Question

我有以下代码：

p1 <- ggplot(df_test, aes(x=AA_Number,y=Energy_Profile,col='red')) + geom_line() + facet_wrap(~Model, ncol=3) + geom_hline(yintercept=-0.03, colour='blue') + geom_line(data=df_templates, colour="green")

print(p1)

它产生这个输出：

plot

我无法将绿色数据合并到一个图中，然后用红色绘制其他三个图。

基本上绿色的情节是我的常数，我希望看到我的红色数据如何与常数不同，方法是将每个图上面的绿色数据叠加成红色。

有人有什么想法吗？

数据：

df_test：

structure(list(Model = 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, 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, 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, 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, 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, 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, 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, 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, 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), .Label = c("102", 
"103", "104", "105", "107", "108", "109", "118", "141", "143", 
"144", "145", "14x", "161", "162", "163", "164", "165", "167", 
"168", "169", "2", "21", "22", "25", "26", "27", "3", "31", "310", 
"32", "36", "37", "39", "51", "510", "52", "53", "54", "57", 
"61", "62", "64", "65", "66", "67", "68", "81", "84", "88", "910", 
"93", "95", "97"), class = "factor"), AA_Number = 1:614, AA = structure(c(1L, 
15L, 1L, 10L, 11L, 4L, 18L, 18L, 1L, 9L, 12L, 7L, 19L, 11L, 14L, 
16L, 4L, 4L, 10L, 11L, 16L, 9L, 11L, 16L, 1L, 18L, 2L, 2L, 10L, 
1L, 2L, 11L, 18L, 8L, 14L, 4L, 19L, 15L, 11L, 6L, 14L, 4L, 1L, 
6L, 1L, 2L, 13L, 11L, 6L, 19L, 1L, 6L, 1L, 17L, 6L, 19L, 10L, 
15L, 4L, 6L, 5L, 2L, 2L, 1L, 6L, 18L, 4L, 5L, 12L, 17L, 5L, 8L, 
2L, 3L, 4L, 14L, 5L, 13L, 3L, 2L, 5L, 13L, 8L, 8L, 7L, 4L, 7L, 
5L, 15L, 13L, 3L, 1L, 6L, 4L, 13L, 4L, 4L, 9L, 13L, 15L, 15L, 
9L, 13L, 7L, 5L, 8L, 1L, 2L, 5L, 15L, 2L, 5L, 2L, 14L, 1L, 16L, 
2L, 7L, 8L, 4L, 9L, 6L, 9L, 6L, 19L, 1L, 19L, 13L, 10L, 6L, 6L, 
16L, 10L, 16L, 6L, 8L, 11L, 2L, 16L, 9L, 15L, 18L, 3L, 10L, 14L, 
19L, 18L, 3L, 1L, 13L, 7L, 12L, 9L, 6L, 5L, 6L, 9L, 14L, 11L, 
16L, 10L, 3L, 19L, 11L, 9L, 14L, 1L, 7L, 19L, 7L, 3L, 7L, 5L, 
2L, 9L, 2L, 10L, 11L, 7L, 5L, 7L, 16L, 14L, 7L, 6L, 3L, 7L, 7L, 
14L, 3L, 7L, 4L, 10L, 17L, 10L, 19L, 9L, 8L, 9L, 1L, 14L, 8L, 
14L, 10L, 13L, 4L, 9L, 19L, 7L, 5L, 4L, 10L, 6L, 9L, 19L, 19L, 
14L, 19L, 15L, 14L, 17L, 6L, 1L, 1L, 10L, 7L, 1L, 11L, 19L, 16L, 
19L, 14L, 16L, 10L, 11L, 6L, 15L, 9L, 10L, 10L, 16L, 9L, 14L, 
14L, 7L, 15L, 5L, 1L, 7L, 5L, 2L, 10L, 2L, 19L, 11L, 7L, 11L, 
7L, 10L, 19L, 15L, 11L, 11L, 5L, 16L, 7L, 4L, 10L, 18L, 1L, 19L, 
10L, 11L, 9L, 19L, 12L, 14L, 14L, 11L, 14L, 4L, 6L, 3L, 16L, 
1L, 1L, 10L, 17L, 5L, 5L, 10L, 1L, 4L, 1L, 5L, 15L, 15L, 13L, 
4L, 14L, 2L, 11L, 4L, 17L, 7L, 11L, 1L, 1L, 15L, 1L, 10L, 11L, 
4L, 18L, 18L, 1L, 9L, 12L, 7L, 19L, 11L, 14L, 16L, 4L, 4L, 10L, 
11L, 16L, 9L, 11L, 16L, 1L, 18L, 2L, 2L, 10L, 1L, 2L, 11L, 18L, 
8L, 14L, 4L, 19L, 15L, 11L, 6L, 14L, 4L, 1L, 6L, 1L, 2L, 13L, 
11L, 6L, 19L, 1L, 6L, 1L, 17L, 6L, 19L, 10L, 15L, 4L, 6L, 5L, 
2L, 2L, 1L, 6L, 18L, 4L, 5L, 12L, 17L, 5L, 8L, 2L, 3L, 4L, 14L, 
