我想制作12个变量的多面图，但在研究对象（P）之间进行划分，同时指定每个变量的最小和最大y值。
Resp_L <- structure(list(P = c("Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat22", "Pat22", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat26", "Pat26", "Pat28", "Pat28", "Pat28", "Pat28", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat31", "Pat31",
"Pat31", "Pat31", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat43", "Pat43",
"Pat43", "Pat43", "Pat43", "Pat44", "Pat44", "Pat44", "Pat44",
"Pat44", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat48", "Pat48", "Pat48",
"Pat48", "Pat48", "Pat48", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat22", "Pat22", "Pat22", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat26", "Pat26", "Pat26", "Pat28", "Pat28", "Pat28", "Pat28",
"Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat31",
"Pat31", "Pat31", "Pat31", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat43",
"Pat43", "Pat43", "Pat43", "Pat43", "Pat44", "Pat44", "Pat44",
"Pat44", "Pat44", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat48", "Pat48",
"Pat48", "Pat48", "Pat48", "Pat48", "Pat22", "Pat22", "Pat22",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat26", "Pat26", "Pat26",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat28", "Pat28", "Pat28",
"Pat28", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat31", "Pat31", "Pat31", "Pat31", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat38",
"Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat43", "Pat43", "Pat43", "Pat43", "Pat43", "Pat44", "Pat44",
"Pat44", "Pat44", "Pat44", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat48",
"Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat22", "Pat22",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat26", "Pat26",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat28", "Pat28",
"Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43", "Pat44",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat22",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat26",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat28",
"Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48",
"Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22", "Pat22",
"Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26", "Pat26",
"Pat28", "Pat28", "Pat28", "Pat28", "Pat3", "Pat3", "Pat3", "Pat3",
"Pat3", "Pat3", "Pat3", "Pat31", "Pat31", "Pat31", "Pat31", "Pat33",
"Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33", "Pat33",
"Pat33", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38", "Pat38",
"Pat38", "Pat38", "Pat43", "Pat43", "Pat43", "Pat43", "Pat43",
"Pat44", "Pat44", "Pat44", "Pat44", "Pat44", "Pat45", "Pat45",
"Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45", "Pat45",
"Pat45", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48", "Pat48"
), X = c(1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L, 7L, 9L, 13L, 15L,
18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L, 16L, 18L, 20L, 5L,
7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L, 22L, 24L, 3L, 5L,
7L, 11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L, 4L, 6L,
9L, 13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L, 2L,
4L, 6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L, 7L,
9L, 13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L, 16L,
18L, 20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L, 22L,
24L, 3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L, 15L,
2L, 4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L, 37L,
39L, 2L, 4L, 6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L,
3L, 7L, 9L, 13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L,
14L, 16L, 18L, 20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L,
19L, 22L, 24L, 3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L,
13L, 15L, 2L, 4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L,
35L, 37L, 39L, 2L, 4L, 6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L,
14L, 16L, 3L, 7L, 9L, 13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L,
5L, 10L, 14L, 16L, 18L, 20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L,
15L, 17L, 19L, 22L, 24L, 3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L,
