Grouping by column in ggplot2 - r

I am an old ggplot2 user, but I am getting a beating from this one. It must be something I am not noticing:
I want a simple line plot across time (x-axis) vs covariances in the y-axis. My grouping is not being picked up by ggplot2. So please consider the MWE code:
umxSim3 %>%
mutate(group = case_when(x2y == 0.4 & aur == 0.8 & aury == 0.8 ~ "x2y0.4_aur0.8_aury0.8",
x2y == 0.2 & aur == 0.6 & aury == 0.6 ~ "x2y0.2_aur0.6_aury0.6")) %>%
drop_na() %>%
ggplot(aes(temp, cova),color = as.factor(group)) +
geom_line(stat = "summary") +
geom_point(stat = "summary") +
theme_bw(12) +
theme(legend.position = c(0.8, 0.8),
panel.border = element_rect(colour = "black"),
legend.background = element_rect(linetype = 1, size = 0.2, colour = 1))+
scale_color_manual(values = cb_palette)
for the data:
structure(list(temp = c(15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 16L, 15L, 15L, 16L, 15L, 16L, 16L, 16L, 16L, 16L, 17L, 16L,
16L, 17L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L,
17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 16L, 18L, 19L, 19L,
19L, 18L, 18L, 19L, 19L, 19L, 17L, 19L, 20L, 20L, 19L, 19L, 18L,
20L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 22L, 21L, 22L, 21L, 21L, 21L, 22L,
22L, 22L, 21L, 22L, 22L, 23L, 23L, 22L, 22L, 23L, 22L, 23L, 23L,
23L, 23L, 23L), immedx = c(0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02), immedy = c(0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01), x2y = c(0, 0, 0.2, 0, 0.2, 0.4, 0, 0.2, 0.2, 0,
0.4, 0.4, 0, 0.4, 0.2, 0.4, 0, 0.2, 0, 0.2, 0.2, 0.2, 0, 0.4,
0.4, 0, 0, 0, 0.4, 0.2, 0.4, 0.2, 0.2, 0, 0.4, 0, 0, 0.2, 0.2,
0.4, 0.4, 0, 0, 0.4, 0.2, 0.2, 0.2, 0, 0.2, 0.4, 0, 0.4, 0, 0.4,
0.2, 0, 0, 0.4, 0.2, 0.4, 0.2, 0.4, 0.4, 0, 0.2, 0.4, 0, 0.2,
0.2, 0.4, 0.4, 0.4, 0, 0.2, 0, 0.2, 0.4, 0, 0, 0, 0.2, 0, 0.4,
0.4, 0.2, 0, 0.4, 0, 0.4, 0.2, 0.2, 0, 0.2, 0.2, 0.4, 0, 0.4,
0, 0.4, 0, 0.2, 0.4), aur = c(0.6, 0.6, 0.6, 0.8, 0.6, 0.6, 0.8,
0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6,
0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8,
0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6,
0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8,
0.8, 0.6, 0.8, 0.8, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8,
0.6, 0.6, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8,
0.8, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.6, 0.8, 0.8,
0.6, 0.8, 0.6, 0.6), aury = c(0.8, 0.6, 0.6, 0.6, 0.8, 0.6, 0.8,
0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6,
0.8, 0.8, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.8, 0.6, 0.6, 0.8, 0.8,
0.8, 0.6, 0.6, 0.6, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6,
0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8,
0.8, 0.6, 0.6, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6,
