Plotting multiple variables in time series with greyscale and shapes [duplicate] - r

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I am trying to make a time-series graph with multiple y values. I would like to change the shape of the different variables so some are solid, some are dashed etc. I would also like all the colors to be on greyscale.
Does anyone know how I can accomplish this?
I know how to melt my data so that I can plot them all together by the value of the variables but right now I cannot get the shapes to change or the greyscale. Thank you in advance.
ggplot(melted_data, aes(x = Distance, y = value, color = variable)) + geom_line()
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, 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, 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, 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), 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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("Mg",
"Mn", "Zn", "Ba"), 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, 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, 0.66783059, 0.672629631,
0.747809615, 0.72338158, 0.666220438, 0.664051795, 0.597260657,
0.689282162, 0.663808452, 0.678551141, 0.672917354, 0.686199986,
0.724202364, 0.746195474, 0.686135659, 0.654148537, 0.713488795,
0.72446665, 0.699529989, 0.630120423, 0.661767463, 0.663290351,
0.705879842, 0.709399338, 0.76228353, 0.714368918, 0.720561695,
0.837036666, 0.923882149, 1.014163852, 1.221410703, 1.315825246,
1.368054705, 1.641746627, 1.630198312, 1.698589629, 1.562956393,
1.427322658, 1.53964983, 1.574583495, 1.527101216, 1.380123116,
1.28649445, 1.29251968, 1.330565441, 1.317758525, 1.19292313,
1.217953538, 1.218591815, 0.746612627, 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, 0.581392042, 0.65277069,
0.65620614, 0.625397246, 0.697647782, 0.6180657, 0.632326126,
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0.832863371, 0.863952475, 0.817833153, 0.748586924, 0.72095701,
0.738213943, 0.672736744, 0.704947698, 0.531743532, 0.634123809,
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0.643172511, 0.597251486, 0.655514695, 0.583437677, 0.557676441,
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0.636198589, 0.565476603, 0.658861425, 0.577557604, 0.629178306,
0.646092809, 0.566079299, 0.60953767, 0.680135261, 0.500802233,
0.704656678, 0.61109605, 0.645344144, 0.667139888, 0.734969576,
0.780062983, 0.783090234, 0.83005691, 0.905356723, 0.933746319,
0.947613375, 0.923115827, 0.873482691, 0.746883952, 0.850273618,
0.795256154, 0.800825928, 0.772630039, 0.749567395, 0.7823457,
0.772609842, 0.736269985, 0.699705666, 0.716860238, 0.65909369
)), row.names = c(NA, -396L), class = "data.frame")


You can use the linetype parameter with the aestethics :
ggplot(data) +
geom_line(aes(x = Distance, y = value, color = variable, linetype = variable))

Related

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,
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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)

Removing outliers in various columns without creating NAs in the whole row

I am trying to remove the outliers from various variables at the same time in my dataset but with the function used it seems that when it finds one outlier it turns the whole row into NA.
That´s a problem because I have to apply the same process to a larger dataset and I am worried that it considerably reduces my sample...
So I would like to just turn the case where the outlier is into NA without turning the whole row into NA. Is that eventually possible?
