Improving my Heatmap in R after error. (pheatmap)

Improving my Heatmap in R after error. (pheatmap) - r

I need to reproduce this heatmap as best as possible in the next couple of days.
My data is an xlsx file with multiple sheets, I cleaned it as good as I could and it should be in an okay format. Do you have an Idea how to create a heatmap as similar as possible to this ? And what would be the most convenient way.
Whenever I use Pheatmap I get this error.. not sure how to fix it.
Error in hclust(d, method = method) :
NA/NaN/Inf in foreign function call (arg 10)
Issue was solved with: (much thanks to #starja)
So I tried to do it to the best of my abilities and I got a heatmap. This was the code in the end!
all_data2 <- cbind(amino,sphingo,hexoses,phospha,lyso,all_data)
matrix_data <- as.matrix(all_data2[, 3:73])
log(matrix_data)
rownames(matrix_data) <- all_data2$`Sample Identification`
pheatmap(
mat = matrix_data
)
Is there any way I can do a seperation into the analyte classes, like in the example given which are basically the data from the different sheets ? and is there a way to enlarge it a bit ?
How could I imputate the NA´s easily, so the resulting data is not changed.
this is the heatmap I do need help improving it!! so anyone who has tips to Improve on my code is very welcome! :)
Here is one of the sheets of the sample data
Heatmap_data <- structure(list(`Sample Identification` = c(2, 2.2, 3, 3.2, 4,
4.2, 6, 6.2, 7, 7.2, 8, 8.2, 9, 9.2, 10, 10.2, 11, 11.2, 12,
12.2, 13, 13.2, 14, 14.2, 15, 15.2, 16, 16.2, 17, 17.2, 18, 18.2,
20, 20.2, 22, 22.2, 23, 23.2, 24, 24.2, 25, 25.2, 26, 26.2, 28,
28.2, 29, 29.2, 33, 33.2, 34, 34.2, 36, 36.2, 37, 37.2, 38, 38.2,
39, 39.2, 40, 40.2, 41, 41.2, 42, 42.2, 43, 43.2, 44, 44.2, 45,
45.2, 46, 46.2, 47, 47.2, 48, 48.2, 49, 49.2, 50, 50.2, 51, 51.2,
52, 52.2, 54, 54.2, 56, 56.2), `Time point` = c("T0", "T1", "T0",
"T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1",
"T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0",
"T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1",
"T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0",
"T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1",
"T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0",
"T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1",
"T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1", "T0", "T1"),
C0 = c(62.9, 37.8, 30.8, 25.3, 30.6, 34.6, 38.7, 43.3, 27.4,
35.3, 28.1, 24, 38.7, 37.1, 59.7, 20.4, 21.6, 20, 29.2, 26.7,
49.7, 40.8, 38.8, 37.9, 29.9, 23.6, 46.7, 52.1, 37.6, 40.4,
38.8, 34.9, 50.1, 45.4, 46.5, 43, 13.8, 25.4, 36.5, 48.8,
19.6, 26.5, 45.8, 13.5, 34.3, 42.6, 28.5, 27.5, 31.8, 35.7,
42.2, 42.6, 41.3, 40.4, 39.1, 39.5, 33.7, 34.1, 49, 37.4,
37.1, 51.2, 51.6, 40.2, 40.6, 35.5, 38.2, 46.5, 25, 26.2,
22.4, 22, 42.8, 39.4, 50.7, 46, 45.7, 47.2, 50.7, 24.4, 31.8,
52.8, 46.9, 39.9, 12.2, 19.7, 26.3, 40.7, 20.5, 16.2), C2 = c(17.8,
6.67, 5.41, 4.56, 11.4, 6.73, 8.24, 7.68, 10.8, 11.9, 7.56,
6.21, 4.71, 5.9, 12.3, 7.09, 5.24, 3.68, 11.5, 15, 8.18,
7.96, 7.86, 7.5, 6.68, 5.65, 21.5, 17.7, 8.91, 7.6, 9.33,
9.27, 19.3, 17.8, 8.64, 10.1, 5.08, 4.88, 6.06, 5.07, 6.79,
11.2, 27.4, 6.57, 7.99, 11.4, 11.3, 9.69, 3.51, 3.7, 19.5,
13.2, 10.1, 5.39, 6.47, 6.09, 9.92, 5.76, 14.3, 9.44, 12.3,
9.57, 11.3, 9, 6.53, 7.01, 9.16, 7.05, 9.06, 5.38, 11.4,
4.29, 7.35, 10.4, 20.1, 12.6, 6.85, 8.07, 7.58, 4.07, 9.85,
17.2, 10.3, 14.3, 4.82, 5.04, 7.1, 7.64, 5.81, 4.17), C3 = c(0.645,
0.543, 0.296, 0.245, 0.267, 0.282, 0.437, 0.528, 0.262, 0.316,
0.204, 0.218, 0.358, 0.32, 0.676, 0.248, 0.278, 0.291, 0.23,
0.263, 0.656, 0.617, 0.438, 0.412, 0.304, 0.273, 0.68, 0.998,
0.429, 0.477, 0.337, 0.267, 0.531, 0.504, 0.716, 0.545, 0.215,
0.652, 0.36, 0.543, 0.199, 0.445, 0.432, 0.231, 0.377, 0.749,
0.409, 0.224, 0.182, 0.259, 0.454, 0.437, 0.36, 0.435, 0.452,
0.473, 0.247, 0.325, 0.493, 0.302, 0.687, 1.02, 0.341, 0.305,
0.247, 0.228, 0.289, 0.525, 0.192, 0.316, 0.17, 0.148, 0.425,
0.513, 0.743, 0.692, 0.395, 0.457, 0.388, 0.318, 0.292, 0.455,
0.604, 0.68, 0.18, 0.275, 0.314, 0.549, 0.182, 0.163), `C3-DC (C4-OH)` = c(0.091,
0.068, NA, NA, 0.089, NA, 0.063, 0.044, 0.072, 0.069, 0.049,
0.063, 0.048, 0.048, 0.113, 0.08, 0.049, NA, 0.117, 0.168,
0.054, 0.055, 0.064, 0.047, 0.051, NA, 0.168, 0.11, 0.056,
0.092, 0.064, 0.083, 0.108, 0.102, 0.084, 0.07, 0.048, NA,
NA, NA, 0.117, 0.097, 0.137, 0.123, 0.066, 0.052, 0.068,
0.06, NA, NA, 0.08, 0.12, 0.062, 0.055, NA, 0.051, 0.093,
0.07, 0.062, 0.046, 0.075, 0.081, 0.073, 0.059, 0.046, 0.046,
0.064, NA, 0.06, 0.059, 0.096, NA, 0.275, 0.315, 0.109, 0.072,
NA, 0.046, 0.057, 0.057, 0.077, 0.177, 0.061, 0.07, 0.07,
0.056, 0.06, 0.057, 0.052, 0.05), C4 = c(0.401, 0.252, 0.176,
0.145, 0.168, 0.158, 0.171, 0.181, 0.413, 0.602, 0.174, 0.198,
0.162, 0.205, 0.795, 0.335, 0.258, 0.215, 0.143, 0.153, 0.515,
0.348, 0.233, 0.264, 0.773, 0.647, 0.537, 0.496, 0.202, 0.856,
0.247, 0.188, 0.7, 1.05, 0.185, 0.2, 0.424, 0.383, 0.202,
0.33, 0.357, 0.541, 0.627, 0.671, 0.643, 0.669, 0.256, 0.156,
0.175, 0.212, 0.66, 0.569, 0.257, 0.287, 0.202, 0.325, 0.157,
0.175, 0.535, 0.356, 0.256, 0.475, 0.217, 0.273, 0.19, 0.191,
0.273, 0.438, 0.263, 0.464, 0.437, 0.123, 0.155, 0.18, 0.753,
0.543, 0.186, 0.242, 0.241, 0.322, 0.266, 0.201, 0.505, 0.707,
0.165, 0.254, 0.213, 0.325, 0.146, 0.153), C5 = c(0.14, 0.148,
0.114, 0.131, 0.081, 0.105, 0.108, 0.147, 0.088, 0.128, 0.074,
0.11, 0.108, 0.105, 1.22, 0.581, 0.105, 0.125, 0.073, 0.105,
0.425, 0.172, 0.119, 0.13, 0.149, 0.176, 0.163, 0.142, 0.125,
1.28, 0.118, 0.124, 0.928, 1.28, 0.13, 0.12, 0.431, 0.161,
0.071, 0.154, 0.176, 0.269, 0.163, 2.81, 0.874, 0.492, 0.089,
0.098, 0.094, 0.094, 0.708, 0.948, 0.105, 0.114, 0.105, 0.174,
0.087, 0.112, 0.216, 0.187, 0.094, 0.222, 0.113, 0.476, 0.091,
0.098, 0.097, 0.171, 0.087, 0.844, 0.483, 0.081, 0.065, 0.188,
0.124, 0.105, 0.098, 0.148, 0.092, 0.188, 0.099, 0.138, 0.148,
0.198, 0.073, 0.104, 0.082, 0.155, 0.061, 0.077), C10 = c(0.183,
0.24, 0.137, 0.144, 0.259, 0.189, 0.175, 0.205, 0.264, 0.235,
0.214, 0.163, 0.203, 0.341, 0.192, 0.146, 0.218, 0.133, 0.224,
0.344, 0.293, 0.233, 0.334, 0.203, 0.21, 0.282, 0.209, 0.309,
0.179, 0.155, 0.422, 0.221, 0.264, 0.29, 0.161, 0.177, 0.185,
0.211, 0.186, 0.127, 0.194, 0.364, 0.805, 0.189, 0.342, 0.231,
0.402, 0.429, 0.156, NA, 0.28, 0.376, 0.176, NA, 0.164, 0.219,
0.141, 0.16, 0.297, 0.277, 0.154, 0.151, 0.162, 0.304, 0.171,
0.227, 0.209, 0.205, 0.2, 0.183, 0.326, NA, 0.188, 0.264,
0.221, 0.154, 0.129, 0.205, 0.194, 0.145, 0.141, 0.221, 0.308,
0.475, 0.188, 0.234, 0.239, 0.241, 0.19, 0.206), `C14:1` = c(0.114,
0.082, 0.097, 0.073, 0.129, 0.105, 0.072, 0.088, 0.183, 0.156,
0.094, 0.066, 0.086, 0.122, 0.107, 0.079, 0.121, 0.067, 0.108,
0.168, 0.11, 0.088, 0.092, 0.08, 0.081, 0.089, 0.179, 0.227,
0.078, 0.049, 0.146, 0.079, 0.155, 0.17, 0.082, 0.097, 0.077,
0.067, 0.082, 0.051, 0.098, 0.181, 0.23, 0.114, 0.135, 0.112,
0.217, 0.165, 0.071, 0.056, 0.169, 0.104, 0.124, 0.051, 0.084,
0.08, 0.137, 0.086, 0.111, 0.101, 0.072, 0.055, 0.124, 0.194,
0.101, 0.129, 0.088, 0.077, 0.11, 0.087, 0.198, 0.065, 0.105,
0.185, 0.227, 0.124, 0.069, 0.073, 0.11, 0.08, 0.086, 0.165,
0.106, 0.173, 0.143, 0.137, 0.096, 0.094, 0.119, 0.102),
`C14:2` = c(0.033, 0.023, NA, NA, 0.036, 0.022, NA, 0.025,
0.054, 0.042, 0.021, 0.021, 0.025, 0.037, 0.024, NA, 0.041,
NA, NA, 0.024, 0.027, NA, 0.028, NA, 0.025, 0.033, 0.055,
0.076, 0.027, NA, 0.028, 0.022, 0.045, 0.048, 0.026, 0.041,
NA, NA, NA, NA, 0.024, 0.059, 0.088, 0.023, 0.054, 0.045,
0.053, 0.045, NA, NA, 0.061, 0.034, 0.034, NA, NA, NA, 0.038,
0.021, 0.042, 0.041, NA, NA, 0.034, 0.073, 0.027, 0.033,
0.022, 0.021, 0.028, NA, 0.061, NA, 0.024, 0.046, 0.055,
0.033, NA, 0.03, 0.028, 0.025, 0.022, 0.038, 0.03, 0.061,
0.037, 0.039, 0.027, 0.023, 0.031, 0.025), C16 = c(0.099,
0.133, 0.152, 0.087, 0.116, 0.099, 0.092, 0.13, 0.181, 0.154,
0.112, 0.095, 0.157, 0.161, 0.159, 0.145, 0.176, 0.095, 0.12,
0.14, 0.109, 0.109, 0.099, 0.078, 0.1, 0.074, 0.117, 0.152,
0.098, 0.063, 0.169, 0.127, 0.233, 0.174, 0.111, 0.134, 0.099,
0.076, 0.123, 0.098, 0.109, 0.099, 0.131, 0.096, 0.196, 0.106,
0.109, 0.105, 0.071, 0.066, 0.127, 0.094, 0.116, 0.07, 0.114,
0.099, 0.137, 0.105, 0.12, 0.108, 0.099, 0.094, 0.128, 0.147,
0.108, 0.119, 0.116, 0.13, 0.088, 0.089, 0.137, 0.076, 0.105,
0.163, 0.18, 0.149, 0.112, 0.111, 0.096, 0.133, 0.123, 0.149,
0.147, 0.178, 0.093, 0.131, 0.097, 0.115, 0.128, 0.111),
C18 = c(0.038, 0.06, 0.066, 0.06, 0.048, 0.046, 0.05, 0.067,
0.078, 0.069, 0.051, 0.05, 0.087, 0.08, 0.057, 0.041, 0.084,
0.057, 0.039, 0.046, 0.057, 0.055, 0.039, 0.036, 0.05, 0.037,
0.038, 0.043, 0.046, 0.041, 0.054, 0.056, 0.098, 0.084, 0.041,
0.053, 0.058, 0.026, 0.058, 0.054, 0.036, 0.043, 0.047, 0.082,
0.081, 0.037, 0.041, 0.037, 0.037, 0.032, 0.053, 0.033, 0.037,
0.033, 0.046, 0.057, 0.061, 0.048, 0.048, 0.049, 0.056, 0.052,
0.07, 0.062, 0.06, 0.055, 0.049, 0.043, 0.037, 0.037, 0.053,
0.026, 0.053, 0.068, 0.072, 0.056, 0.046, 0.044, 0.033, 0.067,
0.05, 0.047, 0.052, 0.072, 0.029, 0.052, 0.053, 0.055, 0.071,
0.07), `C18:1` = c(0.137, 0.145, 0.181, 0.114, 0.142, 0.104,
0.108, 0.191, 0.247, 0.21, 0.167, 0.107, 0.237, 0.209, 0.217,
0.197, 0.23, 0.108, 0.153, 0.192, 0.123, 0.097, 0.148, 0.146,
0.163, 0.126, 0.186, 0.234, 0.177, 0.072, 0.241, 0.13, 0.33,
0.243, 0.13, 0.197, 0.118, 0.077, 0.161, 0.091, 0.133, 0.16,
0.24, 0.096, 0.176, 0.1, 0.173, 0.18, 0.115, 0.066, 0.149,
0.104, 0.14, 0.073, 0.132, 0.11, 0.154, 0.127, 0.168, 0.162,
0.139, 0.094, 0.179, 0.183, 0.129, 0.154, 0.149, 0.131, 0.125,
0.127, 0.251, 0.151, 0.143, 0.236, 0.307, 0.242, 0.12, 0.127,
0.135, 0.166, 0.167, 0.171, 0.164, 0.252, 0.16, 0.21, 0.147,
0.156, 0.167, 0.127), `C18:2` = c(0.038, 0.04, 0.038, 0.033,
0.04, 0.026, 0.027, 0.07, 0.06, 0.053, 0.032, 0.026, 0.061,
0.054, 0.045, 0.046, 0.065, 0.039, 0.018, 0.022, 0.033, 0.023,
0.043, 0.033, 0.054, 0.048, 0.049, 0.064, 0.056, 0.033, 0.047,
0.032, 0.079, 0.055, 0.047, 0.083, 0.022, 0.015, 0.034, 0.021,
0.023, 0.043, 0.06, 0.017, 0.058, 0.044, 0.028, 0.029, 0.028,
0.022, 0.047, 0.031, 0.033, 0.026, 0.029, 0.023, 0.047, 0.038,
0.052, 0.053, 0.033, 0.023, 0.045, 0.05, 0.032, 0.037, 0.033,
0.029, 0.025, 0.026, 0.068, 0.059, 0.04, 0.046, 0.074, 0.068,
0.033, 0.06, 0.027, 0.055, 0.042, 0.038, 0.036, 0.068, 0.034,
0.055, 0.03, 0.039, 0.04, 0.034)), row.names = c(NA, -90L
), class = c("tbl_df", "tbl", "data.frame"))

Here is a basic heatmap with which you can work. I guess problem was that you've had non-numerical values still in your matrix. Notice that for the actual data I only used the numerical values and set the names as the rownames of the matrix.
library(pheatmap)
matrix_data <- as.matrix(Heatmap_data[, 3:15])
rownames(matrix_data) <- Heatmap_data$`Sample Identification`
pheatmap(
mat = matrix_data
)
Created on 2020-09-09 by the reprex package (v0.3.0)