0.6, 0.6, 0.8, 0.8, 0.6, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.8,
0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6,
0.6, 0.8, 0.8, 0.8), cova = c(0.0767650449455536, 0.023479773134789,
0.079250541929235, 0.0497028270157154, 0.217607799079627, 0.168683335594918,
0.150794456586447, 0.27283026101063, 0.690314230152603, 0.0235259541982523,
0.702603056392517, 0.490115926773996, 0.0505076752505145, 1.93010433620619,
0.0798575573537466, 0.171573575141921, 0.0784006741340135, 0.28486748087407,
0.156814170306613, 0.0803151708822509, 0.754445007375547, 0.226801448778645,
0.079735730985492, 0.755994056890623, 0.525290423561159, 0.0511604604964653,
0.0235558887825711, 0.162030264083756, 0.559246976325596, 0.295696528479825,
0.173935711302792, 0.235063054293184, 0.818154209385252, 0.080823259527356,
0.80766448894273, 0.0235752474868944, 0.0516895405415864, 0.305415501682546,
0.0806599420516001, 0.59193655065581, 0.175863895489332, 0.166532200794381,
0.0235877421429864, 2.22373977593273, 0.242470710342392, 0.0809195781739381,
0.249100119500662, 0.0521181251083318, 0.881239650574519, 0.857515819530478,
0.0817077551411662, 0.177436314599814, 0.170404901664453, 2.54251804738705,
0.314120482284585, 0.0824262284334331, 0.0235957929692925, 0.62332889414795,
0.943529017271466, 2.88745078017984, 0.0811150355928273, 0.905481581223059,
3.25958506793805, 0.0524651556999694, 0.321903676726418, 0.178717602990971,
0.173727014344408, 0.255023388431499, 1.00487624869021, 0.653409404225594,
3.66000427224439, 0.951522116145514, 0.0236009729296943, 0.0812621413155391,
0.0830092693966824, 0.260308295568005, 0.179761001622845, 0.0527460575456124,
0.17657007247267, 0.0236043015726037, 0.328852247070274, 0.0834820393132251,
0.995620065250206, 0.682176435239891, 1.06515843598909, 0.0529733721858174,
0.180610243434384, 0.178998254606776, 4.08982898920573, 0.265017899614053,
0.335047641163975, 0.0236064382275949, 0.0814561191768848, 1.12427313098231,
0.709638986736915, 0.0838651574035433, 1.03777649778554, 0.0531572840695336,
0.181301171395821, 0.181068531268503, 0.269210391598863, 0.735814722588949
), ncp = c(0.00000046267086872831, 0.0000000313011696562171,
-2364.79037081984, -0.000000597414327785373, 351.394396380274,
151.990268357971, 0.00000743399141356349, 19.7703358054714, 2440.01464180378,
-7240.58302952931, 70.9914281773526, 149.740843244857, -3309.66286159754,
16647.7277528127, -2582.00822814501, -1561.43493972577, -0.000000475047272630036,
19.6414650627339, 0.0000108720123535022, 315.029701597647, 135.427727860704,
22.4076952430914, 0.0000000233412720263004, -752.562054366463,
80.5575949394988, -0.00000442085001850501, -1179.58693431943,
0.000000114778231363744, -7327.08611100476, -1949.39119122034,
-2388.61869873563, 22.1016658367153, 2824.56886877323, -0.0000042213941924274,