Thank you for your input
#function used for outliers
outliers <- function(x) {
Q1 <- quantile(x, probs=.25, na.rm = TRUE)
Q3 <- quantile(x, probs=.75, na.rm = TRUE)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
x > upper_limit | x < lower_limit
}
remove_outliers <- function(dflinear, cols = names(dflinear)) {
for (col in cols) {
dflinear <- dflinear[!outliers(dflinear[[col]]),]
}
dflinear
}
dflinear_without_outliers<-remove_outliers(dflinear, c("insuline", "glucose", "hdl","ldl"))
#Reproducible sample below
dflinear<- structure(list(id = structure(c("SA01", "SA02", "SA03", "SA04",
"SA05", "SA06", "SA07", "SA08", "SA09", "SA10", "SA11", "SA12",
"SA13", "SA14", "SA15", "SA16", "SA17", "SA18", "SA19", "SA20",
"SA21", "SA22", "SA23", "SA24", "SA25", "SA26", "SA27", "SA28",
"SA29", "SA30", "SA31", "SA32", "SA33", "SA34", "SA35", "SA36",
"SA37", "SA38", "SA39", "SA40", "SA41", "SA42", "SA43", "SA44",
"SA45", "SA46", "SA47", "SA48", "SA49", "SA50", "SA51", "SA52",
"SA53", "SA54", "SA56", "SA57", "SA58", "SA59", "SA60", "SA61",
"SA62", "SA63", "SA64", "SA65", "SA66", "SA67", "SA68", "SA69",
"SA72", "SA73", "SA74", "SA75", "SA76", "SA77", "SA78", "SA79",
"SA80", "SA81", "SA82", "SA83", "SA84", "SA85", "SA86", "SA87",
"SA88", "SA89", "SA90", "SA92", "SA93", "SA94", "SA95", "SA96",
"SA97", "SA99", "SA100", "SA101", "SA102", "SA103", "SA104",
"SA105", "SA107", "SA108", "SA109", "SA110", "SA111", "SA112",
"SA113", "SA114", "SA115", "SA116", "SA118", "SC01", "SC02",
"SC03", "SC04", "SC05", "SC06", "SC07", "SC08", "SC09", "SC10",
"SC11", "SC12", "SC13", "SC14", "SC15", "SC16", "SC17", "SC18",
"SC19", "SC20", "SC21", "SC22", "SC23", "SC24", "SC25", "SC26",
"SC27", "SC28", "SC29", "SC30", "SC31", "SC32", "SC33", "SC34",
"SC35", "SC36", "SC37", "SC38", "M01", "M02", "M03", "M04", "M05",
"M06", "M07", "M08", "M09", "M10", "M11", "M12", "M13", "M14",
"M15", "M16", "M17", "M18", "M19", "M20", "M21", "M22", "M23",
"M24", "M25", "M26", "M27", "M28", "M29", "M30", "M31", "M32",
"M33", "M34", "M35", "M36", "M37", "M38", "M39", "M40", "M41",
"M42", "M43", "M44", "M45", "M46", "M47", "M48", "M49", "M50",
"M51", "M52", "M53", "SA01", "SA02", "SA03", "SA04", "SA05",
"SA06", "SA07", "SA08", "SA09", "SA10", "SA11", "SA12", "SA13",
"SA14", "SA15", "SA16", "SA17", "SA18", "SA19", "SA20", "SA21",
"SA22", "SA23", "SA24", "SA25", "SA26", "SA27", "SA28", "SA29",
"SA30", "SA31", "SA32", "SA33", "SA34", "SA35", "SA36", "SA37",
"SA38", "SA39", "SA40", "SA41", "SA42", "SA43", "SA44", "SA45",
"SA46", "SA47", "SA48", "SA49", "SA50", "SA51", "SA52", "SA53",
"SA54", "SA56", "SA57", "SA58", "SA59", "SA60", "SA61", "SA62",
"SA63", "SA64", "SA65", "SA66", "SA67", "SA68", "SA69", "SA72",
"SA73", "SA74", "SA75", "SA76", "SA77", "SA78", "SA79", "SA80",
"SA81", "SA82", "SA83", "SA84", "SA85", "SA86", "SA87", "SA88",
"SA89", "SA90", "SA92", "SA93", "SA94", "SA95", "SA96", "SA97",