5L, 13L, 3L, 2L, 5L, 13L, 8L, 8L, 7L, 4L, 7L, 5L, 15L, 13L, 3L, 
1L, 6L, 4L, 13L, 4L, 4L, 9L, 13L, 15L, 15L, 9L, 13L, 7L, 5L, 
8L, 1L, 2L, 5L, 15L, 2L, 5L, 2L, 14L, 1L, 16L, 2L, 7L, 8L, 4L, 
9L, 6L, 9L, 6L, 19L, 1L, 19L, 13L, 10L, 6L, 6L, 16L, 10L, 16L, 
6L, 8L, 11L, 2L, 16L, 9L, 15L, 18L, 3L, 10L, 14L, 19L, 18L, 3L, 
1L, 13L, 7L, 12L, 9L, 6L, 5L, 6L, 9L, 14L, 11L, 16L, 10L, 3L, 
19L, 11L, 9L, 14L, 1L, 7L, 19L, 7L, 3L, 7L, 5L, 2L, 9L, 2L, 10L, 
11L, 7L, 5L, 7L, 16L, 14L, 7L, 6L, 3L, 7L, 7L, 14L, 3L, 7L, 4L, 
10L, 17L, 10L, 19L, 9L, 8L, 9L, 1L, 14L, 8L, 14L, 10L, 13L, 4L, 
9L, 19L, 7L, 5L, 4L, 10L, 6L, 9L, 19L, 19L, 14L, 19L, 15L, 14L, 
17L, 6L, 1L, 1L, 10L, 7L, 1L, 11L, 19L, 16L, 19L, 14L, 16L, 10L, 
11L, 6L, 15L, 9L, 10L, 10L, 16L, 9L, 14L, 14L, 7L, 15L, 5L, 1L, 
7L, 5L, 2L, 10L, 2L, 19L, 11L, 7L, 11L, 7L, 10L, 19L, 15L, 11L, 
11L, 5L, 16L, 7L, 4L, 10L, 18L, 1L, 19L, 10L, 11L, 9L, 19L, 12L, 
14L, 14L, 11L, 14L, 4L, 6L, 3L, 16L, 1L, 1L, 10L, 17L, 5L, 5L, 
10L, 1L, 4L, 1L, 5L, 15L, 15L, 13L, 4L, 14L, 2L, 11L, 4L, 17L, 
7L, 11L, 1L), .Label = c("ALA", "ARG", "ASN", "ASP", "GLN", "GLU", 
"GLY", "HIS", "ILE", "LEU", "LYS", "MET", "PHE", "PRO", "SER", 
"THR", "TRP", "TYR", "VAL"), class = "factor"), Energy_Profile = c(-0.017, 
-0.018, -0.02, -0.021, -0.022, -0.024, -0.026, -0.027, -0.028, 
-0.028, -0.028, -0.026, -0.025, -0.024, -0.022, -0.021, -0.02, 
-0.02, -0.021, -0.022, -0.023, -0.024, -0.024, -0.026, -0.026, 
-0.025, -0.024, -0.023, -0.023, -0.022, -0.021, -0.02, -0.02, 
-0.02, -0.019, -0.018, -0.019, -0.019, -0.02, -0.02, -0.022, 
-0.023, -0.024, -0.025, -0.026, -0.027, -0.027, -0.026, -0.026, 
-0.027, -0.027, -0.027, -0.027, -0.026, -0.026, -0.025, -0.024, 
-0.022, -0.021, -0.021, -0.02, -0.02, -0.021, -0.023, -0.024, 
-0.025, -0.026, -0.027, -0.028, -0.028, -0.028, -0.027, -0.027, 
-0.027, -0.027, -0.027, -0.026, -0.026, -0.026, -0.025, -0.025, 
-0.024, -0.024, -0.024, -0.024, -0.024, -0.025, -0.026, -0.027, 
-0.028, -0.028, -0.028, -0.028, -0.028, -0.027, -0.026, -0.025, 
-0.026, -0.026, -0.025, -0.026, -0.027, -0.027, -0.027, -0.025, 
-0.024, -0.023, -0.022, -0.02, -0.019, -0.018, -0.019, -0.019, 
-0.02, -0.021, -0.022, -0.023, -0.024, -0.025, -0.027, -0.029, 
-0.031, -0.034, -0.035, -0.035, -0.035, -0.034, -0.032, -0.03, 
-0.026, -0.024, -0.023, -0.022, -0.022, -0.023, -0.024, -0.027, 
-0.029, -0.032, -0.035, -0.036, -0.037, -0.037, -0.037, -0.036, 
-0.035, -0.033, -0.032, -0.032, -0.031, -0.031, -0.032, -0.033, 
-0.034, -0.036, -0.038, -0.04, -0.041, -0.042, -0.044, -0.045, 
-0.044, -0.043, -0.041, -0.04, -0.037, -0.035, -0.032, -0.031, 
-0.029, -0.029, -0.029, -0.029, -0.03, -0.031, -0.031, -0.031, 
-0.031, -0.029, -0.027, -0.025, -0.023, -0.021, -0.019, -0.018, 
-0.017, -0.018, -0.019, -0.023, -0.026, -0.031, -0.036, -0.04, 
-0.043, -0.045, -0.044, -0.043, -0.039, -0.037, -0.033, -0.03, 
-0.028, -0.027, -0.027, -0.026, -0.025, -0.025, -0.025, -0.024, 
-0.023, -0.023, -0.024, -0.025, -0.026, -0.028, -0.029, -0.029, 
-0.029, -0.029, -0.029, -0.029, -0.028, -0.029, -0.031, -0.033, 