-730.911226568125, -1099.31476127236, 0.00000000339059624820948,
2378.51963226983, -3114.53016945277, 3984.12834500313, 175.787574969581,
0.00000653910683467984, -0.000000110070686787367, 1181.21838523608,
21.8320325434906, -3186.31096406218, 345.549440360701, 13365.6075074154,
14.6078058980493, 2258.65831825051, -700.956671146072, -406.573908048507,
0.0000124756261357106, 1164.89840225065, 19.3309273536142, -0.00000834093225421384,
-6412.21120826376, 69.809013458027, 1435.53987527536, 1438.76661482502,
28.9054297961484, 2385.37075061088, 27660.1530534023, 0.000000160784111358225,
-3822.79946768601, -2315.2593892417, 0.00000594364973949268,
27.1325407072363, 3411.75060408833, 2138.85256412776, 30763.6924561956,
64.9265390225191, -2428.93211152896, -2914.7903949441, 0.00000096066651167348,
678.510061382112, -826.474972046213, 0.000000630076101515442,
0.00000610958522884175, -0.00000443263706983998, -3691.07725347468,
0.00000263373658526689, 12496.6406096131, 111.59293522677, 13.801709866937,
0.0000000575091689825058, -482.465671315367, 0.00000894447293831035,
-944.987300472989, 26.6739735744122, -1686.73710901346, 0.000000106025254353881,
-1838.70931961411, 1602.34840365657, -5004.48179096662, -0.0000282387336483225,
1476.99946196973, 0.000000139494659379125, -3032.83538644376,
0.000000328800524584949, -4550.10670159884, 2268.41146332065),
df = 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),
proximal = c(0.02, 0.02, -0.039, 0.02, 0.02, 0.023, 0.02,
0.02, 0.02, -0.229, 0.02, 0.02, 0.096, 0.02, -0.241, 0.174,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.04, 0.02, 0.02,
0.022, 0.02, 0.129, -0.093, -0.216, 0.02, 0.02, 0.02, -0.061,
0.014, 0.02, -0.025, -0.715, 0.024, 0.02, 0.02, 0.02, 0.02,
0.02, 0.036, 0.02, -6.749, 0.02, 0.02, 0.012, -0.032, 0.02,
0.02, 0.02, 0.02, -0.093, 0.021, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, -0.27, -0.194, 0.02, 0.02, 0.02, 0.02, 0.02,
0.022, -0.071, -0.103, 0.02, 0.02, 0.121, 0.02, 0.02, 0.02,
0.083, 0.02, 0.02, -0.013, 0.02, 0.02, -0.401, 0.02, 0.115,
0.02, 0.084, 0.02, 0.041, 0.018, -0.099, 0.02, 0.02, 0.02,
-0.089, 0.02, -0.056, -0.063), distal = c(0, 0, 0.209, 0,
0.2, 0.396, 0, 0.2, 0.2, 0.145, 0.4, 0.4, -0.09, 0.4, 0.341,
0.273, 0, 0.2, 0, 0.2, 0.2, 0.2, 0, 0.392, 0.4, 0, -0.019,
0, 0.503, 0.29, 0.54, 0.2, 0.2, 0, 0.474, 0, 0, 0.237, 0.621,
0.396, 0.4, 0, 0, 0.4, 0.2, 0.195, 0.2, 6.526, 0.2, 0.4,
0.005, 0.425, 0, 0.4, 0.2, 0, 0.143, 0.4, 0.2, 0.4, 0.2,
0.4, 0.4, 0, 0.512, 0.613, 0, 0.2, 0.2, 0.4, 0.4, 0.396,
0.085, 0.264, 0, 0.2, 0.309, 0, 0, 0, 0.173, 0, 0.4, 0.409,
0.2, 0, 0.708, 0, 0.307, 0.2, 0.131, 0, 0.205, 0.201, 0.594,
0, 0.4, 0, 0.519, 0, 0.238, 0.465), historical = c(0.02,
0.02, 0.02, 0.02, 0.02, 0.026, 0.02, 0.02, 0.02, 0.062, 0.02,