"SA99", "SA100", "SA101", "SA102", "SA103", "SA104", "SA105",
"SA107", "SA108", "SA109", "SA110", "SA111", "SA112", "SA113",
"SA114", "SA115", "SA116", "SA118", "SC01", "SC02", "SC03", "SC04",
"SC05", "SC06", "SC07", "SC08", "SC09", "SC10", "SC11", "SC12",
"SC13", "SC14", "SC15", "SC16", "SC17", "SC18", "SC19", "SC20",
"SC21", "SC22", "SC23", "SC24", "SC25", "SC26", "SC27", "SC28",
"SC29", "SC30", "SC31", "SC32", "SC33", "SC34", "SC35", "SC36",
"SC37", "SC38", "M01", "M02", "M03", "M04", "M05", "M06", "M07",
"M08", "M09", "M10", "M11", "M12", "M13", "M14", "M15", "M16",
"M17", "M18", "M19", "M20", "M21", "M22", "M23", "M24", "M25",
"M26", "M27", "M28", "M29", "M30", "M31", "M32", "M33", "M34",
"M35", "M36", "M37", "M38", "M39", "M40", "M41", "M42", "M43",
"M44", "M45", "M46", "M47", "M48", "M49", "M50", "M51", "M52",
"M53"), label = "Code of PrevenGo", format.spss = "A5", display_width = 12L),
group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("Metab", "SA", "SC"), class = "factor"),
sex = structure(c(2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L), .Label = c("F", "M"), class = "factor"),
time = 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, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), insuline = structure(c(9, 4.1, 3.3, 9.4, 22.9, 16.2,
8.7, 16.7, 21.2, 21, 12.8, 7.3, 38.4, 20.2, 19.6, 6.4, 18.9,
12.1, 8.2, 17, 15.6, 12.5, 19.1, 13.7, 8, 20.1, 19.8, 6.8,
15.4, 14.7, 11.9, 8.8, 7.9, 51.2, 10.8, 8.1, 28.6, 8.6, 27.9,
13.3, 9, 16.3, 13.3, 5.8, 27.3, 4.2, 8.2, 9.9, 20.1, 11.7,
8.7, 18.1, 10.9, 27.4, 14.6, 29.1, 10.2, 20.2, 9.7, 12.3,
18.2, 1.9, 11.6, 14.6, 7.9, 11.2, 13.8, 21.2, 23.8, 18, 23.5,
21.4, 11.4, 12, 6.6, 13.5, 10.4, 25.3, 56.8, 10.7, 21.5,
8.5, 30.2, 5.3, 7.5, 15.9, 11.6, 22.4, 25.2, 6.1, 15.1, 9.3,
24.3, 30.8, 8.9, 9.8, 34.1, 13.4, 23.1, 21.1, 4.8, 20.1,
38.5, 16.1, 34.1, 16.1, 17.7, 41.4, 20.4, 21.5, 36.3, 15.9,
8.8, 6.1, 29, 4, 23.1, 36.8, 16.4, 15.5, 28.8, 15.9, NA,
7.1, 6.1, 10, 9.1, 25.2, 19.1, 6.9, 14.7, 23.1, 19.3, 12.3,
7.3, 5.9, 8, 0.5, 9, 4, 10.4, 21.4, 14.6, 8.8, 24.5, 5.3,
9.8, 17.6, 10.2, 10.7, 23, 14.5, 4.6, 33.3, 23.3, 7.2, 3.7,
13.1, 6.7, 20, 7.5, 9.2, 4.5, 2.1, 7.7, 11.7, 7.6, 22.5,
8.8, 5.1, 14.8, 15.1, 18.8, 24.3, 14, 17.2, 16.2, 23.6, 17.4,
16.5, 12.1, 15.3, 11.4, 8.7, 22.6, 10.5, 7.4, 15.1, 13.1,
24.6, 19.3, 19.7, 14.1, 5.9, 19.7, 14.9, 5.9, 17.2, 16.9,
6.2, 11.2, 4.1, 10, 3.7, 3.6, 11.6, 16.9, NA, 8, 17.3, NA,
18.3, 4, 3.1, 26.4, 12.9, 17.9, 10.3, 22.5, NA, NA, 23.4,
15.1, NA, 11.9, 27, 6.2, NA, 21.5, 11.6, 15.8, 8.6, 15.2,
10.1, 20.6, 21.7, 45.3, 8.3, 19.5, 29.2, 21.5, 11.4, 9.5,
31.8, 35.3, 11.2, 15.4, NA, 8.5, 22.6, 14.3, NA, 11.8, 11.4,
4.2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 35.8, NA, NA,
NA, NA, NA, 19.7, 42.8, 30.6, 12.2, 5.2, 4.9, 20.4, NA, 23.5,
NA, 13.6, 19.4, 6.9, 16.7, 7.2, 14.7, 59.2, 22, 41.4, 18.1,
