Changing the order of plotting levels in Latitice - r

I am trying to get a boxplot with a specific order of the levels that are being plotted.
Using the following data and code I generate the boxplot, but the order in which I need this is 6,12,15,18.
I have tried a number of thing using the with() function but can't make it work.
library(lattice)
rate<-structure(list(Temp = c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L), Rep = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), Ind = structure(c(1L, 1L, 1L, 1L, 5L, 5L,
5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L,
5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L), .Label = c("B", "MBCT",
"MBT", "MSCT", "MST", "S"), class = "factor"), Week = c(1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L), Weight = c(1.756,
1.756, 1.756, 1.756, 0.92, 0.92, 0.92, 0.92, 1.201, 1.201, 1.201,
1.201, 2.601, 2.601, 2.601, 2.601, 2.057, 2.057, 2.057, 2.057,
0.784, 0.784, 0.784, 0.784, 0.663, 0.663, 0.663, 0.663, 1.272,
1.272, 1.272, 1.272, 3.389, 3.389, 3.389, 3.389, 1.433, 1.433,
1.433, 1.433, 3.822, 3.822, 3.822, 3.822, 1.55, 1.55, 1.55, 1.55,
1.198, 1.198, 1.198, 1.198, 1.029, 1.029, 1.029, 1.029, 1.113,
1.113, 1.113, 1.113, 0.261, 0.261, 0.261, 0.261, 0.639, 0.639,
0.639, 0.639, 0.749, 0.749, 0.749, 0.749, 1.083, 1.083, 1.083,
1.083, 1.429, 1.429, 1.429, 1.429, 3.083, 3.083, 3.083, 3.083,
1.061, 1.061, 1.061, 1.061, 1.154, 1.154, 1.154, 1.154, 1.691,
1.691, 1.691, 1.691, 1.185, 1.185, 1.185, 1.185, 0.552, 0.552,
0.552, 0.552, 1.507, 1.507, 1.507, 1.507, 1.175, 1.175, 1.175,
1.175, 1.773, 1.773, 1.773, 1.773, 1.712, 1.712, 1.712, 1.712,
3.784, 3.784, 3.784, 3.784, 0.715, 0.715, 0.715, 0.715, 1.271,
1.271, 1.271, 1.271, 0.788, 0.788, 0.788, 0.788, 1.72, 1.72,
1.72, 1.72, 0.571, 0.571, 0.571, 0.571, 1, 1, 1, 1, 1.037, 1.037,
1.037, 1.037, 1.656, 1.656, 1.656, 1.656, 2.083, 2.083, 2.083,
2.083), Rate = c(0.387, 0.116, -0.141, 0.184, 0.785, 0.151, -0.69,
0.16, 0.477, 0.368, -0.544, 0.49, 0.152, 0.183, -0.137, 0.259,
0.239, 0.292, 0.018, 0.411, 0.322, 0.073, -0.148, 0.287, 0.214,
0.21, -0.579, 0.419, 0.23, 0.271, 0.685, 0.426, 0.248, 0.125,
0.053, 0.176, 0.465, 0.107, 0.02, 0.339, 0.261, 0.327, 0.279,
0.424, 0.308, 0.223, 0.287, 0.383, 0.306, 0.24, 0.258, 0.253,
0.437, 0.315, 0.275, 0.481, 0.372, 0.306, 0.267, 0.449, 0.727,
0.441, 0.624, 1.262, 0.334, 0.447, 0.548, 0.654, 0.846, 0.661,
0.66, 0.734, 0.191, 0.316, 0.551, 0.581, 0.332, 0.403, 0.509,
0.603, 0.411, 0.683, 0.427, 0.516, 0.498, 0.674, 0.371, 0.326,
0.288, 0.435, 0.297, 0.435, 0.165, 0.387, 0.212, 0.345, 0.334,
0.664, 0.526, 0.338, 0.094, 0.066, 0.39, 0.525, 0.215, 0.431,
0.151, 0.361, 0.153, 0.297, 0.127, 0.339, 0.292, 0.434, 0.411,
0.442, 0.25, 0.607, 0.369, 0.567, 0.189, 0.39, 0.372, 0.333,
0.339, 0.327, 0.449, 0.224, 0.086, 0.242, 0.465, 0.374, -0.063,
-0.006, 0.364, 0.308, 0.069, 0.223, 0.397, 0.264, 0.478, 0.345,
0.582, 0.36, 0.426, 0.403, 0.583, 0.544, 0.57, 0.567, 0.388,
0.531, 0.111, 0.125, 0.366, 0.266, 0.26, 0.315, 0.387, 0.549)), .Names = c("Temp",
"Rep", "Ind", "Week", "Weight", "Rate"), class = "data.frame", row.names = c(NA,
-160L))
rate$Temp <- as.character(rate$Temp)
rate$Week <- as.character(rate$Week)
rate$Rep <- as.character(rate$Rep)
rate$Weight<- as.character(rate$Weight)
bwplot(Rate~Temp, rate,
main="Boxplot for data over all weeks by temperature"
)


This can be tackled in the same manner as your question from a month ago. You need to set the order of levels of a factor. I would generally advise you work with factors, unless you have a really good reason to use characters.
rate$Temp <- as.factor(rate$Temp)
levels(rate$Temp) <- c("6", "12", "15", "18")

Related

Getting Wider Violin Plots

I am trying to make some violin plots with ggplot using this dataframe
df = structure(list(nid.weight = c(2.46, 0.319, 1.169, 1.631, 2.03,
0.148, 0.252, 5.614, 2.557, 6.062, 2.939, 6.04, 18.858, 28.727,
18.3, 9.831, 16.298, 17.176, 13.391, 15.044, 35.42, 5.421, 10.073,
15.499, 12.712, 16.046, 23.003, 11.656, 19.79, 20.593, 19.264,
26.35, 13.752, 31.795, 18.604, 18.871, 22.848, 34.46, 14.176,
20.73, 31.97, 18.7, 17.837, 15.875, 14.44, 38.78, 14.595, 21.522,
13.041, 18.051, 20.748, 17.91, 14.831, 9.523, 9.865, 38.2, 19.531,
25.724, 16.208, 18.059, 14.98, 11.9, 14.9, 13, 16.3, 15.555,
0.031, 1.99, 8.924, 21.081, 30.12, 8.658, 0.078, 0.111, 0.373,
0.217, 0.276, 20.993, 12.936, 0.142, 0.188, 0.154, 0.182, 0.14,
0.172, 0.123, 0.187, 0.104, 17.903, 0.18, 21.026, 0.124, 0.108,
21.394, 0.14, 0.189, 0.173, 0.271, 0.124, 0.122, 0.097, 0.16,
8.087, 0.107, 0.149, 0.072, 16.732, 12.663, 0.268, 0.268, 0.315,
0.277, 0.154, 0.233, 0.323, 14.043, 0.424, 0.296, 0.531, 0.287,
0.45, 0.248, 0.475, 0.726, 0.379, 0.623, 0.257, 0.558, 0.34,
13.687, 0.722, 14.936, 0.243, 0.731, 0.216, 0.4, 0.476, 0.114,
1.46, 0.861, 0.861, 1.64, 0.655, 1.096, 0.649, 0.789, 1.083,
1.072, 1.748, 1.404, 0.721, 1.026, 0.305, 0.59, 0.929, 0.937,
1.984, 1.078, 1.632, 3.373, 2.183, 0.546, 2.745, 2.598, 0.789,
0.925, 0.636, 1.184, 1.171, 1, 1.229, 1.503, 1.172, 1.89, 0.946,
0.641, 0.701, 0.228, 0.169, 0.389, 0.894, 3.299, 1.491, 3.022,
1.395, 1.472, 0.7, 1.195, 0.865, 2.414, 0.442, 1.282, 1.228,
1.403, 0.655, 1.34, 2.014, 1.612, 1.08, 0.326, 1.131, 1.133,
1.362, 2.424, 0.565, 0.67, 1.04, 0.997, 1.022, 0.48, 0.837, 0.746,
0.483, 0.696, 0.934, 1.105, 0.86, 0.75, 0.82, 0.48, 2.437, 0.372,
0.234, 0.099, 0.051, 2.716, 0.621, 0.611, 0.384, 0.82, 0.646,
0.68, 0.768, 0.378, 0.305, 2.462, 2.185, 0.598, 1.529, 2.175,
5.242, 7.084, 0.105, 1.29, 1.154, 2.961, 6.741, 1.742, 1.632,
3.47, 1.232, 2.359, 0.111, 1.638, 2.38, 1.162, 5.291, 1.114,
0.487, 0.874, 0.564, 1.318, 5.55, 7.685, 2.543, 0.401, 6.578,
7.53, 7.89, 3.312, 2.555, 0.233, 7.749, 1.289, 0.94, 0.839, 3.408,
6.603, 10.832, 7.353, 8.789, 5.352, 8.341, 3.897, 21.308, 16.963,
14.393, 3.852, 26.156, 21.705, 8.573, 9.504, 8.813, 2.458, 2.22,
32.4, 10.468, 7.66, 18.072, 2.135, 20.67, 4.79, 15.467, 8.484,
4.28, 13.36, 3.515, 7.835, 9.168, 2.443, 4.076, 9.953, 3.515,
5.206, 11.493, 3.059, 5.311, 7.07, 0.045, 5.309, 0.52, 9.56,
19.989, 36.894, 30.305, 21.25, 20.387, 10.685, 26.185, 0.404,
25.427, 5.755, 16.112, 14.832, 16.072, 14.835, 7.67, 8.717, 17.025,
19.564, 30.922, 0.049, 0.632, 0.415, 6.621, 13.701, 21.269, 17.527,
18.9, 16.574, 22.877, 28.866, 27.756, 7.535, 13.557, 19.082,
8.287, 18.617, 17.219, 14.733, 14.484, 12.481, 6.201, 35.361,
19.888, 24.468, 19.198, 29.679, 22.218, 29.408, 36.102, 23.984,
13.494, 30.313, 18.847, 0.731, 6.166, 28.418, 17.481, 20.235,
31.187, 26.49, 32.56, 14.459, 15.121, 2.385, 31.06, 14.626, 18.43,
9.808, 10.926, 10.1, 18.711, 26.396, 17.722, 12.006, 8.995, 17.874,
15.124, 10.318, 15.23, 22.661, 11.005, 6.016, 22.408, 7.561,
13.97, 8.252, 14.08, 10.254, 15.43, 25.756, 14.52, 9.588, 8.775,
29.909, 24.27, 10.459, 18.974, 11.11, 20.189, 16.73, 14.201,
28.025, 19.849, 20.307, 24.715, 10.688, 13.465, 15.817, 21.798,
19.616, 18.622, 12.703, 15.037, 24.377, 21.071, 10.81, 16.02,
15.576, 36.77, 21.363, 17.874, 19.724, 14.749, 9.152, 16.923,
0.065, 37.676, 25.147, 19.729, 18.345, 14.74, 14.938, 16.49,
20.211, 11.397, 15.34, 11.787, 12.373, 11.504, 10.563, 13.459,
12.091, 14.487, 7.769, 10.006, 9.041, 8.031, 9.05, 1.856, 3.405,
0.036, 12.772, 12.104, 8.282, 10.581, 4.867, 11.029, 10.558,
11.115, 16.303, 11.409, 12.732, 11.417, 11.352, 16.167, 23.197,
15.232, 17.714, 14.234, 23.325, 13.902, 13.66, 17.23, 15.176,
20.037, 15.751, 25.133, 15.217, 29.949, 24.001, 26.291, 39.325,
0.101, 0.148, 0.095, 0.194, 0.112, 0.07, 0.13, 0.096, 0.151,
15.518, 11.961, 19.033, 10.798, 0.114, 17.396), Fmaturity = structure(c(3L,
1L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 4L, 3L, 4L, 4L, 5L, 4L, 4L, 4L,
5L, 4L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 4L, 5L, 4L, 5L,
5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 1L, 2L, 5L, 4L, 5L, 4L, 1L, 1L, 2L, 2L, 2L, 5L, 5L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 5L, 2L, 5L, 2L, 2L, 5L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 4L, 2L, 2L, 2L, 4L, 5L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L,
4L, 3L, 4L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 3L, 3L, 4L,
4L, 3L, 4L, 4L, 2L, 2L, 4L, 5L, 3L, 5L, 2L, 4L, 2L, 4L, 3L, 3L,
4L, 3L, 3L, 3L, 3L, 2L, 4L, 2L, 3L, 4L, 3L, 3L, 3L, 1L, 3L, 2L,
5L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 2L, 5L, 3L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 4L, 4L, 1L, 2L, 2L, 3L, 4L, 5L, 5L, 4L, 5L, 4L, 5L, 4L,
4L, 5L, 5L, 3L, 4L, 4L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 4L,
4L, 4L, 5L, 4L, 5L, 4L, 2L, 3L, 5L, 5L, 5L, 5L, 4L, 5L, 4L, 5L,
2L, 5L, 4L, 4L, 4L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L,
5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 1L, 4L,
4L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L, 5L, 5L, 5L,
4L, 4L, 4L, 5L, 4L, 2L, 3L, 1L, 4L, 4L, 5L, 5L, 3L, 5L, 5L, 5L,
5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 4L, 5L, 4L, 4L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
5L, 5L, 5L, 4L, 2L, 4L), levels = c("1", "2", "3", "4", "5"), class = "factor")), class = "data.frame", row.names = c(NA,
-519L))
Starting out I tried using the simplest code
ggplot(df, aes(x=Fmaturity, y=nid.weight)) +
geom_violin()
But my violin graphs are really thin, and they don't seem to help me visualize the data. So I tried using
ggplot(squid, aes(x=Fmaturity, y=nid.weight)) +
geom_violin(scale = "width")
which made the plots wider, except for the first violin plot. And what I'm wondering is:
when I use scale = "width" is that changing the data that the graph is using. I don't want to manipulate the data, I just want to graph it so that I can see the violin plots
Is there a better way to show these violin plots so we can visualize the data at all stages? Can I make them wider without manipulating data?

With scale = "width" the violins are all the same width:
ggplot(df, aes(x=Fmaturity, y=nid.weight)) +
geom_violin(scale = "width")
but the first violin is squashed vertically because...that's just the range of the data. If you want to be able to see the details of each distribution then you need a log scale on the y axis:
ggplot(df, aes(x=Fmaturity, y=nid.weight)) +
geom_violin() +
scale_y_log10()

Plotting individual observations and group means with facets with ggplot2

I'm trying to plot data from a study with three within-subjects (test item, frame, sample size) variables in ggplot. I have summarised and plotted test item on the x axis and have separate lines for sample size and have used facet_grid to separate the two frame conditions. The summarised this data to create within-subjects 95% CI error bars. I'd also like to underlay individual participant's lines. All the advice I have found so far doesn't explain how to plot individual and grouped data when you have facetted the data. Everything I have tried looks messy and doesn't clearly show individual's curves/lines.
Is there a way to do this?
I've considered splitting the data by the facetted conditions and plotting separately but if there is an easier way I would like to find it!
Here's a some of the data:
human_exp1 <- structure(list(sample_size = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("2", "8", "20"), class = "factor"),
sampling_frame = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), .Label = c("category", "property"), class = "factor"),
test_item = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L
), .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"),
id = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L), .Label = c("1",
"2", "3", "4", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23",
"24", "25", "26", "27", "28", "29", "30", "31", "32", "33",
"34", "35", "36", "37", "38", "39", "40", "41", "42", "43",
"44", "45", "46", "47", "48", "49", "50", "85", "86", "87",
"88", "89", "90", "91", "92", "93", "94", "95", "96"), class = "factor"),
response = c(0.75, 0.25, 0.4, 0.5, 0.3, 0.55, 0.65, 0.4,
0.3, 0.5, 0, 0.15, 0.65, 0.65, 0.5, 0.65, 0.8, 0.65, 0.65,
0.75, 0.15, 0.35, 0.6, 0.15, 0.3, 0.5, 0.1, 0.3, 0.5, 0,
0.25, 0.45, 0.75, 0.7, 0.45, 0.65, 0.75, 0.75, 0.3, 0.1,
0.25, 0.15, 0.2, 0.3, 0.35, 0.05, 0.3, 0.5, 0, 0.15, 0.5,
0.1, 0.35, 0.25, 0.5, 0.5, 0, 0.25, 0, 0.3, 0.1, 0.15, 0.35,
0.2, 0, 0.3, 0.5, 0, 0.1, 0.5, 0, 0.3, 0.1, 0.7, 0.45, 0,
0.25, 0, 0.35, 0.1, 0.15, 0.3, 0.1, 0, 0.2, 0.25, 0, 0.1,
0.5, 0, 0.15, 0.3, 0.7, 0.4, 0, 0.05, 0.1, 0.3, 0.1, 0, 0.3,
0.05, 0, 0.25, 0.25, 0, 0.15, 0.5, 0, 0.1, 0, 0.75, 0.6,
0, 0.75, 0.3, 0.9, 0.3, 0.2, 0.95, 0.6, 0.7, 0.6, 0.5, 0,
0, 0.5, 0.9, 0.8, 0.9, 0.75, 0.7, 0.8, 0.5, 0.25, 0.1, 0.05,
0, 0.65, 0.5, 0.3, 0.8, 0.5, 0, 0, 0.5, 0.4, 0.85, 0.5, 0.55,
0.55, 0.35, 0.3, 0.2, 0.15, 0.05, 0, 0.3, 0.15, 0.05, 0.45,
0.5, 0, 0, 0.5, 0.45, 0.55, 0.3, 0.35, 0.4, 0.3, 0.15, 0.2,
0.15, 0, 0, 0.3, 0.1, 0, 0.3, 0.5, 0, 0, 0.5, 0.35, 0.35,
0.25, 0.3, 0.5, 0.35, 0.05, 0.2, 0, 0, 0.05, 0.3, 0.05, 0,
0.3, 0.5, 0, 0, 0.5, 0, 0.55, 0, 0.3, 0.35, 0.2, 0.1, 0.2,
0, 0, 0, 0.3, 0.05, 0, 0.25, 0.5, 0, 0, 0.5, 0, 0.55, 0,
0.25, 0.5, 0.25, 0.8, 0.4, 0.75, 0.7, 0.45, 0.95, 0.85, 0.55,
0.7, 0.5, 0, 0.5, 0.8, 0.8, 0.95, 1, 0.8, 0.7, 1, 0.9, 0.2,
0.7, 0.75, 0.25, 0.7, 0.6, 1, 0.7, 0.5, 0, 1, 0.8, 0.9, 0.8,
0.75, 0.8, 0.85, 1, 0.25, 0.1, 0.2, 0.15, 0.25, 0.6, 0.2,
0, 0.45, 0.5, 0, 0.5, 0.7, 0.35, 0.45, 0.25, 0.75, 0.4, 0.2,
0.1, 0.15, 0.65, 0.1, 0.2, 0.55, 0.05, 0, 0.4, 0.5, 0, 0.5,
0.6, 0.35, 0.35, 0, 0.7, 0.45, 0, 0.1, 0.15, 0.15, 0.15,
0.05, 0.55, 0, 0, 0.35, 0.25, 0, 0.5, 0.55, 0.35, 0.2, 0,
0.8, 0.45, 0, 0.05, 0, 0.6, 0.25, 0.1, 0.5, 0, 0, 0.35, 0.25,
0, 0.5, 0.45, 0.35, 0.2, 0, 0.75, 0.4, 0.1, 0.9, 0.5, 0.95,
0.55, 0.4, 1, 0.65, 0.75, 0.6, 0.5, 0, 0.5, 0.75, 0.85, 0.95,
0.9, 0.6, 0.85, 0.75, 0.5, 0.5, 0.95, 0.3, 0.3, 0.55, 0.45,
0.35, 0.9, 0.5, 0, 0, 0.25, 0.65, 0.9, 0.25, 0.75, 0.65,
0.25, 0.2, 0.2, 0.1, 0.05, 0, 0.1, 0.15, 0.05, 0.4, 0.5,
0, 0, 0.45, 0.4, 0.55, 0.1, 0.5, 0.5, 0.2, 0.1, 0.2, 0.4,
0, 0, 0.1, 0.05, 0, 0.2, 0.5, 0, 0, 0.35, 0.35, 0.55, 0.1,
0.35, 0.4, 0.15, 0.1, 0.2, 0, 0, 0, 0.05, 0, 0, 0.2, 0.5,
0, 0, 0.15, 0, 0.55, 0, 0.2, 0.45, 0.15, 0.05, 0.25, 0, 0,
0, 0.05, 0, 0, 0.2, 0.5, 0, 0, 0.3, 0, 0.55, 0, 0.3, 0.35,
0.05, 0.8, 0.15, 0.8, 0.8, 0.75, 1, 0.7, 0.5, 0.95, 0.5,
0, 0.5, 0.9, 0.85, 1, 1, 1, 0.8, 1, 1, 0.15, 0.75, 0.8, 0.4,
1, 0.5, 1, 0.85, 0.5, 0, 1, 0.85, 1, 0.85, 0.9, 0.9, 0.85,
1, 0.1, 0, 0.25, 0.3, 0.4, 0.65, 0, 0, 0.6, 0.5, 0, 0, 0.75,
0.65, 0.65, 0.45, 0.7, 0.5, 0, 0.1, 0, 0.2, 0.3, 0.4, 1,
0, 0, 0.6, 0.5, 0, 0, 0.7, 0.35, 0.55, 0, 0.85, 0.3, 0, 0.1,
0, 0.25, 0.25, 0.1, 0.65, 0, 0, 0.65, 0.25, 0, 0, 0.65, 0.35,
0.3, 0.05, 0.85, 0.3, 0, 0.05, 0, 0.15, 0.25, 0.1, 0.5, 0,
0, 0.45, 0.25, 0, 0, 0.6, 0.35, 0.3, 0, 0.65, 0.25, 0, 0.95,
0.6, 1, 0.75, 0.65, 0.5, 0.55, 0.9, 0.8, 0.5, 0, 1, 0.9,
0.95, 1, 0.95, 0.5, 0.85, 0.8, 0.5, 0.55, 0.95, 0.45, 0.55,
0.5, 0.4, 0.35, 0.8, 0.5, 0, 0, 0.35, 0.65, 1, 0.45, 0.5,
0.55, 0.25, 0.15, 0.3, 0.25, 0.15, 0, 0, 0, 0, 0.35, 0.5,
0, 0, 0.4, 0.35, 0.5, 0.05, 0.25, 0.4, 0, 0.05, 0.2, 0.45,
0, 0, 0, 0, 0, 0.25, 0.5, 0, 0, 0.3, 0.35, 0.5, 0, 0, 0.35,
0, 0.05, 0.25, 0, 0, 0, 0, 0, 0, 0.15, 0.5, 0, 0, 0.15, 0,
0.5, 0, 0, 0.3, 0, 0.05, 0.25, 0, 0, 0, 0, 0, 0, 0.2, 0.5,
0, 0, 0.15, 0, 0.5, 0, 0, 0.35, 0)), row.names = c(NA, -684L
), class = c("tbl_df", "tbl", "data.frame"))
I used summarySEwithin to summarise the data:
within <- Rmisc::summarySEwithin(data = human_exp1, measurevar = "response",
withinvars = c("sample_size", "sampling_frame", "test_item"),
idvar = "id")
I used the summarised data to plot the group means in ggplot. Particularly so I could compute within-ss confidence intervals for the means.
pd <- position_dodge(0.1)
ggplot(within, aes(x=test_item, y=response, colour=factor(sample_size), group=factor(sample_size)))+
geom_point(position=pd, size=5)+
geom_line(position=pd, size = .8)+
facet_grid(cols = vars(sampling_frame))+
geom_errorbar(aes(ymin=response-ci, ymax=response+ci), width=1, position=pd, size=1)+
ylim(0, 1)+
theme_bw()+
scale_x_discrete(
breaks=c("1","2","3", "4", "5", "6"),
labels=c("S1", "S2", "T1", "T2", "T3", "T4")
)+
# theme(legend.position = c(.9, .85))+
labs(x = "Test Item", y = "Generalisation Response")
I then summarised the data and grouped by all the grouping variables including id
gd <- human %>%
group_by(id, test_item, sample_size, sampling_frame) %>%
summarise(response = mean(response))%>%
ungroup()
gd
I then tried many different versions of geom_line() with the gd summarised data to add individual lines.
Any help would be much appreciated. I would like the individual lines to appear as faint grey lines behind the group mean lines.
Here is what I have with the within-subjects grouped data
Here is what I get when I try to add individual lines with geom_line(data = human, aes(x=test_item, y=response, group=id))

Is this what you want? I grouped the individual lines by both id and sample_size to get single lines:
ggplot(within, aes(x=test_item, y=response, colour=factor(sample_size), group=factor(sample_size)))+
geom_point(position=pd, size=5)+
geom_line(position=pd, size = .8)+
facet_grid(cols = vars(sampling_frame))+
geom_errorbar(aes(ymin=response-ci, ymax=response+ci), width=1, position=pd, size=1)+
ylim(0, 1)+
theme_bw()+
scale_x_discrete(
breaks=c("1","2","3", "4", "5", "6"),
labels=c("S1", "S2", "T1", "T2", "T3", "T4")
)+
# theme(legend.position = c(.9, .85))+
labs(x = "Test Item", y = "Generalisation Response") +
geom_line(data=human_exp1, alpha=0.2, color="black", aes(x=test_item, Y=response, group=interaction(id,sample_size)))

Is this what you are lookong for?
library(dplyr)
library(ggplot2)
within %>%
ungroup() %>%
group_by(test_item, sample_size) %>%
summarise(mean = mean(response), ci = sd(response)) -> smry
pd <- "jitter"
ggplot(within, aes(x = test_item, y = response)) +
geom_point(aes(colour = sample_size), position = pd) +
geom_errorbar(
data = smry,
mapping = aes(y = mean, ymin = mean - ci, ymax = mean + ci),
size = 1
)+
facet_grid(cols = vars(sampling_frame)) +
ylim(0, 1) +
scale_x_discrete(
breaks = c("1","2","3", "4", "5", "6"),
labels = c("S1", "S2", "T1", "T2", "T3", "T4")
) +
labs(x = "Test Item", y = "Generalisation Response") +
theme_bw()
# theme(legend.position = c(.9, .85))+

Create new conditional columns with factors using fewer scripts

I would like to know if there is a way to more elegantly rewrite this piece of script. I have tried case_when but it throws an error message when I try to have several of them within one mutate function. Here is the dput for the file
structure(list(todays_date = structure(c(1L, 1L, 1L, 1L, 2L,
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6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 8L, 9L, 9L,
9L, 9L), .Label = c("04/11/2019", "05/11/2019", "06/11/2019",
"07/11/2019", "08/11/2019", "12/11/2019", "13/11/2019", "14/11/2019",
"15/11/2019"), class = "factor"), p_initials = structure(c(34L,
54L, 1L, 71L, 16L, 77L, NA, 55L, 56L, 122L, 20L, 53L, 116L, 48L,
36L, 14L, 44L, 55L, 89L, 96L, 105L, 83L, 92L, 98L, 38L, 5L, 70L,
47L, 10L, 10L, 107L, 67L, 70L, 24L, 25L, 32L, 65L, 24L, 124L,
87L, 75L, 80L, 26L, 31L, 112L, 40L, 45L, 117L, 10L, 23L, 11L,
69L, 7L, 8L, 6L, 79L, 81L, 46L, 108L, 13L, 3L, 61L, 82L, 65L,
90L, 102L, 101L, 59L, 93L, 70L, 74L, 29L, 62L, 78L, 67L, 13L,
64L, 119L, 22L, 43L, 10L, 38L, 50L, 104L, 3L, 2L, 125L, 13L,
88L, 4L, 96L, 106L, 84L, 109L, 17L, 74L, 10L, 91L, 63L, 89L,
7L, 120L, 12L, 38L, 95L, 27L, 9L, 86L, 42L, 99L, 70L, 110L, 103L,
74L, 111L, 72L, 85L, 68L, 76L, 73L, 70L, 21L, 77L, 37L, 8L, 66L,
70L, 123L, 94L, 61L, 115L, 25L, 120L, 67L, 119L, 19L, 71L, 21L,
34L, 57L, 42L, 57L, 100L, 18L, 30L, 19L, 105L, 113L, 39L, 60L,
15L, 33L, 95L, 121L, 52L, 97L, 102L, 5L, 58L, 81L, 114L, 119L,
28L, 3L, 7L, 51L, 35L), .Label = c("BA", "BB", "BD", "BE", "BH",
"BI", "BM", "BS", "BY", "CA", "CB", "CD", "CE", "CF", "CG", "CGA",
"CGG", "CI", "CK", "CL", "CM", "CO", "CP", "CS", "CT", "CZ",
"DK", "DO", "DPH", "DT", "GA", "GB", "GG", "IA", "IB", "Ik",
"IK", "IM", "IP", "IS", "ITF", "KA", "KB", "KBA", "KF", "KG",
"KJ", "KK", "KM", "KO", "KP", "KR", "KS", "KY", "NB", "ND", "NF",
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"ZN", "ZP", "ZS", "ZSS", "ZT", "ZTM", "ZTN", "ZZ"), class = "factor"),
village = structure(c(2L, 2L, 2L, 2L, 3L, 3L, 8L, 1L, 1L,
1L, 8L, 8L, 8L, 8L, 6L, 6L, 8L, 8L, 8L, 8L, 8L, 1L, 1L, 1L,
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3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 1L, 1L, 1L, 1L, 3L,
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"40094", "240007", "240013", "240071", "240078", "040018",
"040125", "40147", "80034", "80049"), new_compound_id = c(40080L,
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30203L, 30229L, NA, NA, 80007L, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, 30828L, 40094L, NA, NA, NA, NA, NA, NA, NA, NA,
NA), num_sleep_space = c(2L, 3L, 2L, 2L, 3L, 4L, 2L, 3L,
6L, 4L, 8L, 5L, 1L, 2L, 4L, 4L, 3L, 6L, 3L, 10L, 2L, 3L,
9L, 8L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 3L, 4L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L,
2L, 3L, 3L, 5L, 5L, 3L, 3L, 2L, 5L, 4L, 3L, 2L, 4L, 3L, 4L,
3L, 4L, 5L, 2L, 2L, 3L, 5L, 3L, 5L, 4L, 3L, 2L, 4L, 3L, 4L,
4L, 5L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 7L, 2L, 3L, 2L, 4L, 3L,
3L, 3L, 2L, 3L, 4L, 3L, 3L, 2L, 5L, 4L, 4L, 4L, 4L, 2L, 3L,
2L, 4L, 1L, 2L, 1L, 5L, 5L, 1L, 4L, 3L, 3L, 4L, 4L, 4L, 6L,
8L, 8L, 9L, 7L, 7L, 3L, 7L, 3L, 4L, 4L, 4L, 2L, 10L, 12L,
4L, 4L, 10L, 5L, 3L, 8L, 4L, 5L, 4L, 3L, 3L), receive_new_net = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = "yes", class = "factor"), note_net_type.num_net_given = c(2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 6L, 4L, 6L, 7L, 1L, 3L, 3L, 3L,
3L, 5L, 4L, 4L, 3L, 2L, 4L, 3L, 3L, 6L, 5L, 3L, 3L, 2L, 2L,
3L, 3L, 6L, 3L, 4L, 2L, 3L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 4L,
3L, 4L, 4L, 4L, 2L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 1L, 3L,
3L, 5L, 5L, 3L, 4L, 4L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 1L, 3L,
4L, 3L, 2L, 4L, 3L, 4L, 4L, 5L, 4L, 3L, 3L, 2L, 2L, 3L, 3L,
3L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L,
7L, 2L, 3L, 2L, 4L, 3L, 3L, 3L, 2L, 3L, 4L, 4L, 3L, 2L, 4L,
4L, 4L, 4L, 4L, 2L, 3L, 2L, 4L, 2L, 2L, 2L, 5L, 5L, 1L, 4L,
3L, 3L, 6L, 4L, 3L, 5L, 6L, 6L, 5L, 7L, 6L, 3L, 8L, 5L, 4L,
5L, 5L, 4L, 10L, 15L, 4L, 4L, 8L, 5L, 3L, 7L, 4L, 5L, 4L,
3L, 3L), note_net_type.date_new_net = structure(c(2L, 2L,
2L, 2L, 14L, 11L, 14L, 12L, 12L, 14L, 14L, 12L, 14L, 14L,
11L, 12L, 21L, 14L, 21L, 11L, 21L, 14L, 11L, 11L, 15L, 2L,
2L, 8L, 10L, 9L, 9L, 22L, 21L, 23L, 23L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L,
6L, 21L, 2L, 2L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
12L, 14L, 14L, 12L, 15L, 17L, 11L, 16L, 14L, 14L, 11L, 14L,
21L, 2L, 2L, 2L, 2L, 2L, 4L, 21L, 9L, 9L, 23L, 23L, 23L,
23L, 23L, 14L, 1L, 14L, 14L, 14L, 13L, 14L, 14L, 4L, 4L,
4L, 21L, 21L, 21L, 21L, 21L, 9L, 21L, 21L, 21L, 21L, 21L,
21L, 23L, 23L, 23L, 23L, 23L, 4L, 4L, 4L, 4L, 14L, 12L, 16L,
18L, 14L, 14L, 14L, 23L, 23L, 14L, 4L, 4L, 2L, 14L, 12L,
14L, 14L, 14L, 16L, 12L, 12L, 14L, 12L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 18L, 4L, 2L, 19L, 19L, 16L, 20L, 2L, 3L, 5L,
2L, 2L), .Label = c("12/07/2019", "15/06/2019", "15/07/2019",
"16/06/2019", "16/07/2019", "17/06/2019", "17/10/2019", "18/06/2019",
"19/06/2019", "20/06/2019", "20/07/2019", "21/07/2019", "22/06/2019",
"22/07/2019", "23/06/2019", "23/07/2019", "24/06/2019", "24/07/2019",
"25/06/2019", "25/07/2019", "29/06/2019", "29/10/2019", "30/06/2019"
), class = "factor"), note_net_type.brand_net_given = structure(c(6L,
6L, 6L, 6L, 6L, 6L, 6L, 9L, 9L, 9L, 9L, 9L, 2L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 3L, 5L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 1L, 6L, 9L, 9L, 6L, 12L, 1L, 11L, 12L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 4L, 7L, 3L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 4L, 7L, 6L, 12L, 13L, 12L, 6L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 12L, 12L, 7L, 7L, 1L, 12L, 12L, 12L,
10L, 7L, 5L, 7L, 7L), .Label = c("", "Pema.net", "PERMA .NET",
"PERMA,NET", "PERMA. NET", "Perma.net", "PERMA.NET", "Perman.net",
"Permanet", "PERMANET", "Permanet.2", "PERMANET.2", "PERMANT.2"
), class = "factor"), note_net_type.help_hang_net = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("no", "yes"), class = "factor"), net_shape = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = "rectangular", class = "factor"), other_net_shape = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA), num_old_net = c(2L, 3L, 2L, 2L, 4L, 6L, 3L, 3L, 4L,
2L, 4L, 5L, 1L, 3L, 6L, 4L, 3L, 2L, 4L, 4L, 3L, 1L, 4L, 4L,
3L, 0L, 2L, 0L, 1L, 3L, 2L, 3L, 2L, 3L, 2L, 5L, 4L, 3L, 6L,
6L, 4L, 5L, 6L, 4L, 6L, 5L, 6L, 6L, 5L, 4L, 4L, 4L, 3L, 6L,
6L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 6L,
5L, 1L, 3L, 4L, 5L, 4L, 5L, 0L, 0L, 2L, 4L, 3L, 4L, 4L, 5L,
4L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L,
3L, 2L, 5L, 4L, 5L, 3L, 3L, 7L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
2L, 3L, 4L, 2L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 3L, 2L,
4L, 2L, 2L, 5L, 5L, 1L, 4L, 3L, 3L, 5L, 3L, 4L, 5L, 7L, 7L,
7L, 7L, 8L, 3L, 7L, 5L, 3L, 3L, 4L, 3L, 9L, 8L, 4L, 4L, 6L,
4L, 1L, 1L, 4L, 5L, 4L, 3L, 3L), num_hh_members = c(4L, 5L,
4L, 3L, 4L, 6L, 5L, 6L, 7L, 7L, 12L, 9L, 7L, 9L, 7L, 5L,
7L, 8L, 8L, 9L, 6L, 3L, 8L, 7L, 5L, 6L, 5L, 5L, 5L, 4L, 4L,
6L, 6L, 6L, 7L, 6L, 3L, 5L, 7L, 8L, 7L, 6L, 7L, 6L, 6L, 7L,
6L, 8L, 7L, 7L, 4L, 5L, 5L, 8L, 6L, 5L, 5L, 6L, 7L, 2L, 5L,
5L, 7L, 5L, 8L, 6L, 8L, 5L, 8L, 7L, 6L, 6L, 7L, 10L, 8L,
10L, 5L, 5L, 6L, 5L, 4L, 5L, 5L, 6L, 6L, 4L, 4L, 3L, 5L,
7L, 8L, 7L, 5L, 10L, 10L, 6L, 2L, 4L, 6L, 4L, 10L, 5L, 5L,
5L, 5L, 6L, 12L, 5L, 5L, 4L, 7L, 5L, 5L, 5L, 4L, 5L, 5L,
5L, 6L, 5L, 9L, 5L, 5L, 5L, 6L, 9L, 9L, 6L, 10L, 6L, 5L,
5L, 11L, 10L, 3L, 6L, 5L, 5L, 11L, 8L, 5L, 9L, 10L, 18L,
12L, 12L, 19L, 6L, 15L, 10L, 9L, 7L, 10L, 8L, 22L, 30L, 5L,
6L, 19L, 11L, 5L, 15L, 7L, 7L, 6L, 5L, 6L), hh_member_count = c(4L,
5L, 4L, 3L, 4L, 6L, 5L, 6L, 7L, 7L, 12L, 9L, 7L, 9L, 7L,
5L, 7L, 8L, 8L, 9L, 6L, 3L, 8L, 7L, 5L, 6L, 5L, 5L, 5L, 4L,
4L, 6L, 6L, 6L, 7L, 6L, 3L, 5L, 7L, 8L, 7L, 6L, 7L, 6L, 6L,
7L, 6L, 8L, 7L, 7L, 4L, 5L, 5L, 8L, 6L, 5L, 5L, 6L, 7L, 2L,
5L, 5L, 7L, 5L, 8L, 6L, 8L, 5L, 8L, 7L, 6L, 6L, 7L, 10L,
8L, 10L, 5L, 5L, 6L, 5L, 4L, 5L, 5L, 6L, 6L, 4L, 4L, 3L,
5L, 7L, 8L, 7L, 5L, 10L, 10L, 6L, 2L, 4L, 6L, 4L, 10L, 5L,
5L, 5L, 5L, 6L, 12L, 5L, 5L, 4L, 7L, 5L, 5L, 5L, 4L, 5L,
5L, 5L, 6L, 5L, 9L, 5L, 5L, 5L, 6L, 9L, 9L, 6L, 10L, 6L,
5L, 5L, 11L, 10L, 3L, 6L, 5L, 5L, 11L, 8L, 5L, 9L, 10L, 18L,
12L, 12L, 19L, 6L, 15L, 10L, 9L, 7L, 10L, 8L, 22L, 30L, 5L,
6L, 19L, 11L, 5L, 15L, 7L, 7L, 6L, 5L, 6L)), class = "data.frame", row.names = c(NA,
-167L))
and the script I want to rewrite
comp_df <- comp_df %>% mutate(`sleep space category` = ifelse(num_sleep_space == 1, "1", ifelse(num_sleep_space >=2
& num_sleep_space <=4 ,"2-4",ifelse(num_sleep_space >=5 & num_sleep_space <=9,
"5-9", ifelse(num_sleep_space >9, ">9", NA)))),
`sleep space category` = factor(`sleep space category` , levels=c("1","2-4","5-9",">9")),
`number of nets given` = ifelse(note_net_type.num_net_given == 1, "1",
ifelse(note_net_type.num_net_given >=2 & note_net_type.num_net_given <=4 ,"2-4",
ifelse(note_net_type.num_net_given >=5 & note_net_type.num_net_given <=9,"5-9",
ifelse(note_net_type.num_net_given >9, ">9", NA)))),
`number of nets given` = factor(`number of nets given`, levels = c("1","2-4","5-9",">9")),
`net surplus/gap` = num_sleep_space - note_net_type.num_net_given,
`number of household members` = ifelse(hh_member_count >= 1 & hh_member_count<= 5, "1-5",
ifelse(hh_member_count >=6 & hh_member_count <=10,"6-10",ifelse(hh_member_count >10, ">10", NA)))) %>%
mutate(`number of household members` = factor(`number of household members`,
levels = c("1-5","6-10",">10")))

I can see why you want to refactor your code!
You are trying to reinvent the cut function using ifelse statements and without taking advantage of the ability to seperate logic out into simple chunks using functions.
Your whole complex code can be replaced with this:
cut4 <- function(x) cut(x, c(0, 1.5, 4.5, 9.5, 20), c("1", "2-4", "5-9", ">9"))
cut3 <- function(x) cut(x, c(0, 5.5, 10.5, 50), c("1-5", "6-10", ">10"))
comp_df <- comp_df %>%
mutate(`sleep space category` = cut4(num_sleep_space),
`number of nets given` = cut4(note_net_type.num_net_given),
`net surplus/gap` = num_sleep_space - note_net_type.num_net_given,
`number of household members` = cut3(hh_member_count))

combine facet_grid (ggplot2) with denscomp (fitdistrplus)

First off, I am an R newbie. I am trying to apply density plots to various groups within my data. Using fitdistrplus, I have created a single distribution density plot for all of my data.
plot(my_data, pch=20)
plotdist(my_data$Capture_Rate, histo = TRUE, demp = TRUE)
fit_w <- fitdist(my_data$Capture_Rate, "weibull")
fit_g <- fitdist(my_data$Capture_Rate, "gamma")
fit_ln <- fitdist(my_data$Capture_Rate, "lnorm")
par(mfrow=c(2,2))
plot.legend <- c("Weibull", "lognormal", "gamma")
denscomp(list(fit_w, fit_ln, fit_g), legendtext = plot.legend)
Using facet_grid in ggplot, I have created a grid of histograms for each grouping of my data.
df_data <- data.frame(my_data)
cdat <- ddply(df_data, c("sYear", "Season"), summarise, Capture_Rate.mean=mean(Capture_Rate))
ggplot(df_data, aes(x=Capture_Rate, fill=sYear))+
geom_histogram(binwidth = .025,
alpha = .5,
position = "identity")+
#geom_density(alpha=.2, fill="#FF6666")+
geom_vline(data=cdat, aes(xintercept=Capture_Rate.mean),
color="red", linetype="dashed", size=1)+
facet_grid(Season ~ sYear)
What I am looking for is to combine the two results where I get a density plot for each histogram in my grouping grid. Thank you for the assistance.
Sample Data:
a <- dput(my_data)
structure(list(Schedule_Name = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Actuals ", class = "factor"),
Sub_Fleet = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = "38K", class = "factor"), sDate = structure(c(17664,
17665, 17666, 17667, 17668, 17669, 17670, 17672, 17674, 17675,
17676, 17677, 17678, 17679, 17680, 17681, 17682, 17683, 17684,
17685, 17686, 17687, 17688, 17689, 17690, 17691, 17692, 17693,
17694, 17696, 17697, 17698, 17699, 17700, 17701, 17702, 17703,
17704, 17705, 17706, 17707, 17708, 17710, 17711, 17712, 17713,
17714, 17715, 17716, 17717, 17718, 17719, 17720, 17721, 17722,
17723, 17724, 17725, 17728, 17729, 17730, 17731, 17732, 17733,
17734, 17735, 17736, 17737, 17738, 17739, 17740, 17741, 17742,
17743, 17744, 17745, 17746, 17747, 17748, 17749, 17750, 17751,
17753, 17754, 17755, 17758, 17759, 17761, 17762, 17763, 17764,
17765, 17766, 17767, 17768, 17769, 17770, 17771, 17772, 17773,
17774, 17775, 17776, 17777, 17778, 17779, 17781, 17782, 17783,
17784, 17785, 17786, 17787, 17788, 17789, 17790, 17791, 17792,
17793, 17794, 17795, 17796, 17797, 17798, 17799, 17800, 17801,
17802, 17803, 17804, 17805, 17806, 17807, 17808, 17809, 17810,
17811, 17812, 17813, 17814, 17815, 17816, 17817, 17818, 17819,
17820, 17821, 17822, 17823, 17824, 17825, 17826, 17827, 17828,
17829, 17830, 17831, 17832, 17833, 17834, 17835, 17836, 17837,
17838, 17839, 17840, 17841, 17842, 17843, 17844, 17845, 17846,
17847, 17848, 17849, 17850, 17851, 17852, 17853, 17854, 17855,
17856, 17857, 17858, 17859, 17860, 17861, 17862, 17863, 17864,
17865, 17866, 17867, 17868, 17869, 17870, 17871, 17872, 17873,
17874, 17875, 17876, 17877, 17878, 17879, 17880, 17881, 17882,
17883, 17884, 17885, 17886, 17887, 17888, 17889, 17890, 17891,
17892, 17893, 17894, 17895, 17896, 17897, 17898, 17899, 17900,
17901, 17902, 17903, 17904, 17905, 17906, 17907, 17908, 17909,
17910, 17911, 17912, 17913, 17914, 17915, 17916, 17917, 17918,
17919, 17920, 17921, 17922, 17923, 17924, 17925, 17926, 17927,
17928, 17929, 17930, 17931, 17932, 17933, 17934, 17935, 17936,
17937, 17938, 17939, 17940, 17941, 17942, 17943, 17944, 17945,
17946, 17947, 17948, 17949, 17950, 17951, 17952, 17953, 17954,
17955, 17956, 17957, 17958, 17959, 17960, 17961, 17962, 17963,
17964, 17965, 17966, 17967, 17968, 17969, 17970, 17971, 17972,
17973, 17974, 17975, 17976, 17977, 17978, 17979, 17980, 17981,
17982, 17983, 17984, 17985, 17986, 17987, 17988, 17989, 17990,
17991, 17992, 17993, 17994, 17995, 17996, 17997, 17998, 17999,
18000, 18001, 18002, 18003, 18004, 18005, 18006, 18007, 18008,
18009, 18010, 18011, 18012, 18013, 18014, 18015, 18016, 18017,
18018, 18019, 18020, 18021, 18022, 18023, 18024, 18025, 18026,
18027, 18028, 18029, 18030, 18031, 18032, 18033, 18034, 18035,
18036, 18037, 18038, 18039, 18040, 18041, 18042, 18043, 18044,
18045, 18046, 18047, 18048, 18049, 18050, 18051, 18052, 18053,
18054, 18055, 18056, 18057, 18058, 18059, 18060, 18061, 18062,
18063, 18064, 18065, 18066, 18067, 18068, 18069, 18070, 18071,
18072, 18073, 18074, 18075, 18076, 18077, 18078, 18079, 18080,
18081, 18082, 18083, 18084, 18085, 18086, 18087, 18088, 18089,
18090, 18091, 18092), class = "Date"), Active_Tails = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 8L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 17L, 18L, 18L, 19L, 19L, 19L, 20L, 21L, 21L,
21L, 22L, 22L, 23L, 24L, 25L, 26L, 26L, 26L, 26L, 25L, 26L,
26L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 29L, 30L, 30L, 31L,
32L, 33L, 33L, 34L, 34L, 34L, 35L, 35L, 36L, 36L, 36L, 37L,
37L, 37L, 37L, 38L, 40L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 45L, 46L,
46L, 46L, 46L, 46L, 46L, 47L, 48L, 48L, 49L, 49L, 49L, 49L,
50L, 51L, 51L, 52L, 52L, 52L, 52L, 53L, 53L, 54L, 55L, 55L,
55L, 55L, 56L, 56L, 56L, 58L, 58L, 58L, 58L, 60L, 59L, 59L,
60L, 60L, 60L, 60L, 61L, 62L, 63L, 63L, 63L, 63L, 65L, 65L,
65L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 67L, 67L, 67L, 67L,
67L, 68L, 68L, 68L, 68L, 69L, 69L, 69L, 69L, 69L, 69L, 69L,
69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L,
69L, 69L, 69L, 69L, 69L, 69L, 69L, 70L, 70L, 70L, 69L, 70L,
70L, 71L, 71L, 70L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 70L, 70L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L), MX_Credits = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 3L, 4L, 3L, 2L, 4L, 4L, 1L, 3L, 2L, 4L,