0.02, -0.15, 0.02, -0.225, -0.121, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.085, 0.02, 0.02, 0.037, 0.02, 0.124,
-0.082, -0.202, 0.02, 0.02, 0.02, 0.07, 0.024, 0.02, -0.022,
-0.69, 0.023, 0.02, 0.02, 0.02, 0.02, 0.02, 0.038, 0.02,
0.151, 0.02, 0.02, 0.082, -0.063, 0.02, 0.02, 0.02, 0.02,
0.159, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, -0.117,
-0.033, 0.02, 0.02, 0.02, 0.02, 0.02, 0.025, -0.086, -0.044,
0.02, 0.022, -0.168, 0.02, 0.02, 0.02, 0.299, 0.02, 0.02,
-0.015, 0.02, 0.02, -0.128, 0.02, -0.088, 0.02, 0.035, 0.02,
0.041, 0.019, -0.218, 0.02, 0.02, 0.02, -0.062, 0.02, -0.088,
-0.057)), class = "data.frame", row.names = c(NA, -102L))
For this data I am getting:
Whereas I expected it to be colored by two groups:
2y == 0.4 & aur == 0.8 & aury == 0.8 ~ "x2y0.4_aur0.8_aury0.8",
x2y == 0.2 & aur == 0.6 & aury == 0.6 ~ "x2y0.2_aur0.6_aury0.6")


You need to move the color specification inside the aes call:
umxSim3 %>%
mutate(group = case_when(x2y == 0.4 & aur == 0.8 & aury == 0.8 ~ "x2y0.4_aur0.8_aury0.8",
x2y == 0.2 & aur == 0.6 & aury == 0.6 ~ "x2y0.2_aur0.6_aury0.6")) %>%
drop_na() %>%
ggplot(aes(temp, cova, color = as.factor(group))) +
geom_line(stat = "summary") +
geom_point(stat = "summary") +
theme_bw(12) +
theme(legend.position = c(0.8, 0.8),
panel.border = element_rect(colour = "black"),
legend.background = element_rect(linetype = 1, size = 0.2, colour = 1))+
scale_color_manual(values = c('red', 'blue'), name = 'Group')
Note you didn't provide the cb_palette object, so I have just set the colors to red and blue for this example.

Related

ggplot: How to color/fill area between ROC curves and diagonal?

I have this ROC curve
Written with this code:
ggplot(a, aes(y = TPR, x = FPR, color = model)) +
geom_line() +
geom_segment(aes(y = 0, yend = 1, x = 0, xend = 1), color = "grey50")
I want to color the space between red and green curve, and the area between the green curve and the diagonal.
I tried to color the expected output manually in free hand (my apologies for the artistic skills)
I sought solutions using geom_area() but could not get it work.
How can I fill these area?
Here is my data sample. My apologies for many datapoints, but that was the only way I could reproduce "the full curves" reaching (0,0) and (1,1).
a <- structure(list(model = 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, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 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("Null model",
"SSA+", "SSA-"), class = "factor"), risk = c(1, 1, 1, 1, 1, 0.99,
0.99, 0.99, 0.98, 0.98, 0.97, 0.97, 0.97, 0.96, 0.95, 0.95, 0.94,
0.93, 0.92, 0.91, 0.91, 0.91, 0.91, 0.9, 0.89, 0.89, 0.88, 0.87,
0.87, 0.85, 0.85, 0.81, 0.81, 0.8, 0.78, 0.77, 0.76, 0.76, 0.76,
0.76, 0.75, 0.74, 0.72, 0.69, 0.69, 0.69, 0.67, 0.66, 0.65, 0.65,
0.64, 0.63, 0.63, 0.6, 0.59, 0.58, 0.58, 0.57, 0.57, 0.57, 0.53,