10.5, 19.8, 17.4, NA, 25.9, NA, 8.3, 25.9, 5.7, 17.1, 25.2,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.3, 9.1, 14.8,
13.7, 6.2, 17.9, 13.9, 14.6, 70.4, 23.6, 13.8, 15.2, 9.9,
14, 27.6, 14.3, 23.7, 11, 12.1, 13.5, 21, NA, 7.2, 12.3,
4.4, 6.2, 3.9, 15, 9.6, NA, 9, 10.3, NA, 13.3, 6, 11.3, 17.6,
8.5, 10, NA, 11.8, 10.4, 26.2, NA, 10, 5.7, 16.3, 4.7, 20.3,
7.7, 14.6, 9.4, 6.3, 10, 11.1, 6.7, 42.5, NA, NA, NA, 7.7,
18.6, NA, 16.7, 25.4, 21.8, 26.8, 10.2, 13.8, 11.6, 19.1,
8.3, 3.8, 31.1, NA, 7.1, 11.1, 8.7, 19, 16, 31.8, 11.7, 3.4,
17.6, 12.3, 5.1, 17.5, 6.7, 3.8, 16.6, 6.1), format.spss = "F4.2", display_width = 11L),
glucose = structure(c(90, 95, 79, 85, 95, 97, 86, 74, 88,
95, 94, 88, 86, 94, 86, 95, 97, 88, 88, 88, 83, 103, 79,
67, 88, 79, 90, 79, 97, 94, 85, 83, 88, 97, 81, 95, 92, 94,
99, 79, 83, 92, 81, 92, 79, 94, 83, 79, 81, 92, 86, 95, 92,
95, 92, 85, 94, 81, 86, 85, 99, 92, 85, 72, 86, 81, 79, 86,
97, 88, 92, 97, 83, 103, 97, 95, 85, 77, 77, 83, 99, 90,
77, 77, 83, 92, 88, 83, 88, 86, 88, 97, 101, 99, 88, 101,
94, 86, 85, 83, 86, 88, 92, 94, 94, 90, 160, 94, 83, 95,
97, 88, 88, 95, 90, 92, 113, 104, 85, 101, 91.8, 99, 94,
85, 85, 83, 86, 88, 95, 79, 101, 92, 83, 90, 85, 95, 88,
79, 90, 79, 94, 99, 83, 85, 85, 77, 99, 81, 92, 86.4, 95.4,
82.8, 73.8, 81, 90, 82.8, 79.2, 90, 82.8, 91.8, 90, 84.6,
84.6, 84.6, 77.4, 77.4, 75.6, 88.2, 79.2, 92, 90, 113, 81,
81, 81, 84.6, 88.2, 73.8, 81, 81, 82.8, 79.2, 70.2, 91.8,
97.2, 82.8, 70.2, 91.8, 93.6, 86.4, 93.6, 73.8, 95.4, 81,
97.2, 77.4, 90, 82.8, 86.4, 88.2, 88.2, 73.8, 90, 92, 83,
86, 99, NA, 86, 81, NA, 99, 83, 86, 76, 90, 85, 90, 92, NA,
NA, 79, 79, NA, 86, 81, 88, NA, 90, 86, 92, 85, 92, 83, 92,
90, 92, 95, 94, 88, 90, 86, 88, 101, 95, 92, 81, NA, 92,
90, 81, NA, 90, 81, 88, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 85, NA, NA, NA, NA, NA, 85, 88, 86, 88, 106, 101, 88,
NA, 79, NA, 85, 99, 92, 79, 88, 88, 95, 81, 86, 77, 81, 92,
97, NA, 86, NA, 88, 94, 81, 86, 85, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 85, 88, 95, 83, 92, 112, 94, 95, 108,
97, 90, 88, 86, 97, 95, 88, 90, 88, 77, 94, 81, NA, 79, 83,
95, 88, 81, 92, 92, NA, 88, 86, NA, 85, 85, 97, 81, 88, 90,
NA, 77.4, 94, 83, NA, 95, 85, 92, 83, 95, 88, 94, 94, 88,
77, 90, 86, 92, NA, NA, NA, 95, 92, NA, 90, 103, 90, 85,
92, 83, 81, 94, 81, 79, 94, NA, 92, 99, 95, 84, 95, 72, 90,
79, 97.5, 85, 88, 79, 81, 72, 85, 88), format.spss = "F4.2", display_width = 11L),
hdl = structure(c(54, 55, 48, 38, 46, 50, 45, 38, 50, 43,
39, 32, 35, 34, 40, 48, 53, 33, 42, 34, 41, 48, 51, 38, 53,
38, 37, 44, 37, 33, 54, 47, 51, 39, 44, 54, 32, 53, 39, 36,
58, 41, 34, 43, 40, 49, 49, 50, 37, 36, 54, 47, 35, 40, 50,
44, 40, 43, 45, 41, 34, 50, 46, 46, 50, 53, 53, 45, 37, 70,
51, 55, 51, 58, 58, 49, 44, 37, 32, 64, 41, 63, 46, 55, 46,
65, 43, 55, 42, 56, 39, 50, 38, 46, 45, 53, 53, 39, 45, 47,