-0.034, -0.036, -0.037, -0.039, -0.039, -0.039, -0.038, -0.037, 
-0.036, -0.035, -0.033, -0.033, -0.032, -0.031, -0.03, -0.029, 
-0.028, -0.027, -0.025, -0.023, -0.022, -0.021, -0.02, -0.019, 
-0.019, -0.018, -0.019, -0.018, -0.018, -0.017, -0.017, -0.016, 
-0.015, -0.014, -0.013, -0.012, -0.011, -0.011, -0.013, -0.014, 
-0.017, -0.02, -0.024, -0.027, -0.03, -0.031, -0.032, -0.03, 
-0.029, -0.027, -0.025, -0.023, -0.021, -0.021, -0.022, -0.023, 
-0.025, -0.026, -0.028, -0.031, -0.033, -0.035, -0.035, -0.035, 
-0.036, -0.036, -0.034, -0.033, -0.031, -0.028, -0.025, -0.023, 
-0.021, -0.019, -0.017, -0.015, -0.014, -0.013, -0.012, -0.011, 
-0.011, -0.011, -0.012, -0.013, -0.016, -0.018, -0.02, -0.022, 
-0.024, -0.026, -0.027, -0.027, -0.026, -0.025, -0.024, -0.023, 
-0.022, -0.021, -0.02, -0.021, -0.022, -0.023, -0.024, -0.024, 
-0.024, -0.025, -0.025, -0.023, -0.022, -0.021, -0.021, -0.02, 
-0.018, -0.018, -0.018, -0.017, -0.016, -0.016, -0.016, -0.016, 
-0.016, -0.017, -0.019, -0.02, -0.021, -0.023, -0.024, -0.026, 
-0.027, -0.027, -0.027, -0.028, -0.028, -0.028, -0.028, -0.027, 
-0.026, -0.025, -0.023, -0.022, -0.02, -0.02, -0.02, -0.02, -0.021, 
-0.022, -0.023, -0.025, -0.025, -0.026, -0.026, -0.026, -0.026, 
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-0.027, -0.025, -0.024, -0.023, -0.022, -0.02, -0.019, -0.018, 
-0.019, -0.019, -0.02, -0.021, -0.022, -0.023, -0.024, -0.025, 
-0.027, -0.029, -0.031, -0.034, -0.035, -0.036, -0.035, -0.034, 
-0.032, -0.03, -0.027, -0.025, -0.024, -0.023, -0.023, -0.024, 
-0.025, -0.027, -0.03, -0.032, -0.035, -0.036, -0.037, -0.037, 
-0.037, -0.036, -0.035, -0.033, -0.032, -0.032, -0.031, -0.031, 
-0.032, -0.033, -0.034, -0.036, -0.038, -0.04, -0.041, -0.042, 
-0.044, -0.044, -0.044, -0.043, -0.041, -0.039, -0.037, -0.034, 
-0.031, -0.03, -0.029, -0.028, -0.029, -0.029, -0.03, -0.031, 
-0.031, -0.032, -0.031, -0.029, -0.027, -0.025, -0.023, -0.021, 
-0.019, -0.018, -0.017, -0.018, -0.019, -0.023, -0.026, -0.031, 
-0.036, -0.04, -0.043, -0.045, -0.044, -0.043, -0.039, -0.037, 
-0.033, -0.03, -0.028, -0.027, -0.026, -0.026, -0.025, -0.025, 
-0.024, -0.023, -0.023, -0.024, -0.026, -0.027, -0.029, -0.03, 
-0.031, -0.031, -0.031, -0.03, -0.03, -0.029, -0.028, -0.029, 
-0.029, -0.031, -0.033, -0.035, -0.036, -0.039, -0.039, -0.04, 
-0.039, -0.039, -0.038, -0.037, -0.037, -0.036, -0.036, -0.035, 
-0.034, -0.032, -0.031, -0.028, -0.026, -0.024, -0.023, -0.022, 
-0.022, -0.021, -0.022, -0.022, -0.022, -0.022, -0.022, -0.021, 
-0.021, -0.02, -0.019, -0.018, -0.017, -0.015, -0.014, -0.014, 
-0.015, -0.017, -0.02, -0.023, -0.026, -0.029, -0.032, -0.033, 
-0.033, -0.031, -0.03, -0.029, -0.027, -0.025, -0.023, -0.023, 
-0.024, -0.025, -0.027, -0.029, -0.032, -0.034, -0.037, -0.039, 
-0.04, -0.041, -0.041, -0.04, -0.039, -0.036, -0.033, -0.03, 
-0.026, -0.023, -0.021, -0.019, -0.017, -0.016, -0.015, -0.015, 
-0.014, -0.014, -0.013, -0.013)), .Names = c("Model", "AA_Number", 
"AA", "Energy_Profile"), row.names = c(NA, 614L), class = "data.frame")