Related

Error in imputation values with missing values in MFA

My data structure is based in 151 individuals x 51 variables (1 categorical variable made up by 3 categories or groups(OO, NUTS, LFD), and 50 continuous numerical variables). The background of the experiment is based in 3 interventions in which patients go through different treatments. The variables are gene expression results in the form of numbers, which I intend to form clusters or to observe groups.
Of course, there are MISSING values. The missing values due to lack of samples of the individuals could be related to the clinical trial itself. However, in my database, the one I am going to attach a slice sample, missingness I would say is completely at random. Why is that? The individuals that attended to periodical meetings, get their blood collected and afterwards due to processing erros, have several genes amplified, and others not amplified, from my standpoint, is random. It is true that in the origin could be related somehow to the clinical trial.
Reviewing the potential approaches, I found the missMDA package, which I have been reviewing lately. My first doubt, which I think is correct is to confirm if the MFA is the best approach to analyse my database. Other options could be:
PCA excluding the categorical variable to run quantitative variables?
FAMD looks like more to combine quantitative and qualitative, not very much as a grouping variable as it is my case
The 'gene' example database (provided in missMDA package) is similar to mine, and the approach is MFA, and the theorical basis (I am not an expert) looks like to me correct. However following the steps in 'missMDA: A Package for Handling Missing Values
in Multivariate Data Analysis' I find the next errors (I have tried several options formulating the group):
#The ncp estimated excluding the group using PCA approach (just quantiative continuous variabes was 5, the variables are scaled type = "s"
res.mfa <- imputeMFA(PCA[, -1], group = c(2:51), type = "s", ncp = 5)
# Error in if (type[g] == "s") { : missing value where TRUE/FALSE needed
res.mfa <- imputeMFA(df[, -1], group = 50, type = "s", ncp = 5)
#Error in (cumsum(group.mod)[g - 1] + 1):cumsum(group.mod)[g] : NA/NaN argument
The df example reduced to body limitation (is already standardized (scale funtion))
df <- structure(list(grup_int = structure(c(3L, 3L, 3L, 2L, 3L, 1L,
1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L,
1L, 1L, 1L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 2L,
1L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 2L,
1L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L,
1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L,
3L), levels = c("OO", "NUTS", "LFD"), label = "GENERAL: Grupo de intervención", class = "factor"),
ppara = structure(c(2.882, -0.091, -0.519, NA, NA, NA, 0.102,
NA, NA, -0.292, NA, 0.73, 0.555, -0.11, -1.022, -0.027, 0.114,
NA, 0.593, -0.768, -0.53, NA, NA, -0.224, -0.327, -0.952,
-0.185, NA, NA, -0.52, -1.175, 0.286, NA, -0.398, NA, -0.144,
NA, NA, NA, -0.903, NA, -0.258, -0.897, -0.751, -0.662, -0.628,
-0.779, NA, NA, 0.743, 0.142, NA, NA, NA, NA, -0.607, -0.739,
-0.437, NA, -1.152, -1.272, 0.608, 1.317, -0.547, 3.081,
0.647, -0.799, -0.682, -0.354, NA, 0.031, NA, 1.992, 3.665,
NA, NA, -0.027, -0.318, -0.916, NA, NA, NA, -0.31, -1.113,
-0.089, -0.391, NA, 0.134, -0.574, -0.291, -0.012, NA, NA,
-0.607, NA, -1.018, -0.702, NA, 1.624, 0.841, 0.869, NA,
0.373, -0.857, 0.007, 0.099, NA, -0.613, -0.005, 1.059, 1.525,
2.504, NA, NA, -0.737, -0.864, NA, NA, -0.901, -0.257, 0.968,
NA, -0.743, -0.023, NA, NA, NA, -0.219, NA, 0.226, -0.366,
-0.788, NA, -0.942, -0.215, NA, -0.659, -0.532, 3.052, 1.426,
NA, 0.366, -0.52, 0.377, NA, 1.421, NA, NA, NA, NA, NA), dim = c(151L,
1L), "`scaled:center`" = 1.35197894736842, "`scaled:scale`" = 0.835827593173089),
ppard = structure(c(0.214, -0.376, -0.152, -0.121, -0.147,
0.681, 0.373, 0.144, 0.291, -0.554, -0.344, -0.463, 0.565,
-0.612, -1.073, -0.038, 0.373, -0.801, 0.052, -0.765, -0.891,
0.56, 0.059, -0.396, -0.447, -0.83, -0.088, -0.543, -0.126,
-0.678, -0.769, 0.079, NA, -0.345, NA, -0.12, -0.845, 0.182,
-0.234, -0.785, 0.257, -0.035, -0.426, -0.428, -0.668, -0.51,
-0.626, -0.264, -0.588, -0.508, -0.184, NA, 2.647, -0.403,
-0.99, -0.727, 0.047, -0.487, -0.286, -0.865, -0.841, 3.273,
0.653, -0.439, 3.509, 0.653, -0.971, -0.298, -0.343, NA,
0.44, 0.143, 0.847, 5.239, 0.973, 2.861, 0.36, -0.538, 4.029,
-0.154, -0.361, -0.047, -0.222, -1.302, -0.047, -0.005, NA,
0.507, -0.244, -0.38, -0.116, -0.958, NA, -0.194, NA, -0.713,
-0.498, 0.239, 0.405, 1.012, 0.542, 0.22, 0.846, -0.455,
-0.003, 0.477, -0.096, -0.301, 0.85, 0.575, 0.606, 0.713,
NA, -1.197, -0.766, -0.846, -0.583, -0.53, -0.246, -1.062,
-0.221, -0.265, -1.083, -0.449, -1.117, -0.899, 0.146, -0.561,
-0.471, 0.171, -0.263, -0.33, 0.028, -0.625, -0.564, 4.08,
-0.444, 0.092, 0.581, 0.14, -0.112, -0.349, 0.198, 0.077,
NA, -0.409, NA, 0.05, 0.489, NA, 0.903), dim = c(151L, 1L
), "`scaled:center`" = 1.40111428571429, "`scaled:scale`" = 0.811830324669782),
pparg = structure(c(1.298, -0.171, 0.056, 0.017, -0.428,
0.257, 0.719, -0.1, 1.28, -0.19, 0.344, -0.629, 1.572, -0.713,
-0.739, 1.027, 0.22, -0.321, -1.283, -1.283, 0.1, -0.38,
0.257, -0.325, -0.572, -0.705, -0.442, -1.129, NA, -1.129,
-0.869, 2.129, NA, 0.044, NA, -0.352, -0.568, 1.976, -0.823,
-0.843, 0.529, -0.046, -0.223, -0.646, -0.308, 0.227, 0.028,
-0.352, -0.097, 0.054, -0.512, NA, 3.624, 0.399, -0.588,
-0.986, -0.672, -0.612, 0.362, -1.199, -0.896, -0.675, 0.354,
-0.641, 3.271, 0.3, -1.196, -0.789, -0.285, NA, -0.244, -0.156,
1.765, 4.562, 1.241, NA, -0.407, -0.84, 2.605, 0.016, -0.619,
-0.317, -0.472, -1.337, -0.555, -0.932, NA, 0.312, -0.4,
-1.241, 0.22, -0.937, NA, -0.134, NA, -0.241, -0.176, 0.138,
-0.716, 0.312, 0.061, -0.682, 0.609, 0.174, -0.07, -0.554,
-0.224, 0.107, 0.228, 0.491, 2.581, 1.164, NA, -0.368, -0.816,
-0.471, -0.126, -0.144, -0.281, -0.635, 0.618, 0.649, -1.601,
-0.913, -1.271, -0.756, 0.529, 0.047, -0.922, 1.729, -1.05,
0.01, 0.8, 1.488, 0.452, 1.876, -0.13, 0.485, -0.041, 0.211,
-0.859, -0.621, -0.515, -0.177, NA, -0.632, NA, -0.545, 1.322,
NA, 1.602), dim = c(151L, 1L), "`scaled:center`" = 1.2910652173913, "`scaled:scale`" = 0.700929843387113),
nr1h3 = structure(c(0.407, 0.244, -0.42, -0.013, -0.829,
0.492, 0.881, 0.171, -0.709, 0.22, 0.026, -0.45, 0.262, 1.012,
-0.847, 0.484, -0.607, -0.601, -0.821, -0.194, -0.568, 0.673,
-0.607, -0.458, -0.494, -0.492, 0.623, -0.93, -0.058, -0.41,
-0.784, 0.052, -2.094, 0.07, NA, 0.272, 0.024, 0.458, 0.832,
-0.75, 0.074, 0.766, -0.158, 0.463, -0.184, -0.469, -0.269,
-0.387, -0.337, -0.271, 1.308, NA, NA, -0.497, -0.944, -1.006,
-1.024, -0.783, 0.276, -1.132, -0.827, -0.343, 2.697, -0.497,
NA, 0.897, NA, -0.294, 0.517, NA, 0.236, 0.241, 0.679, NA,
2.847, 2.11, 0.069, 1.473, 4.445, NA, NA, NA, -0.207, -1.697,
-0.069, 0.105, NA, 1.004, NA, NA, -0.428, -1.257, NA, 1.736,
NA, -0.691, 0.09, 0.226, 0.674, -0.55, 0.699, -0.258, 0.906,
-0.691, 0.768, 0.484, -0.199, -0.62, 0.128, 0.742, 2.149,
0.737, NA, -1.296, NA, -0.545, 0.495, -0.888, -0.926, -0.97,
-0.279, -0.028, -1.396, -1.321, -1.254, -0.858, -0.592, -0.691,
-0.885, 0.077, 0.641, -0.643, -0.286, -0.932, -0.77, 4.228,
-0.589, 0.254, 0.947, -0.461, -0.469, 0.023, -0.476, -0.071,
NA, -0.445, NA, -0.207, 0.469, NA, 2.62), dim = c(151L, 1L
), "`scaled:center`" = 1.30208396946565, "`scaled:scale`" = 0.609461200104939),
nr1h2 = structure(c(-0.49, 0.333, -0.525, -0.673, -0.442,
-0.029, 0.504, 0.169, -0.193, -0.641, -0.405, 0.01, 1.152,
0.002, -0.478, -0.514, -0.077, -0.61, -0.404, -0.544, -0.434,
0.448, 0.162, -0.463, -0.447, -0.81, -0.209, -0.194, 0.637,
-0.427, -0.366, -0.211, -1.252, -0.205, NA, -0.003, -0.277,
0.027, 0.172, -0.569, 0.343, 0.601, -0.468, -0.254, 0.001,
0.092, -0.416, -0.224, -0.518, -0.356, -0.401, NA, 1.952,
-0.607, -0.721, -0.481, -0.375, -0.499, -0.01, -0.822, -0.704,
0.01, 0.686, -0.429, 6.483, 0.214, -0.302, 0.019, -0.323,
NA, 0.566, 0.441, 0.273, 2.845, 0.409, 4.469, 0.115, -0.21,
4.747, 0.324, -0.453, -0.528, -0.057, -0.943, -0.463, -0.127,
NA, 0.056, -0.343, -0.05, -0.539, -0.785, NA, -0.327, NA,
-0.466, -0.413, -0.181, 0.43, 0.548, -0.098, 0.565, 0.487,
-0.571, 0.046, 0.251, 0.113, 0.382, 0.343, 0.919, 0.215,
0.433, NA, -0.757, -0.711, -0.656, -0.525, -0.482, -0.268,
-0.714, -0.101, 0.093, -0.493, -0.626, -0.845, -0.7, -0.48,
-0.404, -0.668, -0.028, 0.2, -0.506, -0.078, -0.84, -0.54,
3.735, -0.366, -0.326, 0.271, 0.199, -0.624, -0.504, 0.573,
-0.131, NA, -0.212, NA, 0.092, 0.175, NA, 0.61), dim = c(151L,
1L), "`scaled:center`" = 1.38648936170213, "`scaled:scale`" = 1.07206008371747),
rxra = structure(c(0.003, -0.137, -0.372, -0.339, 0.001,
-0.083, 0.371, -0.04, -0.286, -0.707, -0.405, 0.067, 0.205,
-0.515, -0.48, -0.25, -0.28, -0.408, -0.649, -0.735, -0.722,
0.391, -0.008, 0.584, -0.517, -0.531, -0.268, -0.405, NA,
0.39, -0.025, -0.02, NA, -0.119, NA, -0.586, -0.449, -0.152,
-0.343, -0.126, -0.528, 0.012, -0.769, 0.074, -0.213, -0.501,
-0.624, -0.602, -0.847, -0.655, -0.619, NA, 2.641, -0.764,
-0.931, -0.833, 0.084, -0.065, -0.135, -0.77, -0.665, 0.893,
0.544, -0.512, 7.66, 0.111, -0.297, 0.248, -0.305, NA, 0.041,
0.515, 0.562, 4.191, 0.701, 1.726, -0.371, -0.225, 2.191,
0.063, -0.714, 0.049, 0.049, -1.099, -0.875, -0.101, NA,
-0.152, -0.058, 0.084, -0.564, -0.49, NA, 0.138, NA, -0.592,
-0.782, 0.033, 0.165, 0.161, 0.576, 1.449, 2.191, -0.21,
-0.182, 0.547, 0.163, 0.057, 1.104, 0.842, 0.208, 0.941,
NA, -0.817, -0.543, -0.4, -0.344, -0.747, -0.811, -1.077,
-0.334, 0.312, -0.541, -0.726, -0.872, -0.365, -0.232, 0.04,
-0.334, -0.111, -0.304, -0.476, 0.172, -0.389, -0.233, 3.064,
-0.104, -0.063, -0.049, 0.686, -0.851, -0.243, -0.098, 0.067,
NA, -0.076, NA, 0.112, 0.429, NA, 0.241), dim = c(151L, 1L
), "`scaled:center`" = 1.39937410071942, "`scaled:scale`" = 0.869319134487005),
rxrb = structure(c(-0.489, -0.548, 0.591, 0.137, 0.814, 0.161,
-0.204, -0.242, -0.408, -0.65, -0.242, 0.987, 0.468, -0.6,
-1.373, -0.739, -0.917, -0.565, -0.633, -0.833, -0.81, 0.509,
0.217, 0.388, -0.067, -0.614, -0.055, -0.185, 0.416, 0.94,
-0.399, 0.106, -1.652, -0.902, NA, -0.224, -0.289, -0.568,
0.219, -0.61, -0.913, 0.299, -0.743, -0.183, -0.251, -0.699,
-1.232, -0.43, -0.297, -0.664, -0.273, NA, 3.264, 0.207,
-0.849, -0.253, -0.134, -0.762, 0.206, -0.693, -0.627, 0.747,
-0.139, -0.043, 4.043, 0.643, -0.319, -0.16, -0.185, NA,
0.083, 0.399, 0.147, 6.611, 0.054, -1.572, 0.721, -0.009,
-1.597, -0.062, 1.607, 0.272, -0.169, -1.139, 1.191, 0.293,
NA, 0.196, 0.383, -0.531, 0.02, 3.971, NA, 0.246, NA, -0.154,
-0.51, 0.083, 0.838, 0.19, 0.446, 0.083, 0.219, 1.676, -0.353,
0.421, -0.163, 0.091, 0.758, 0.625, 0.79, 0.084, NA, -0.956,
-0.775, -0.26, -0.446, -0.898, -0.439, -0.713, -0.93, -0.587,
-0.918, -0.161, -0.408, -0.216, -0.311, -0.168, 0.107, 0.15,
0.634, 0.853, 0.179, -0.47, -0.796, -1.533, 0.429, -0.155,
0.578, 0.741, 0.193, 0.067, -0.334, -0.016, NA, -0.006, NA,
0.053, 0.837, NA, -0.265), dim = c(151L, 1L), "`scaled:center`" = 1.39233333333333, "`scaled:scale`" = 0.828513485248186),
cyp27a1 = structure(c(-0.366, -0.342, -0.257, -0.731, 0.498,
-0.251, 1.016, -0.209, 0.086, -0.84, 0.399, -0.466, 0.022,
-0.54, -0.085, 0.018, -0.248, -0.82, -0.666, -0.821, -0.443,
0.187, -0.135, -0.433, 0.494, -0.57, -0.332, -0.088, NA,
-0.004, -0.705, 0.502, NA, -0.313, NA, -0.471, -0.423, -0.398,
-0.272, -0.82, 0.102, 0.183, -0.703, -0.155, -0.437, -0.427,
-0.739, -0.292, -0.586, -0.574, -0.284, NA, NA, -0.42, -1.099,
-0.879, -0.527, -0.609, -0.227, -0.827, -0.765, 6.111, 1.02,
-0.66, NA, 0.53, -0.424, 0.055, -0.184, NA, 0.633, 0.364,
0.418, NA, 0.229, 2.879, -0.264, -0.285, 3.73, 0.569, 0.053,
-0.368, -0.2, -1.217, -0.249, -0.318, NA, 0.37, 0.15, -0.23,
0.258, -0.623, NA, 0.183, NA, -0.321, -0.534, 0.537, -0.046,
0.809, 0.899, 1.415, 1.484, -0.158, -0.109, 0.863, -0.256,
0.281, 0.94, 1.062, 1.988, 0.064, NA, -0.455, -0.959, -0.437,
-0.657, -0.82, 0.022, -0.927, -0.821, 0.878, -1.189, -0.121,
-1.278, -0.811, -0.653, -0.54, -0.576, -0.266, 0.11, -0.433,
-0.075, -0.617, -0.682, 4.587, 0.149, -0.146, 0.384, 0.917,
-0.38, -0.17, 0.082, -0.301, NA, -0.176, NA, 0.135, -0.007,
NA, 2.875), dim = c(151L, 1L), "`scaled:center`" = 1.37367647058824, "`scaled:scale`" = 0.862860015103544),
abca1 = structure(c(-0.275, 0.339, -0.655, -0.414, -0.611,
0.548, -0.053, -0.25, 0.157, -0.472, -0.555, -0.329, 0.027,
-0.017, -0.442, 0.044, -0.191, -0.52, -0.685, -0.778, -0.503,
0.33, 0.045, -0.593, -0.279, -0.651, -0.192, 0.161, -0.159,
0.023, -0.304, 1.422, NA, -0.06, NA, -0.379, -0.488, -0.313,
-0.336, -0.056, -0.7, -0.563, -0.631, -0.275, -0.742, -0.394,
-0.655, -0.432, -0.616, -0.642, -0.141, NA, 0.3, -0.336,
-0.975, -0.667, -0.418, -0.325, 0.318, -0.852, -0.577, -0.766,
0.772, -0.559, 7.974, 0.385, -0.653, -0.261, -0.474, NA,
-0.007, 0.203, 0.908, 2.893, 1.629, 1.134, -0.125, 0.149,
4.345, 1.047, 0.223, -0.071, -0.296, -0.814, -0.64, 0.255,
NA, -0.277, -0.065, 0.479, -0.375, -0.422, NA, 0.292, NA,
-0.422, 0.515, 0.309, 0.11, 0.717, 0.468, 1.66, 1.869, -0.286,
0.075, 0.288, 0.092, -0.088, 0.473, -0.19, 0.548, 0.37, NA,
-0.824, -0.697, -0.561, -0.549, -0.529, -0.846, -0.768, -0.818,
0.101, -0.316, -0.727, -0.192, -0.498, -0.784, 0.324, -0.654,
0.626, -0.297, -0.268, -0.002, -0.21, -0.193, 2.531, 0.516,
-0.403, -0.064, 0.461, -0.481, 0.154, 0.215, -0.275, NA,
-0.527, NA, -0.642, 0.003, NA, 0.59), dim = c(151L, 1L), "`scaled:center`" = 1.66513571428571, "`scaled:scale`" = 1.46050537823946)), row.names = c("50109018",
"50109019", "50109025", "50109026", "50109027", "50118001", "50202099",
"50203004", "50203006", "50203008", "50203009", "50203010", "50203011",
"50203012", "50203013", "50203014", "50203015", "50203016", "50203017",
"50203019", "50203020", "50203022", "50203026", "50203027", "50203029",
"50203030", "50203031", "50203032", "50430001", "50508026", "50508027",
"50521001", "50521002", "50527001", "50601001", "50705001", "60901020",
"60901021", "60901023", "60901024", "60901026", "60901027", "60901028",
"60901029", "60901030", "60901031", "60901033", "60901034", "60901035",
"60901036", "60901037", "60901038", "70107034", "70111021", "70111022",
"70111023", "70111024", "70201047", "70204055", "70204056", "70211014",
"70710002", "70713001", "70713002", "70802011", "70802012", "70802013",
"70802015", "71801001", "71801002", "71801003", "110104017",
"110104019", "110104023", "110104024", "110104027", "110104028",
"110104029", "110104030", "110110005", "110113001", "110113003",
"110113005", "110113006", "110113007", "110113008", "110606056",
"110606061", "111201006", "111201007", "111201014", "111201017",
"111201019", "111201026", "111202007", "111202009", "111202015",
"120715011", "120715012", "120715019", "120715020", "120715021",
"120715022", "120715025", "120715026", "120715027", "120715029",
"120715030", "120715032", "120715033", "120715034", "120715035",
"120715037", "130102008", "130102009", "130102010", "130102012",
"130102013", "130102014", "130104004", "130105044", "130105045",
"130106034", "130106037", "130106038", "130108008", "130108009",
"140101088", "140101091", "140101096", "140101097", "140101099",
"140102087", "140102088", "140102089", "140102090", "140102092",
"140102095", "140103019", "140103020", "140103023", "140103024",
"140103026", "140103027", "140103028", "140103029", "140103030",
"140103033", "140103035", "140103036", "140103038"), class = "data.frame")