48, 32, 45, 45, 36, 60, 30, 43, 43, 57, 36, 56, 45, 40, 40,
61, 50, 29, 55, 38, 35, 47, 42, 50, 46, 26, 60, 33, 36, 34,
44, 59, 45, 44, 55, 45, 53, 38, 50, 40, 57, 46, 48, 45, 43,
49, 53, 39, 46, 39, 36, 39, 36, 42, 40, 50, 63, 46, 45, 39,
43, 30, 57, 46, 40, 39, 39, 53, 40, 54, 56, 40, 37, 48, 43,
29, 46, 45, 82, 31, 34, 37, 41, 63, 34, 50, 37, 51, 36, 42,
41, 34, 55, 40, 42, 60, 36, 38, 52, 57, 48, 48, 46, 47, 50,
41, 48, NA, 40, 45, NA, 43, 58, 42, 48, 44, 46, 47, 55, NA,
NA, 38, 52, NA, 53, 31, 51, NA, 32, 51, 41, 38, 57, 36, 50,
41, 60, 65, 39, 52, 36, 36, 49, 43, 34, 44, 41, NA, 50, 52,
37, NA, 58, 45, 34, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
46, NA, NA, NA, NA, NA, 59, 55, 50, 46, 58, 58, 42, NA, 31,
NA, 48, 43, 66, 55, 51, 41, 50, 38, 46, 41, 43, 38, 48, NA,
46, NA, 56, 44, 46, 48, 49, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 63, 41, 39, 46, 58, 53, 33, 53, 48, 33, 44, 46,
49, 48, 44, 55, 44, 39, 32, 46, 50, NA, 47, 53, 39, 51, 61,
48, 32, NA, 42, 46, NA, 49, 48, 52, 39, 40, 38, NA, 31, 46,
48, NA, 51, 58, 43, 49, 43, 65, 41, 61, 49, 35, 37, 36, 58,
NA, NA, NA, 38, 45, NA, 58, 31, 49, 52, 65, 32, 45, 39, 37,
41, 34, NA, 42, 51, 39, 48, 36, 35, 55, 38, 48, 53, 41, 39,
49, 63, 41, 47), label = "HDL-Cholesterol", format.spss = "F3.2", display_width = 11L),
ldl = structure(c(100, 104, 171, 153, 107, 152, 87, 101,
70, 137, 96, 95, 98, 94, 92, 102, 63, 104, 62, 75, 125, 117,
114, 132, 112, 146, 121, 91, 113, 120, 96, 96, 95, 87, 96,
134, 98, 92, 88, 101, 133, 113, 77, 128, 97, 169, 136, 96,
74, 59, 121, 66, 109, 103, 116, 86, 87, 124, 88, 94, 77,
98, 90, 133, 79, 78, 98, 129, 62, 62, 96, 72, 85, 98, 101,
132, 69, 196, 76, 125, 105, 108, 89, 108, 123, 51, 92, 50,
121, 105, 80, 103, 59, 96, 89, 65, 77, 90, 92, 65, 123, 96,
80, 128, 92, 124, 96, 83, 120, 145, 114, 134, 116, 65, 91,
103, 84, 123, 99, 96, 61, 82, 85, 116, 116, 113, 121, 69,
82, 100, 108, 99, 144, 152, 158, 128, 112, 89, 119, 61, 99,
147, 109, 121, 92, 115, 95, 62, 72, 130, 96, 76, 117, 96,
108, 131, 120, 67, 99, 105, 63, 63, 103, 128, 92, 120, 146,
106, 103, 94, 85, 122, 111, 102, 143, 74, 87, 80, 67, 140,
85, 87, 101, 94, 122, 124, 82, 150, 92, 84, 119, 98, 89,
97, 117, 122, 111, 86, 90, 110, 107, 150, 103, 94, 149, 159,
91, NA, 109, 126, NA, 167, 77, 90, 103, 80, 68, 75, 55, NA,
NA, 74, 113, NA, 102, 116, 84, NA, 66, 85, 114, 111, 101,
95, 92, 86, 96, 90, 92, 77, 91, 108, 86, 118, 85, 127, 99,
NA, 160, 80, 63, NA, 123, 86, 94, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 106, NA, NA, NA, NA, NA, 70, 85, 70, 96,
102, 117, 101, NA, 146, NA, 94, 122, 122, 94, 110, 121, 39,
72, 48, 109, 110, 60, 95, NA, 83, NA, 79, 87, 113, 103, 55,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 157, 103, 56,
92, 114, 78, 97, 106, 117, 61, 72, 83, 91, 122, 106, 103,
89, 51, 89, 153, 90, NA, 132, 132, 110, 84, 84, 96, 72, NA,
104, 122, NA, 80, 113, 106, 62, 72, 121, NA, 102, 125, 130,
NA, 111, 119, 66, 109, 119, 91, 92, 120, 160, 93, 117, 126,
88, NA, NA, NA, 115, 100, NA, 200, 79, 95, 99, 89, 123, 108,
82, 108, 81, 103, NA, 103, 149, 116, 115, 122, 95, 106, 89,
128, 118, 123, 51, 90, 130, 119, 120), label = "LDL-Cholesterol", format.spss = "F4.2", display_width = 11L)), row.names = c(NA,
-404L), class = c("tbl_df", "tbl", "data.frame"), reshapeLong = list(
varying = list(c("age_1", "age_2"), c("whz_1", "whz_2"),
c("haz_1", "haz_2"), c("waz_1", "waz_2"), c("zbmi_1",
"zbmi_2"), c("wc_1", "wc_2"), c("abc_1", "abc_2"), c("PA_1",
"PA_2"), c("PAextra_1", "PAextra_2"), c("TVweekdays_1",
"TVweekdays_2"), c("TVweekend_1", "TVweekend_2"), c("kidmed_1",
"kidmed_2"), c("totalcholesterol_1", "totalcholesterol_2"
), c("ldl_1", "ldl_2"), c("hdl_1", "hdl_2"), c("triglycerides_1",
"triglycerides_2"), c("glucose_1", "glucose_2"), c("insuline_1",
"insuline_2"), c("hba1c_1", "hba1c_2"), c("homair_1",
"homair_2"), c("fatmass_1", "fatmass_2"), c("energykcal_1",
"energykcal_2"), c("protein_1", "protein_2"), c("proteinpc_1",
"proteinpc_2"), c("carbohydrates_1", "carbohydrates_2"
), c("carbohydratespc_1", "carbohydratespc_2"), c("sugar_1",
"sugar_2"), c("sugarpc_1", "sugarpc_2"), c("starch_1",
"starch_2"), c("fruitportions_1", "fruitportions_2"),
c("vegetablesportions_1", "vegetablesportions_2"), c("vegetalfiber_1",
"vegetalfiber_2"), c("solublefiber_1", "solublefiber_2"
), c("insolublefiber_1", "insolublefiber_2"), c("lipids_1",
"lipids_2"), c("lipidspc_1", "lipidspc_2"), c("sfa_1",
"sfa_2"), c("sfapc_1", "sfapc_2"), c("mufa_1", "mufa_2"
), c("mufapc_1", "mufapc_2"), c("pufa_1", "pufa_2"),
c("pufapc_1", "pufapc_2"), c("cholesterolintake_1", "cholesterolintake_2"
)), v.names = c("age", "whz", "haz", "waz", "zbmi", "wc",
"abc", "PA", "PAextra", "TVweekdays", "TVweekend", "kidmed",
"totalcholesterol", "ldl", "hdl", "triglycerides", "glucose",
"insuline", "hba1c", "homair", "fatmass", "energykcal", "protein",
"proteinpc", "carbohydrates", "carbohydratespc", "sugar",
"sugarpc", "starch", "fruitportions", "vegetablesportions",
"vegetalfiber", "solublefiber", "insolublefiber", "lipids",
" lipidspc", "sfa", "sfapc", "mufa", "mufapc", "pufa", "pufapc",
"cholesterolintake"), idvar = c("id", "group"), timevar = "time"))

You can drop the outliers by changing your remove_outlier function to this:
remove_outliers <- function(dflinear, cols = names(dflinear)) {
for (col in cols) {
dflinear[,col] <- ifelse(outliers(dflinear[[col]]),NA,dflinear[[col]])
}
dflinear
}
But I would think very carefully about whether this is a good approach to outlier detection and removal. This procedure is removing values that look like regular parts of the distribution. With a lot of values you would expect some to be outside of the range Q3+1.5IQR etc.