3L, 5L, 8L, 13L, 15L, 2L, 4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L,
15L, 17L, 33L, 35L, 37L, 39L, 2L, 4L, 6L, 8L, 12L, 14L, 1L, 3L,
5L, 7L, 11L, 14L, 16L, 3L, 7L, 9L, 13L, 15L, 18L, 20L, 1L, 3L,
5L, 9L, 1L, 5L, 10L, 14L, 16L, 18L, 20L, 5L, 7L, 9L, 13L, 3L,
5L, 7L, 13L, 15L, 17L, 19L, 22L, 24L, 3L, 5L, 7L, 11L, 13L, 17L,
19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L, 4L, 6L, 9L, 13L, 5L, 7L,
10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L, 2L, 4L, 6L, 8L, 12L,
14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L, 7L, 9L, 13L, 15L, 18L,
20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L, 16L, 18L, 20L, 5L, 7L,
9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L, 22L, 24L, 3L, 5L, 7L,
11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L, 4L, 6L, 9L,
13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L, 2L, 4L,
6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L, 7L, 9L,
13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L, 16L, 18L,
20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L, 22L, 24L,
3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L,
4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L,
2L, 4L, 6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L,
7L, 9L, 13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L,
16L, 18L, 20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L,
22L, 24L, 3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L,
15L, 2L, 4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L,
37L, 39L, 2L, 4L, 6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L, 14L,
16L, 3L, 7L, 9L, 13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L,
10L, 14L, 16L, 18L, 20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L,
17L, 19L, 22L, 24L, 3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L, 3L,
5L, 8L, 13L, 15L, 2L, 4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L, 15L,
17L, 33L, 35L, 37L, 39L, 2L, 4L, 6L, 8L, 12L, 14L, 1L, 3L, 5L,
7L, 11L, 14L, 16L, 3L, 7L, 9L, 13L, 15L, 18L, 20L, 1L, 3L, 5L,
9L, 1L, 5L, 10L, 14L, 16L, 18L, 20L, 5L, 7L, 9L, 13L, 3L, 5L,
7L, 13L, 15L, 17L, 19L, 22L, 24L, 3L, 5L, 7L, 11L, 13L, 17L,
19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L, 4L, 6L, 9L, 13L, 5L, 7L,
10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L, 2L, 4L, 6L, 8L, 12L,
14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L, 7L, 9L, 13L, 15L, 18L,
20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L, 16L, 18L, 20L, 5L, 7L,
9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L, 22L, 24L, 3L, 5L, 7L,
11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L, 4L, 6L, 9L,
13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L, 2L, 4L,
6L, 8L, 12L, 14L, 1L, 3L, 5L, 7L, 11L, 14L, 16L, 3L, 7L, 9L,
13L, 15L, 18L, 20L, 1L, 3L, 5L, 9L, 1L, 5L, 10L, 14L, 16L, 18L,
20L, 5L, 7L, 9L, 13L, 3L, 5L, 7L, 13L, 15L, 17L, 19L, 22L, 24L,
3L, 5L, 7L, 11L, 13L, 17L, 19L, 23L, 3L, 5L, 8L, 13L, 15L, 2L,
4L, 6L, 9L, 13L, 5L, 7L, 10L, 12L, 15L, 17L, 33L, 35L, 37L, 39L,
2L, 4L, 6L, 8L, 12L, 14L), variable = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
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"L_IL.2", "L_IL.5", "L_IL.6", "L_IL.8", "L_IL.10", "L_IL.13",
"L_IL12p70", "L_IL1ß", "L_TNF", "L_IFN.y", "L_IL17A"), class = "factor"),
value = c(0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
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439.8, 843.2, 879.3, 349.2, 435.9, 1123.1, 776.2, 2126.2,
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1259L, 1260L), class = "data.frame")
以下是12个最大值和最小值的两个字符串，其顺序与刻面时变量的顺序相对应：
MaxY <- c(0.1, 9.8, 3, 327839.8, 191712.6, 72.2, 5.2, 10, 53, 21.2, 13.4,
18.4)
MinY <- c(0.02, 0, 0, 29.8, 132.8, 0, 0, 0, 0, 0, 0, 0)
我的阴谋尝试：
dat_split <- split(Resp_L, Resp_L$P)
plots <-
lapply(dat_split, function(df)
ggplot(df, aes(x = X, y = value)) +
geom_point() + geom_line(col='blue')+ ylab('')+ ylim(MinY, MaxY)+
theme(plot.title = element_text(size = 10, face = "bold") )+
ggtitle(df$P[1]) + facet_grid(variable ~ ., scales = 'free_x'))
gridExtra::grid.arrange(grobs = plots)
但是它不会接受这种指定ylim()的方式，并且我无法弄清楚如何以可能的方式组织数据。
我收到此错误：
Error in limits.numeric(c(...), "y") : length(lims) == 2 is not TRUE