0.53, 0.52, 0.5, 0.46, 0.46, 0.46, 0.45, 0.44, 0.42, 0.41, 0.4,
0.4, 0.39, 0.38, 0.37, 0.35, 0.31, 0.29, 0.27, 0.27, 0.26, 0.24,
0.23, 0.2, 0.19, 0.19, 0.18, 0.18, 0.16, 0.15, 0.15, 0.11, 0.11,
0.09, 0.07, 0.06, 0.04, 0.93, 0.92, 0.92, 0.91, 0.91, 0.9, 0.9,
0.9, 0.9, 0.89, 0.86, 0.86, 0.86, 0.86, 0.86, 0.85, 0.85, 0.84,
0.83, 0.82, 0.81, 0.81, 0.81, 0.8, 0.79, 0.78, 0.78, 0.77, 0.77,
0.76, 0.75, 0.74, 0.74, 0.74, 0.73, 0.72, 0.71, 0.7, 0.66, 0.65,
0.65, 0.64, 0.63, 0.61, 0.6, 0.59, 0.56, 0.54, 0.52, 0.51, 0.51,
0.5, 0.47, 0.45, 0.45, 0.43, 0.42, 0.42, 0.38, 0.36, 0.34, 0.32,
0.32, 0.31, 0.3, 0.3, 0.29, 0.28, 0.27, 0.27, 0.26, 0.24, 0.23,
0.18, 0.16, 0.14, 0.13, 0.13, 0.12, 0.09), TPR = c(0.02, 0.03,
0.05, 0.07, 0.08, 0.1, 0.11, 0.13, 0.15, 0.16, 0.18, 0.2, 0.21,
0.23, 0.25, 0.26, 0.28, 0.3, 0.31, 0.33, 0.34, 0.34, 0.36, 0.38,
0.38, 0.39, 0.41, 0.43, 0.44, 0.44, 0.44, 0.46, 0.48, 0.49, 0.49,
0.51, 0.52, 0.54, 0.56, 0.57, 0.59, 0.61, 0.62, 0.62, 0.64, 0.66,
0.67, 0.69, 0.7, 0.72, 0.74, 0.74, 0.75, 0.75, 0.77, 0.77, 0.79,
0.8, 0.8, 0.82, 0.82, 0.82, 0.84, 0.84, 0.84, 0.85, 0.85, 0.87,
0.89, 0.9, 0.92, 0.92, 0.93, 0.93, 0.95, 0.95, 0.95, 0.97, 0.98,
0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0.03, 0.05, 0.07, 0.08, 0.1, 0.11, 0.11,
0.13, 0.15, 0.15, 0.16, 0.18, 0.21, 0.23, 0.25, 0.25, 0.26, 0.26,
0.28, 0.31, 0.33, 0.33, 0.33, 0.34, 0.38, 0.39, 0.43, 0.49, 0.51,
0.56, 0.59, 0.61, 0.62, 0.66, 0.69, 0.7, 0.7, 0.72, 0.72, 0.74,
0.75, 0.75, 0.77, 0.77, 0.79, 0.79, 0.79, 0.8, 0.82, 0.84, 0.84,
0.85, 0.87, 0.89, 0.89, 0.89, 0.89, 0.9, 0.92, 0.93, 0.93, 0.93,
0.93, 0.93, 0.93, 0.95, 0.98, 0.98, 0.98, 0.98, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1), FPR = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.03, 0.03, 0.03, 0.05, 0.05, 0.05, 0.05,
0.05, 0.08, 0.11, 0.11, 0.11, 0.11, 0.13, 0.13, 0.13, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.21, 0.21, 0.24, 0.24, 0.26, 0.26, 0.26, 0.29, 0.29, 0.32,
0.34, 0.34, 0.37, 0.39, 0.39, 0.42, 0.42, 0.42, 0.42, 0.42, 0.45,
0.45, 0.47, 0.47, 0.5, 0.53, 0.53, 0.53, 0.55, 0.58, 0.61, 0.63,
0.66, 0.68, 0.71, 0.74, 0.76, 0.76, 0.79, 0.82, 0.84, 0.87, 0.89,
0.92, 0.95, 0.97, 1, 0, 0, 0, 0, 0, 0, 0.03, 0.03, 0.03, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.08, 0.08, 0.11, 0.11, 0.11, 0.11,
0.13, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.21, 0.24, 0.26, 0.26, 0.29, 0.29, 0.29, 0.32, 0.32,
0.34, 0.34, 0.37, 0.39, 0.39, 0.39, 0.39, 0.42, 0.42, 0.45, 0.45,
0.47, 0.5, 0.53, 0.53, 0.53, 0.53, 0.55, 0.58, 0.61, 0.63, 0.66,
0.66, 0.66, 0.71, 0.74, 0.76, 0.76, 0.79, 0.82, 0.84, 0.87, 0.89,
0.92, 0.95, 0.97, 1)), row.names = c(NA, -178L), class = c("data.table",
"data.frame"))

You can use geom_ribbon. The ymax will be TPR, and since the diagonal occurs at TPR = FPR, the ymin will be FPR.