Eg, this is the qqnorm for the ldl variable. Doesn't look like any problematic values at all really, but your procedure is throwing out the top five and the lowest value:

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,
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, 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,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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), .Label = c("Size5", "Size15", "Size25",
"Size50", NA), class = "factor"), value = c(-0.0011241931, 2.85943772727272e-05,
-0.00243256027727273, -0.00254402339069767, -0.00157743184782609,
-0.00148078985416667, -0.00331852698695652, 0.000848599395833333,
-0.00297349628181818, -0.000345186711111111, -0.000501344534782609,
-0.000800074080851064, -0.00221098331428571, 0.00129236565531915,
-0.00131892916086957, -0.00177610596170213, -0.00319288334468085,
0.00113381609787234, -0.00107374819148936, -0.00144620190638298,
0.000141176244444444, -0.000204472952173913, -0.00153988329130435,
3.81790045454546e-05, -0.0011685701173913, 0.000593231404255319,
0.00081186575, 0.000795386733333333, -0.00260122034583333, -0.0021938114875,
0.000553855472340425, -0.0059582546893617, 0.868693839468085,
-0.002801906475, -0.00463708955833333, -0.00272099271111111,
0.00108590003333333, -0.00247600357826087, -0.00372859965957447,
0.000286994587234042, 0.000746639587234043, 0.000276679165957447,
-0.001046501125, 0.0001712814375, -0.0024568735875, 0.000996786336170213,
-0.00182627393191489, -0.000544925195652174, -0.00357635526086957,
-0.00054329072173913, 0.00013407957826087, -0.00103177774347826,
0.000500834638297872, 0.793977075161702, -7.5436685106383e-05,
-0.000485326586956522, -0.000606974591304348, 0.00238437179166667,
0.00100126108888889, -0.000513758953191489, 8.69377181818182e-05,
-0.00123867083829787, 1.3438354628383, -0.00031518505, -0.00191560200454545,
0.805996328804348, -2.61513454545454e-05, -0.000103250163636364,
-0.000778098191489362, -0.0015234903, -0.00114211408636364, -0.00159511182978723,
1.87802078087826, 6.72123086956521e-05, 0.00100891171111111,
-0.000902390629787234, 0.0011009548, 0.000312209177777778, -0.000768029209090909,
-0.000303889769565218, 2.55336222222222e-05, -0.000205546365217391,
0.000216974877272727, 1.47901930388571, -0.000277860133333333,
-0.000214537138095238, -0.00209356162222222, 8.97567761904762e-05,
-0.00559485620909091, 0.000268375711111111, 1.39724291668889,
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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

R ggline changing x axis labels on chart without changing the actual data

I have a chart
I which to change the names of the values along the axis from 1 2 3 4 to Chrome, Internet Explorer, Firefox, Netscape. However I do not know how to do this
I have found a similar thread, but cant seem to get the changes I need
Any ideas please?
