571782831204, 5.18810443566122
), width.3q = c(`75%` = 8.955, `75%` = 15.45, `75%` = 14.905,
`75%` = 14.02, `75%` = 12.925, `75%` = 15.0675, `75%` = 11.0775,
`75%` = 7.44, `75%` = 15.4, `75%` = 15.635, `75%` = 14.24, `75%` = 15.82,
`75%` = 14.25, `75%` = 3.7125, `75%` = 13.1875, `75%` = 13.2475,
`75%` = 15.31), Mean.leaves = c(6, 5.9, 6.21428571428571, 5.83333333333333,
4.7, 6, 4.63888888888889, 4, 5.84615384615385, 7.22857142857143,
7.56451612903226, 7.69565217391304, 6.31111111111111, 3, 7.45454545454545,
7.31578947368421, 7.18518518518519), sd.leaves = c(1.4142135623731,
1.59513148186739, 2.79360569949175, 2.48022481874429, 2.62809751564209,
1.4142135623731, 1.64147630029935, 0.816496580927726, 2.39486630428185,
3.30850698765999, 2.44695171853683, 1.66336959782615, 2.20353251747179,
0, 2.59536716784557, 2.85794542680383, 3.91286868046211), Leaves.3q = c(`75%` = 6.5,
`75%` = 7, `75%` = 8, `75%` = 7.25, `75%` = 5, `75%` = 7, `75%` = 6,
`75%` = 4.5, `75%` = 7, `75%` = 9, `75%` = 9, `75%` = 8, `75%` = 8,
`75%` = 3, `75%` = 8, `75%` = 10, `75%` = 7.5), cld.ht = c("ab",
"b", "b", "b", "b", "ab", "b", "b", "b", "b", "a", "b", "b",
"b", "b", "b", "b")), class = c("grouped_df", "tbl_df", "tbl",
"data.frame"), row.names = c(NA, -17L), groups = structure(list(
Cross = c("CBG X CBG", "CBG X UCBG", "CBG X USBG1", "CBG X USBG2",
"NTBG X CBG", "NTBG X SDZ", "NTBG X USBG1", "NTBG X USBG2",
"SDZ X CBG", "SDZ X SDZ", "SDZ X UCBG", "SDZ X USBG1", "SDZ X USBG2",
"USBG1 X UCBG", "USBG1 X USBG1", "USBG1 X USBG2", "USBG2 X USBG2"
), Relatedness = c(0.42, -0.28, -0.04, 0.02, 0.24, -0.12,
-0.01, 0.11, -0.09, 0.4, -0.17, -0.19, -0.24, -0.06, 0.3,
0.25, 0.38), .rows = structure(list(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -17L), .drop = TRUE))
我的自定义标签看起来像这样，并且符合预期的情节顺序；
bi.labels<-c('CBG\nX\nCBG', 'SDZ\nX\nSDZ', 'USBG2\nX\nUSBG2', 'USBG1\nX\nUSBG1',
'USBG1\nX\nUSBG2', 'NTBG\nX\nCBG', 'NTBG\nX\nUSBG2','CBG\nX\nUSBG2',
'NTBG\nX\nUSBG1', 'CBG\nX\nUSBG1', 'USBG1\nX\nUCBG', 'SDZ\nX\nCBG',
'USBG2\nX\nUCBG', 'NTBG\nX\nSDZ', 'SDZ\nX\nUCBG',
'SDZ\nX\nUSBG1', 'SDZ\nX\nUSBG2', 'CBG\nX\nUCBG')
现在，我正在尝试绘制这样的图表；
ggplot(data = df, aes(x = Rel.factor, y = Height)) +
geom_boxplot() +
scale_x_discrete(limits=rev, labels = bi.labels) +
labs(y = "Height", x = "Cross") +
#theme_classic(base_size = 12) +
geom_text(data = df.sum, aes(x = Rel.factor, y = height.3q, label = cld.ht, size = 3.5, vjust=-0.3, hjust =-0.25))
我期待一个图表，其中箱形图按“Rel.factor”的降序（从高到低）排序，并且 CLD 匹配它们各自的“Cross”，并且我的自定义标签名称也匹配相应的“Cross”。我觉得也许我把它过于复杂化了，我擅长 :) 非常感谢任何帮助！