4L, 3L, 3L, 4L, 2L, 5L, 5L, 4L, 4L, 6L, 7L, 2L, 4L, 6L, 4L,
7L, 9L, 6L, 4L, 7L, 3L, 9L, 6L, 9L, 7L, 7L, 8L, 7L, 5L, 8L,
10L, 11L, 9L, 6L, 8L, 5L, 7L, 6L, 9L, 10L, 8L, 10L, 7L, 9L,
11L, 9L, 10L, 11L, 8L, 10L, 11L, 11L, 9L, 8L, 9L, 13L, 13L,
16L, 15L, 10L, 13L, 16L, 12L, 10L, 14L, 17L, 12L, 12L, 13L,
15L, 18L, 14L, 24L, 15L, 20L, 17L, 17L, 14L, 22L, 19L, 21L,
23L, 16L, 19L, 23L, 16L, 22L, 17L, 17L, 15L, 22L, 21L, 16L,
19L, 19L, 18L, 14L, 23L, 23L, 25L, 17L, 15L, 22L, 21L, 17L,
19L, 17L, 20L, 23L, 22L, 22L, 22L, 19L, 19L, 25L, 22L, 25L,
25L, 21L, 22L, 24L, 24L, 22L, 20L, 26L, 22L, 22L, 26L, 25L,
24L, 27L, 27L, 26L, 24L, 28L, 23L, 27L, 25L, 25L, 27L, 27L,
23L, 28L, 23L, 23L, 29L, 32L, 23L, 19L, 30L, 27L, 30L, 29L,
25L, 29L, 26L, 24L, 30L, 30L, 33L, 24L, 31L, 30L, 28L, 28L,
29L, 35L, 33L, 30L, 33L, 35L, 37L, 32L, 32L, 36L, 30L, 31L,
33L, 33L, 31L, 33L, 33L, 37L, 33L, 33L, 38L, 37L, 37L, 38L,
34L, 36L, 38L, 28L, 35L, 30L, 33L, 38L, 39L, 30L, 34L, 32L,
28L, 37L, 33L, 36L, 39L, 33L, 36L, 34L, 39L, 28L, 39L, 39L,
32L, 30L, 35L, 33L, 37L, 25L, 32L, 30L, 28L, 39L, 36L, 33L,
38L, 40L, 37L, 33L, 35L, 43L, 30L, 32L, 40L, 36L, 30L, 31L,
41L, 29L, 31L, 38L, 41L, 34L, 35L, 42L, 34L, 33L, 40L, 33L,
31L, 38L, 37L, 29L, 33L, 35L, 38L, 34L, 33L, 36L, 39L, 33L,
33L, 31L, 33L, 36L, 33L, 38L, 33L, 30L, 28L, 30L, 28L, 37L,
34L, 33L, 33L, 34L, 35L, 31L, 38L, 30L, 35L, 30L, 45L, 35L,
31L, 30L, 26L, 26L, 35L, 34L, 26L, 34L, 36L, 31L, 31L), Capture_Rate = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5,
1, 1, 0.5, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5, 1,
0.33, 1, 1, 0.75, 0.5, 1, 1, 0.25, 0.6, 0.4, 0.8, 0.8, 0.6,
0.6, 0.8, 0.4, 1, 1, 0.67, 0.67, 1, 1, 0.25, 0.4, 0.6, 0.4,
0.64, 0.82, 0.55, 0.36, 0.64, 0.25, 0.75, 0.5, 0.75, 0.58,
0.58, 0.62, 0.54, 0.36, 0.57, 0.71, 0.79, 0.6, 0.38, 0.5,
0.31, 0.44, 0.38, 0.56, 0.63, 0.47, 0.56, 0.39, 0.47, 0.58,
0.47, 0.5, 0.52, 0.38, 0.48, 0.5, 0.5, 0.39, 0.33, 0.36,
0.5, 0.5, 0.62, 0.58, 0.4, 0.5, 0.62, 0.44, 0.37, 0.5, 0.61,
0.43, 0.43, 0.46, 0.52, 0.6, 0.47, 0.77, 0.47, 0.61, 0.52,
0.5, 0.41, 0.65, 0.54, 0.6, 0.64, 0.44, 0.53, 0.62, 0.43,
0.59, 0.46, 0.45, 0.38, 0.54, 0.51, 0.39, 0.46, 0.46, 0.44,
0.34, 0.56, 0.53, 0.58, 0.4, 0.35, 0.51, 0.49, 0.4, 0.44,
0.4, 0.44, 0.5, 0.48, 0.48, 0.48, 0.41, 0.41, 0.53, 0.46,
0.52, 0.51, 0.43, 0.45, 0.49, 0.48, 0.43, 0.39, 0.5, 0.42,
0.42, 0.5, 0.47, 0.45, 0.5, 0.49, 0.47, 0.44, 0.51, 0.41,
0.48, 0.45, 0.43, 0.47, 0.47, 0.4, 0.47, 0.39, 0.39, 0.48,
0.53, 0.38, 0.32, 0.49, 0.44, 0.48, 0.46, 0.4, 0.46, 0.4,
0.37, 0.46, 0.45, 0.5, 0.36, 0.47, 0.45, 0.42, 0.42, 0.43,
0.52, 0.49, 0.45, 0.49, 0.51, 0.54, 0.47, 0.47, 0.52, 0.43,
0.45, 0.48, 0.48, 0.45, 0.48, 0.48, 0.54, 0.48, 0.48, 0.55,
0.54, 0.54, 0.55, 0.49, 0.52, 0.55, 0.41, 0.51, 0.43, 0.48,
0.55, 0.57, 0.43, 0.49, 0.46, 0.4, 0.53, 0.48, 0.51, 0.56,
0.46, 0.51, 0.49, 0.55, 0.39, 0.55, 0.55, 0.45, 0.42, 0.49,
0.46, 0.52, 0.35, 0.46, 0.43, 0.39, 0.55, 0.51, 0.46, 0.54,
0.56, 0.52, 0.46, 0.49, 0.61, 0.42, 0.45, 0.56, 0.51, 0.42,
0.44, 0.58, 0.41, 0.44, 0.54, 0.58, 0.48, 0.49, 0.59, 0.48,
0.46, 0.56, 0.46, 0.44, 0.54, 0.52, 0.41, 0.46, 0.49, 0.54,
0.48, 0.46, 0.51, 0.55, 0.46, 0.46, 0.44, 0.46, 0.51, 0.46,
0.54, 0.46, 0.42, 0.39, 0.42, 0.39, 0.52, 0.48, 0.46, 0.46,
0.48, 0.49, 0.44, 0.54, 0.42, 0.49, 0.42, 0.63, 0.49, 0.44,
0.42, 0.37, 0.37, 0.49, 0.48, 0.37, 0.48, 0.51, 0.44, 0.44
), Total_SPR_IML = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Capture_Rate_w_SPR_IML = c(1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5,
1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5, 1, 0.33, 1, 1,
0.75, 0.5, 1, 1, 0.25, 0.6, 0.4, 0.8, 0.8, 0.6, 0.6, 0.8,
0.4, 1, 1, 0.67, 0.67, 1, 1, 0.25, 0.4, 0.6, 0.4, 0.64, 0.82,
0.55, 0.36, 0.64, 0.25, 0.75, 0.5, 0.75, 0.58, 0.58, 0.62,
0.54, 0.36, 0.57, 0.71, 0.79, 0.6, 0.38, 0.5, 0.31, 0.44,
0.38, 0.56, 0.63, 0.47, 0.56, 0.39, 0.47, 0.58, 0.47, 0.5,
0.52, 0.38, 0.48, 0.5, 0.5, 0.39, 0.33, 0.36, 0.5, 0.5, 0.62,
0.58, 0.4, 0.5, 0.62, 0.44, 0.37, 0.5, 0.61, 0.43, 0.43,
0.46, 0.52, 0.6, 0.47, 0.77, 0.47, 0.61, 0.52, 0.5, 0.41,
0.65, 0.54, 0.6, 0.64, 0.44, 0.53, 0.62, 0.43, 0.59, 0.46,
0.45, 0.38, 0.54, 0.51, 0.39, 0.46, 0.46, 0.44, 0.34, 0.56,
0.53, 0.58, 0.4, 0.35, 0.51, 0.49, 0.4, 0.44, 0.4, 0.44,
0.5, 0.48, 0.48, 0.48, 0.41, 0.41, 0.53, 0.46, 0.52, 0.51,
0.43, 0.45, 0.49, 0.48, 0.43, 0.39, 0.5, 0.42, 0.42, 0.5,
0.47, 0.45, 0.5, 0.49, 0.47, 0.44, 0.51, 0.41, 0.48, 0.45,
0.43, 0.47, 0.47, 0.4, 0.47, 0.39, 0.39, 0.48, 0.53, 0.38,
0.32, 0.49, 0.44, 0.48, 0.46, 0.4, 0.46, 0.4, 0.37, 0.46,
0.45, 0.5, 0.36, 0.47, 0.45, 0.42, 0.42, 0.43, 0.52, 0.49,
0.45, 0.49, 0.51, 0.54, 0.47, 0.47, 0.52, 0.43, 0.45, 0.48,
0.48, 0.45, 0.48, 0.48, 0.54, 0.48, 0.48, 0.55, 0.54, 0.54,
0.55, 0.49, 0.52, 0.55, 0.41, 0.51, 0.43, 0.48, 0.55, 0.57,
0.43, 0.49, 0.46, 0.4, 0.53, 0.48, 0.51, 0.56, 0.46, 0.51,
0.49, 0.55, 0.39, 0.55, 0.55, 0.45, 0.42, 0.49, 0.46, 0.52,
0.35, 0.46, 0.43, 0.39, 0.55, 0.51, 0.46, 0.54, 0.56, 0.52,
0.46, 0.49, 0.61, 0.42, 0.45, 0.56, 0.51, 0.42, 0.44, 0.58,
0.41, 0.44, 0.54, 0.58, 0.48, 0.49, 0.59, 0.48, 0.46, 0.56,
0.46, 0.44, 0.54, 0.52, 0.41, 0.46, 0.49, 0.54, 0.48, 0.46,
0.51, 0.55, 0.46, 0.46, 0.44, 0.46, 0.51, 0.46, 0.54, 0.46,
0.42, 0.39, 0.42, 0.39, 0.52, 0.48, 0.46, 0.46, 0.48, 0.49,
0.44, 0.54, 0.42, 0.49, 0.42, 0.63, 0.49, 0.44, 0.42, 0.37,
0.37, 0.49, 0.48, 0.37, 0.48, 0.51, 0.44, 0.44), sYear = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("2018 -",
"2019 -"), class = "factor"), sYear_Month = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L), .Label = c("2018-05",
"2018-06", "2018-07", "2018-08", "2018-09", "2018-10", "2018-11",
"2018-12", "2019-01", "2019-02", "2019-03", "2019-04", "2019-05",
"2019-06", "2019-07"), class = "factor"), Season = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("0.Winter 1H",
"1.Winter 2H", "2.Spring", "3.Summer", "4.Fall"), class = "factor"),
Year_Season = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L), .Label = c("2018-0.Winter 1H", "2018-2.Spring",
"2018-3.Summer", "2018-4.Fall", "2019-1.Winter 2H", "2019-2.Spring",
"2019-3.Summer"), class = "factor")), row.names = c(NA, 418L
), class = "data.frame")