df_templates：

structure(list(Model = 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, 
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, 
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3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("2kqx_renumberedA", 
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6L, 11L, 1L, 2L, 6L, 10L, 17L, 5L, 5L, 10L, 1L, 1L, 1L, 6L, 1L, 
15L, 13L, 4L, 14L, 2L, 11L, 16L, 17L), .Label = c("ALA", "ARG", 
"ASN", "ASP", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS", 
"MET", "PHE", "PRO", "SER", "THR", "TRP", "TYR", "VAL"), class = "factor"), 
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    -0.033, -0.032, -0.031, -0.032, -0.032, -0.033, -0.034, -0.035, 
    -0.036, -0.037, -0.038, -0.039)), .Names = c("Model", "AA_Number", 
"AA", "Energy_Profile"), class = "data.frame", row.names = c(NA, 
-532L))

在我提供的df_test数据中，我只能在达到字符数限制时添加一个图。

Answer 1

您的数据框df_templates也有分面，因为它与Model中提供的列facet_wrap()相同。例如，如果您将此列重命名为Model2

colnames(df_templates)<-c("AA_Number","AA","Energy_Profile","Model2")

然后这个数据框不是刻面的。

ggplot(df_test, aes(x=AA_Number,y=Energy_Profile,col='red')) + geom_line()  + 
  geom_hline(yintercept=-0.03, colour='blue') + 
   geom_line(data=df_templates,colour="green")+
  facet_wrap(~Model,ncol=3)

enter image description here

为facet_wrap（）中的所有绘图添加一个常量线

1 个答案: