How to make beanplot and boxplot in the same chart? - r

seasons <- structure(list(values = c(204, 339, 304, 434, 334, 212, 361,
102, 298, 369, 149, 227, 278, 199, 360, 211, 219, 209, 177, 299,
262, 285, 237, 227, 216, 229, 317, 321, 327, 123, 84, 321, 442,
263, 225, 290, 259, 219, 244, 325, 257, 672, 762, 381, 698, 578,
576, 386, 834, 790, 815, 736, 517, 556, 685, 781, 703, 1071,
537, 784, 753, 790, 489, 878, 433, 742, 638, 731, 1017, 850,
804, 612, 923, 1000, 855, 750, 921, 676, 621, 781, 703, 1054,
156, 312, 267, 152, 352, 155, 215, 184, 186, 221, 352, 183, 307,
353, 507, 255, 159, 109, 343, 377, 209, 260, 193, 231, 111, 167,
233, 360, 488, 347, 208, 178, 371, 276, 263, 166, 486, 119, 153,
315, 226, 158, 142, 78, 75, 156, 53, 103, 141, 94, 94, 55, 84,
35, 82, 65, 150, 30, 201, 184, 94, 119, 150, 70, 63, 50, 74,
160, 49, 52, 135, 105, 129, 75, 83, 85, 84, 85, 77, 147, 100,
46), ind = 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, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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, 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("spring",
"summer", "autumn", "winter"), class = "factor", scores = structure(c(3,
1, 2, 4), .Dim = 4L, .Dimnames = list(c("autumn", "spring", "summer",
"winter"))))), .Names = c("values", "ind"), row.names = c(NA,
-164L), class = "data.frame")
I made a boxplot and beanplot below.
boxplot(seasons$values~seasons$ind, ylim= c(0,1200))
beanplot(seasons$values~seasons$ind, ylim= c(0,1200),
col = c("#CAB2D6", "#33A02C", "#B2DF8A"), border = "#CAB2D6", side="second")
I want to make a chart containing these boxplot and beanplot at the same time.
This would make comparison easy. Thai is why I made same ylim on both plots.
Is there any way I can do?


With beanplot package, use add=TRUE:
boxplot(seasons$values~seasons$ind, ylim= c(0,1200))
beanplot(seasons$values~seasons$ind, ylim= c(0,1200), col = c("#CAB2D6", "#33A02C", "#B2DF8A"), border = "#CAB2D6", side="second", add=T)


Try with ggplot:
ggplot(seasons, aes(x=ind, y=values))+geom_boxplot()+geom_violin(fill='lightblue', alpha=0.5)+geom_jitter(position = position_jitter(width = .1))

Related

My interaction term doesn't cover all groups

I am trying to execute linear-mixed models with 3 factors as groups. However running the syntax, I just get 2 terms: time:grup_int2 or time:grup_int3 as you can see in the picture. There should be another term for time:grup_int1. My reference factor group is the level 1, however I don't know how to relevel in the loop, and I intended to post a example with a database provided by the package. The problem is with the database provided by the package works... and I get 3 terms
My code with genes_wide dataframe (plenty of NAs)
#pivot wider to make horizontal data
genes_wide <- genes1 %>% pivot_wider(names_from = gen, values_from = dCt)
genes_wide$time [genes_wide$time == "1"] <- 1
genes_wide$time [genes_wide$time == "3"] <- 3
genes_wide$time<-as.numeric(genes_wide$time)
# if they need to be relevel
#genes_wide$grup_int <- relevel(genes_wide$grup_int, "2")
# names of variables
genes.names <- colnames(genes_wide)[13:67]
no.genes <- length(genes.names)
# create a named list to hold the fitted models
gene.list <- as.list(1:no.genes)
names(gene.list) <- genes.names
# loop over gene names
for(i in genes.names){
# create temporary data matrix and model formula
#1st: adjusting per several variables (edad0 = numeric, grup_int = factor, sexo = factor, time = numeric, id = numeric (need to change?)
#2nd: adjusting just per group
tmp <- genes_wide[, c(i, "grup_int","time", "id", "edad0", "sexo")]
fml <- as.formula( paste( i, "~", paste(c("time", "time:grup_int", "edad0", "sexo"), collapse="+")))
gene.list[[i]] <- lme(fml, random= ~ time|id , method="REML", data=tmp, control = lmeControl(opt = "optim"), na.action = na.omit)
}
# List of data.frames/tibble
result <- lapply(gene.list, function(x) tidy(x))
# Solution 2
result <- dplyr::bind_rows(result, .id = "var")
result2 <- result %>% dplyr::select( var, term, p.value)
**
Example database
**
However running the exact syntax with an example database (+ 5 variables to iterate) from the nlme package, the result is different
#adding variables to df problem
BodyWeight$var1 <- rnorm(176, 47 ,3)
BodyWeight$var2 <- rnorm(176, 50 ,10)
BodyWeight$var3 <- rnorm(176, 68, 7)
BodyWeight$var4 <- rnorm(176, 150 ,10)
BodyWeight$var5 <- rnorm(176, 140, 7)
# names of variables
var.names <- colnames(BodyWeight)[5:9]
no.var <- length(var.names)
# create a named list to hold the fitted models
var.list <- as.list(1:no.var)
names(var.list) <- var.names
# loop over gene names
for(i in var.names){
#2nd: adjusting just per group
tmp <- BodyWeight[, c(i, "Diet","Time", "Rat", "weight")]
fml <- as.formula( paste( i, "~", paste(c("Time:Diet", "weight"), collapse="+")))
var.list[[i]] <- lme(fml, random= ~ Time|Rat , method="REML", data=tmp, control = lmeControl(opt = "optim"), na.action = na.omit)
}
# List of data.frames/tibble
result <- lapply(var.list, function(x) tidy(x))
# Solution
result <- dplyr::bind_rows(result, .id = "var")
result2 <- result %>% dplyr::select( var, term, p.value)
As you can see I get 3 terms:
Time:Diet1
Time:Diet2
Time:Diet3
My original database is made up by more variables, but time is numeric, as well as grouping variables are factor (grup_int and Diet respectively). The original database contains NAs
I was wondering if it is possible to relevel to get the interaction remaining
UPDATE
I don't know how to put in words, what is happening when I get this:
What does it mean? time:grup_int1 is significant but time:grup_int2 not? Is time:grup_int1 the meaning for statistical differences between group 1 ant the others groups (2 and 3) across time ? It doesn't make any sense to be significant for one but not for the others. How can I get an overall p-value comparison between groups?
The df used for the loop
structure(list(weight = c(240, 250, 255, 260, 262, 258, 266,
266, 265, 272, 278, 225, 230, 230, 232, 240, 240, 243, 244, 238,
247, 245, 245, 250, 250, 255, 262, 265, 267, 267, 264, 268, 269,
260, 255, 255, 265, 265, 268, 270, 272, 274, 273, 275, 255, 260,
255, 270, 270, 273, 274, 273, 276, 278, 280, 260, 265, 270, 275,
275, 277, 278, 278, 284, 279, 281, 275, 275, 260, 270, 273, 274,
276, 271, 282, 281, 284, 245, 255, 260, 268, 270, 265, 265, 267,
273, 274, 278, 410, 415, 425, 428, 438, 443, 442, 446, 456, 468,
478, 405, 420, 430, 440, 448, 460, 458, 464, 475, 484, 496, 445,
445, 450, 452, 455, 455, 451, 450, 462, 466, 472, 555, 560, 565,
580, 590, 597, 595, 595, 612, 618, 628, 470, 465, 475, 485, 487,
493, 493, 504, 507, 518, 525, 535, 525, 530, 533, 535, 540, 525,
530, 543, 544, 559, 520, 525, 530, 540, 543, 546, 538, 544, 553,
555, 548, 510, 510, 520, 515, 530, 538, 535, 542, 550, 553, 569
), Time = c(1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64), Rat = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L), levels = c("2", "3", "4", "1", "8", "5", "6", "7", "11",
"9", "10", "12", "13", "15", "14", "16"), class = c("ordered",
"factor")), Diet = 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,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 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), levels = c("1", "2", "3"), class = "factor"),
var1 = c(46.804, 45.792, 43.572, 50.587, 52.268, 45.402,
45.996, 48.566, 47.933, 48.336, 43.964, 52.441, 45.077, 45.699,
51.348, 47.888, 50.245, 48.111, 42.373, 44.231, 46.461, 45.639,
44.993, 50.391, 47.997, 48.368, 44.939, 47.805, 49.195, 51.419,
50.242, 45.856, 48.115, 48.053, 44.318, 50.493, 40.48, 47.418,
49.441, 49.414, 49.776, 49.043, 41.456, 48.52, 46.079, 43.058,
43.649, 43.967, 44.799, 48.081, 50.728, 51.207, 50.01, 47.346,
43.816, 52.575, 47.962, 49.061, 45.438, 43.822, 43.022, 48.864,
41.669, 45.58, 42.206, 42.788, 43.988, 46.636, 45.651, 42.502,
42.93, 49.196, 48.314, 46.412, 49.436, 47.193, 48.806, 45.142,
48.4, 48.419, 47.937, 42.605, 46.027, 45.325, 43.811, 45.922,
47.262, 46.566, 47.016, 46.111, 50.573, 47.436, 47.859, 47.839,
48.239, 46.762, 46.581, 55.9, 42.816, 49.636, 47.514, 39.489,
46.513, 49.323, 51.504, 45.558, 42.402, 45.153, 48.632, 47.908,
46.033, 44.963, 49.312, 45.733, 51.149, 45.755, 47.247, 46.674,
49.121, 43.411, 43.735, 44.777, 44.942, 48.207, 50.962, 47.1,
44.167, 50.007, 48.531, 46.749, 47.69, 47.396, 45.074, 49.203,
42.869, 47.713, 46.186, 52.191, 46.809, 47.578, 45.365, 53.228,
45.896, 45.428, 42.702, 47.328, 46.108, 49.857, 45.128, 43.772,
46.479, 51.573, 51.255, 45.33, 45.651, 48.521, 49.006, 51.906,
48.924, 53.693, 48.555, 48.367, 49.659, 47.839, 47.829, 48.212,
45.867, 51.965, 48.275, 49.091, 44.54, 44.978, 50.431, 48.915,
51.569, 46.008), var2 = c(53.273, 35.7, 44.406, 62.644, 42.971,
49.393, 52.585, 30.572, 52.156, 58.566, 44.631, 50.967, 48.583,
60.878, 50.712, 48.741, 47.584, 54.774, 62.288, 49.952, 33.225,
39.251, 53.158, 46.74, 46.796, 40.343, 58.491, 48.12, 41.854,
55.315, 44.553, 55.674, 47.404, 56.085, 53.443, 56.382, 74.641,
44.822, 73.191, 46.409, 66.453, 60.929, 51.394, 56.812, 53.244,
48.993, 54.763, 35.624, 63.346, 38.389, 48.288, 61.422, 45.707,
50.213, 42.629, 64.381, 39.218, 58.923, 61.569, 39.279, 54.748,
63.149, 54.639, 56.494, 66.907, 36.303, 52.297, 46.288, 62.852,
53.063, 43.096, 70.983, 49.175, 43.663, 44.456, 55.17, 51.383,
56.945, 51.471, 49.053, 63.062, 46.386, 48.094, 43.282, 35.509,
48.079, 48.223, 34.605, 42.669, 40.146, 53.089, 40.911, 39.396,
39.507, 41.481, 41.729, 28.367, 62.781, 59.154, 50.788, 84.282,
58.214, 53.404, 46.312, 36.983, 61.712, 53.109, 37.285, 50.834,
32.306, 47.969, 36.232, 46.744, 37.876, 50.369, 59.684, 40.427,
44.2, 46.955, 56.388, 46.667, 46.373, 49.932, 60.3, 27.732,
56.772, 54.569, 45.164, 37.747, 57.827, 49.998, 23.109, 53.339,
51.024, 58.875, 55.315, 40.637, 37.781, 47.209, 57.51, 46.956,
66.715, 51.034, 52.54, 60.342, 34.439, 58.08, 64.298, 56.374,
64.829, 49.392, 48.939, 38.746, 40.048, 46.856, 61.697, 51.253,
54.024, 61.166, 45.834, 43.578, 39.212, 67.632, 35.208, 23.676,
50.577, 46.832, 42.866, 57.808, 53.292, 42.474, 62.779, 34.747,
55.367, 27.928, 57.481), var3 = c(67.483, 67.977, 70.749,
76.851, 77.351, 59.821, 67.203, 72.635, 67.767, 67.627, 64.729,
70.388, 71.889, 61.95, 54.182, 71.049, 56.33, 68.982, 70.615,
68.416, 60.661, 73.205, 68.086, 72.611, 62.082, 74.026, 69.073,
67.488, 79.656, 79.612, 71.016, 61.284, 76.297, 62.713, 79.335,
68.155, 67.184, 73.889, 61.59, 68.273, 57.056, 66.95, 60.504,
77.121, 73.056, 60.739, 75.891, 74.363, 82.829, 86.902, 59.38,
77.63, 67.613, 72.666, 68.903, 71.631, 68.86, 57.653, 65.119,
60.825, 63.301, 64.945, 65.89, 80.092, 79.951, 65.475, 66.491,
72.355, 71.909, 70.339, 83.508, 59.686, 62.102, 67.291, 52.908,
70.508, 63.2, 66.62, 75.138, 72.311, 66.394, 64.812, 74.063,
51.087, 55.095, 56.906, 55.88, 71.916, 81.805, 67.339, 66.459,
70.005, 82.891, 65.456, 86.611, 65.401, 72.647, 59.198, 73.558,
66.536, 69.83, 77.101, 65.494, 62.129, 71.86, 85.406, 70.619,
79.225, 71.806, 62.86, 79.615, 65.049, 63.472, 63.46, 80.215,
74.033, 58.949, 69.519, 66.39, 68.751, 69.356, 75.201, 54.815,
50.592, 76.019, 62.177, 66.677, 69.495, 82.428, 70.743, 59.458,
75.326, 69.615, 67.157, 69.793, 66.205, 75.328, 76.311, 64.36,
62.207, 74.16, 51.799, 66.378, 67.964, 66.937, 59.779, 59.735,
64.992, 66.817, 60.595, 65.388, 50.404, 61.742, 65.789, 77.047,
56.86, 69.756, 66.338, 57.149, 63.407, 73.601, 66.042, 67.421,
51.208, 47.265, 69.465, 63.471, 75.845, 74.938, 69.009, 81.807,
81.195, 64.288, 77.085, 64.698, 65.747), var4 = c(143.048,
139.381, 152.849, 163.726, 155.712, 146.957, 140.231, 148.321,
141.917, 156.873, 165.845, 158.824, 157.305, 144.521, 145.253,
134.892, 153.597, 124.482, 153.096, 134.954, 171.238, 147.43,
150.983, 129.945, 150.652, 142.259, 156.631, 152.215, 152.196,
153.54, 139.712, 168.495, 152.043, 148.538, 164.067, 172.445,
144.175, 144.015, 149.093, 135.921, 156.787, 148.292, 144.958,
137.332, 148.185, 151.678, 160.774, 168.479, 175.839, 148.211,
163.747, 145.416, 131.502, 154.997, 152.35, 125.955, 167.116,
141.734, 135.958, 162.752, 139.482, 149.315, 144.425, 144.654,
160.05, 153.767, 148.532, 143.102, 135.553, 143.305, 153.021,
127.982, 165.047, 144.142, 136.568, 145.793, 143.913, 158.002,
154.043, 167.63, 165.472, 143.681, 166.417, 152.012, 145.843,
144.448, 138.782, 141.475, 150.717, 140.765, 164.657, 133.655,
151.908, 160.084, 138.25, 155.93, 142.756, 170.935, 128.346,
149.986, 154.099, 141.688, 150.616, 149.999, 139.195, 160.921,
148.261, 144.611, 154.183, 157.2, 130.512, 155.004, 125.064,
124.813, 150.074, 145.615, 152.238, 144.241, 149.079, 159.354,
153.398, 146.609, 155.032, 145.522, 143.296, 139.502, 148.944,
143.446, 155.094, 148.208, 154.417, 153.249, 154.544, 140.746,
156.798, 146.069, 134.905, 145.318, 137.198, 142.087, 137.117,
151.782, 147.245, 148.583, 152.535, 151.265, 152.272, 162.014,
150.055, 154.343, 139.717, 148.752, 140.933, 146.761, 145.311,
146.361, 155.264, 154.984, 168.623, 129.31, 147.492, 139.828,
161.22, 145.89, 139.146, 138.104, 148.543, 155.836, 143.594,
159.32, 163.648, 152.058, 160.412, 133, 155.305, 144.094),
var5 = c(135.794, 136.92, 131.548, 132.091, 143.587, 130.393,
133.678, 143.005, 136.036, 128.161, 134.036, 144.237, 125.767,
133.32, 137.047, 140.3, 137.92, 144.883, 140.901, 140.632,
136.315, 138.138, 140.855, 124.721, 143.153, 144.514, 142.092,
144.765, 139.205, 134.905, 141.815, 122.992, 138.249, 125.918,
125.236, 136.503, 137.462, 130.757, 135.141, 135.413, 134.991,
137.143, 153.329, 148.779, 138.249, 133.978, 132.454, 139.955,
122.84, 135.984, 140.941, 134.203, 146.091, 146.766, 136.259,
151.474, 139.683, 132.913, 142.39, 134.625, 124.717, 145.74,
135.647, 135.119, 144.182, 133.961, 130.387, 141.314, 129.26,
127.154, 154.74, 142.327, 142.23, 139.433, 148.245, 132.123,
127.374, 134.518, 133.422, 143.66, 129.306, 138.31, 147.787,
137.415, 138.686, 143.896, 140.973, 128.592, 139.53, 146.29,
145, 129.926, 132.338, 155.193, 132.286, 140.86, 135.343,
137.037, 140.147, 131.872, 137.858, 142.137, 131.587, 121.455,
142.264, 138.533, 138.769, 136.296, 141.055, 133.475, 148.136,
136.175, 143.947, 152.026, 137.649, 142.466, 144.819, 153.745,
138.95, 138.146, 133.404, 147.928, 145.928, 146.421, 145.156,
134.289, 127.955, 148.223, 129.987, 139.295, 141.035, 150.867,
136.412, 139.705, 142.013, 129.377, 143.521, 145.595, 142.936,
137.703, 133.344, 144.46, 143.001, 137.017, 149.985, 144.242,
158.029, 151.835, 133.262, 123.893, 136.338, 143.951, 136.71,
136.007, 147.102, 134.472, 128.058, 145.521, 142.379, 135.869,
144.135, 141.297, 149.133, 134.008, 138.566, 117.773, 151.701,
140.288, 144.17, 140.886, 151.585, 136.438, 150.399, 140.535,
142.601, 136.449)), row.names = c(NA, 176L), class = c("nfnGroupedData",
"nfGroupedData", "groupedData", "data.frame"), outer = ~Diet, formula = weight ~
Time | Rat, labels = list(x = "Time", y = "Body weight"), units = list(
x = "(days)", y = "(g)"), FUN = structure(function (x)
max(x, na.rm = TRUE), source = "function(x) max(x, na.rm = TRUE)"), order.groups = TRUE)

The problem (i.e., difference between what you get in your original model and what you get in the other example you posted) is not because of the levelling of the factors, but because of a difference in model specification. In the first model results you posted you include Time + Time:Diet - the "main effect" of Time and the product regressors with Diet. However in the second set of models - the ones with the dummy data you provided - you do not include the main effect in the model. This is what produces the difference. I show the difference below. First, load the data.
BodyWeight <- structure(list(weight = c(240, 250, 255, 260, 262, 258, 266,
266, 265, 272, 278, 225, 230, 230, 232, 240, 240, 243, 244, 238,
247, 245, 245, 250, 250, 255, 262, 265, 267, 267, 264, 268, 269,
260, 255, 255, 265, 265, 268, 270, 272, 274, 273, 275, 255, 260,
255, 270, 270, 273, 274, 273, 276, 278, 280, 260, 265, 270, 275,
275, 277, 278, 278, 284, 279, 281, 275, 275, 260, 270, 273, 274,
276, 271, 282, 281, 284, 245, 255, 260, 268, 270, 265, 265, 267,
273, 274, 278, 410, 415, 425, 428, 438, 443, 442, 446, 456, 468,
478, 405, 420, 430, 440, 448, 460, 458, 464, 475, 484, 496, 445,
445, 450, 452, 455, 455, 451, 450, 462, 466, 472, 555, 560, 565,
580, 590, 597, 595, 595, 612, 618, 628, 470, 465, 475, 485, 487,
493, 493, 504, 507, 518, 525, 535, 525, 530, 533, 535, 540, 525,
530, 543, 544, 559, 520, 525, 530, 540, 543, 546, 538, 544, 553,
555, 548, 510, 510, 520, 515, 530, 538, 535, 542, 550, 553, 569
), Time = c(1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64), Rat = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L), levels = c("2", "3", "4", "1", "8", "5", "6", "7", "11",
"9", "10", "12", "13", "15", "14", "16"), class = c("ordered",
"factor")), Diet = 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,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 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), levels = c("1", "2", "3"), class = "factor"),
var1 = c(46.804, 45.792, 43.572, 50.587, 52.268, 45.402,
45.996, 48.566, 47.933, 48.336, 43.964, 52.441, 45.077, 45.699,
51.348, 47.888, 50.245, 48.111, 42.373, 44.231, 46.461, 45.639,
44.993, 50.391, 47.997, 48.368, 44.939, 47.805, 49.195, 51.419,
50.242, 45.856, 48.115, 48.053, 44.318, 50.493, 40.48, 47.418,
49.441, 49.414, 49.776, 49.043, 41.456, 48.52, 46.079, 43.058,
43.649, 43.967, 44.799, 48.081, 50.728, 51.207, 50.01, 47.346,
43.816, 52.575, 47.962, 49.061, 45.438, 43.822, 43.022, 48.864,
41.669, 45.58, 42.206, 42.788, 43.988, 46.636, 45.651, 42.502,
42.93, 49.196, 48.314, 46.412, 49.436, 47.193, 48.806, 45.142,
48.4, 48.419, 47.937, 42.605, 46.027, 45.325, 43.811, 45.922,
47.262, 46.566, 47.016, 46.111, 50.573, 47.436, 47.859, 47.839,
48.239, 46.762, 46.581, 55.9, 42.816, 49.636, 47.514, 39.489,
46.513, 49.323, 51.504, 45.558, 42.402, 45.153, 48.632, 47.908,
46.033, 44.963, 49.312, 45.733, 51.149, 45.755, 47.247, 46.674,
49.121, 43.411, 43.735, 44.777, 44.942, 48.207, 50.962, 47.1,
44.167, 50.007, 48.531, 46.749, 47.69, 47.396, 45.074, 49.203,
42.869, 47.713, 46.186, 52.191, 46.809, 47.578, 45.365, 53.228,
45.896, 45.428, 42.702, 47.328, 46.108, 49.857, 45.128, 43.772,
46.479, 51.573, 51.255, 45.33, 45.651, 48.521, 49.006, 51.906,
48.924, 53.693, 48.555, 48.367, 49.659, 47.839, 47.829, 48.212,
45.867, 51.965, 48.275, 49.091, 44.54, 44.978, 50.431, 48.915,
51.569, 46.008), var2 = c(53.273, 35.7, 44.406, 62.644, 42.971,
49.393, 52.585, 30.572, 52.156, 58.566, 44.631, 50.967, 48.583,
60.878, 50.712, 48.741, 47.584, 54.774, 62.288, 49.952, 33.225,
39.251, 53.158, 46.74, 46.796, 40.343, 58.491, 48.12, 41.854,
55.315, 44.553, 55.674, 47.404, 56.085, 53.443, 56.382, 74.641,
44.822, 73.191, 46.409, 66.453, 60.929, 51.394, 56.812, 53.244,
48.993, 54.763, 35.624, 63.346, 38.389, 48.288, 61.422, 45.707,
50.213, 42.629, 64.381, 39.218, 58.923, 61.569, 39.279, 54.748,
63.149, 54.639, 56.494, 66.907, 36.303, 52.297, 46.288, 62.852,
53.063, 43.096, 70.983, 49.175, 43.663, 44.456, 55.17, 51.383,
56.945, 51.471, 49.053, 63.062, 46.386, 48.094, 43.282, 35.509,
48.079, 48.223, 34.605, 42.669, 40.146, 53.089, 40.911, 39.396,
39.507, 41.481, 41.729, 28.367, 62.781, 59.154, 50.788, 84.282,
58.214, 53.404, 46.312, 36.983, 61.712, 53.109, 37.285, 50.834,
32.306, 47.969, 36.232, 46.744, 37.876, 50.369, 59.684, 40.427,
44.2, 46.955, 56.388, 46.667, 46.373, 49.932, 60.3, 27.732,
56.772, 54.569, 45.164, 37.747, 57.827, 49.998, 23.109, 53.339,
51.024, 58.875, 55.315, 40.637, 37.781, 47.209, 57.51, 46.956,
66.715, 51.034, 52.54, 60.342, 34.439, 58.08, 64.298, 56.374,
64.829, 49.392, 48.939, 38.746, 40.048, 46.856, 61.697, 51.253,
54.024, 61.166, 45.834, 43.578, 39.212, 67.632, 35.208, 23.676,
50.577, 46.832, 42.866, 57.808, 53.292, 42.474, 62.779, 34.747,
55.367, 27.928, 57.481), var3 = c(67.483, 67.977, 70.749,
76.851, 77.351, 59.821, 67.203, 72.635, 67.767, 67.627, 64.729,
70.388, 71.889, 61.95, 54.182, 71.049, 56.33, 68.982, 70.615,
68.416, 60.661, 73.205, 68.086, 72.611, 62.082, 74.026, 69.073,
67.488, 79.656, 79.612, 71.016, 61.284, 76.297, 62.713, 79.335,
68.155, 67.184, 73.889, 61.59, 68.273, 57.056, 66.95, 60.504,
77.121, 73.056, 60.739, 75.891, 74.363, 82.829, 86.902, 59.38,
77.63, 67.613, 72.666, 68.903, 71.631, 68.86, 57.653, 65.119,
60.825, 63.301, 64.945, 65.89, 80.092, 79.951, 65.475, 66.491,
72.355, 71.909, 70.339, 83.508, 59.686, 62.102, 67.291, 52.908,
70.508, 63.2, 66.62, 75.138, 72.311, 66.394, 64.812, 74.063,
51.087, 55.095, 56.906, 55.88, 71.916, 81.805, 67.339, 66.459,
70.005, 82.891, 65.456, 86.611, 65.401, 72.647, 59.198, 73.558,
66.536, 69.83, 77.101, 65.494, 62.129, 71.86, 85.406, 70.619,
79.225, 71.806, 62.86, 79.615, 65.049, 63.472, 63.46, 80.215,
74.033, 58.949, 69.519, 66.39, 68.751, 69.356, 75.201, 54.815,
50.592, 76.019, 62.177, 66.677, 69.495, 82.428, 70.743, 59.458,
75.326, 69.615, 67.157, 69.793, 66.205, 75.328, 76.311, 64.36,
62.207, 74.16, 51.799, 66.378, 67.964, 66.937, 59.779, 59.735,
64.992, 66.817, 60.595, 65.388, 50.404, 61.742, 65.789, 77.047,
56.86, 69.756, 66.338, 57.149, 63.407, 73.601, 66.042, 67.421,
51.208, 47.265, 69.465, 63.471, 75.845, 74.938, 69.009, 81.807,
81.195, 64.288, 77.085, 64.698, 65.747), var4 = c(143.048,
139.381, 152.849, 163.726, 155.712, 146.957, 140.231, 148.321,
141.917, 156.873, 165.845, 158.824, 157.305, 144.521, 145.253,
134.892, 153.597, 124.482, 153.096, 134.954, 171.238, 147.43,
150.983, 129.945, 150.652, 142.259, 156.631, 152.215, 152.196,
153.54, 139.712, 168.495, 152.043, 148.538, 164.067, 172.445,
144.175, 144.015, 149.093, 135.921, 156.787, 148.292, 144.958,
137.332, 148.185, 151.678, 160.774, 168.479, 175.839, 148.211,
163.747, 145.416, 131.502, 154.997, 152.35, 125.955, 167.116,
141.734, 135.958, 162.752, 139.482, 149.315, 144.425, 144.654,
160.05, 153.767, 148.532, 143.102, 135.553, 143.305, 153.021,
127.982, 165.047, 144.142, 136.568, 145.793, 143.913, 158.002,
154.043, 167.63, 165.472, 143.681, 166.417, 152.012, 145.843,
144.448, 138.782, 141.475, 150.717, 140.765, 164.657, 133.655,
151.908, 160.084, 138.25, 155.93, 142.756, 170.935, 128.346,
149.986, 154.099, 141.688, 150.616, 149.999, 139.195, 160.921,
148.261, 144.611, 154.183, 157.2, 130.512, 155.004, 125.064,
124.813, 150.074, 145.615, 152.238, 144.241, 149.079, 159.354,
153.398, 146.609, 155.032, 145.522, 143.296, 139.502, 148.944,
143.446, 155.094, 148.208, 154.417, 153.249, 154.544, 140.746,
156.798, 146.069, 134.905, 145.318, 137.198, 142.087, 137.117,
151.782, 147.245, 148.583, 152.535, 151.265, 152.272, 162.014,
150.055, 154.343, 139.717, 148.752, 140.933, 146.761, 145.311,
146.361, 155.264, 154.984, 168.623, 129.31, 147.492, 139.828,
161.22, 145.89, 139.146, 138.104, 148.543, 155.836, 143.594,
159.32, 163.648, 152.058, 160.412, 133, 155.305, 144.094),
var5 = c(135.794, 136.92, 131.548, 132.091, 143.587, 130.393,
133.678, 143.005, 136.036, 128.161, 134.036, 144.237, 125.767,
133.32, 137.047, 140.3, 137.92, 144.883, 140.901, 140.632,
136.315, 138.138, 140.855, 124.721, 143.153, 144.514, 142.092,
144.765, 139.205, 134.905, 141.815, 122.992, 138.249, 125.918,
125.236, 136.503, 137.462, 130.757, 135.141, 135.413, 134.991,
137.143, 153.329, 148.779, 138.249, 133.978, 132.454, 139.955,
122.84, 135.984, 140.941, 134.203, 146.091, 146.766, 136.259,
151.474, 139.683, 132.913, 142.39, 134.625, 124.717, 145.74,
135.647, 135.119, 144.182, 133.961, 130.387, 141.314, 129.26,
127.154, 154.74, 142.327, 142.23, 139.433, 148.245, 132.123,
127.374, 134.518, 133.422, 143.66, 129.306, 138.31, 147.787,
137.415, 138.686, 143.896, 140.973, 128.592, 139.53, 146.29,
145, 129.926, 132.338, 155.193, 132.286, 140.86, 135.343,
137.037, 140.147, 131.872, 137.858, 142.137, 131.587, 121.455,
142.264, 138.533, 138.769, 136.296, 141.055, 133.475, 148.136,
136.175, 143.947, 152.026, 137.649, 142.466, 144.819, 153.745,
138.95, 138.146, 133.404, 147.928, 145.928, 146.421, 145.156,
134.289, 127.955, 148.223, 129.987, 139.295, 141.035, 150.867,
136.412, 139.705, 142.013, 129.377, 143.521, 145.595, 142.936,
137.703, 133.344, 144.46, 143.001, 137.017, 149.985, 144.242,
158.029, 151.835, 133.262, 123.893, 136.338, 143.951, 136.71,
136.007, 147.102, 134.472, 128.058, 145.521, 142.379, 135.869,
144.135, 141.297, 149.133, 134.008, 138.566, 117.773, 151.701,
140.288, 144.17, 140.886, 151.585, 136.438, 150.399, 140.535,
142.601, 136.449)), row.names = c(NA, 176L), class = c("nfnGroupedData",
"nfGroupedData", "groupedData", "data.frame"), outer = ~Diet, formula = weight ~
Time | Rat, labels = list(x = "Time", y = "Body weight"), units = list(
x = "(days)", y = "(g)"), FUN = structure(function (x)
max(x, na.rm = TRUE), source = "function(x) max(x, na.rm = TRUE)"), order.groups = TRUE)
Here is the model without the main effect of Time. You can see, you get the desired result - coefficients for each value of Diet.
library(name)
mod0 <- lme(var1 ~ Time:Diet + weight, random= ~ Time|Rat , method="REML", data=BodyWeight, control = lmeControl(opt = "optim"), na.action = na.omit)
summary(mod0)
#> Linear mixed-effects model fit by REML
#> Data: BodyWeight
#> AIC BIC logLik
#> 905.3992 933.6741 -443.6996
#>
#> Random effects:
#> Formula: ~Time | Rat
#> Structure: General positive-definite, Log-Cholesky parametrization
#> StdDev Corr
#> (Intercept) 0.73280912 (Intr)
#> Time 0.02632007 -0.912
#> Residual 2.73566273
#>
#> Fixed effects: var1 ~ Time:Diet + weight
#> Value Std.Error DF t-value p-value
#> (Intercept) 46.94072 1.3142431 156 35.71692 0.0000
#> weight 0.00016 0.0033845 156 0.04685 0.9627
#> Time:Diet1 -0.00470 0.0160669 156 -0.29282 0.7701
#> Time:Diet2 0.00369 0.0194774 156 0.18956 0.8499
#> Time:Diet3 0.02582 0.0213189 156 1.21119 0.2277
#> Correlation:
#> (Intr) weight Tm:Dt1 Tm:Dt2
#> weight -0.940
#> Time:Diet1 -0.698 0.497
#> Time:Diet2 0.311 -0.533 0.109
#> Time:Diet3 0.422 -0.635 0.026 0.620
#>
#> Standardized Within-Group Residuals:
#> Min Q1 Med Q3 Max
#> -2.75835293 -0.70479591 0.02987446 0.58694850 3.08058470
#>
#> Number of Observations: 176
#> Number of Groups: 16
Now, let's look at the model specified with the Time main effect as well. Here, you can see that you get the Time coefficient and one for all but the reference level of Diet interacted with Time. The omission comes because Time = Time:Diet1 + Time:Diet2 + Time:Diet3, so these four terms are perfectly collinear.
mod1 <- lme(var1 ~ Time + Time:Diet + weight, random= ~ Time|Rat , method="REML", data=BodyWeight, control = lmeControl(opt = "optim"), na.action = na.omit)
summary(mod1)
#> Linear mixed-effects model fit by REML
#> Data: BodyWeight
#> AIC BIC logLik
#> 905.3992 933.6741 -443.6996
#>
#> Random effects:
#> Formula: ~Time | Rat
#> Structure: General positive-definite, Log-Cholesky parametrization
#> StdDev Corr
#> (Intercept) 0.73280912 (Intr)
#> Time 0.02632007 -0.912
#> Residual 2.73566273
#>
#> Fixed effects: var1 ~ Time + Time:Diet + weight
#> Value Std.Error DF t-value p-value
#> (Intercept) 46.94072 1.3142431 156 35.71692 0.0000
#> Time -0.00470 0.0160669 156 -0.29282 0.7701
#> weight 0.00016 0.0033845 156 0.04685 0.9627
#> Time:Diet2 0.00840 0.0238626 156 0.35188 0.7254
#> Time:Diet3 0.03053 0.0263585 156 1.15811 0.2486
#> Correlation:
#> (Intr) Time weight Tm:Dt2
#> Time -0.698
#> weight -0.940 0.497
#> Time:Diet2 0.724 -0.585 -0.770
#> Time:Diet3 0.767 -0.588 -0.816 0.752
#>
#> Standardized Within-Group Residuals:
#> Min Q1 Med Q3 Max
#> -2.75835293 -0.70479591 0.02987446 0.58694850 3.08058470
#>
#> Number of Observations: 176
#> Number of Groups: 16
You could recover all the same information from the second model. The effect of Time for Diet1 is just the coefficient on Time. The effect of Time for Diet2 is the effect of Time plus the effect of Time:Diet2 and its standard error is the square root of the variance of the time effect, plus the variance of the interaction coefficient plus two times the covariance of the two coefficients (this comes from the equation for the calculation of the variance of a linear combination of random variables).
b <- fixef(mod1)
V <- vcov(mod1)
## Time:Diet1
d1 <- c(b[2], sqrt(V[2,2]))
d1 <- c(d1, d1[1]/d1[2])
d1 <- c(d1, 2*pt(abs(d1[3]), 156, lower.tail=FALSE))
## Time:Diet2
d2 <- c(b[2] + b[4], sqrt(V[2,2] + V[4,4] + 2*V[2,4]))
d2 <- c(d2, d2[1]/d2[2])
d2 <- c(d2, 2*pt(abs(d2[3]), 156, lower.tail=FALSE))
## Time:Diet2
d3 <- c(b[2] + b[5], sqrt(V[2,2] + V[5,5] + 2*V[2,5]))
d3 <- c(d3, d3[1]/d3[2])
d3 <- c(d3, 2*pt(abs(d3[3]), 156, lower.tail=FALSE))
d <- rbind(d1, d2, d3)
colnames(d) <- c("Estimate", "SE", "t-stat", "p-value")
rownames(d) <- c("Time:Diet1", "Time:Diet2", "Time:Diet3")
d
#> Estimate SE t-stat p-value
#> Time:Diet1 -0.004704694 0.01606695 -0.2928182 0.7700503
#> Time:Diet2 0.003692093 0.01947738 0.1895580 0.8499019
#> Time:Diet3 0.025821390 0.02131895 1.2111944 0.2276525
Compare this result with the one from the first model above you can see that they are the same:
#> Estimate SE DF t-stat p-value
#> Time:Diet1 -0.00470 0.0160669 156 -0.29282 0.7701
#> Time:Diet2 0.00369 0.0194774 156 0.18956 0.8499
#> Time:Diet3 0.02582 0.0213189 156 1.21119 0.2277
Created on 2023-02-01 by the reprex package (v2.0.1)
So, the answer to how you get a coefficient for the interaction of each level of Diet and Time is to leave out the main effect of Time. That said, I would encourage you to include the main effect of Diet in the model as well - this would allow the intercepts to vary according to Diet.

Only one level of a four-level factor is showing in ggplot graph

I am using the following code to produce a boxplot with a density plot:
ggplot(data_alk_pan, aes(x = PAN, y = MEAS_RESULT, fill = PAN)) +
theme(legend.position = "none") +
theme(text = element_text(size = 10)) +
scale_x_discrete(labels = paste(levels(data_alk_pan$PAN), "\n(N=",table(data_alk_pan$PAN),")", sep="")) +
labs(y = "Concentration (mg/l)", x = "Pan") +
ggdist::stat_halfeye(
aes(thickness = stat(pdf*n)),
adjust = 0.5,
justification = -.5,
.width = 0,
point_colour = NA
) +
geom_boxplot(alpha = 0.5, varwidth = TRUE)
It works perfectly for every other factor I have in my data (i.e. if I point to a different column in my dataframe other than PAN) but for some reason it will only show the first level of the factor PAN.
I have checked that R recognises all of the levels in the factor by running the following:
levels(data_alk_pan$PAN)
which returns
[1] "Pan_01" "Pan_02" "Pan_03" "Pan_all"
which is what I would expect. However, only the first level (Pan_01) is showing in the plot. All of the levels have data (i.e. n>0 for all levels).
Any thoughts on what I am missing or what I can do to get all four levels to show in the plot?
Updated query to include some of the data:
> dput(data_alk_pan[1:200, c(8,13)])
structure(list(MEAS_RESULT = c(170, 180, 150, 140, 160, 140,
330, 130, 130, 210, 130, 130, 310, 400, 200, 160, 160, 160, 170,
170, 220, 170, 200, 240, 200, 190, 200, 180, 190, 240, 250, 260,
280, 270, 250, 290, 260, 260, 250, 230, 270, 260, 220, 260, 250,
310, 270, 86, 270, 150, 290, 290, 300, 290, 270, 210, 280, 300,
390, 270, 190, 240, 240, 260, 210, 250, 210, 220, 190, 260, 210,
190, 240, 280, 240, 210, 220, 220, 200, 250, 310, 260, 290, 280,
180, 280, 310, 310, 300, 300, 280, 190, 180, 180, 200, 210, 180,
280, 390, 290, 320, 310, 320, 260, 330, 330, 390, 300, 350, 270,
190, 180, 310, 270, 300, 270, 330, 310, 320, 310, 340, 330, 340,
270, 340, 57, 360, 28, 320, 170, 21, 370, 320, 330, 430, 340,
300, 350, 300, 300, 340, 260, 270, 230, 180, 210, 210, 240, 230,
260, 250, 230, 250, 270, 280, 170, 190, 190, 190, 180, 190, 250,
250, 260, 270, 180, 260, 260, 260, 260, 250, 260, 280, 290, 130,
150, 190, 180, 190, 240, 270, 260, 230, 220, 170, 190, 270, 270,
260, 280, 260, 260, 270, 280, 290, 270, 190, 230, 240, 170),
PAN = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 1L,
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, 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, 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, 3L,
3L, 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("Pan_01",
"Pan_02", "Pan_03", "Pan_all"), class = "factor")), row.names = c(NA,
200L), class = "data.frame")

When I run your code there seems no problem. Try to update your packages and run them in a clean environment. Here is a reproducible example:
library(ggplot2)
library(ggdist)
ggplot(data_alk_pan, aes(x = PAN, y = MEAS_RESULT, fill = PAN)) +
theme(legend.position = "none") +
theme(text = element_text(size = 10)) +
scale_x_discrete(labels = paste(levels(data_alk_pan$PAN), "\n(N=",table(data_alk_pan$PAN),")", sep="")) +
labs(y = "Concentration (mg/l)", x = "Pan") +
ggdist::stat_halfeye(
aes(thickness = stat(pdf*n)),
adjust = 0.5,
justification = -.5,
.width = 0,
point_colour = NA
) +
geom_boxplot(alpha = 0.5, varwidth = TRUE)
Created on 2022-09-01 with reprex v2.0.2

putting segments on a plot with multiple plots and y variables

Is there a way to put arrows on a plot with multiple plots with different y axes? I would like to put arrows across the time series on the same x axis locations but different y locations. I cant just use "annotate("segment", x = 37, xend = 84, y = 0.0, yend = 0.0,colour = "black", size = 1, arrow = arrow(ends='both'))" because then it puts them at 0 on the y axis for all variables when I actually want to just put the arrows at the bottom of the y axis which is different for every variable.
Current code:
fin_plot <- ggplot(melted_data, aes(x = `Distance`, y = value, group = variable)) + geom_line() + theme_bw() + labs(y="", x= "") + theme_classic() + theme(text=element_text(size=16, family="serif", face = "bold", color = "black")) +
facet_wrap(variable~., scales = "free_y",ncol=2) +
scale_x_continuous(limits = c(0, 250),labels = scales::number_format(accuracy = 1)) + theme(axis.line = element_line(colour = 'black', size = 1)) +
theme(axis.ticks = element_line(colour = "black", size = 1)) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))+ theme(axis.ticks.length = unit(.3, "cm")) + coord_capped_cart(bottom='right', left='none', gap = 0.15) + geom_vline(xintercept=c(58, 132, 204, 250, 309), linetype='dashed', col = 'black')
Current output
desired output
data:
melted_data <- structure(list(Distance = c(1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,
106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,
145, 146, 147, 148, 149, 1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106,
107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132,
133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,
146, 147, 148, 149, 1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,
121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62,
63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
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, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L), .Label = c("Mg", "Mn", "Zn", "Ba", "All"), class = "factor"),
value = c(0.903247645, 0.912560748, 0.896003508, 0.909572697,
0.883631829, 0.905722594, 0.892465355, 0.909271173, 0.880506202,
0.889278401, 0.878534542, 0.959209459, 0.913303825, 0.929893977,
0.97778374, 0.9885554, 0.929716333, 1.028422583, 1.025638955,
1.011352651, 1.041343955, 1.092562951, 1.129761801, 1.088857171,
1.107257284, 1.116728405, 1.103053734, 1.041662037, 1.134182243,
1.104550315, 1.086952767, 1.106004784, 1.057688595, 1.034347579,
1.04641385, 1.139270945, 1.048446018, 1.033827731, 1.075554754,
1.029893202, 1.074749532, 1.001626205, 0.977053541, 0.987467665,
0.999540478, 0.945184816, 0.959677178, 0.962807712, 0.967023936,
1.024286493, 0.881264816, 0.967181342, 1.000316876, 0.956168258,
1.003214572, 1.00047837, 0.940103474, 0.929875987, 0.928227112,
0.982410241, 0.983035162, 0.976666772, 1.019755049, 1.075189042,
0.975380543, 0.981316782, 0.986876269, 1.026690916, 1.052379934,
1.001547298, 0.979888683, 1.008209647, 0.976098272, 0.944479556,
0.996767684, 1.018077758, 1.028862706, 1.08510417, 1.08963868,
1.048481179, 1.139954126, 1.107066353, 1.122920581, 1.23904326,
1.19449336, 1.179971969, 1.165865352, 1.068804094, 1.099436469,
1.073307737, 1.07045113, 1.101007051, 1.011962649, 1.11202545,
1.097883672, 1.05361424, 0.993283703, 1.046635444, 1.04951188,
1.055736151, 1.063705172, 0.977095039, 1.015650848, 1.029367222,
1.003814349, 0.973376993, 1.021665177, 0.925511352, 1.014703757,
0.933654542, 1.027336075, 0.961163947, 1.022921765, 0.910164297,
0.937410814, 0.935246588, 0.925900983, 0.934477753, 0.927973832,
0.946372309, 0.950554394, 0.9386026, 1.000712639, 0.947846812,
0.953585987, 0.967735737, 0.927914753, 0.943303715, 0.935435884,
0.987648375, 0.902379461, 0.939086878, 1.018529942, 0.973874968,
0.974093087, 0.984149676, 0.948669001, 0.934863295, 1.011232041,
0.942884239, 0.978044788, 1.023700208, 1.011714275, 0.999153709,
1.06822476, 0.967735328, 1.131133479, 1.011068503, 1.034903609,
0.086720869, 0.113119382, 0.088197332, 0.081547788, 0.079373211,
0.07888827, 0.072865285, 0.079637996, 0.066314774, 0.097585729,
0.185034982, 0.214466904, 0.294317625, 0.481389256, 0.531196058,
0.715842439, 0.865098887, 0.987242052, 1.081028291, 1.240920518,
1.313524957, 1.543771699, 1.78495042, 1.746572555, 2.048760527,
2.101438775, 1.967474033, 2.000286925, 2.014020838, 1.924470659,
1.75696549, 1.786681246, 1.633290961, 1.455799758, 1.315346538,
1.435348984, 1.27887702, 1.152818928, 1.095127218, 0.987502349,
1.062278922, 0.898540082, 0.83617998, 0.889057689, 0.825563648,
0.788347646, 0.790973555, 0.775541228, 0.815063004, 0.848723108,
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0.733734124, 0.787221188, 0.685452005, 0.740552711, 0.707414697,
0.781271754, 0.72652958, 0.729470139, 0.71649368, 0.681176551,
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0.692246657, 0.673560118, 0.820384633, 0.740567172, 0.72070082,
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0.818368055, 0.696689824, 0.748702805, 0.717457681, 0.766243608,
0.805305259, 0.855909762, 0.803357905, 0.889646097, 0.854456208,
1.067795473, 1.051422575, 1.17061972, 1.138440648, 1.052796919,
1.040998633, 1.161739158, 1.025956799, 0.971567748, 1.072911493,
0.952121155, 1.040392714, 1.069745522, 1.068549198, 1.090194087,
1.214584829, 1.157485471, 1.245813376, 1.336359991, 1.204038397,
1.126255292, 1.131057736, 0.922042386, 1.037566449, 1.100852394,
1.121842367, 0.998657748, 1.006938923, 1.002800377, 0.897387497,
0.93902937, 0.889327622, 0.802133735, 0.855245047, 0.860702407,
0.704324249, 0.905827093, 0.760155095, 0.760247698, 0.655991619,
0.677006743, 0.668001976, 0.623410532, 0.569302474, 0.523713794,
0.690042836, 0.539115342, 0.528696218, 0.57851915, 0.60294784,
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0.577535469, 0.573427383, 0.672984155, 0.735286828, 0.7532343,
0.881292245, 0.801132661, 1.122761046, 1.137397845, 1.173190388,
1.138033979, 1.126494557, 1.144871399, 1.087042815, 0.981750792,
0.992888445, 0.955352455, 1.074357698, 1.027127808, 1.083248059,
1.010304962, 1.037776316, 1.052809984, 0.959161909, 0.939369893,
0.932304641, 0.912110856, 1.035278327, 0.825391661, 0.883818816,
0.880397247, 0.775385156, 0.860535004, 0.75878312, 0.764243502,
0.788209749, 0.736029937, 0.746966542, 0.762295984, 0.804665042,
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0.912631032, 0.926629248, 0.807376002, 0.795165332, 0.776764645,
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3.117943428)), row.names = c(NA, -745L), class = "data.frame")

Use geom_segment - this allows you to make use of the faceting variable. You will then want to pass a data frame with the respective x/xend/y/yend.
library(dplyr)
## create a data frame first for the segments
## it makes sense to use the mininimum of your y for each facet
annot_df <- melted_data %>%
group_by(variable) %>%
summarise(y = min(value), yend = min(value), x = 25, xend = 75)
ggplot(melted_data, aes(x = Distance, y = value, group = variable)) +
geom_line() +
## now use the new data frame for geom_segment
geom_segment(data = annot_df, aes(x = x, xend = xend, y = y, yend = yend),
arrow = arrow(ends = "both", length = unit(5, "pt"))) +
facet_wrap(variable~., scales = "free_y",ncol=2)
Created on 2022-07-14 by the reprex package (v2.0.1)

Heatmap in R does not display properly

This is the code I have for the heatmap.
sd1<-melt(Mstressed,id.vars = "Period")
library(plotly)
P1 <- ggplot(data=sd1, aes(x=Period, y=variable, fill=value)) +
geom_tile() +
ggtitle("Stress Portfolio Returns") +
scale_fill_gradientn(colors=colorRampPalette(c("lightgray","royalblue","seagreen","orange","red","brown"))(500),name="Returns") +
labs(x = "Period",y="Size") +
theme_bw()
ggplotly(P1)
Here is sd1 which is already in the melted format:
Period variable value
1 1 Size5 -1.124193e-03
2 2 Size5 2.859438e-05
3 3 Size5 -2.432560e-03
4 4 Size5 -2.544023e-03
5 5 Size5 -1.577432e-03
6 6 Size5 -1.480790e-03
and here it is sd1
> dput(sd1)
structure(list(Period = c(1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,
106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,
145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,
158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,
171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,
184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222,
223, 224, 1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
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96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,
161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,
174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,
187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199,
200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212,
213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 1,
2, 3, 4, 5, 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, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 220, 221, 222, 223, 224, 1, 2, 3, 4, 5, 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, 51, 52, 53, 54, 55,
56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,
103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128
), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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-0.000307806654782609, -0.00256542482085106, -0.00344343351428571,
0.171732012655319, 0.16290640195913, 0.0771365308982979, -0.00323413486468085,
0.239592012997872, 0.0929770842485106, 0.143919237313617, 0.0872022225044444,
-0.000374014952173913, -0.00198149831130435, 0.150230880804545,
0.0495306889826087, 0.00207576486425532, 0.21266222853, 6.23467733333334e-05,
-0.000502785265833333, 0.1146813025125, 0.00236195601234043,
-0.0055398928293617, 0.0850331580480851, 0.091095192845, -0.00308972659833333,
-0.00268806053111111, -0.000621778406666667, -0.00211494293826087,
0.0788185111804255, 0.187436011507234, -0.00105928309276596,
0.273111877825957, 0.091654408075, 0.1881146334375, -0.0012845890675,
0.000535671256170213, 0.261780701048085, -0.00130433873565217,
0.0672782252391304, 0.123152524898261, 0.122219372418261, 0.108913352556522,
0.00154812281829787, 0.0787473360617021, 0.208401497354894, 0.0494821097730435,
-0.00100385527130435, 0.00179214179166667, -0.00113253543111111,
0.125312734706809, 0.00112138269818182, -0.000824940378297872,
0.133558478718298, 0.06196782193, 0.0853069711554545, 0.227115156824348,
-0.000573424825454545, 0.147897520156364, 0.0948538439485106,
-0.00102526364, 0.136662496333636, 0.0497748200102128, 0.327559354018261,
0.158837548148696, 0.111405826131111, 0.472707361930213, 0.15095515508,
0.108453752477778, -0.000742863369090909, 0.000265612850434783,
0.187649188042222, 0.277873906394783, 0.417418378297273, 0.383188042745714,
0.188476248766667, -0.000679375438095238, -0.000940556842222222,
0.14747278479619, -0.00116911116909091, 0.193063097831111, 0.443910019148889,
0.156860665528889, -0.00140947317111111, 0.194701012465116, 0.179753437755349,
0.0271931040711111, -0.00148793193106383, -0.000247921611304348,
0.0215570688078049, 0.193278326948837, 0.269718930653333, 0.00265989913047619,
0.00111463559674419, 0.0727459556427273, 0.178373261021818, 0.0498605999422222,
0.00285280777363636, 0.28503765544, 0.0225589752241861, 0.105547501674783,
0.00522300738930233, 0.000134479296190476, 0.0587813904836364,
0.02802175016, 0.17572127429, 0.184946720591111, -0.00234486358,
-0.00230577149043478, 0.194051282172558, 0.250351470368182, 0.143461193228889,
-0.00273105342, 0.0943393136254545, 0.184555788855556, 0.0659578766466666,
0.294674815488889, -0.000197858810909091, 0.135317324614545)), row.names = c(NA,
800L), class = "data.frame")
This is the plot i get which is incorrect, I am very confused as I am sure it was working in the past that code, as well as I am sure the syntax is correct! Obviously i must be missing something.