ggplot(a, aes(y = TPR, x = FPR)) +
geom_ribbon(aes(ymin = FPR, ymax = TPR, fill = model)) +
geom_line(aes(group = model), color = "black") +
geom_segment(aes(y = 0, yend = 1, x = 0, xend = 1), color = "grey50") +
scale_fill_manual(values = c("#ba6329", "#5f7c37")) +
coord_equal() +
theme_light(base_size = 16)

Removing primary y-axis labels and ticks while keep labels and ticks of secondary y-axis

I want to remove primary y-axis labels and ticks while keeping labels and ticks of the secondary y-axis. I have used the following code
library(tidyverse)
data %>%
pivot_longer(cols = -c(Dependent, Sig_pair_count)) %>%
ggplot() +
geom_col(aes(x = Dependent, y = Sig_pair_count, alpha = 0.3, width=1)) +
geom_line(aes(x = Dependent, y = value*2.5, colour = name)) +
scale_y_continuous(sec.axis = sec_axis(~./2.5, name="Reflectance")) +
theme_bw(base_size = 12) + xlab("Wavelength") + ylab("") +
scale_colour_manual(values = c("blue", "red")) +
theme(legend.position = "none", axis.ticks.y = element_blank(),
text = element_text(family = "serif", color = "black", size = 15),
axis.text = element_text(family = "serif", color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
which returns me
You can see that though the variable of primary y-axis has values 0 and 1, the plot shows 0-2. How can I have the values between 0-1 and have the plot like this
Data
data = structure(list(Dependent = 350:1799, Sig_pair_count = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), PUSA44 = c(0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.06,
0.06, 0.06, 0.06, 0.07, 0.07, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09,
0.1, 0.1, 0.11, 0.11, 0.11, 0.12, 0.12, 0.13, 0.13, 0.14, 0.14,
0.15, 0.15, 0.16, 0.16, 0.17, 0.17, 0.18, 0.18, 0.19, 0.19, 0.2,
0.2, 0.21, 0.21, 0.22, 0.22, 0.23, 0.23, 0.24, 0.24, 0.25, 0.25,
0.26, 0.26, 0.27, 0.27, 0.27, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29,
0.29, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,
0.3, 0.3, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.3, 0.3,
0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33,
0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33,
0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33,
0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32,
0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.31, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.3, 0.3, 0.3, 0.3,
0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28,
0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28,
0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28,
0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28,
0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28,
0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28,
0.28, 0.28, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27,
0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.26, 0.26, 0.26, 0.26, 0.26,
0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12,
0.12, 0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13,
0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14,
0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15), PB6 = c(0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07,
0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05,
0.06, 0.06, 0.06, 0.07, 0.07, 0.08, 0.08, 0.09, 0.09, 0.1, 0.1,
0.11, 0.11, 0.12, 0.12, 0.13, 0.13, 0.14, 0.14, 0.15, 0.15, 0.16,
0.17, 0.17, 0.18, 0.19, 0.19, 0.2, 0.21, 0.21, 0.22, 0.23, 0.24,
0.24, 0.25, 0.26, 0.27, 0.27, 0.28, 0.29, 0.29, 0.3, 0.31, 0.31,
0.32, 0.32, 0.33, 0.34, 0.34, 0.35, 0.35, 0.36, 0.36, 0.36, 0.37,
0.37, 0.37, 0.38, 0.38, 0.38, 0.38, 0.39, 0.39, 0.39, 0.39, 0.39,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42,