So, the solution for the empirical density is going to slightly easier than do the theoretical distributions. First, let's setup some dummy data, since we don't have any of yours to play around with.
set.seed(123)
# Setup some facets
idx <- expand.grid(c("A", "B"), c("C", "D"))
# For each facet, generate some numbers
df <- apply(idx, 1, function(x){
data.frame(row = x[[1]],
col = x[[2]],
# chose 10 as mean, since Weibull can't be negative
x = rnorm(100, 10))
})
df <- do.call(rbind, df)
Now for the empirical case, we can simply take the density in each facet. We can do this, because ggplot has included kernel density estimates as a stat function.
ggplot(df, aes(x)) +
geom_histogram(binwidth = 0.1) +
# To line up the histogram with KDE, we multiply y-values by binwidth
geom_line(aes(y = ..count..*0.1, colour = "empirical"), stat = "density") +
facet_grid(row ~ col)
Which looks like this:
Because we don't have any ggplot stat functions for the theoretical densities -at least not ones that are panel specific- we would have to pre-compute the xy-coordinates for the theoretical distributions in a separate data.frame:
# Loop over facets
dists <- apply(idx, 1, function(i){
# Grab data belonging to facet
dat <- df$x[df$row == i[[1]] & df$col == i[[2]]]
# Setup x-values
xseq <- seq(min(dat), max(dat), length.out = 100)
# Specify distributions of interest
dists <- c("weibull", "lnorm", "gamma")
# Loop over distributions
fits <- lapply(setNames(dists, dists), function(dist) {
# Estimate parameters
ests <- fitdist(dat, dist)$estimate
# Get y-values
y <- do.call(paste0("d", dist), c(list(x = xseq), as.list(ests)))
# Multiplied by length(dat) to match absolute counts
y * length(dat)
})
# Format everything neatly in a data.frame
out <- lapply(dists, function(j) {
data.frame(row = i[[1]],
col = i[[2]],
x = xseq,
y = fits[[j]],
distr = j)
})
# Combine all distributions
do.call(rbind, out)
})
# Combine all facets
dists <- do.call(rbind, dists)
Now that we've done that tedious work, we can finally plot it:
ggplot(df, aes(x)) +
geom_histogram(binwidth = 0.1) +
geom_line(data = dists, aes(y = y * 0.1, colour = distr)) +
facet_grid(row ~ col)
Adapt as necessary for your own data. Good luck!
EDIT: Now with example data
Assume df is the data.frame from which you've posted the dput() output. I've included a condition that checks if the length of the facet data is longer than 2 and wether the variance is non-zero, so as to skip data from which we wouldn't be able to make any estimates anyway. Furthermore, I've converted variable names to be compatible with how you named them in your data.frame.
idx <- expand.grid(levels(df$Season), levels(df$sYear))
# Loop over facets
dists <- apply(idx, 1, function(i){
dat <- df$Capture_Rate[df$Season == i[[1]] & df$sYear == i[[2]]]
print(length(dat))
if (length(dat) < 2 | var(dat) == 0) {
return(NULL)
}
xseq <- seq(min(dat), max(dat), length.out = 100)
dists <- c("weibull", "lnorm", "gamma")
fits <- lapply(setNames(dists, dists), function(dist) {
ests <- fitdist(dat, dist)$estimate
y <- do.call(paste0("d", dist), c(list(x = xseq), as.list(ests)))
y * length(dat)
})
out <- lapply(dists, function(j) {
data.frame(Season = i[[1]],
sYear = i[[2]],
x = xseq,
y = fits[[j]],
distr = j)
})
do.call(rbind, out)
})
dists <- do.call(rbind, dists)
ggplot(df, aes(x=Capture_Rate, fill=sYear))+
geom_histogram(binwidth = .025,
alpha = .5,
position = "identity") +
geom_line(data = dists, aes(x, y * .025, colour = distr), inherit.aes = FALSE) +
facet_grid(Season ~ sYear)

Newbie attempting linear mixed effects model in R studio - TOTAL FAIL

After searching over an hour (this forum, Youtube, class notes, google) I've found no help for my question. I'm a complete newb who knows nothing about R or stats.
I'm attempting to create a linear mixed effects model in R. I'm measuring leaf width across three different locations (Jacksonville FL, Augusta GA, & Atlanta GA), and within those three locations there is a high-nitrogen and low-nitrogen plot. I have 150 leaf measurements from 50 trees.
My limited understanding tells me that the leaf width is the continuous response variable, and city and plot are the discrete explanatory variables. The random effect would be the individual trees, since the leaf width within a single tree is non-independent.
I've used "nlme" to make a model:
leaf.width.model <- lme(width ~ city*plot, (1|tree.id), data=leaf)
I then ran an ANOVA test, and it suggested there's something going on with city and the interaction between city and plot. This is where I'm stuck. I want to make a plot that has lines for all three cities, but I haven't a clue how to do that. When I try to use the plot function, I just get a boxplot.
I've literally tried for hours and am more lost and confused than before.
1) How can I make this graph?
2) What other tests should I do to analyze and/or visualize this data?
I am forever grateful for any help at all. I really want to learn R and stats very badly, but I'm getting discouraged.
Thank you,
Rich
P.S Here is the output of the dput function:
> dput(tree) structure(list(tree.id = structure(c(24L, 24L, 32L, 25L, 25L, 24L, 24L, 32L, 25L, 25L, 43L, 45L, 45L, 43L, 23L, 23L, 45L, 45L, 23L, 23L, 41L, 41L, 38L, 11L, 11L, 38L, 41L, 41L, 11L, 11L, 14L, 14L, 29L, 13L, 13L, 14L, 14L, 29L, 13L, 13L, 4L, 4L, 1L, 1L, 20L, 1L, 1L, 20L, 6L, 8L, 8L, 5L, 5L, 6L, 4L, 4L, 8L, 8L, 5L, 5L, 9L, 9L, 10L, 10L, 12L, 12L, 13L, 13L, 22L, 22L, 23L, 23L, 24L, 24L, 25L, 25L, 25L, 25L, 40L, 40L, 41L, 41L, 38L, 38L, 39L, 39L, 14L, 14L, 14L, 15L, 15L, 28L, 28L, 29L, 29L, 35L, 35L, 36L, 36L, 37L, 37L, 42L, 42L, 43L, 43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 2L, 1L, 3L, 3L, 4L, 4L, 7L, 11L, 11L, 16L, 16L, 20L, 20L, 21L, 21L, 17L, 17L, 18L, 18L, 19L, 19L, 26L, 26L, 27L, 27L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L, 34L, 48L), .Label = c("Tree_112", "Tree_112 ", "Tree_115", "Tree_130", "Tree_137", "Tree_139", "Tree_140", "Tree_141", "Tree_153", "Tree_154", "Tree_156", "Tree_159", "Tree_166", "Tree_169", "Tree_171", "Tree_180", "Tree_182", "Tree_184", "Tree_185", "Tree_202", "Tree_213", "Tree_218", "Tree_222", "Tree_227", "Tree_239", "Tree_242", "Tree_246", "Tree_247", "Tree_252", "Tree_260", "Tree_267", "Tree_269", "Tree_271", "Tree_272", "Tree_291", "Tree_293", "Tree_298", "Tree_327", "Tree_329", "Tree_336", "Tree_350", "Tree_401", "Tree_403", "Tree_405", "Tree_407", "Tree_409", "Tree_420", "Tree_851"), class = "factor"), city = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Atlanta", "Augusta", "Jacksonville"), class = "factor"), plot = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("High-N", "Low-N"), class = "factor"), width = c(0.66, 0.716, 0.682, 0.645, 0.645, 0.696, 0.733,
0.707, 0.668, 0.686, 0.617, 0.733, 0.73, 0.615, 0.669, 0.746, 0.687, 0.682, 0.76, 0.713, 0.651, 0.664, 0.679, 0.729, 0.756,
0.669, 0.647, 0.713, 0.767, 0.685, 0.69, 0.731, 0.781, 0.729,
0.725, 0.739, 0.769, 0.791, 0.676, 0.688, 0.719, 0.753, 0.748,
0.791, 0.785, 0.78, 0.723, 0.756, 0.664, 0.645, 0.653, 0.615,
0.591, 0.642, 0.693, 0.716, 0.694, 0.676, 0.662, 0.629, 0.665,
0.748, 0.726, 0.693, 0.715, 0.714, 0.764, 0.732, 0.61, 0.721,
0.703, 0.713, 0.746, 0.752, 0.662, 0.733, 0.707, 0.674, 0.734,
0.79, 0.732, 0.794, 0.703, 0.712, 0.737, 0.731, 0.747, 0.746,
0.787, 0.709, 0.716, 0.764, 0.77, 0.764, 0.802, 0.663, 0.777,
0.642, 0.779, 0.81, 0.724, 0.645, 0.68, 0.637, 0.695, 0.768,
0.761, 0.7, 0.759, 0.726, 0.696, 0.794, 0.774, 0.799, 0.747,
0.606, 0.691, 0.733, 0.707, 0.698, 0.706, 0.72, 0.694, 0.697,
0.737, 0.716, 0.73, 0.706, 0.667, 0.734, 0.528, 0.695, 0.684,
0.763, 0.733, 0.809, 0.6, 0.676, 0.718, 0.759, 0.609, 0.665,
0.667, 0.647, 0.701, 0.663, 0.688, 0.693, 0.899)), .Names = c("tree.id", "city", "plot", "width"), class = "data.frame", row.names = c(NA, -149L))
Thank you all so much for your comments, I sincerely appreciate everyone's help!

As suggested in comments, a line plot might not make sense for your data, as you are studying how width varies in discrete categories (in separate cities and separate plots). Boxplots would make sense as you can make them for each of the interactions of city and plot. To give you a sense of what you can do I generated some fake data and made an example of the sort of plot that might be helpful to you:
# fake data
leaf <- data.frame(tree.id = rep(1:50, each = 3),
city = rep(c("Jackson", "Augusta", "Atlanta"), each = 50),
plot = rep(1:6, each = 25))
# I'll make the average of width different for each plot
leaf$width <- rnorm(nrow(leaf), leaf$plot, 1)
# plotting the data
library(ggplot2) # this is a great library for plotting in R
ggplot(leaf, aes(x = factor(plot), y = width, color = factor(plot))) +
facet_grid(~city, scales = 'free_x') + # This creates a subplot for each city
geom_boxplot() +
geom_point(position = "jitter") +
theme_bw()
In this plot I added the points (the leaf widths for each individual tree) but I 'jittered' them, meaning perturbing their position slightly so that they do not pile up on top of each other and are all visible. You could remove this if you liked.
Exploratory data analysis should be fun! And I think visualization is a good place to start when beginning in statistics. Hopefully this will prove helpful to you.

leaf.width.model <- lme(width ~ city*plot, (1|tree.id), data=leaf)
In this model if you want to plot something, you are probably trying to answer:
How much is the average leaf width for all trees in each city for each type of plot.
To show this information in a figure, you need to plot width on y axis plot plot(high and low nitrogen) on x axis and group the data by city. Then you will get the 3 lines you are taking about. However, you need to get the average width in each group as you only want to show city variation.
To get this plot from raw data: (Using fake data provided by gfgm)
set.seed(100)
leaf <- data.frame(tree.id = rep(1:50, each = 3),
city = rep(c("Jackson", "Augusta", "Atlanta"), each = 50),
plot = rep(c(1, 0), each = 25))
# I'll make the average of width different for each plot
leaf$width <- rnorm(nrow(leaf), leaf$plot, 1)
library(plotly)
library(tidyverse)
leaf %>%
group_by(city,plot) %>%
summarise(avwidth = mean(width, na.rm=T),
avsd = 1.96*sd(width, na.rm=T)/sqrt(25)) %>%
plot_ly(x = ~plot, y = ~avwidth, color= ~city,
type="scatter", mode="markers+lines",
error_y = ~list(array=avsd)
)

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