It seems like there was a problem where the number of groups is 1. There are a few lines of code that look like:
for (g in 2:length(group))
ind.var[[g]] <- (cumsum(group.mod)[g - 1] + 1):cumsum(group.mod)[g]
With one group, this fails because rather than counting up from 2 to the number of groups, it's counting down from 2 to 1. If you make this line active only if the length(group) > 1 with an if() statement, then it works. I implemented this in a fork of the missMDA package from CRAN. Here's how an example.
df <- structure(list(grup_int = structure(c(3L, 3L, 3L, 2L, 3L, 1L,
1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L,
1L, 1L, 1L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 2L,
1L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 2L,
1L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L,
1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L,
3L), levels = c("OO", "NUTS", "LFD"), label = "GENERAL: Grupo de intervención", class = "factor"),
ppara = structure(c(2.882, -0.091, -0.519, NA, NA, NA, 0.102,
NA, NA, -0.292, NA, 0.73, 0.555, -0.11, -1.022, -0.027, 0.114,
NA, 0.593, -0.768, -0.53, NA, NA, -0.224, -0.327, -0.952,
-0.185, NA, NA, -0.52, -1.175, 0.286, NA, -0.398, NA, -0.144,
NA, NA, NA, -0.903, NA, -0.258, -0.897, -0.751, -0.662, -0.628,
-0.779, NA, NA, 0.743, 0.142, NA, NA, NA, NA, -0.607, -0.739,
-0.437, NA, -1.152, -1.272, 0.608, 1.317, -0.547, 3.081,
0.647, -0.799, -0.682, -0.354, NA, 0.031, NA, 1.992, 3.665,
NA, NA, -0.027, -0.318, -0.916, NA, NA, NA, -0.31, -1.113,
-0.089, -0.391, NA, 0.134, -0.574, -0.291, -0.012, NA, NA,
-0.607, NA, -1.018, -0.702, NA, 1.624, 0.841, 0.869, NA,
0.373, -0.857, 0.007, 0.099, NA, -0.613, -0.005, 1.059, 1.525,
2.504, NA, NA, -0.737, -0.864, NA, NA, -0.901, -0.257, 0.968,
NA, -0.743, -0.023, NA, NA, NA, -0.219, NA, 0.226, -0.366,
-0.788, NA, -0.942, -0.215, NA, -0.659, -0.532, 3.052, 1.426,
NA, 0.366, -0.52, 0.377, NA, 1.421, NA, NA, NA, NA, NA), dim = c(151L,
1L), "`scaled:center`" = 1.35197894736842, "`scaled:scale`" = 0.835827593173089),
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pparg = structure(c(1.298, -0.171, 0.056, 0.017, -0.428,
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1L), "`scaled:center`" = 1.38648936170213, "`scaled:scale`" = 1.07206008371747),
rxra = structure(c(0.003, -0.137, -0.372, -0.339, 0.001,
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rxrb = structure(c(-0.489, -0.548, 0.591, 0.137, 0.814, 0.161,
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cyp27a1 = structure(c(-0.366, -0.342, -0.257, -0.731, 0.498,
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abca1 = structure(c(-0.275, 0.339, -0.655, -0.414, -0.611,
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"`scaled:center`" = 1.66513571428571,
"`scaled:scale`" = 1.46050537823946)), row.names = c("50109018",
"50109019", "50109025", "50109026", "50109027", "50118001", "50202099",
"50203004", "50203006", "50203008", "50203009", "50203010", "50203011",
"50203012", "50203013", "50203014", "50203015", "50203016", "50203017",
"50203019", "50203020", "50203022", "50203026", "50203027", "50203029",
"50203030", "50203031", "50203032", "50430001", "50508026", "50508027",
"50521001", "50521002", "50527001", "50601001", "50705001", "60901020",
"60901021", "60901023", "60901024", "60901026", "60901027", "60901028",
"60901029", "60901030", "60901031", "60901033", "60901034", "60901035",
"60901036", "60901037", "60901038", "70107034", "70111021", "70111022",
"70111023", "70111024", "70201047", "70204055", "70204056", "70211014",
"70710002", "70713001", "70713002", "70802011", "70802012", "70802013",
"70802015", "71801001", "71801002", "71801003", "110104017",
"110104019", "110104023", "110104024", "110104027", "110104028",
"110104029", "110104030", "110110005", "110113001", "110113003",
"110113005", "110113006", "110113007", "110113008", "110606056",
"110606061", "111201006", "111201007", "111201014", "111201017",
"111201019", "111201026", "111202007", "111202009", "111202015",
"120715011", "120715012", "120715019", "120715020", "120715021",
"120715022", "120715025", "120715026", "120715027", "120715029",
"120715030", "120715032", "120715033", "120715034", "120715035",
"120715037", "130102008", "130102009", "130102010", "130102012",
"130102013", "130102014", "130104004", "130105044", "130105045",
"130106034", "130106037", "130106038", "130108008", "130108009",
"140101088", "140101091", "140101096", "140101097", "140101099",
"140102087", "140102088", "140102089", "140102090", "140102092",
"140102095", "140103019", "140103020", "140103023", "140103024",
"140103026", "140103027", "140103028", "140103029", "140103030",
"140103033", "140103035", "140103036", "140103038"), class = "data.frame")
remotes::install_github("davidaarmstrong/missmda", force=TRUE)
library(missMDA)
res.mfa <- imputeMFA(df[, -1], group = 9, type = "s", ncp = 5)

What is Error: Aesthetics must be valid data columns Problematic aesthetic(s) in geom_rect and how to fix it?

I would like to make shade on certain date each year in time-series plot that is similar to this: Using geom_rect for time series shading in R
However, I got error:
Error: Aesthetics must be valid data columns. Problematic aesthetic(s): x = Date.
Did you mistype the name of a data column or forget to add after_stat()?
What is the meaning of that error? I'm sure the error come from geom_rect function, but I don't know how to fix it.
Here is the example of my data:
structure(list(Date = structure(c(4018, 4019, 4020, 4021, 4022,
4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033,
4034, 4035, 4036, 4037, 4038, 4039, 4040, 4041, 4042, 4043, 4044,
4045, 4046, 4047, 4048, 4049, 4050, 4051, 4052, 4053, 4054, 4055,
4056, 4057, 4058, 4059, 4060, 4061, 4062, 4063, 4064, 4065, 4066,
4067, 4068, 4069, 4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077,
4078, 4079, 4080, 4081, 4082, 4083, 4084, 4085, 4086, 4087, 4088,
4089, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 4097, 4098, 4099,
4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110,
4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120, 4121,
4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132,
4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4141, 4142, 4143,
4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 4152, 4153, 4154,
4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165,
4166, 4167, 4168, 4169, 4170, 4171, 4172, 4173, 4174, 4175, 4176,
4177, 4178, 4179, 4180, 4181, 4182, 4183, 4184, 4185, 4186, 4187,
4188, 4189, 4190, 4191, 4192, 4193, 4194, 4195, 4196, 4197, 4198,
4199, 4200, 4201, 4202, 4203, 4204, 4205, 4206, 4207, 4208, 4209,
4210, 4211, 4212, 4213, 4214, 4215, 4216, 4217, 4218, 4219, 4220,
4221, 4222, 4223, 4224, 4225, 4226, 4227, 4228, 4229, 4230, 4231,
4232, 4233, 4234, 4235, 4236, 4237, 4238, 4239, 4240, 4241, 4242,
4243, 4244, 4245, 4246, 4247, 4248, 4249, 4250, 4251, 4252, 4253,
4254, 4255, 4256, 4257, 4258, 4259, 4260, 4261, 4262, 4263, 4264,
4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 4273, 4274, 4275,
4276, 4277, 4278, 4279, 4280, 4281, 4282, 4283, 4284, 4285, 4286,
4287, 4288, 4289, 4290, 4291, 4292, 4293, 4294, 4295, 4296, 4297,
4298, 4299, 4300, 4301, 4302, 4303, 4304, 4305, 4306, 4307, 4308,
4309, 4310, 4311, 4312, 4313, 4314, 4315, 4316, 4317, 4318, 4319,
4320, 4321, 4322, 4323, 4324, 4325, 4326, 4327, 4328, 4329, 4330,
4331, 4332, 4333, 4334, 4335, 4336, 4337, 4338, 4339, 4340, 4341,
4342, 4343, 4344, 4345, 4346, 4347, 4348, 4349, 4350, 4351, 4352,
4353, 4354, 4355, 4356, 4357, 4358, 4359, 4360, 4361, 4362, 4363,
4364, 4365, 4366, 4367, 4368, 4369, 4370, 4371, 4372, 4373, 4374,
4375, 4376, 4377, 4378, 4379, 4380, 4381, 4382, 4383, 4384, 4385,
4386, 4387, 4388, 4389, 4390, 4391, 4392, 4393, 4394, 4395, 4396,
4397, 4398, 4399, 4400, 4401, 4402, 4403, 4404, 4405, 4406, 4407,
4408, 4409, 4410, 4411, 4412, 4413, 4414, 4415, 4416, 4417, 4418,
4419, 4420, 4421, 4422, 4423, 4424, 4425, 4426, 4427, 4428, 4429,
4430, 4431, 4432, 4433, 4434, 4435, 4436, 4437, 4438, 4439, 4440,
4441, 4442, 4443, 4444, 4445, 4446, 4447, 4448, 4449, 4450, 4451,
4452, 4453, 4454, 4455, 4456, 4457, 4458, 4459, 4460, 4461, 4462,
4463, 4464, 4465, 4466, 4467, 4468, 4469, 4470, 4471, 4472, 4473,
4474, 4475, 4476, 4477, 4478, 4479, 4480, 4481, 4482, 4483, 4484,
4485, 4486, 4487, 4488, 4489, 4490, 4491, 4492, 4493, 4494, 4495,
4496, 4497, 4498, 4499, 4500, 4501, 4502, 4503, 4504, 4505, 4506,
4507, 4508, 4509, 4510, 4511, 4512, 4513, 4514, 4515, 4516, 4517,
4518, 4519, 4520, 4521, 4522, 4523, 4524, 4525, 4526, 4527, 4528,
4529, 4530, 4531, 4532, 4533, 4534, 4535, 4536, 4537, 4538, 4539,
4540, 4541, 4542, 4543, 4544, 4545, 4546, 4547, 4548, 4549, 4550,
4551, 4552, 4553, 4554, 4555, 4556, 4557, 4558, 4559, 4560, 4561,
4562, 4563, 4564, 4565, 4566, 4567, 4568, 4569, 4570, 4571, 4572,
4573, 4574, 4575, 4576, 4577, 4578, 4579, 4580, 4581, 4582, 4583,
4584, 4585, 4586, 4587, 4588, 4589, 4590, 4591, 4592, 4593, 4594,
4595, 4596, 4597, 4598, 4599, 4600, 4601, 4602, 4603, 4604, 4605,
4606, 4607, 4608, 4609, 4610, 4611, 4612, 4613, 4614, 4615, 4616,
4617, 4618, 4619, 4620, 4621, 4622, 4623, 4624, 4625, 4626, 4627,
4628, 4629, 4630, 4631, 4632, 4633, 4634, 4635, 4636, 4637, 4638,
4639, 4640, 4641, 4642, 4643, 4644, 4645, 4646, 4647, 4648, 4649,
4650, 4651, 4652, 4653, 4654, 4655, 4656, 4657, 4658, 4659, 4660,
4661, 4662, 4663, 4664, 4665, 4666, 4667, 4668, 4669, 4670, 4671,
4672, 4673, 4674, 4675, 4676, 4677, 4678, 4679, 4680, 4681, 4682,
4683, 4684, 4685, 4686, 4687, 4688, 4689, 4690, 4691, 4692, 4693,
4694, 4695, 4696, 4697, 4698, 4699, 4700, 4701, 4702, 4703, 4704,
4705, 4706, 4707, 4708, 4709, 4710, 4711, 4712, 4713, 4714, 4715,
4716, 4717, 4718, 4719, 4720, 4721, 4722, 4723, 4724, 4725, 4726,
4727, 4728, 4729, 4730, 4731, 4732, 4733, 4734, 4735, 4736, 4737,
4738, 4739, 4740, 4741, 4742, 4743, 4744, 4745, 4746, 4747), class = "Date"),
Cu = c(1.25, 1.25, 1.25, 1.25, 1.15, 1.15, 1.15, 1.15, 1.15,
1.15, 1.15, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75,
2.66, 2.66, 2.66, 2.66, 2.66, 2.66, 1.24, 1.24, 1.24, 1.24,
1.24, 1.24, 1.24, 3.71, 3.71, 3.71, 3.71, 3.71, 3.71, 3.71,
1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 2.13, 2.13, 2.13,
2.13, 2.13, 2.13, 2.13, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73,
0.73, 0.47, 0.47, 0.47, 0.47, 0.47, 0.47, 0.47, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, NA, NA, NA, NA, NA, NA, 1.12,
1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.71, 1.71, 1.71, 1.71,
1.71, 1.71, 1.71, 1.71, NA, NA, NA, NA, NA, NA, 1.28, 1.28,
1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 0.9, 0.9, 0.9, 0.9, 0.9,
0.9, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, NA, NA, NA, NA, NA, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76,
0.76, 0.76, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.65, 0.65,
0.65, 0.65, 0.65, 0.65, 0.65, 0.76, 0.76, 0.76, 0.76, 0.76,
0.76, 0.76, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84,
NA, NA, NA, NA, NA, NA, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68,
0.68, 0.68, NA, NA, NA, NA, NA, NA, 1.16, 1.16, 1.16, 1.16,
1.16, 1.16, 1.16, 0.67, 0.67, 0.67, 0.67, 0.67, 0.67, 0.67,
0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.6, 0.6, 0.6,
0.6, 0.6, 0.6, 0.6, 1.72, 1.72, 1.72, 1.72, 1.72, 1.72, 1.72,
1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, NA,
NA, NA, NA, NA, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31,
1.89, 1.89, 1.89, 1.89, 1.89, 1.89, 1.89, 0.7, 0.7, 0.7,
0.7, 0.7, 0.7, 0.7, 4.35, 4.35, 4.35, 4.35, 4.35, 4.35, 4.35,
1.48, 1.48, 1.48, 1.48, 1.48, 1.48, 1.48, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4.5, 1.95, 1.95, 1.95, 1.95, 1.95, 1.95, 1.95,
1.95, NA, NA, NA, NA, NA, NA, 1.41, 1.41, 1.41, 1.41, 1.41,
1.41, 1.41, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 3.44,
3.44, 3.44, 3.44, 3.44, 3.44, 3.44, 3.01, 3.01, 3.01, 3.01,
3.01, 3.01, 3.01, 3.28, 3.28, 3.28, 3.28, 3.28, 3.28, 3.28,
0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 3.08, 3.08, 3.08,
3.08, 3.08, 3.08, 3.08, 3.08, 3.38, 3.38, 3.38, 3.38, 3.38,
3.38, 1.86, 1.86, 1.86, 1.86, 1.86, 1.86, 1.86, 1.85, 1.85,
1.85, 1.85, 1.85, 1.85, 1.85, 2.25, 2.25, 2.25, 2.25, 2.25,
2.25, 2.25, 2.25, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 2.93, 2.93,
2.93, 2.93, 2.93, 2.93, 2.93, 1.09, 1.09, 1.09, 1.09, 1.09,
1.09, 1.09, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.57,
0.57, 0.57, 0.57, 0.57, 0.57, 0.57, 0.57, 0.57, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.49, 0.49, 0.49, 0.49,
0.49, 0.49, 0.49, 0.49, 0.49, 0.49, NA, NA, NA, NA, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.51, 0.51, 0.51, 0.51, 0.51,
0.51, 0.51, 0.51, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 2.48, 2.48, 2.48, 2.48, 2.48, 2.48,
0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.72, 0.72, 0.72,
0.72, 0.72, 0.72, 0.72, 1.35, 1.35, 1.35, 1.35, 1.35, 1.35,
1.35, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 1.84, 1.84,
1.84, 1.84, 1.84, 1.84, 1.84, 2.56, 2.56, 2.56, 2.56, 2.56,
2.56, 2.56, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.73,
1.73, 1.73, 1.73, 1.73, 1.73, 1.73, 1.33, 1.33, 1.33, 1.33,
1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33,
1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33,
1.33, 1.33, 1.33, 1.33, 2.23, 2.23, 2.23, 2.23, 2.23, 2.23,
2.23, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 3.7, 3.7,
3.7, 3.7, 3.7), Pb = c(3.58, 3.58, 3.58, 3.58, 3, 3, 3, 3,
3, 3, 3, 3.89, 3.89, 3.89, 3.89, 3.89, 3.89, 3.89, 3.89,
5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 4.24, 4.24, 4.24, 4.24, 4.24,
4.24, 4.24, 4.08, 4.08, 4.08, 4.08, 4.08, 4.08, 4.08, 3.42,
3.42, 3.42, 3.42, 3.42, 3.42, 3.42, 3.11, 3.11, 3.11, 3.11,
3.11, 3.11, 3.11, 1.68, 1.68, 1.68, 1.68, 1.68, 1.68, 1.68,
0.67, 0.67, 0.67, 0.67, 0.67, 0.67, 0.67, 1.19, 1.19, 1.19,
1.19, 1.19, 1.19, 1.19, 1.63, 1.63, 1.63, 1.63, 1.63, 1.63,
1.63, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.25, 2.25,
2.25, 2.25, 2.25, 2.25, 2.25, 1.93, 1.93, 1.93, 1.93, 1.93,
1.93, 1.93, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98,
1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.44, 1.44, 1.44, 1.44, 1.44,
1.44, 1.44, 1.44, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 1.02,
1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.31, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.31, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.3,
0.3, 0.3, 0.3, 0.3, 0.3, 0.3, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76,
1.88, 1.88, 1.88, 1.88, 1.88, 1.88, 1.88, 1.88, NA, NA, NA,
NA, NA, NA, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 4.57,
4.57, 4.57, 4.57, 4.57, 4.57, 4.57, 3.93, 3.93, 3.93, 3.93,
3.93, 3.93, 3.93, 7.19, 7.19, 7.19, 7.19, 7.19, 7.19, 7.19,
7.55, 7.55, 7.55, 7.55, 7.55, 7.55, 7.55, 5.37, 5.37, 5.37,
5.37, 5.37, 5.37, 5.37, 4.64, 4.64, 4.64, 4.64, 4.64, 4.64,
4.64, 9.34, 9.34, 9.34, 9.34, 9.34, 9.34, 9.34, 9.34, 4.98,
4.98, 4.98, 4.98, 4.98, 4.98, 4.11, 4.11, 4.11, 4.11, 4.11,
4.11, 4.11, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 2.1, 2.1,
2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 3.31, 3.31, 3.31, 3.31, 3.31,
3.31, 3.11, 3.11, 3.11, 3.11, 3.11, 3.11, 3.11, 3.05, 3.05,
3.05, 3.05, 3.05, 3.05, 3.05, 2.24, 2.24, 2.24, 2.24, 2.24,
2.24, 2.24, 2.24, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
2.47, 2.47, 2.47, 2.47, 2.47, 2.47, 2.47, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.27,
1.27, 1.27, 1.27, 1.27, 1.27, 1.27, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 2.72, 2.72, 2.72, 2.72, 2.72,
2.72, 2.72, 2.72, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61,
NA, NA, NA, NA, NA, NA, 2.26, 2.26, 2.26, 2.26, 2.26, 2.26,
2.26, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11,
1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11,
1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.63,
1.63, 1.63, 1.63, 1.63, 1.63, 1.63, 1.27, 1.27, 1.27, 1.27,
1.27, 1.27, 1.27, 1.48, 1.48, 1.48, 1.48, 1.48), V = c(0.847,
0.847, 0.847, 0.847, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83,
0.83, 1.178, 1.178, 1.178, 1.178, 1.178, 1.178, 1.178, 1.178,
1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.351, 1.351, 1.351, 1.351,
1.351, 1.351, 1.351, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92,
0.92, 0.633, 0.633, 0.633, 0.633, 0.633, 0.633, 0.633, 0.755,
0.755, 0.755, 0.755, 0.755, 0.755, 0.755, 0.268, 0.268, 0.268,
0.268, 0.268, 0.268, 0.268, 0.116, 0.116, 0.116, 0.116, 0.116,
0.116, 0.116, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145,
0.138, 0.138, 0.138, 0.138, 0.138, 0.138, 0.138, 0.392, 0.392,
0.392, 0.392, 0.392, 0.392, 0.392, 0.438, 0.438, 0.438, 0.438,
0.438, 0.438, 0.438, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34,
0.34, 0.517, 0.517, 0.517, 0.517, 0.517, 0.517, 0.517, 0.517,
0.269, 0.269, 0.269, 0.269, 0.269, 0.269, 0.673, 0.673, 0.673,
0.673, 0.673, 0.673, 0.673, 0.673, 0.161, 0.161, 0.161, 0.161,
0.161, 0.161, 0.535, 0.535, 0.535, 0.535, 0.535, 0.535, 0.535,
0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.091, 0.091,
0.091, 0.091, 0.091, 0.091, 0.091, 0.091, 0.121, 0.121, 0.121,
0.121, 0.121, 0.121, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035,
0.035, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.108,
0.108, 0.108, 0.108, 0.108, 0.108, 0.108, 0.278, 0.278, 0.278,
0.278, 0.278, 0.278, 0.278, 0.162, 0.162, 0.162, 0.162, 0.162,
0.162, 0.162, 0.162, 0.162, NA, NA, NA, NA, NA, 0.064, 0.064,
0.064, 0.064, 0.064, 0.064, 0.064, 0.064, NA, NA, NA, NA,
NA, NA, 0.062, 0.062, 0.062, 0.062, 0.062, 0.062, 0.062,
0.095, 0.095, 0.095, 0.095, 0.095, 0.095, 0.095, 0.031, 0.031,
0.031, 0.031, 0.031, 0.031, 0.031, 0.343, 0.343, 0.343, 0.343,
0.343, 0.343, 0.343, 0.767, 0.767, 0.767, 0.767, 0.767, 0.767,
0.767, 0.442, 0.442, 0.442, 0.442, 0.442, 0.442, 0.442, 1.085,
1.085, 1.085, 1.085, 1.085, 1.085, 1.085, 0.711, 0.711, 0.711,
0.711, 0.711, 0.711, 0.711, 1.036, 1.036, 1.036, 1.036, 1.036,
1.036, 1.036, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624,
0.624, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
0.132, 0.132, 0.132, 0.132, 0.132, 0.132, 0.132, 0.032, 0.032,
0.032, 0.032, 0.032, 0.032, 0.032, 0.032, 0.134, 0.134, 0.134,
0.134, 0.134, 0.134, 0.065, 0.065, 0.065, 0.065, 0.065, 0.065,
0.065, 0.201, 0.201, 0.201, 0.201, 0.201, 0.201, 0.201, 0.109,
0.109, 0.109, 0.109, 0.109, 0.109, 0.109, 1.189, 1.189, 1.189,
1.189, 1.189, 1.189, 1.189, 0.479, 0.479, 0.479, 0.479, 0.479,
0.479, 0.479, 0.565, 0.565, 0.565, 0.565, 0.565, 0.565, 0.565,
0.243, 0.243, 0.243, 0.243, 0.243, 0.243, 0.243, 0.142, 0.142,
0.142, 0.142, 0.142, 0.142, 0.142, 2.73, 2.73, 2.73, 2.73,
2.73, 2.73, 2.73, 1.848, 1.848, 1.848, 1.848, 1.848, 1.848,
1.848, 2.126, 2.126, 2.126, 2.126, 2.126, 2.126, 2.126, 2.59,
2.59, 2.59, 2.59, 2.59, 2.59, 2.59, 2.077, 2.077, 2.077,
2.077, 2.077, 2.077, 2.077, 0.912, 0.912, 0.912, 0.912, 0.912,
0.912, 0.912, 1.944, 1.944, 1.944, 1.944, 1.944, 1.944, 1.944,
1.944, 1.38, 1.38, 1.38, 1.38, 1.38, 1.38, 1.384, 1.384,
1.384, 1.384, 1.384, 1.384, 1.384, 0.807, 0.807, 0.807, 0.807,
0.807, 0.807, 0.807, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573,
0.573, 0.573, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.598,
0.598, 0.598, 0.598, 0.598, 0.598, 0.598, 0.535, 0.535, 0.535,
0.535, 0.535, 0.535, 0.535, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063,
0.063, NA, NA, NA, NA, NA, NA, NA, 0.139, 0.139, 0.139, 0.139,
0.139, 0.139, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 0.015, 0.015, 0.015, 0.015, 0.015, 0.015, 0.015,
0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.081, 0.081,
0.081, 0.081, 0.081, 0.081, 0.081, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051,
0.051, 0.051, 0.428, 0.428, 0.428, 0.428, 0.428, 0.428, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.044, 0.044, 0.044,
0.044, 0.044, 0.044, 0.044, 0.057, 0.057, 0.057, 0.057, 0.057,
0.057, 0.057, 0.057, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0.548, 0.548, 0.548, 0.548, 0.548, 0.548, 0.048,
0.048, 0.048, 0.048, 0.048, 0.048, 0.048, 0.019, 0.019, 0.019,
0.019, 0.019, 0.019, 0.019, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.051,
0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.105, 0.105, 0.105,
0.105, 0.105, 0.105, 0.105, 0.105, NA, NA, NA, NA, NA, NA,
0.261, 0.261, 0.261, 0.261, 0.261, 0.261, 0.261, 0.176, 0.176,
0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176,
0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176,
0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.327,
0.327, 0.327, 0.327, 0.327, 0.327, 0.327, 0.254, 0.254, 0.254,
0.254, 0.254, 0.254, 0.254, 0.258, 0.258, 0.258, 0.258, 0.258
), Zn = c(6.19, 6.19, 6.19, 6.19, 8.7, 8.7, 8.7, 8.7, 8.7,
8.7, 8.7, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 9.91, 9.91,
9.91, 9.91, 9.91, 9.91, 7.8, 7.8, 7.8, 7.8, 7.8, 7.8, 7.8,
11.89, 11.89, 11.89, 11.89, 11.89, 11.89, 11.89, 6.86, 6.86,
6.86, 6.86, 6.86, 6.86, 6.86, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6,
7.6, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.18, 2.18, 2.18,
2.18, 2.18, 2.18, 2.18, 2.18, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 3.09, 3.09, 3.09, 3.09, 3.09, 3.09,
3.09, 4.43, 4.43, 4.43, 4.43, 4.43, 4.43, 4.43, 2.28, 2.28,
2.28, 2.28, 2.28, 2.28, 2.28, 3.25, 3.25, 3.25, 3.25, 3.25,
3.25, 3.25, 3.25, 3.25, NA, NA, NA, NA, NA, 3.71, 3.71, 3.71,
3.71, 3.71, 3.71, 3.71, 3.71, 0.89, 0.89, 0.89, 0.89, 0.89,
0.89, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.11, 1.11,
1.11, 1.11, 1.11, 1.11, 1.11, 1.11, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.87, 0.87, 0.87, 0.87, 0.87,
0.87, 0.87, 0.79, 0.79, 0.79, 0.79, 0.79, 0.79, 0.79, 0.79,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.53,
1.53, 1.53, 1.53, 1.53, 1.53, 1.53, 1.53, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 1.65, 1.65, 1.65, 1.65, 1.65, 1.65, 1.65,
1.65, NA, NA, NA, NA, NA, NA, 0.88, 0.88, 0.88, 0.88, 0.88,
0.88, 0.88, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.49,
1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.8, 1.8, 1.8, 1.8, 1.8,
1.8, 1.8, 2.86, 2.86, 2.86, 2.86, 2.86, 2.86, 2.86, 0.97,
0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 2.2, 2.2, 2.2, 2.2, 2.2,
2.2, 2.2, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 3.06,
3.06, 3.06, 3.06, 3.06, 3.06, 3.06, 1.86, 1.86, 1.86, 1.86,
1.86, 1.86, 1.86, 2.41, 2.41, 2.41, 2.41, 2.41, 2.41, 2.41,
1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 2.88, 2.88, 2.88,
2.88, 2.88, 2.88, 2.88, 2.35, 2.35, 2.35, 2.35, 2.35, 2.35,
2.35, 2.35, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 2.02, 2.02,
2.02, 2.02, 2.02, 2.02, 2.02, 3.31, 3.31, 3.31, 3.31, 3.31,
3.31, 3.31, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 7, 7, 7, 7,
7, 7, 7, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 9.07, 9.07, 9.07,
9.07, 9.07, 9.07, 9.07, 3.28, 3.28, 3.28, 3.28, 3.28, 3.28,
3.28, 1.55, 1.55, 1.55, 1.55, 1.55, 1.55, 1.55, 9.58, 9.58,
9.58, 9.58, 9.58, 9.58, 9.58, 7.54, 7.54, 7.54, 7.54, 7.54,
7.54, 7.54, 13.86, 13.86, 13.86, 13.86, 13.86, 13.86, 13.86,
14.27, 14.27, 14.27, 14.27, 14.27, 14.27, 14.27, 9.59, 9.59,
9.59, 9.59, 9.59, 9.59, 9.59, 7.08, 7.08, 7.08, 7.08, 7.08,
7.08, 7.08, 18.08, 18.08, 18.08, 18.08, 18.08, 18.08, 18.08,
18.08, 10.95, 10.95, 10.95, 10.95, 10.95, 10.95, 7.36, 7.36,
7.36, 7.36, 7.36, 7.36, 7.36, 6.18, 6.18, 6.18, 6.18, 6.18,
6.18, 6.18, 5.25, 5.25, 5.25, 5.25, 5.25, 5.25, 5.25, 5.25,
5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 6.39, 6.39, 6.39, 6.39, 6.39,
6.39, 6.39, 4.33, 4.33, 4.33, 4.33, 4.33, 4.33, 4.33, 2.92,
2.92, 2.92, 2.92, 2.92, 2.92, 2.92, 0.89, 0.89, 0.89, 0.89,
0.89, 0.89, 0.89, 0.89, 0.89, NA, NA, NA, NA, NA, NA, NA,
1.06, 1.06, 1.06, 1.06, 1.06, 1.06, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 5.21, 5.21, 5.21, 5.21, 5.21,
5.21, 5.21, NA, NA, NA, NA, NA, NA, 2.09, 2.09, 2.09, 2.09,
2.09, 2.09, 2.09, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03,
2.03, NA, NA, NA, NA, NA, NA, 2.63, 2.63, 2.63, 2.63, 2.63,
2.63, 2.63, 4.31, 4.31, 4.31, 4.31, 4.31, 4.31, 4.31, 1.77,
1.77, 1.77, 1.77, 1.77, 1.77, 1.77, 4.15, 4.15, 4.15, 4.15,
4.15, 4.15, 4.15, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22,
2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22,
2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22,
2.22, 5.24, 5.24, 5.24, 5.24, 5.24, 5.24, 5.24, 2.7, 2.7,
2.7, 2.7, 2.7, 2.7, 2.7, 7.47, 7.47, 7.47, 7.47, 7.47)), row.names = 167:896, class = "data.frame")
Here is my code:
library (ggplot2)
library (dplyr)
library (tidyr)
shade <-
df1 %>%
transmute(year = year(Date)) %>%
unique() %>%
mutate(
from = as.Date(paste0(year, "-02-21")),
to = as.Date(paste0(year, "-04-30"))
)
ggplot(df1, aes(x=Date)) +
geom_rect(data = shade, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf), color='grey', alpha=0.2) +
geom_line( aes(y=V, color='V')) + geom_line( aes(y= Cu / coeff, color = 'Cu')) +
geom_line( aes(y= Pb / coeff, color = 'Pb')) + geom_line( aes(y= / coeff, color = 'Zn')) +
scale_y_continuous(name = "V", sec.axis = sec_axis(~.*coeff, name = "Cu, Pb, Zn"))+
theme_bw()+ theme(legend.position = c(0.2, 0.9),legend.direction="horizontal")+labs(color = NULL, fill = NULL)
If you have any idea what happened and how to fix it, please let me know. Thank you.
Best regards.

Try to use only ggplot(df1) and not putting aes() inside ggplot(), each geom_line read df1 and geom_rect read shade.
ggplot(df1) +
geom_line( aes(x=Date, y=V, color='V')) +
geom_line( aes(x=Date, y= Cu, color = 'Cu')) +
geom_line( aes(x=Date, y= Pb, color = 'Pb')) +
geom_line( aes(x=Date, y= Zn, color = 'Zn')) +
geom_rect(data = shade, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf), color='grey', alpha=0.2) +
scale_y_continuous(name = "V", sec.axis = sec_axis(~., name = "Cu, Pb, Zn"))+
theme_bw()+ theme(legend.position = c(0.2, 0.9),legend.direction="horizontal")+labs(color = NULL, fill = NULL)
# or u can use
ggplot() +
geom_line(data = df1, aes(x=Date, y=V, color='V')) +
geom_line(data = df1, aes(x=Date, y= Cu, color = 'Cu')) +
geom_line(data = df1, aes(x=Date, y= Pb, color = 'Pb')) +
geom_line(data = df1, aes(x=Date, y= Zn, color = 'Zn')) +
geom_rect(data = shade, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf), color='grey', alpha=0.2) +
scale_y_continuous(name = "V", sec.axis = sec_axis(~., name = "Cu, Pb, Zn"))+
theme_bw()+ theme(legend.position = c(0.2, 0.9),legend.direction="horizontal")+labs(color = NULL, fill = NULL)

In my opinion the error you are getting is beacuse geom_rect() is trying to find columns (Date in this case) in previously declared aes().
I couldn't test my theory since there are some problems with your code (e.g. no coeff in df1 object).