0.42, 0.42, 0.42, 0.42, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.4,
0.4, 0.4, 0.4, 0.4, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39,
0.39, 0.39, 0.39, 0.39, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38,
0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.37, 0.37, 0.37, 0.37, 0.37,
0.37, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38,
0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38,
0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.39, 0.39, 0.39, 0.39,
0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.42, 0.42, 0.42, 0.42,
0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42,
0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42,
0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42,
0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41,
0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.39, 0.39, 0.39, 0.39, 0.39,
0.39, 0.39, 0.39, 0.39, 0.39, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38,
0.38, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.34, 0.34, 0.34, 0.34,
0.34, 0.34, 0.34, 0.34, 0.34, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33,
0.33, 0.33, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.31, 0.31, 0.31,
0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.3, 0.3, 0.3, 0.3, 0.3,
0.3, 0.3, 0.3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12,
0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.13,
0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14,
0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.18, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19,
0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22,
0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24,
0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23,
0.23, 0.23, 0.23, 0.23, 0.23, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22,
0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22,
0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21,
0.21, 0.21, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.19, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2)), class = "data.frame", row.names = c(NA,
-1450L))

As far as I get it there is no need for secondary axis. Simply map value on y, position the y-axis on the right and set the limits of the yaxis to the desired range via coord_cartesian. Try this:
library(tidyverse)
data %>%
pivot_longer(cols = -c(Dependent, Sig_pair_count)) %>%
ggplot() +
geom_col(aes(x = Dependent, y = Sig_pair_count, alpha = 0.3, width=1)) +
geom_line(aes(x = Dependent, y = value, colour = name)) +
scale_y_continuous(name="Reflectance", position = "right") +
coord_cartesian(ylim = c(0, 0.5)) +
theme_bw(base_size = 12) +
xlab("Wavelength") +
ylab("") +
scale_colour_manual(values = c("blue", "red")) +
theme(legend.position = "none", axis.ticks.y = element_blank(),
text = element_text(family = "serif", color = "black", size = 15),
axis.text = element_text(family = "serif", color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#> Warning: Ignoring unknown aesthetics: width
#> Warning: Removed 162 rows containing missing values (position_stack).

Why do geom_rect() add many multiple layers when used with facet_wrap()?

I am adding a color bar using geom_rect() in combination with facet_wrap(), but for some reason 30 layers are added, which means the bar is completely dark even though I use alpha = 0.2.
I can export to powerpoint and delete all the extra layes manually, but that is a huuuge pain. Is there a way to fix this problem?
I have tried to restart the terminal and to only load the package needed, thinking maybe the error occured do to a loaded function, but no, it does not seem to be the case.