Given your data and script, the plot looks ok on my computer (see below). Which version have you used?
library("plotly")
P1 <- ggplot(data=sd1, aes(x=Period, y=variable, fill=value)) +
geom_tile() +
ggtitle("Stress Portfolio Returns") +
scale_fill_gradientn(colors=colorRampPalette(c("lightgray","royalblue","seagreen","orange","red","brown"))(500),name="Returns") +
labs(x = "Period",y="Size") +
theme_bw()
ggplotly(P1)
I recommend to follow the advice of #Dan Adams. It may also help to check the versions of R and packages. Here a part of my SessionInfo (only R version and attached packages shown).
> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
# ... omitted
other attached packages:
[1] plotly_4.10.0 ggplot2_3.3.5
# ... omitted

is it possible to modify xaxis to show only one sets of date in ggplot2

I have a data frame like this:
Server
Date Server Space
1 2010-01-30 server1 100
2 2010-02-28 Server1 400
3 2010-03-30 Server1 300
4 2010-04-30 Server2 200
5 2010-05-30 Server2 500
6 2010-06-30 Server2 300
based on this data frame, I use aggregate to group Harddisk usage by month and store it in a data frame called z, as below.
z
Month Value
1 2010-01 600
2 2010-02 700
3 2010-03 800
4 2010-04 900
5 2010-05 800
6 2010-06 900
I am trying to create a stack chart on x and put a line on the total based on z on top of the same stack chart using ggplot2.
syntax is below:
I had to do this to avoid (Error: Discrete value supplied to continuous scale)
server$Date<-as.character(server$Date)
then run this:
ggplot(server, aes(Date, Space)) + geom_bar(aes(fill=Server), stat="identity", position="stack") + theme_bw() + scale_x_discrete(name="Date") + scale_y_continuous("Space") + opts(axis.title.x = theme_text(face="bold", colour="#990000", size=15),axis.text.x = theme_text(angle=90), axis.title.y = theme_text(face="bold", colour="#990000", angle=90, size=15)) + geom_smooth(data=z,aes(Month,Value,group=1), method="lm", size=2, color="darkblue")
This works but on my xaxis, I have two sets of data, which looks very cluttered. Is it possible to hide or suppress the dates from x data frame and only show z$Month on xaxis?
I can use this:
scale_x_date(labels = date_format("%m-%Y"))
But when I do this, xaxis is now showing 01-1970 for all data points.
*Update *
Let me ask this question in other way. I like to create a stack chart and on top of the stack, put a linear line using geom_smooth() to show where the total usage is going. I was thinking use x data frame to build the stack chart, then create another data frame for the total value and create the geom_smooth() line based on the total data frame as z. Doing it this way, puts 2 data values to the xaxis.
Rather than doing it this way, is it possible to draw geom_smooth() on the stack bar based on x data frame?
This is the dput of the data frame:
server <- structure(list(Date = structure(c(1325394000, 1325480400, 1325566800,
1325653200, 1325739600, 1325826000, 1325912400, 1325998800, 1326085200,
1326171600, 1326258000, 1326344400, 1326430800, 1326517200, 1326603600,
1326690000, 1326776400, 1325394000, 1325480400, 1325566800, 1325653200,
1325739600, 1325826000, 1325912400, 1325998800, 1326085200, 1326171600,
1326258000, 1326344400, 1326430800, 1326517200, 1326603600, 1326690000,
1326776400, 1325394000, 1325480400, 1325566800, 1325653200, 1325739600,
1325826000, 1325912400, 1325998800, 1326085200, 1326171600, 1326258000,
1326344400, 1326430800, 1326517200, 1326603600, 1326690000, 1326776400,
1328072400, 1328158800, 1328245200, 1328331600, 1328418000, 1328504400,
1328590800, 1328677200, 1328763600, 1328850000, 1328936400, 1329022800,
1329109200, 1329195600, 1329282000, 1329368400, 1329454800, 1328072400,
1328158800, 1328245200, 1328331600, 1328418000, 1328504400, 1328590800,
1328677200, 1328763600, 1328850000, 1328936400, 1329022800, 1329109200,
1329195600, 1329282000, 1329368400, 1329454800, 1328072400, 1328158800,
1328245200, 1328331600, 1328418000, 1328504400, 1328590800, 1328677200,
1328763600, 1328850000, 1328936400, 1329022800, 1329109200, 1329195600,
1329282000, 1329368400, 1329454800, 1330578000, 1330664400, 1330750800,
1330837200, 1330923600, 1331010000, 1331096400, 1331182800, 1331269200,
1331355600, 1331442000, 1331524800, 1331611200, 1331697600, 1331784000,
1331870400, 1331956800, 1330578000, 1330664400, 1330750800, 1330837200,
1330923600, 1331010000, 1331096400, 1331182800, 1331269200, 1331355600,
1331442000, 1331524800, 1331611200, 1331697600, 1331784000, 1331870400,
1331956800, 1330578000, 1330664400, 1330750800, 1330837200, 1330923600,
1331010000, 1331096400, 1331182800, 1331269200, 1331355600, 1331442000,
1331524800, 1331611200, 1331697600, 1331784000, 1331870400, 1331956800
), class = c("POSIXct", "POSIXt"), tzone = ""), Server = 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, 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, 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, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("ServerA", "ServerB",
"ServerC"), class = "factor"), Space = c(100, 110, 110, 120,
120, 130, 130, 140, 140, 150, 150, 160, 160, 170, 170, 180, 200,
220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460,
480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720,
740, 760, 780, 800, 820, 840, 860, 880, 100, 110, 110, 120, 120,
130, 130, 140, 140, 150, 150, 160, 160, 170, 170, 180, 200, 220,
240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480,
500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740,
760, 780, 800, 820, 840, 860, 880, 550, 110, 560, 120, 570, 130,
580, 140, 590, 150, 600, 160, 610, 170, 620, 180, 200, 550, 570,
590, 610, 630, 650, 670, 690, 710, 730, 750, 600, 620, 640, 660,
680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920,
940, 960, 980, 1000, 1020, 1000)), .Names = c("Date", "Server",
"Space"), row.names = c(NA, 153L), class = "data.frame")
z <- structure(list(Month = c("2012-01", "2012-02", "2012-03"), Value = c(21140,
21140, 32010)), .Names = c("Month", "Value"), row.names = c(NA,
-3L), class = "data.frame")

Since you are working with dates (and not DateTimes), you'll find it easier if both your date columns are as.Date objects:
server$Date <- as.Date(server$Date)
z$Month <- as.Date(paste0(z$Month, "-01"))
ggplot(server, aes(Date, Space)) +
geom_bar(aes(fill=Server), stat="identity", position="stack") +
geom_point(data=z,aes(Month,Value,group=1), color="darkblue") +
geom_line(data=z,aes(Month,Value,group=1), color="darkblue")

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