How to get minimum of y when x is maximum in R?

I want to find the minimum of y where x is maximum. I could able to add a line in the plot when y is minimum using the following code
library(tidyverse)
ggplot(data = df, aes(x = x, y = y)) + geom_point() +
geom_hline(yintercept=min(df$y), size=1, color = "darkgreen")
Now how can I fit a line in the plot for minimum of y where x is maximum?
Data
df = structure(list(x = c(0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
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, 0.09, 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, 0.09, 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, 0.09, 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, 0.09, 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, 0.09, 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, 0.09, 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, 0.09, 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, 0.09,
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, 0.09, 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, 0.09, 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, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
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, 0.17, 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, 0.17, 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, 0.17,
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, 0.17, 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, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 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, 0.25, 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, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31,
0.31, 0.31), y = c(25.07, 25.41, 26.08, 25.3, 26.95, 26.85, 24.69,
26.94, 26.49, 27.1, 26.14, 27.25, 28.5, 25.38, 26.07, 27.92,
24.6, 29.24, 25.96, 25.91, 25.66, 26.8, 26.84, 25.37, 26.41,
26.34, 25.54, 28.53, 29.08, 27.31, 26.07, 26.57, 26.52, 26.39,
25.85, 29.28, 26.31, 27.12, 25.15, 26.24, 26.21, 25.68, 26.54,
28.72, 25.73, 26.76, 25.89, 26.77, 27.29, 26.34, 26.07, 26.54,
25.95, 29, 26.16, 25.23, 25.17, 27.07, 27.93, 26.12, 27.2, 26.89,
26.7, 25.96, 26.61, 26.37, 27.95, 28.98, 25.76, 25.09, 25.57,
25.94, 27.55, 25.1, 26.73, 27.24, 26.47, 25.44, 25.28, 27.65,
26.52, 27.42, 25.57, 25.46, 26.9, 27.26, 26.49, 25.39, 27, 26.22,
26.82, 25.72, 26.01, 28.61, 26.4, 27.95, 27.51, 28.26, 25.5,
25.99, 24.69, 24.71, 26.25, 25.65, 26.84, 26.27, 27.25, 27.93,
25.47, 27.29, 26.69, 24.92, 28.24, 25.37, 25.19, 25.52, 26.11,
25.84, 26.19, 26.88, 25.8, 28.66, 26.29, 28.75, 26.64, 28.22,
26.11, 26.95, 26.19, 26.18, 28.2, 29.21, 26.31, 27.06, 25.71,
25.92, 26, 25.32, 27.95, 25.59, 26.09, 25.47, 27.01, 26.37, 26.49,
25.46, 25.94, 26.8, 25.76, 25.88, 26.27, 28.79, 26.68, 25.04,
29.48, 26.73, 26.24, 26.72, 25.58, 25.48, 29.57, 26.44, 27.47,
26.64, 25.61, 26.53, 26.26, 26.24, 25.9, 24.72, 25.35, 25.19,
25.43, 26.94, 25.56, 26.47, 29.26, 25.93, 27.03, 26.04, 25.78,
24.97, 25.52, 25.29, 26.46, 26.13, 26.22, 25.93, 26.48, 25, 25.36,
25.76, 26.27, 26.44, 29.59, 25.84, 26.53, 29.47, 26.25, 25.85,
27.48, 27.83, 24.66, 26.03, 25.44, 27.62, 24.93, 25.71, 26.47,
24.74, 28.73, 27.31, 26.26, 26.36, 25.41, 28.74, 26.6, 26.54,
25.76, 26.2, 27.67, 25.13, 27.53, 25.28, 28.25, 26.41, 24.92,
27.85, 28.75, 27.02, 25.99, 29.48, 28.9, 26.45, 29.36, 26.05,
24.99, 26.38, 25.63, 25.39, 28.12, 25.29, 26.61, 28.36, 26.38,
25.47, 26.66, 26.3, 25.03, 26.58, 26.22, 25.86, 26.71, 26.32,
27.1, 24.44, 27.18, 25.27, 24.97, 26.01, 26.79, 24.75, 27.22,
26.62, 28.4, 26.75, 26.25, 27.26, 25.74, 26.54, 26.21, 26.85,
26.9, 28.89, 24.82, 25.1, 25.51, 27.16, 28.76, 26.03, 26.23,
25.23, 24.6, 24.95, 26.3, 25.61, 28.81, 26.82, 27.79, 25.59,
25.78, 26.33, 26.19, 25.22, 26.68, 27.4, 26.59, 25.4, 24.63,
29.42, 26.28, 24.83, 26, 26.05, 26.28, 25.78, 26.92, 27.42, 26.65,
26.47, 27.53, 26.88, 26.93, 25.67, 27.7, 25.48, 25.61, 25.58,
25.13, 27.68, 26.64, 26.87, 25.17, 28.55, 27.07, 24.5, 28.05,
25.63, 27.62, 26.39, 24.58, 24.93, 26.36, 24.98, 27.78, 27.39,
25.14, 24.63, 24.83, 26.59, 27.97, 25.23, 27.15, 25.69, 26.44,
28.37, 27, 26.08, 25.9, 24.45, 25.13, 25.25, 26.32, 26.08, 25.03,
26.84, 25.4, 25.59, 26.19, 25.3, 26.31, 26, 26.72, 29.05, 27.35,
25.41, 26.49, 27.48, 27.36, 24.99, 26.89, 25.39, 26.98, 25.39,
26.55, 24.61, 26.57, 25.38, 24.58, 25.76, 26.2, 24.82, 25.41,
24.93, 29.98, 26.97, 27.26, 25.44, 25.37, 27.02, 26.52, 25.61,
25.61, 27.98, 26.67, 26.18, 26.05, 26.16, 25.7, 24.65, 25.85,
26.35, 27.73, 27.3, 28.1, 26.27, 26.32, 24.66, 29.12, 25.98,
25.99, 25.59, 27.15, 24.46, 26.39, 28.18, 25.28, 25.56, 28.61,
24.19, 25.87, 26.49, 25.02, 25.17, 25.21, 25.54, 25, 26.39, 24.11,
26.37, 26.33, 26.8, 25.96, 25.06, 26.61, 29.23, 26.79, 26.26,
26.97, 26.23, 25.67, 25.66, 27.04, 25.17, 27.5, 26.26, 26.33,
26.97, 27.09, 29.59, 26.08, 26.1, 25.51, 25.38, 26.2, 24.18,
25.1, 28.04, 26.04, 26.51, 26.1, 28.68, 24.97, 27.75, 26.97,
24.71, 25.35, 24.36, 25.26, 24.62, 26.73, 25.52, 26.66, 28.15,
26.24, 25.66, 26.29, 24.8, 25.34, 26.41, 26.12, 24.9, 26.99,
26.76, 26.06, 26.47, 27.56, 26.8, 25.35, 27.91, 30.09, 28.37,
26.66, 24.79, 26.24, 27.25, 26.78, 27.71, 29.42, 27.04, 26.19,
25.14, 25.48, 25.86, 25.74, 25.35, 26.39, 25.68, 24.09, 25.73,
26.53, 26.31, 24.83, 26.5, 27.77, 24.99, 24.9, 26.3, 24.87, 24.9,
24.77, 26.45, 25.4, 26.87, 24.74, 25.81, 27.29, 24.83, 26.84,
26.16, 27.79, 24.66, 25.54, 28.88, 28.08, 26.31, 26.13, 26.45,
27.64, 28.25, 25.95, 25.45, 24.33, 26.25, 24.95, 25.72, 24.78,
27.33, 27.26, 26.46, 25.19, 25.37, 25.49, 25.9, 25.7, 28.46,
27.07, 26.32, 26.07, 25.24, 24.97, 24.98, 26.31, 28.19, 26, 26.56,
24.95, 29.22, 25.95, 25.17, 25.35, 26.48, 26.82, 25.39, 25.69,
28.38, 26.08, 27.1, 24.33, 24.13, 28.34, 26.03, 25.57, 27.45,
24.53, 26.33, 24.99, 25.96, 29.34, 25.03, 25.39, 25.33, 25.85,
25.33, 25.21, 25.67, 26.03, 26.55, 27.1, 27.56, 25.04, 24.75,
25.24, 26.54, 27.77, 26.65, 27.2, 24.68, 24.97, 28.13, 26.73,
27.64, 26.03, 24.58, 28.3, 25.95, 26.23, 24.89, 28.47, 26.46,
24.76, 26.92, 24.99, 24.48, 25.15, 25.46, 24.56, 25.69, 24.73,
25.27, 25.85, 26.39, 29.7, 28.71, 27.84, 26.23, 27.37, 25.79,
26.56, 26.28, 27.08, 26.26, 27.04, 25.17, 28.08, 25.91, 24.55,
24.37, 24.24, 25.34, 25.76, 25.4, 24.53, 26.77, 25.73, 25.22,
26.04, 26.41, 26.72, 24.57, 24.95, 25.12, 29.65, 25.43, 27.92,
25.48, 25.99, 24.68, 25.01, 25.19, 25.05, 24.84, 27.53, 27.3,
25.14, 25.26, 26.25, 24.16, 25.17, 29.84, 27.84, 26.37, 26.17,
24.89, 28.2, 24.96, 26.18, 27.66, 26.67, 25.62, 25.51, 24.62,
24.07, 28.08, 26.57, 24.94, 25.93, 25.64, 28.85, 25.43, 29.82,
25.36, 25.79, 25.87, 26.43, 25.67, 27.15, 25.58, 25.76, 25.61,
25.48, 25.6, 27.17, 25.12, 25.84, 24.64, 26.68, 25.85, 25.01,
27.78, 26.41, 25.64, 27.38, 25.62, 27.36, 24.26, 27.07, 25.28,
28.9, 27.72, 28.67, 27.81, 26, 25.74, 25.53, 25.36, 25.27, 28.96,
25.18, 26.69, 26.17, 27.21, 26.29, 24.79, 25.43, 26.28, 25.9,
29.1, 29.92, 25.84, 27.45, 27.68, 27.06, 28.01, 25.04, 25.85,
24.77, 25.24, 26.68, 26.6, 24.98, 26.26, 25.26, 24.6, 28.85,
27.91, 25.04, 24.6, 26.89, 29.48, 25.89, 27.53, 26.18, 26.46,
25.52, 24.98, 24.5, 25.43, 27.62, 25.83, 26.26, 25.98, 25.68,
27.79, 29.98, 25.93, 24.92, 24.98, 25.2, 26.34, 24.69, 25.11,
26.83, 25.41, 25.12, 26.17, 25.22, 26.12, 27.73, 26.1, 26.64,
26.13, 25.8, 27.12, 25.67, 25.61, 25.24, 25.73, 24.54, 25.17,
26.78, 25.68, 27.67, 24.52, 25.79, 25.01, 25.8, 26.55, 26.01,
24.86, 25.09, 24.34, 25.38, 25.41, 24.88, 25.35, 26.3, 26.2,
24.89, 24.36, 26.35, 27.07, 26.59, 24.49, 25.85, 26.24, 27.06,
24.31, 26.31, 24.79, 25.83, 26.51, 25.58, 26.06, 26.01, 26.05,
26.08, 26.31, 26.62, 27.17, 24.63, 25.25, 25.11, 28.05, 25.12,
27.64, 24.65, 26.26, 25.62, 27.77, 27.78, 25.75, 25.1, 24.61,
25.88, 25, 25.18, 25.19, 27.09, 24.97, 25.09, 25.83, 24.42, 27.14,
24.53, 25.15, 29.05, 26.91, 26.24, 24.54, 25.43, 24.98, 26.1,
26.77, 25.39, 24.47, 25.97, 24.71, 25.64, 25, 24.95, 26.77, 25.05,
25.99, 25.13, 25.49, 26.85, 27.99, 25.58, 27.1, 24.86, 25.79,
24.91, 25.14, 25.94, 28.91, 26.94, 28.68, 27.05, 26.56, 25.08,
26.04, 25.06, 25.4, 28.18, 26.55, 24.92, 25.11, 25.18, 25.92,
26.73, 25.81, 24.63, 25.22, 26.75, 24.93, 27.33, 25.03, 27.62,
24.57, 26.61, 25.23, 27.41, 28.26, 25.15, 25.05, 27.96, 26.35,
26.65, 26.21, 24.59, 24.45, 28.26, 24.83, 25.86, 26.46, 24.89,
24.92, 25.16, 28.93, 26.57, 27.26, 27.33, 25.6, 28.13, 25.49,
26.93, 26.14, 25.32, 25.18, 25.41, 25.12, 29.18, 27.16, 25.91,
26.28, 26.68, 25.4, 25.18, 24.91, 27.04, 25.98, 26.75, 25.45,
25.1, 24.48, 25.45, 25.47, 28.67, 26.48, 24.95, 24.96, 26.56,
25.71, 25.17, 25.28, 25.62, 24.94, 26.83, 26.85, 25.72, 26.97,
25.43, 25.06, 25.71, 29.29, 24.76, 24.74, 24.92, 25.47, 24.73,
24.66, 27.16, 27.38, 26.05, 25.08, 25.25, 24.93, 24.8, 27.16,
26.05, 26.3, 26.24, 24.67, 27.68, 25.33, 26.6, 25.11, 25.8, 26.09,
25, 25.69, 28.47, 26.03, 26.54, 26.14, 24.36, 24.93, 24.84, 27.97,
27.07, 24.65, 24.52, 25.75, 25.96, 25.02, 24.93, 26.95, 26.47,
28.13, 28.46, 25.61, 25.49, 24.59, 24.98, 24.22, 24.48, 25.04,
28.85, 25.05, 25.15, 28.31, 25.3, 25.04, 25.05, 25.12, 25.14,
24.84, 25.32, 26.85, 26.56, 25.02, 25.2, 24.52, 27.45, 25.32,
25.81, 25.08, 25.25, 25.31, 24.53, 26.31, 28.57, 26.28, 25.94,
25.1, 26.83, 26.3, 27.26, 25.19, 25.02, 26.39, 26.91, 25.07,
24.82, 26.25, 24.42, 26.82, 25.3, 25.02, 29.23, 24.77, 26.03,
25.47, 27.41, 26.08, 25.68, 25.33, 27.77, 27.3, 25.14, 24.87,
25.11, 27.38, 26.44, 25.33, 26.15, 24.58, 25.91, 25.13, 28.4,
24.98, 25.41, 28.53, 25.1, 25.05, 24.91, 24.49, 26.04, 25.68,
26.78, 26.85, 25.52, 24.69, 26.25, 27.75, 24.99, 27.75, 24.62,
24.6, 26.68, 26.42, 24.39, 25.37, 29.87, 28.33, 26.35, 25.36,
25.16, 27.47, 26.62, 24.72, 25.81, 24.92, 26.78, 25.27, 25.18,
27.6, 24.71, 25.74, 24.46, 24.98, 24.93, 25.68, 25.15, 24.96,
25.98, 25.1, 26.16, 25.35, 25.15, 25.53, 26.25, 26.85, 25.66,
25.74, 26.03, 25.55, 25.56, 24.46, 24.88, 25.04, 24.64, 24.97,
26.04, 27.14, 24.43, 27.8, 27.43, 25.31, 26.36, 25.38, 25.49,
25.49, 24.67, 27.26, 25.05, 25.51, 25.16, 26.02, 24.87, 25.18,
24.98, 25.13, 25.62, 25.29, 26.67, 24.91, 25.06, 25.65, 25.09,
24.72, 27.21, 25.7, 25.92, 24.89, 25.02, 26.66, 27.21, 26.8,
25.89, 26.68, 25.91, 24.93, 24.38, 25.08, 25.25, 25.55, 27.11,
25.57, 25.33, 24.95, 24.74, 25.03, 25.39, 25.55, 24.71, 26.94,
26.84, 24.56, 28.64, 25.14, 26, 27.58, 27.59, 26.5, 27.26, 26.24,
26.71, 25.03, 26.69, 24.42, 26.2, 24.83, 26.41, 25.28, 26.42,
25.29, 25.04, 24.55, 24.52, 25.66, 25.06, 27.86, 26.58, 26.05,
24.78, 25.18, 25.58, 27.45, 25.25, 24.95, 24.62, 24.6, 25.4,
25.07, 25.24, 24.96, 26.49, 26.34, 25.88, 25.63, 24.84, 24.96,
24.79, 25.3, 25.59, 25.83, 26, 26.83, 25.75, 27.09, 25.02, 25.29,
26.19, 24.68, 24.45, 25.22, 25.43, 27.69, 24.57, 25.15, 27.44,
26.55, 25.09, 28, 26.16, 24.93, 24.98, 25.34, 28.66, 27.59, 26.33,
24.41, 25.22, 24.87, 24.56, 25.3, 25.01, 25.44, 24.94, 26.52,
25.68, 24.71, 26.83, 24.93, 24.35, 26.48, 25.25, 25.79, 26.16,
27.29, 24.71, 25.37, 26.77, 25.52, 25, 24.63, 25.34, 24.67, 25.23,
25.17, 24.93, 27.37, 25.94, 25.75, 25.1, 25.51, 26.22, 26.6,
25.03, 25.01, 26.51, 24.85, 24.94, 25.23, 29.16, 27.08, 25.4,
24.86, 24.64, 26.44, 24.26, 24.87, 25.54, 25.09, 26.84, 25.24,
24.89, 26.6, 25.86, 26.75, 24.8, 26.29, 26.09, 27.93, 25.42,
25.17, 27.11, 27.4, 27.12, 25.33, 25.15, 25.34, 25.14, 24.63,
25.97, 24.75, 25.76, 26.9, 26.62, 25.24, 28.55, 26.48, 25.15,
25.92, 26.12, 24.97, 25.79, 25.91, 25.09, 25.67, 24.75, 28.64,
25.43, 25.12, 25.13, 25, 24.88, 27.3, 25.39, 25.4, 25, 24.81,
24.53, 24.97, 28, 24.29, 24.82, 25.24, 26.76, 25.51, 26.54, 25.17,
25.24, 25.79, 26.43, 26.61, 25.43, 26.87, 25.29, 26.79, 28.5,
24.92, 26.59, 25.43, 26.19, 24.79, 24.77, 26.85, 25.21, 25.34,
26.31, 25.17, 26.13, 25.89, 26.4, 25.01, 24.66, 27.5, 27.16,
25.53, 26.56, 24.66, 24.55, 24.68, 25.14, 24.87, 27.14, 25.27,
24.91, 25.32, 24.54, 25.63, 24.81, 25.47, 25.39, 25.32, 24.78,
26.57, 25.95, 26.87, 27.17, 26.22, 26.29, 25.25, 28.22, 24.85,
25.36, 26.98, 25.21, 25.77, 24.91, 25.17, 25.13, 25.17, 25.24,
24.87, 24.68, 24.7, 25.01, 24.79, 25.43, 25.7, 26.15, 26.18,
25.06, 26.63, 25.94, 27.58, 26.78, 27, 29.61, 26.22, 24.58, 26.57,
26.14, 25.96, 25.4, 25.51, 25.31, 25.47, 25.37, 26.82, 24.84,
24.58, 25.61, 25.42, 27.35, 26.95, 24.88, 25.4, 24.9, 25.36,
25, 28.6, 24.89, 25.39, 25.55, 25.83, 24.78, 27.19, 27.66, 24.8,
24.81, 25.23, 28, 25.89, 25.02, 24.73, 26.39, 26.47, 25.97, 24.64,
25.08, 25.36, 26.7, 25.05, 26.7, 25.78, 25.08, 25.24, 26.63,
26.26, 25.1, 27.41, 27.42, 25.92, 24.58, 24.84, 26.53, 25.68,
26.33, 27.22, 25.31, 25.66, 26.32, 24.57, 26.13, 25.13, 24.74,
25.59, 24.57, 25.15, 24.64, 24.92, 25.28, 25.33, 24.99, 25.15,
24.88, 27.58, 25.17, 25.31, 25.71, 24.82, 25.87, 25.11, 25.49,
25.45, 25.28, 27.09, 27, 25.75, 25.31, 24.67, 25.28, 25.75, 26.91,
24.93, 25.41, 25.7, 26.15, 26.27, 25.49, 25.35, 25.41, 25.83,
25.38, 25.94, 26.44, 25.01, 25.62, 25.76, 26.16, 25.65, 26.66,
24.76, 24.75, 28.5, 26.76, 26.52, 25.61, 25.51, 26.5, 28.09,
26.37, 25.13, 25.59, 25.38, 24.72, 26.47, 26.49, 25.49, 25.4,
27.92, 24.84, 27.99, 26.8, 24.58, 25.22, 24.75, 25.52, 26.25,
26.3, 27.29, 24.95, 27.54, 26, 26.45, 26.88, 24.51, 25.81, 27.48,
25.22, 25.29, 25.95, 25.21, 26.38, 25.22, 25.99, 25.67, 27, 24.97,
24.74, 26.3, 26.55, 25.02, 25.24, 25.1, 25.17, 24.95, 25, 26.52,
24.7, 26.31, 25.68, 25.27, 25.19, 25.07, 27.34, 26.08, 27.12,
25.53, 26.33, 25.13, 25.21, 25.53, 25.19, 27.8, 27.62, 25.57,
26.18, 25.25, 25.1, 24.56, 25.61, 25.61, 26.18, 25.63, 26.69,
25.56, 26.21, 25.42, 26.45, 26.03, 25.99, 25.23, 26.22, 25.18,
25.17, 25.27, 24.83, 26.96, 25.43, 25.09, 25.52, 25.74, 25.52,
25.22, 25.1, 25.53, 25.35, 25.18, 26.75, 26.52, 24.8, 26.3, 26.4,
25.01, 28.4, 25.34, 25.14, 26.99, 24.65, 24.77, 25.18, 25.89,
26.29, 26.01, 26.05, 26.77, 25.5, 24.62, 25.21, 26.21, 25.53,
26.54, 25.44, 24.84, 24.76, 25.58, 27.2, 25.07, 25.23, 25.68,
25.17, 25.7, 27.02, 25.46, 25.05, 24.92, 25.15, 24.66, 25.41,
26.48, 25.16, 24.57, 27.27, 26.74, 24.88, 25.89, 25.91, 25.26,
26.62, 24.94, 26.4, 24.79, 25.56, 24.97, 24.2, 24.18, 25.15,
26.35, 27.25, 25.22, 25.8, 24.55, 25.95, 24.39, 26.42, 24.27,
25.11, 25.09, 26.13, 24.84, 24.49, 24.7, 25.14, 25.05, 25.54,
27.19, 26.79, 24.95, 26.6, 25.58, 26.28, 25.25, 26.41, 25.92,
26.67, 25.03, 24.25, 25.9, 25.38, 25.52, 25.27, 25.93, 26.2,
24.49, 24.21, 25.11, 25.76, 25.47, 26.09, 24.23, 24.36, 25.81,
25.45, 25.38, 25.38, 24.4, 26.68, 24.17, 24.75, 24.69, 24.4,
24.66, 25.68, 24.96, 25.88, 24.89, 25.49, 25.23, 25.43, 24.57,
24.37, 24.41, 24.38, 24.91, 26.21, 25.31, 26.22, 24.47, 25.4,
25.4, 26.01, 25.96, 25.4, 25.04, 24.79, 26.14, 26.43, 24.76,
24.63, 25.14, 24.62, 24.83, 26.38, 26.75, 25.43, 24.68, 25.14,
24.78, 25.85, 25.04, 26.19, 27.04, 24.58, 24.52, 26.25, 26.43,
24.69, 26.68, 24.78, 24.95, 24.59, 24.47, 24.65, 25.14, 25.46,
26.3, 24.98, 26.42, 25.14, 26.28, 26.7, 26.2, 26.46, 25.63)), row.names = c(NA,
1890L), class = "data.frame")

You could filter for it:
yy <- df %>%
filter(x == max(x)) %>%
filter(y == min(y)) %>%
pull(y) %>%
first()
and use it in your plot:
ggplot(data = df, aes(x = x, y = y)) +
geom_point() +
geom_hline(yintercept=min(yy), size=1, color = "darkgreen")

How to perform rolling regression in R with this dataset?

Let's suppose I have the following dataframe made of up 219 rows. The dataset is not perfectly monthly for some structural reasons.
df = structure(list(X1 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X2 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X3 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X4 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X5 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X6 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X7 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X8 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86)), row.names = c(NA, -219L), class = "data.frame")
Then, what I want to do is setting up a rolling regression in a time window that encompasses, say, 2 years (24 months). To do so, I run the following codes:
library(rollRegres)
library(zoo)
roll_model1 = roll_regres(X1 ~ ., df, 24L, do_compute = c("sigmas", "r.squareds"), do_downdates = TRUE)
roll_model2 = rollapply(df, width = 24, FUN = function(x) coef(lm(X1 ~ ., data = as.data.frame(x))), by.column = FALSE, align = "right")
In the first case, the model doesn't work. In the second case, I only get results for the intercept (and only coefficinets). Besides, I don't understand why there are 196 coefficient observations.
Can anyone help me run a rolling regression over 2 years window with this dataset?
Thanks!

All columns of df are the same
all(df == df[, 1])
## [1] TRUE
so it can perfectly predict X1 using X2 and the others are not needed so it gives NA.
Regarding the rollapply code it only gave coefficients because that is what you asked for coef(lm(...)) . Your function should return a vector of whatever it is you want to get out.
It does a regression for rows 1:24, rows 2:25, ... rows 196:219 so clearly there are 196 such sets so the result has 196 rows. If you specify fill=NA then it will pad it with NAs to give the same number of rows as df.
Note that rollapplyr is available which defaults to align = "right".
Here is a possible function that returns a variety of information:
library(broom)
stats <- function(x) {
fm <- lm(X1 ~., as.data.frame(x))
c(coef(fm), unlist(glance(fm)))
}
rollapplyr(df, width = 24, FUN = stats, by.column = FALSE)

How to impute missing values not at random?

My data consists of 202 cases, each stand for a single interview. The variables reflect the interviewers' and interviewees' behaviours during different parts of the interview: p1, g1, pA, gA. in some interviews, certain parts were not carried out. part p1 wasn't carried out in one interview. part g1 wasn't conducted in 46 cases. part pA wasn't conducted with 14 subjects and gA with 27.
Different variables are different facets of the same underlying concept or latent variable. for example, all four variables belonging to part pA - pAx1, pAx2, pAx3, pAx4 - are different measures of the interviewee's cooperativeness during part pA.
I would like to impute the missing values while accounting for the fact that there is a pattern for values to be missing, such that if a value is missing for a variable of part pA, e.g., pAx1, then, necessarily that the other values pertaining to part pA - pAx2, pAx3, pAx4 - are also missing.
Help would be much appreciated!
this is my data -
df <- structure(list(p1x1 = c(0.54, 0.77, 0.84, 0.84, 0.75, 0.35, 0.67,
0.23, 0.9, 0.81, 0.76, 0.85, 0.61, 0.8, 0.1, 0.81, 0.96, 0.68,
0.83, 0.8, 0.89, 0.85, 1, 0.83, 0.52, 0.74, 0.47, 0.51, 1, 0.83,
0.93, 0, 0.31, 0.95, 0, 0.39, 0.84, 0.62, 0.81, 0.58, 0.7, 0.54,
0.94, 0.76, 0.76, 0.14, 0.67, 0.65, 1, 0.69, 0.31, 0.43, 0.83,
0.79, 0.94, 0.84, 0.28, 0.76, 0.78, 0.91, 0.89, 0.63, 0.76, 0.34,
0.91, 1, 0.72, 0.89, 0.43, 0.85, 0.8, 0.45, 0.12, 0.19, 0.91,
0.74, 0.88, 0.62, 0.92, 0.72, 0.54, 0.59, 0.74, 0.8, 1, 0.66,
0.48, 0.7, 0.96, 0.87, 0.65, 0.61, 0.79, 0.8, 0.93, 0.83, 0.88,
0.76, 0.58, 0.79, 0.65, 0.88, 0.37, 0.74, 0.63, 0.64, 0.58, 0.86,
0.62, 0.57, 0.09, 0.61, 0.29, 0.9, 0.91, 0.73, 0.92, 0.9, 0.56,
0.89, 0.89, 0.62, 0.24, 0.65, 0.76, 0.69, 0.42, 0.8, 0.39, 0.58,
0.72, 0.73, 0.48, NA, 0.5, 0.72, 0.91, 0.58, 0.8, 0, 0.47, 0.5,
0.85, 0.93, 0.81, 0.89, 0.93, 0.55, 0.78, 0.72, 0.77, 0.44, 0.57,
0.78, 0.84, 0.83, 0.62, 0.3, 0.67, 0.96, 0.62, 0.73, 0.29, 0.76,
0.86, 0.7, 0.54, 0.28, 0.74, 0.67, 0.17, 0.05, 0.62, 0.76, 0.73,
1, 0.7, 0.92, 0.31, 1, 0.33, 0.59, 0.62, 0.78, 0.26, 0.76, 0.7,
0.81, 0.82, 0.81, 0.83, 0.3, 0.79, 0, 0.72, 0.67, 0.78, 0.11,
0.32, 0.39, 0.6, 0.7), p1x2 = c(0, 0.08, 0.32, 0.11, 0.12, 0,
0.17, 0.08, 0.38, 0.12, 0, 0.15, 0.25, 0.05, 0, 0.15, 0.13, 0.08,
0.08, 0.13, 0.06, 0.46, 0.21, 0.14, 0.19, 0.11, 0.24, 0.08, 0.36,
0.08, 0.29, 0, 0, 0.14, 0, 0.07, 0.16, 0.04, 0.33, 0.32, 0.22,
0.08, 0.29, 0.06, 0.43, 0.07, 0.06, 0.16, 0.18, 0.19, 0.08, 0.1,
0.17, 0.21, 0.06, 0.11, 0.06, 0.24, 0.22, 0.13, 0.21, 0.26, 0.1,
0, 0.23, 0.44, 0.21, 0.16, 0, 0.15, 0.4, 0.07, 0, 0, 0.31, 0.1,
0.38, 0.43, 0.16, 0.12, 0.12, 0.18, 0.3, 0.45, 0.33, 0.02, 0.19,
0.15, 0.15, 0.2, 0.02, 0.04, 0.21, 0.27, 0.07, 0.14, 0.06, 0.05,
0.37, 0.05, 0.35, 0.25, 0.21, 0.09, 0.08, 0.08, 0.06, 0.71, 0.04,
0.05, 0, 0.04, 0.32, 0.4, 0.55, 0.12, 0.08, 0, 0.19, 0.33, 0.11,
0.06, 0.02, 0.29, 0.12, 0.03, 0.04, 0.33, 0.27, 0.25, 0, 0, 0.19,
NA, 0.08, 0.32, 0.48, 0.08, 0.07, 0, 0.11, 0.17, 0.2, 0.33, 0.19,
0.22, 0.33, 0.09, 0.28, 0.28, 0, 0.44, 0.27, 0.17, 0.32, 0.06,
0.29, 0, 0.1, 0.25, 0.22, 0.45, 0, 0.09, 0.14, 0.33, 0, 0.24,
0.21, 0.06, 0, 0, 0.5, 0.52, 0.36, 0.4, 0.2, 0.33, 0.14, 0.12,
0.08, 0.17, 0.31, 0, 0, 0.16, 0.02, 0, 0.45, 0.19, 0, 0, 0.02,
0, 0.25, 0.43, 0.39, 0, 0.21, 0, 0.02, 0.25), p1x3 = c(0.46,
0.12, 0.21, 0.47, 0.29, 0.4, 0.33, 0.38, 0.21, 0.12, 0.41, 0.1,
0.29, 0.45, 0.9, 0.3, 0.22, 0.18, 0, 0.27, 0.17, 0.23, 0, 0.28,
0.19, 0.16, 0.59, 0.38, 0.07, 0.25, 0.36, 1, 0.75, 0.14, 1, 0.43,
0.21, 0.42, 0.1, 0.42, 0.39, 0.53, 0.06, 0.35, 0.33, 0.64, 0.28,
0.29, 0.24, 0.19, 0.69, 0.61, 0.08, 0.37, 0.06, 0.26, 0.56, 0.34,
0.48, 0.17, 0.25, 0.11, 0.14, 0.24, 0.14, 0.07, 0.28, 0.37, 0.46,
0.35, 0.6, 0.52, 0.81, 0.39, 0.07, 0.23, 0.08, 0.19, 0.08, 0.44,
0.73, 0.3, 0.11, 0.15, 0.25, 0.32, 0.24, 0.44, 0.07, 0.13, 0.22,
0.26, 0.29, 0.2, 0.29, 0.28, 0.06, 0.29, 0.42, 0.05, 0.6, 0.25,
0.68, 0.26, 0.42, 0.31, 0.36, 0.14, 0.29, 0.03, 0.5, 0.14, 0.54,
0.3, 0.05, 0.35, 0.38, 0.3, 0.06, 0.11, 0.3, 0.41, 0.44, 0.47,
0.18, 0.28, 0.67, 0, 0.45, 0.25, 0.28, 0.27, 0.24, NA, 0.42,
0.24, 0.48, 0.21, 0.2, 1, 0.79, 0.33, 0.1, 0.07, 0.19, 0.28,
0.13, 0.45, 0.17, 0.17, 0.08, 0.62, 0.2, 0.26, 0.12, 0.17, 0.29,
0.7, 0.33, 0.04, 0.38, 0.18, 0.71, 0.24, 0.21, 0.41, 0.31, 0.56,
0, 0.39, 0.83, 0.65, 0.62, 0, 0.32, 0, 0.4, 0.08, 0.43, 0.65,
0.25, 0.28, 0.31, 0.09, 0.71, 0.08, 0.09, 0.17, 0.09, 0.24, 0.33,
0.52, 0.21, 1, 0.28, 0, 0.22, 0.89, 0.32, 0.48, 0.53, 0.45),
p1x4 = c(0, 0.71, 0.78, 0.73, 0.73, 0.75, NA, 0, 0.78, 1,
0.8, 0.71, 0.88, 0.9, NA, 0.73, 1, 0.57, 0.83, 0.67, 0.67,
1, 1, 0.47, 0, 0.86, NA, 0.4, 0.88, 0.86, 1, NA, 0.33, 0.73,
0, 0.28, 0.89, 0.62, 0.45, 0.4, 0.75, 0.42, 0.8, 0.5, 0.67,
0.33, 0.54, 0.25, 0.9, 0.54, NA, 0.33, 0, 0.67, 0.82, 0.62,
NA, 0.62, 0.5, NA, 0.81, 0, 0.6, 0, 0.88, 0, 0.45, 0.8, 0,
0.89, NA, 0.47, NA, 0.3, 0.25, NA, 0, 0, 0.82, 0, 0.5, 0.53,
0.61, 0.58, 1, 0, 0.23, 0.53, 0.78, 0, 0.33, 0.57, 0.57,
0.89, 1, 0.6, 0.88, 0.9, 0.5, 0.56, 0.42, 0.75, NA, 0.71,
0, 0.59, NA, NA, 0.33, 0.4, 0.22, 0.33, 0.3, 0.86, 0.7, 0.78,
1, 0.92, 0, 0.89, 0.61, 0.6, 0.16, 0.4, 0.55, 0, 0.36, 0.6,
0, 0.43, 0.5, 0.42, 0.36, NA, 0.33, 0.8, 0.81, 0, 0.62, 0,
0.56, 0.6, 0, 0.88, 0.67, 0.83, 1, 0.36, 0, 0.4, 0, 0.29,
0.45, 0.82, 0.67, 0.8, 0.59, 0.17, 0.24, 0, 0, 0.69, 0.25,
0.56, 0.38, 0.64, NA, 0, 0.64, 0.75, NA, NA, 0.44, 0.65,
0.67, 1, 0.78, NA, 0.17, 0.9, 0, 0.53, 0.22, 1, 0, 0, 0.53,
0.56, 1, 0.77, 0, 0, 0, NA, 0.73, 0.33, 0.71, NA, 0, 0, 0.46,
0.78), p1y1 = c(0.42, 0.27, 0.63, 0.32, 0.46, 0.8, 0.5, 0.31,
0.59, 0.38, 0.24, 0.55, 0.71, 0.7, 0.8, 0.59, 0.35, 0.08,
0.33, 0.6, 0.22, 0.46, 0.43, 0.38, 0.33, 0.32, 0.41, 0.24,
0.43, 0.33, 0.64, 1, 0.44, 0.33, 0.5, 0.25, 0.53, 0.29, 0.33,
0.89, 0.26, 0.34, 0.59, 0.35, 0.48, 0.43, 0.44, 0.45, 0.53,
0.46, 0.69, 0.18, 0.54, 0.32, 0.41, 0.58, 0.17, 0.28, 0.26,
0.35, 0.43, 0.58, 0.33, 0.07, 0.27, 0.59, 0.59, 0.58, 0.14,
0.54, 1, 0.24, 0.35, 0.24, 0.29, 0.13, 0.88, 0.38, 0.48,
0.16, 0.35, 0.36, 0.41, 0.45, 1, 0.22, 0.33, 0.22, 0.15,
0.27, 0.02, 0.35, 0.57, 0.6, 0.5, 0.52, 0.41, 0.57, 0.42,
0.53, 0.35, 0.31, 0.58, 0.34, 0.37, 0.5, 0.44, 0.71, 0.46,
0.16, 0.32, 0.39, 0.43, 0.6, 0.86, 0.38, 0.33, 0.55, 0.5,
0.56, 0.19, 0.38, 0.13, 0.53, 0.65, 0.22, 0.46, 0.4, 0.42,
0.5, 0.32, 0.42, 0.33, 0, 0.5, 0.56, 0.26, 0.12, 0.47, 0.5,
0.53, 0, 0.55, 0.4, 0.29, 0.17, 0.33, 0.45, 0.72, 0.33, 0.77,
0.75, 0.6, 0.25, 0.48, 1, 0.33, 0.5, 0.59, 0.38, 0.22, 0.45,
0.35, 0.24, 0.57, 0.48, 0.31, 0.36, 0.32, 0.56, 0.46, 0.25,
0.25, 0.64, 0.91, 0.67, 0.5, 0.92, 0.17, 0.47, 0.83, 0.24,
0.23, 0.43, 0.32, 0.55, 0.14, 0.09, 0.73, 0.29, 0.39, 0.39,
0.32, 1.2, 0.39, 0.48, 0.39, 0.33, 0.74, 0.55, 0.29, 0.6),
g1y2 = c(0.46, 0.79, 0.83, 0.44, NA, 0.84, NA, NA, 1.44,
0.55, 0.86, 0.35, 0.63, 1.05, NA, 1.45, 0.67, 0.85, 0.45,
1.13, 0.42, 0.45, 0.6, 1.12, 1, 0.63, NA, NA, 0.68, 1.09,
1.28, NA, 1.17, 0.93, NA, 0.45, 0.5, 1.06, 0.51, 0.86, 1.09,
1.28, 0.83, 0.94, 1.1, NA, 0.95, NA, 1.1, 0.94, NA, 0.31,
1.33, 0.97, 0.57, 0.94, NA, NA, 0.79, NA, 1.02, 0.62, 1.11,
0.52, 0.97, 0.89, NA, 1, 0.46, 0.85, NA, 0.5, NA, 1.25, 0.75,
NA, 0.71, 1, 0.6, 0.51, 0.8, 0.86, 1.03, 0.8, 0.79, 0.6,
NA, 0.87, 0.57, 0.36, 0.64, 0.43, 0.88, 1.14, 0.76, NA, 0.71,
0.77, 0.7, 0, 0.94, 0.93, NA, 0.47, NA, 0.98, NA, NA, NA,
0.44, 1, 0.62, 0.7, 0.96, 0.94, 0.74, 0.65, 0.86, 1.5, 0.92,
NA, 1.11, 0.75, 1.09, 0.79, 0.6, 0.75, 0.71, NA, 0.62, 1.08,
0.58, 0.62, NA, 0.67, 1.11, 1.11, 0.32, 0.77, NA, 1.5, 0.47,
NA, 0.93, NA, 0.4, NA, 0.94, 1, 0.72, 0.85, 0.73, 0.79, 0.32,
0.81, 0.92, 0.93, NA, 1, 0.7, 0.88, 1, NA, 0.85, 1, 0.92,
0.67, NA, 0.68, 0.64, NA, NA, 0.67, 1, NA, 1.08, 1.21, NA,
NA, 1, NA, 0.72, 0.5, 0.95, 1, 0.79, 0.65, 0.72, 1.03, 0.86,
0.84, NA, 1.11, NA, 0.97, NA, 0.85, NA, NA, 1.22, 0.31, 0.81
), g1y3 = c(0.21, 0.05, 0.13, 0, NA, 0.18, NA, NA, 0.12,
0.1, 0.27, 0.08, 0.11, 0.35, NA, 0.36, 0.33, 0.03, 0.27,
0.13, 0.17, 0.05, 0.4, 0.06, 0.5, 0.07, NA, NA, 0.08, 0.18,
0.11, NA, 0.5, 0.13, NA, 0.27, 0.17, 0.06, 0.14, 0.29, 0.18,
0.05, 0.12, 0.19, 0.05, NA, 0.2, NA, 0.3, 0.28, NA, 0.38,
0.33, 0.12, 0.05, 0.29, NA, NA, 0.15, NA, 0.07, 0.12, 0.06,
0, 0.05, 0.09, NA, 0.09, 0, 0.15, NA, 0.12, NA, 0.12, 0.12,
NA, 0.06, 0.25, 0.08, 0, 0.06, 0.14, 0.09, 0.16, 0.07, 0.07,
NA, 0.1, 0.11, 0.36, 0.06, 0.29, 0.19, 0.14, 0.05, NA, 0.09,
0.04, 0.04, 0, 0.1, 0.21, NA, 0.07, NA, 0.14, NA, NA, NA,
0.08, 0, 0.23, 0.03, 0.15, 0.18, 0.04, 0.15, 0.1, 0.5, 0.08,
NA, 0.05, 0.5, 0.27, 0.03, 0.1, 0.09, 0.18, NA, 0.1, 0.15,
0.18, 0.23, NA, 0.1, 0.05, 0.33, 0.05, 0.31, NA, 0.08, 0,
NA, 0.31, NA, 0.2, NA, 0.18, 0.17, 0.11, 0.15, 0.04, 0.14,
0.09, 0.06, 0.08, 0.21, NA, 0.12, 0.04, 0.27, 0.14, NA, 0.07,
0.11, 0.12, 0, NA, 0.04, 0.18, NA, NA, 0.09, 0.17, NA, 0.08,
0.12, NA, NA, 0.15, NA, 0.13, 0.3, 0.09, 0.12, 0.09, 0.18,
0.1, 0.16, 0.29, 0.05, NA, 0.17, NA, 0.06, NA, 0.08, NA,
NA, 0.11, 0.2, 0.19), g1y4 = c(0, 0, 0, 0, NA, 0, NA, NA,
0, 0, 0, 0, 0, 0, NA, 0, 0, 0.17, 0, 0, 0, 0, 0, 0, 0, 0,
NA, NA, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, NA, NA, 0, NA, 0,
0, 0, 0.1, 0, 0, NA, 0, 0, 0, NA, 0, NA, 0, 0, NA, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0,
0, 0, 0, 0, 0, NA, 0, NA, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA,
0, 0.08, 0, 0, 0, NA, 0, 0, NA, 0, NA, 0, NA, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0,
0, NA, NA, 0, 0, NA, 0, 0, NA, NA, 0, NA, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, NA, 0, NA, 0, NA, 0, NA, NA, 0, 0, 0), g1y5 = c(0.21,
0.11, 0.13, 0.25, NA, 0, NA, NA, 0.12, 0.25, 0, 0.23, 0.37,
0.05, NA, 0, 0, 0.1, 0.18, 0.13, 0.33, 0.36, 0.1, 0.06, 0,
0.2, NA, NA, 0.16, 0, 0, NA, 0.17, 0, NA, 0.09, 0.2, 0.06,
0.3, 0.14, 0, 0, 0.12, 0.25, 0, NA, 0, NA, 0, 0.06, NA, 0.23,
0, 0, 0.3, 0, NA, NA, 0.06, NA, 0, 0.5, 0.03, 0.07, 0.28,
0.08, NA, 0.15, 0.15, 0, NA, 0.31, NA, 0, 0, NA, 0.37, 0,
0.2, 0.34, 0.1, 0, 0, 0, 0.21, 0.37, NA, 0.03, 0.18, 0.18,
0.24, 0.21, 0, 0, 0.05, NA, 0.13, 0.12, 0.32, 0, 0, 0, NA,
0.25, NA, 0, NA, NA, NA, 0.28, 0, 0.15, 0.22, 0, 0.12, 0.13,
0.15, 0, 0, 0, NA, 0, 0, 0, 0.24, 0.4, 0.06, 0.18, NA, 0.38,
0, 0.21, 0, NA, 0.29, 0.02, 0, 0.26, 0, NA, 0, 0.35, NA,
0, NA, 0.2, NA, 0, 0, 0, 0, 0.12, 0, 0.5, 0.1, 0.2, 0, NA,
0.08, 0.36, 0, 0, NA, 0.07, 0, 0.08, 0, NA, 0.28, 0.11, NA,
NA, 0.03, 0, NA, 0, 0, NA, NA, 0, NA, 0.06, 0.1, 0, 0, 0.27,
0.11, 0.17, 0.08, 0, 0.11, NA, 0, NA, 0, NA, 0.15, NA, NA,
0, 0.4, 0), g1y6 = c(0.68, 0.47, 0.43, 0.44, NA, 0.47, NA,
NA, 0.44, 0.65, 0.32, 0.77, 0.63, 0.7, NA, 0.45, 0.67, 0.24,
0.91, 0.47, 0.92, 0.77, 0.8, 0.21, 0.5, 0.6, NA, NA, 0.43,
0.18, 0.22, NA, 1, 0.13, NA, 0.73, 0.67, 0.31, 0.6, 0.43,
0.27, 0.26, 0.5, 0.75, 0.08, NA, 0.2, NA, 0.5, 0.44, NA,
0.85, 0.33, 0.34, 0.54, 0.29, NA, NA, 0.3, NA, 0.13, 0.75,
0.17, 0.57, 0.44, 0.28, NA, 0.5, 0.46, 0.38, NA, 0.69, NA,
0.25, 0.62, NA, 0.57, 0.25, 0.52, 0.54, 0.29, 0.14, 0.11,
0.32, 0.55, 0.53, NA, 0.27, 0.5, 0.91, 0.52, 0.86, 0.44,
0.14, 0.3, NA, 0.38, 0.31, 0.56, 1, 0.16, 0.29, NA, 0.6,
NA, 0.14, NA, NA, NA, 0.68, 0.29, 0.77, 0.46, 0.19, 0.47,
0.35, 0.8, 0.28, 0.5, 0.15, NA, 0.05, 0.5, 0.36, 0.47, 0.7,
0.31, 0.53, NA, 0.71, 0.31, 0.61, 0.69, NA, 0.62, 0.11, 0.33,
0.84, 0.43, NA, 0.17, 0.59, NA, 0.52, NA, 1, NA, 0.29, 0.25,
0.5, 0.31, 0.45, 0.36, 0.82, 0.52, 0.6, 0.25, NA, 0.48, 0.47,
0.39, 0.23, NA, 0.26, 0.11, 0.33, 0.67, NA, 0.44, 0.46, NA,
NA, 0.42, 0.17, NA, 0.17, 0.25, NA, NA, 0.23, NA, 0.32, 0.7,
0.32, 0.12, 0.45, 0.49, 0.45, 0.32, 0.43, 0.37, NA, 0.39,
NA, 0.11, NA, 0.35, NA, NA, 0.11, 0.8, 0.31), g1y7 = c(0.46,
0.42, 0.3, 0.44, NA, 0.29, NA, NA, 0.31, 0.55, 0.05, 0.69,
0.53, 0.35, NA, 0.09, 0.33, 0.21, 0.64, 0.33, 0.75, 0.73,
0.4, 0.15, 0, 0.53, NA, NA, 0.35, 0, 0.11, NA, 0.5, 0, NA,
0.45, 0.5, 0.25, 0.47, 0.14, 0.09, 0.21, 0.38, 0.56, 0.02,
NA, 0, NA, 0.2, 0.17, NA, 0.46, 0, 0.22, 0.49, 0, NA, NA,