ggline(DF8, x = "web_browser_as_numeric", y = "ages",
add = c("mean_se", "jitter"),
order = c("1", "2", "3", "4"),
ylab = "Age", xlab = "Browser")
dput(DF8)
structure(list(ages = c(49, 47, 53, 45, 49, 51, 45, 45, 51, 43,
49, 51, 45, 49, 37, 45, 47, 59, 55, 39, 53, 51, 43, 51, 49, 47,
41, 53, 49, 39, 47, 51, 55, 43, 59, 49, 53, 57, 47, 41, 55, 47,
53, 41, 57, 43, 49, 57, 55, 61), web_browser_as_numeric = structure(c(1L,
1L, 4L, 1L, 3L, 3L, 2L, 1L, 4L, 1L, 1L, 1L, 3L, 4L, 1L, 2L, 1L,
3L, 3L, 2L, 1L, 1L, 1L, 3L, 4L, 3L, 4L, 4L, 1L, 2L, 1L, 1L, 3L,
1L, 1L, 2L, 1L, 2L, 3L, 4L, 2L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 4L,
1L), .Label = c("1", "2", "3", "4"), class = c("ordered", "factor"
))), row.names = c(NA, -50L), class = "data.frame")

R get linear regression equation for boxplots

I didn´t found a sufficient answer in this forum yet, so I decided to raise my own question.
I want to get the linear regression equation of a linear fit from a boxplot. I have this data:
library(ggplot2)
data <- structure(list(x = 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, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
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, 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
), .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"),
y = c(169, 79.5, 78.5, 75, 99.5, 68, 14, 30.5, 107.5, 51,
43, 33, 21.5, 35, 11, 1, 38, 54.5, 26.5, 143, 158, 171, 31.5,
67.5, 1, 57.5, 12, 36.5, 1, 23.5, 22.5, 71, 141, 218, 7.5,
1, 129, 144.5, 76, 46.5, 75.5, 45, 12, 24, 67, 65.5, 44.5,
37.5, 25.5, 19, 15, 1, 17.5, 50, 22.5, 90, 226, 220, 32,
69.5, 1, 79.5, 7, 44, 1, 15.5, 22, 75.5, 178, 153, 4.5, 1,
159, 89, 57, 71, 98.5, 47.5, 18.5, 30, 119, 57.5, 41, 33.5,
30, 31, 10, 1, 12, 43.5, 20.5, 98, 146.5, 145, 34, 64.5,
1, 40.5, 17, 41, 1, 14.5, 16.5, 71, 181, 168, 2, 1, 159,
103, 69, 65.5, 97.5, 37.5, 21, 15.5, 120.5, 46, 27, 29.5,
16.5, 20, 7.5, 1, 15.5, 42.5, 21.5, 111, 102.5, 124, 20.5,
51.5, 1, 22.5, 15, 42, 1, 13, 13.5, 64.5, 138, 155, 4.9,
1, 190, 89.5, 74.5, 79, 78, 59.5, 19.5, 21, 88.5, 44, 18,
19, 10, 13, 4, 1, 9.5, 44, 17, 140.5, 98, 112.5, 29.5, 62.56,
1, 31, 11.5, 49.5, 1, 10, 8.5, 40.5, 121, 141, 2.5, 1, 170,
87.5, 92, 77, 65, 34, 8, 26, 98, 51.5, 26, 19, 9, 8.5, 7.5,
1, 4.5, 0, 15.5, 80, 69, 59, 28, 44.5, 1, 38.5, 10, 51.5,
1, 3, 5, 65, 107, 152, 5, 1)), row.names = c(NA, -216L), class = "data.frame")
p <- ggplot(data = data) +
aes(x = x,
y = y) +
geom_boxplot(outlier.shape = NA) + geom_jitter(shape = 1, position = position_jitter(0.1)) +
ylim(0, NA) +
theme_light() +
geom_smooth(method = "lm",se = TRUE, formula = y ~ x, aes(group = 1))
print(p)
fit <- lm(y ~ x, data = data)
fit
which results in this output:
How can I extract the regression equation for this dataset? The function fit <- lm(y ~ x, data = data) just gives me one intercept and 5 coefficients, which is not my desired output. I want a simple regression equation in the form of y = a + bx.
How can I put this equation into the diagramm? I´ve already looked into ggpmisc::stat_poly_eq(), but this doesn´t seem to work with boxplot linear regression.
Can you guys help me out?

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