0.15, NA, 0.07, 0.62, 0.11, 0.57, 0.38, 0.19, NA, 0.41, 0.46,
0.23, NA, 0.56, NA, 0.12, 0.5, NA, 0.51, 0, 0.44, 0.54, 0.22,
0, 0.03, 0.16, 0.48, 0.47, NA, 0.17, 0.39, 0.55, 0.45, 0.57,
0.25, 0, 0.24, NA, 0.29, 0.27, 0.52, 1, 0.06, 0.07, NA, 0.53,
NA, 0, NA, NA, NA, 0.6, 0.29, 0.54, 0.43, 0.04, 0.29, 0.3,
0.65, 0.17, 0, 0.08, NA, 0, 0, 0.09, 0.44, 0.6, 0.22, 0.35,
NA, 0.62, 0.15, 0.42, 0.46, NA, 0.52, 0.06, 0, 0.79, 0.11,
NA, 0.08, 0.59, NA, 0.21, NA, 0.8, NA, 0.12, 0.08, 0.39,
0.15, 0.41, 0.21, 0.73, 0.45, 0.52, 0.04, NA, 0.36, 0.43,
0.12, 0.09, NA, 0.2, 0, 0.21, 0.67, NA, 0.4, 0.29, NA, NA,
0.33, 0, NA, 0.08, 0.12, NA, NA, 0.08, NA, 0.19, 0.4, 0.23,
0, 0.36, 0.32, 0.34, 0.16, 0.14, 0.32, NA, 0.22, NA, 0.06,
NA, 0.27, NA, NA, 0, 0.6, 0.12), pAx1 = c(0.2, 0.56, 0.67,
NA, 0.7, 0.5, 1, NA, 1, NA, 1, 0.67, 0.67, 0.57, 0.85, 0.91,
0.82, 0.65, 1, 0.8, 0.67, 1, 0.67, 0.5, 0.64, 0.45, 0.8,
0.74, 0.67, 0, 1, 0.42, NA, 0.4, 0.77, 0.62, 1, 0.44, 0.59,
0.4, 0.5, 0.14, 0.93, 0.82, 0.85, 0.8, 0.71, 0.62, 0.6, 1,
0.95, 0.4, 0.6, 0.75, 0.36, 1, 0.53, 0.63, 0.67, 0.65, 0.82,
0.43, 0.5, NA, 0.76, 0.78, 1, 0.88, 0.6, 0.57, 0.77, 0, 0.71,
0.46, 0.9, 0.89, 0.95, 0.14, 1, 0.4, 0.31, NA, 1, 1, 0.92,
1, NA, 0.91, 0.94, 1, 0.83, 0.67, 1, 1, 0.62, 0.5, 0.9, 0.76,
0.61, 0.29, 0.58, 0.67, 0.88, 0.45, 0.86, 0.53, 0.88, 1,
0.65, NA, 0.12, 0.79, 0.92, 1, 0.83, 0.8, 0.79, 1, 0, NA,
0.5, 0.47, 0.52, 0.86, 1, 1, 0.5, 1, 0.14, 0.58, 0.7, 0.5,
0.56, 0.42, 0.3, 0.18, 1, 0.61, 0.25, 0.83, 0.75, 0.78, 0.6,
1, 0.38, 0, NA, 0.67, 0, 0.53, NA, 0.89, 0, 0.75, 0.67, 0.75,
1, 0.75, 0.59, 0.67, NA, 0, 0.33, 0.25, 0.8, 0.58, NA, 0.19,
0.89, 0.67, 0.11, 0.43, 0, 0.09, 1, NA, 0.71, 0.15, 0, 0.81,
0.4, 0.58, 0.17, 0, 0, 0.5, 0.38, 0.5, 0, 0.72, 1, 0.33,
0, 0.91, 0, 0.12, 0.04, 0.4, 0.43, 0.34, 0, 1), pAx2 = c(0,
0, 0.17, NA, 0.05, 0.07, 0, NA, 0.5, NA, 0, 0.08, 0.17, 0,
0.1, 0, 0.05, 0.06, 1, 0.4, 0, 0, 0, 0.05, 0.27, 0, 0.13,
0.29, 0, 0, 0, 0, NA, 0, 0.23, 0.12, 0, 0, 0.12, 0, 0.1,
0.29, 0.43, 0.18, 0.4, 0, 0.14, 0, 0, 0.33, 0.5, 0.2, 0,
0.75, 0.07, 0, 0, 0.11, 0, 0.35, 0.41, 0.29, 0.08, NA, 0.06,
0.24, 0, 0.24, 0, 0.05, 0.27, 0, 0.07, 0, 0, 0.11, 0.29,
0.14, 0.25, 0.2, 0.12, NA, 0.33, 0.83, 0.23, 0, NA, 0.05,
0.1, 0, 0.1, 0.33, 0.2, 0, 0, 0, 0, 0.18, 0.11, 0.14, 0.5,
0.33, 0.12, 0.03, 0.18, 0.05, 0.08, 0.18, 0.08, NA, 0, 0,
0.08, 0.67, 0.5, 0.13, 0.04, 0, 1, NA, 0, 0.05, 0, 0.14,
0.25, 0, 0.12, 0, 0, 0.16, 0, 0, 0, 0.12, 0, 0.14, 0.75,
0.44, 0.25, 0.06, 0, 0.17, 0.2, 0.08, 0.38, 0.33, NA, 0.17,
0, 0.24, NA, 0.11, 0, 0, 0, 0.25, 0.38, 0.08, 0.05, 0.12,
NA, 0.5, 0, 0, 0.4, 0.12, NA, 0.62, 0.17, 0, 0, 0.04, 1,
0.45, 1, NA, 0.07, 0.11, 0, 0.06, 0, 0.15, 0.17, 0, 0, 0,
0.12, 0.04, 0, 0.17, 0, 0, 0.03, 0.14, 0.21, 0.25, 0.04,
0.02, 0.07, 0.02, 0, 0), pAx3 = c(0.5, 0.38, 0.33, NA, 0.5,
0.21, 0, NA, 0.25, NA, 0, 0.08, 0.22, 0.29, 0.25, 0.36, 0.45,
0.19, 0, 0.2, 0, 0, 0, 0.35, 0.45, 0.1, 0.27, 0.09, 0.67,
0, 0, 0.58, NA, 0.6, 0.31, 0.38, 1, 0.56, 0.24, 0.4, 0.5,
0.29, 0.07, 0.36, 0.6, 0, 0.21, 0.38, 0.4, 0, 0.1, 0.2, 0,
0, 0.21, 1, 0.42, 0.21, 0.28, 0.29, 0.12, 0.43, 0.58, NA,
0.06, 0.19, 0, 0.18, 0.4, 0.43, 0.35, 0.5, 0.36, 0.08, 0,
0.03, 0.11, 0.09, 0, 0.4, 0.59, NA, 0.33, 0.33, 0, 1, NA,
0.14, 0.1, 0, 0.24, 0.33, 0.2, 0, 0.12, 0.5, 0, 0.29, 0.17,
0, 0.17, 0, 0, 0.52, 0.23, 0.5, 0.38, 0.18, 0.38, NA, 0.38,
0.11, 0.31, 0.33, 0.33, 0.07, 0.39, 0, 0, NA, 0.5, 0.68,
0.43, 0.43, 0.5, 0, 0.25, 1, 0.64, 0, 0.3, 0.25, 0.44, 0.42,
0.2, 0.39, 0.25, 0.33, 0, 0.17, 0.75, 0.56, 0.4, 0.08, 0.12,
0.67, NA, 0.5, 1, 0.41, NA, 0.78, 0.5, 0.12, 0.33, 0.5, 0,
0.33, 0.64, 0.29, NA, 0.62, 0.71, 0.12, 0, 0.46, NA, 0.31,
0.11, 0, 0.56, 0.57, 1, 0.27, 0, NA, 0.21, 0.22, 1, 0.19,
0.6, 0.15, 0.28, 1, 1, 0, 0.25, 0.18, 0.62, 0.5, 0.5, 0.5,
0.4, 0.18, 0.14, 0.25, 0.28, 0.32, 0.43, 0.5, 0.5, 0.33),
pAx4 = c(NA, 0.12, NA, NA, 0.69, 0.29, 0.92, NA, NA, NA,
NA, 0.71, 0.82, 0.4, 0.46, 1, 0.2, 0.5, NA, NA, NA, NA, NA,
0, 0.38, 0, 0.73, 0.46, NA, NA, NA, 0.2, NA, NA, 0.43, 0.43,
NA, NA, 0.38, 0.5, NA, NA, 0.71, 0.83, 0.7, NA, 0.75, 0.5,
NA, 1, 0.86, NA, 0, NA, 0.69, 1, 0.29, 0.6, 0.71, 1, 0.8,
NA, NA, NA, 0.73, 0.59, NA, 0, NA, 0.6, 0.38, NA, NA, 0.75,
0.54, 0, 0, 0, NA, NA, 0.19, NA, NA, NA, 1, 0, NA, 0.91,
NA, NA, NA, NA, NA, NA, NA, NA, 0.88, 0, 0.53, 0.29, NA,
NA, 0, 0.43, 0, 0.28, 0.6, 0.67, 0.42, NA, NA, NA, 0.91,
NA, NA, NA, 0, NA, NA, NA, 0, 0.67, NA, NA, NA, 0, NA, NA,
0.07, 0.3, NA, NA, 0, 0.28, 0, 0, NA, 0.67, NA, 0.78, NA,
0.75, NA, NA, NA, NA, NA, NA, 0, 0, NA, 1, NA, 0.62, NA,
NA, NA, 0.67, 0.69, 0, NA, 0, 0.25, 0, NA, 0.5, NA, 0.08,
0.92, NA, NA, 0.56, NA, NA, NA, NA, 0.6, 0, NA, 0.67, 0.33,
0.32, NA, NA, NA, NA, 0.67, 0, NA, 0.57, 1, 0, NA, 0.73,
NA, 0.12, 0, 0.21, 0, 0, NA, NA), pAy1 = c(0.1, 0.19, 0.5,
0, 0.2, 0.07, 0.15, 0, 0.75, 0, 1, 0.5, 0.17, 0.71, 0.2,
0.27, 0.27, 0.1, 2, 0.6, 1, 1, 0, 0.4, 0.18, 0.2, 0.13, 0.4,
0.33, 1, 0, 0.5, 0, 0.8, 0.35, 0.25, 1, 0.33, 0.35, 0.4,
0.4, 0.29, 0.57, 0.64, 0.35, 0.8, 0.21, 0.25, 0.8, 0, 0.8,
0.2, 0.6, 0.75, 0.14, 0, 0.05, 0.37, 0.11, 0.41, 0.35, 0.14,
0.25, 0, 0.24, 0.37, 0, 0.41, 0, 0.38, 0.65, 0, 0.64, 0.46,
0.13, 0.23, 0.42, 0.32, 0.5, 0.2, 0.31, 0, 1, 0.5, 0.77,
1, 0, 0.32, 0.32, 0.5, 0.1, 0.67, 0.4, 0, 0, 0.17, 0.5, 0.59,
0.17, 0.86, 0.75, 0.67, 0.42, 0.42, 0.41, 0.34, 0.23, 0.09,
0.27, 0, 0.25, 0.21, 0.23, 0.67, 0.17, 0.4, 0.11, 0, 0, 0,
0, 0.37, 0.24, 1.29, 1.25, 1.5, 0.62, 0, 0.36, 0.16, 0.2,
0.25, 0.22, 0.46, 0.6, 0.37, 0.75, 0.56, 0.75, 0.33, 0.5,
0.5, 0.6, 0.23, 0.38, 0, 0, 0.67, 1, 0.41, 0, 0.33, 0.5,
0, 0.67, 0, 0.75, 0.08, 0.5, 0.33, 0, 0.25, 0.24, 0.25, 1.2,
0.58, 0, 0.5, 0.28, 0, 0.56, 0.26, 0, 0.27, 1, 0, 0.39, 0.15,
0.67, 0.31, 1, 0.11, 0.17, 1, 0.2, 1, 0.12, 0.11, 0.38, 0.28,
0.5, 0.33, 0.07, 0.36, 0.38, 0.38, 0.04, 0.15, 0.21, 0.57,
0.62, 1), gAy2 = c(NA, 0.4, 1.27, 0.25, 1.03, 1, NA, 0.6,
1.23, 0.69, 0.78, 0.81, 0, 1.07, NA, 1.11, 0.38, 0.59, 0.29,
NA, 0.33, 0.38, 0.2, NA, 0.5, 0.5, 0.67, 0.67, 1, NA, NA,
0.64, NA, 0.8, 0.44, 0.31, NA, 0.73, 0.52, 0.84, 1.08, 1.25,
0.36, 0.36, 1, 0.25, 0.4, 0.82, 1.14, 0.77, 0.76, 0, 0.9,
1, 0, 0.68, 0.67, 1.08, 1, 1.13, NA, 0.5, 0.73, 0.33, 0.92,
0.88, NA, 1.26, 1, 0.8, 1.18, 0.29, 0.78, 1.14, 0.62, 0,
0.62, 0.61, 0.43, 0.27, 0, 1.07, NA, 0.5, 1.25, 0.18, 0.71,
1, 0.85, 0.1, NA, 0.5, 0.71, 1.18, 0.71, 0.42, 1.4, 1, 0.89,
0.33, 0.91, 0.32, 0.52, 0.5, 1.04, 0.9, 0.64, 0.2, 1.33,
NA, 0.67, 0.78, NA, NA, 0.57, 0.88, NA, NA, 0.22, 0.67, 0.55,
1, 0, 0.81, 1, 0.62, 0.75, 0.67, 0.55, 0.2, NA, NA, 0.5,
0.6, 0.6, 1.05, 0.89, 0, 1, 0.73, 0.57, 1.33, 0.16, NA, 0.69,
0, 0.56, 0.14, 1, 0.8, 1.25, 3, 0.81, 0.5, 1.67, NA, 0, 0.8,
1.25, 0.6, 0.79, NA, 0.52, 1.2, 0.84, 1, 0.46, 0.18, 0.62,
0.71, 0.4, 0.12, 0.2, 1.25, 1, NA, 0.92, 0.38, 0.58, 1.38,
1, 0.7, NA, 0.4, 0.69, 0.89, 0.36, 0.67, 0.87, 0.38, 1.08,
0.94, NA, 0.73, 0.29, 0.83, NA, 1, 0.47, 0.98, 0.11, 2),
gAy3 = c(NA, 0.2, 0, 0, 0.08, 1, NA, 0.2, 0, 0.15, 0.07,
0, 1, 0.1, NA, 0.22, 0, 0.18, 0.43, NA, 0.11, 0.15, 0.4,
NA, 0.75, 0.5, 0.5, 0.22, 1, NA, NA, 0.14, NA, 0.4, 0.33,
0.62, NA, 0.13, 0, 0.16, 0.17, 0.38, 0.36, 0.27, 0.56, 0.38,
0.3, 0.06, 0.14, 0, 0.12, 0.11, 0.03, 0.25, 0.5, 0.11, 1,
0.08, 0, 0.33, NA, 0.04, 0.09, 0.67, 0, 0.38, NA, 0, 0, 0,
0.09, 0.07, 0.33, 0.14, 0.23, 0, 0, 0.13, 0, 0, 0, 0, NA,
0, 0.12, 0, 0.14, 1, 0, 0.4, NA, 0.38, 0, 0, 0, 0.25, 0,
1, 0.11, 0.08, 0.05, 0.21, 0.14, 0.09, 0.08, 0.1, 0.18, 0.3,
0.67, NA, 0, 0.11, NA, NA, 0.07, 0.38, NA, NA, 0.11, 0.33,
0.27, 0.5, 0, 0.05, 0, 0.12, 0.15, 1, 0.06, 0, NA, NA, 0,
0.6, 0, 0.05, 0.21, 0.2, 0.5, 0.18, 0.29, 1, 0, NA, 0.08,
0, 0.22, 0.14, 0, 0.1, 0, 1, 0.05, 0.3, 0, NA, 1, 0.3, 0.12,
0.1, 0.02, NA, 0.09, 0.2, 0.05, 0.5, 0.06, 0.36, 0.12, 0.06,
0.13, 0, 0.1, 0.5, 0.17, NA, 0.15, 0.15, 0.25, 0, 0.2, 0.04,
NA, 0, 0, 0, 0, 0, 0.33, 0.12, 0, 0.08, NA, 0.13, 0.14, 0.5,
NA, 1, 0.47, 0.1, 0, 1), gAy4 = c(NA, 0, 0, 0, 0, 0, NA,
0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0,
0, 0, 0, 0, NA, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0,
0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, NA, 0, 0, NA, NA,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA,
0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
NA, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0.57, 0, 0, 0, 0, 0, 0, NA,
0, 0, 0, NA, 0, 0, 0, 0, 0), gAy5 = c(NA, 0.4, 0.18, 0.33,
0.08, 0, NA, 0, 0, 0.08, 0.15, 0, 0, 0.13, NA, 0, 0.19, 0,
0.14, NA, 0.44, 0.31, 0, NA, 0, 0, 0, 0.11, 0, NA, NA, 0.18,
NA, 0, 0, 0, NA, 0.2, 0.1, 0.32, 0.25, 0, 0.21, 0.27, 0,
0.12, 0, 0.06, 0.14, 0.31, 0.08, 0.22, 0.1, 0, 0, 0.21, 0,
0.13, 0.09, 0, NA, 0.25, 0.18, 0, 0.08, 0, NA, 0.05, 0, 0.08,
0, 0.36, 0, 0, 0.31, 0, 0.2, 0.13, 0.57, 0.45, 1, 0, NA,
0, 0, 0.45, 0.14, 0, 0, 0.1, NA, 0.5, 0.29, 0, 0, 0.25, 0,
0, 0, 0.33, 0.07, 0.21, 0.24, 0.25, 0.15, 0.1, 0, 0.3, 0,
NA, 0.14, 0.11, NA, NA, 0.21, 0, NA, NA, 0.17, 0, 0.27, 0,
0.75, 0.05, 0, 0.38, 0.1, 0, 0.36, 0.4, NA, NA, 0.5, 0, 0.6,
0.05, 0, 0.33, 0, 0.18, 0, 0, 0.72, NA, 0, 0, 0.22, 0.29,
0.5, 0.1, 0, 0, 0.05, 0, 0, NA, 0, 0, 0, 0, 0.05, NA, 0.35,
0, 0.05, 0, 0.17, 0.18, 0.56, 0.24, 0.33, 0.5, 0.2, 0, 0,
NA, 0.08, 0.08, 0.17, 0.12, 0.3, 0.15, NA, 0.4, 0.23, 0.09,
0.09, 0.29, 0.2, 0.25, 0.08, 0.18, NA, 0.27, 0.29, 0, NA,
0, 0, 0, 0, 0), gAy6 = c(NA, 0.8, 0.27, 0.67, 0.37, 1, NA,
0.6, 0, 0.31, 0.46, 0.25, 1, 0.53, NA, 0.33, 0.57, 0.45,
1, NA, 1, 0.85, 1, NA, 0.75, 0.5, 0.83, 0.78, 1, NA, NA,
0.68, NA, 0.6, 0.78, 0.75, NA, 0.6, 0.52, 0.79, 0.75, 0.38,
0.93, 0.91, 0.56, 1, 0.8, 0.29, 0.57, 0.62, 0.4, 1, 0.33,
0.5, 1, 0.54, 1, 0.36, 0.13, 0.4, NA, 0.46, 0.36, 0.67, 0.33,
0.5, NA, 0.16, 0, 0.4, 0.36, 0.86, 0.78, 0.14, 0.85, 1, 0.4,
0.52, 0.79, 0.45, 1, 0, NA, 1, 0.12, 0.82, 0.5, 1, 0.23,
1, NA, 0.88, 0.53, 0, 0.14, 0.75, 0, 1, 0.11, 0.88, 0.25,
0.74, 0.67, 0.62, 0.38, 0.32, 0.55, 1, 0.67, NA, 0.38, 0.44,
NA, NA, 0.57, 0.38, NA, NA, 0.94, 0.33, 1, 0.5, 1, 0.52,
0, 0.62, 0.35, 1, 0.61, 0.8, NA, NA, 1, 0.9, 0.8, 0.21, 0.37,
1, 0.5, 0.82, 0.57, 1, 0.88, NA, 0.31, 1, 0.78, 0.86, 1,
0.4, 1, 1, 0.33, 0.5, 0.5, NA, 1, 0.5, 0.12, 0.6, 0.36, NA,
0.87, 0.2, 0.16, 1, 0.63, 1, 0.88, 0.29, 0.73, 0.88, 0.8,
0.5, 0.17, NA, 0.38, 0.69, 0.75, 0.5, 0.8, 0.37, NA, 0.8,
0.69, 0.14, 0.36, 0.57, 0.73, 0.75, 0.08, 0.35, NA, 0.6,
0.86, 0.83, NA, 1, 0.73, 0.24, 0.95, 1), gAy7 = c(NA, 0.6,
0.27, 0.67, 0.29, 0, NA, 0.4, 0, 0.15, 0.39, 0.25, 0, 0.43,
NA, 0.11, 0.57, 0.27, 0.57, NA, 0.89, 0.69, 0.6, NA, 0, 0,
0.33, 0.56, 0, NA, NA, 0.55, NA, 0.2, 0.44, 0.12, NA, 0.47,
0.52, 0.63, 0.58, 0, 0.57, 0.64, 0, 0.62, 0.5, 0.24, 0.43,
0.62, 0.28, 0.89, 0.3, 0.25, 0.5, 0.43, 0, 0.28, 0.13, 0.07,
NA, 0.43, 0.27, 0, 0.33, 0.12, NA, 0.16, 0, 0.4, 0.27, 0.79,
0.44, 0, 0.62, 1, 0.4, 0.39, 0.79, 0.45, 1, 0, NA, 1, 0,
0.82, 0.36, 0, 0.23, 0.6, NA, 0.5, 0.53, 0, 0.14, 0.5, 0,
0, 0, 0.79, 0.2, 0.53, 0.52, 0.53, 0.31, 0.22, 0.36, 0.7,
0, NA, 0.38, 0.33, NA, NA, 0.5, 0, NA, NA, 0.83, 0, 0.73,
0, 1, 0.48, 0, 0.5, 0.2, 0, 0.55, 0.8, NA, NA, 1, 0.3, 0.8,
0.16, 0.16, 0.8, 0, 0.64, 0.29, 0, 0.88, NA, 0.23, 1, 0.56,
0.71, 1, 0.3, 1, 0, 0.29, 0.2, 0.5, NA, 0, 0.2, 0, 0.5, 0.33,
NA, 0.78, 0, 0.11, 0.5, 0.57, 0.64, 0.75, 0.24, 0.6, 0.88,
0.7, 0, 0, NA, 0.23, 0.54, 0.5, 0.5, 0.6, 0.33, NA, 0.8,
0.69, 0.14, 0.36, 0.57, 0.4, 0.62, 0.08, 0.27, NA, 0.47,
0.71, 0.33, NA, 0, 0.27, 0.15, 0.95, 0)), row.names = c(NA,
-202L), class = "data.frame")

If it makes sense to impute the values, then even if you do not have the 4 questions of a part, you can predict them using the relationship between variables and the similarities between observations.
To take into account the colinearities, you can use methods based on low rank,
see the package missMDA for instance with imputePCA or imputeMFA function, in addition you can have a look at the website
https://rmisstastic.netlify.com/
for information,
Best,
JJ

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