dat <- structure(list(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, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L), .Label = c("alat", "asat", "chol", "cl",
"fhb", "ft3", "ft4", "ggt", "gluk", "hdlch", "hrs", "k", "kreae",
"ldh", "ldlch", "lipa", "mg", "na", "phos", "prot", "trig", "tsh"
), class = "factor"), diff_A = c(0.03, -0.02, -0.01, 0, -0.03,
-0.01, -0.01, 0.03, 0.02, 0, 0.01, 0.0099999999999999, 0, 0.02,
0.01, 0, -0.03, -0.02, -0.01, 0.02, 0, -0.01, 0.02, 0.02, 0,
0.03, -0.02, -0.01, 0, 0.00999999999999995, 0, 0, 0.01, 0, -0.0299999999999999,
0.04, 0.03, -0.04, 0, 0.02, 0.03, -0.03, -0.01, -0.05, -0.07,
-0.01, -0.00999999999999995, 0.02, 0, 0.0099999999999999, 0.01,
0.06, -0.04, 0.01, 0.06, -0.03, 0.01, 0.03, 0.02, 0, 0.02, 0,
0.00999999999999995, 0.00999999999999995, -0.01, 0.01, -0.02,
0.02, 0.02, 0.13, 0.0999999999999996, 0.2, 0, 0, 0.3, 0, -0.2,
0.100000000000001, 0, -0.2, 0.0999999999999996, -0.1, 0, -0.300000000000001,
-0.0999999999999996, 0, 0, -0.2, 0.100000000000001, -0.100000000000001,
-0.3, -0.0999999999999996, -0.0999999999999996, 0.3, 0, 0.2,
0, 0, 0.100000000000001, -0.2, 0, 0, -0.199999999999999, 0.0999999999999996,
-0.0999999999999996, 1, -1, 0, 0, 3, -1, 0, 0, -1, -2, 0, -2,
0, -1, 1, 0, -2, -2, 0, 1, -1, 1, -1, 3, -2, 0, 0, -1, -1, 1,
0, 0, -1, 0, 0, 1, 0, 2, 1, -1, 1, 0, 3, 2, -3, 4, 1, -2, 2,
1, 3, 0, 2, 2, 4, -2, -1, 1, 1, 1, 1, 1, 3, 5, 0, 1.1, -1, 1,
1, 1, 0.23, -0.71, 0.21, 0.0599999999999996, -0.4, 0.59, -0.0299999999999994,
0.0899999999999999, 0.15, -0.0700000000000003, -0.04, -0.0999999999999996,
0.13, -0.79, -0.27, -0.18, -0.0600000000000001, -0.26, 0.24,
0.63, -0.0500000000000007, -0.28, -0.31, 0.43, -0.2, -0.0499999999999998,
0.149999999999999, -0.319999999999999, 0.0999999999999996, 0.34,
0.0499999999999998, -0.1, 0.3, 0.0699999999999998, 0.0600000000000001,
0.699999999999999, 0.6, 0, 0.300000000000001, -0.199999999999999,
-0.0299999999999994, -0.299999999999999, -0.0999999999999996,
-0.199999999999999, 0.0999999999999996, 0, 0.0999999999999979,
0.0999999999999996, -0.0999999999999996, -0.200000000000001,
-0.0299999999999994, -0.300000000000001, -0.9, -0.0999999999999996,
0.5, -0.5, 0.0999999999999996, -0.0999999999999996, 0.4, -0.200000000000001,
0.300000000000001, 0, -0.199999999999999, -0.4, 0.4, -0.0999999999999996,
0.5, 0.800000000000001, -0.100000000000001, 0.5, 0.02, 0.01,
-0.02, -0.01, 0.05, 0, 0.02, 0, -0.00999999999999995, 0, -0.01,
0.0599999999999999, -0.01, 0.03, 0.01, 0.04, 0.07, 0.05, -0.01,
-0.06, 0.03, -0.03, 0, -0.03, 0.04, 0.01, -0.01, 0, 0.02, -0.03,
0.02, 0.03, 0.03, -0.02, 0, -0.0999999999999996, 0.100000000000001,
0.0999999999999996, -0.199999999999999, -0.4, -0.6, -0.0999999999999996,
0.2, 0, 0.1, -0.0999999999999996, 0.0999999999999996, -0.1, 0.0999999999999996,
-0.100000000000001, 0.0999999999999996, -0.7, -0.2, 0.4, 0.399999999999999,
-0.0999999999999996, -0.0999999999999996, -0.100000000000001,
-0.2, -0.100000000000001, 0.100000000000001, -0.0999999999999996,
-0.1, 0.100000000000001, -0.3, 0, 0, -0.300000000000001, -0.1,
0.3, 0.01, -0.02, 0.01, -0.0900000000000001, 0.11, 0.00999999999999979,
-0.01, -0.01, 0.04, -0.0699999999999998, -0.04, -0.03, 0.03,
-0.0399999999999998, 0.1, 0, 0.03, -0.0700000000000001, -0.0599999999999998,
0.04, 0.03, 0.12, -0.0900000000000001, 0.1, -0.0600000000000001,
0.0700000000000001, 0.02, 0, -0.0399999999999998, 0.0900000000000003,
-0.02, -0.03, 0.03, 0.11, 0, 1, 2, 0, 1, 20, 8, 4, 9, -12, -23,
1, -13, -2, 2, -10, 0, 2, 2, 2, 2, 7, 9, -7, 6, 1, -9, -3, 0,
-12, 12, -2, 1, 14, -3, 4, 0, 0, 0.1, 0, 0.0999999999999996,
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0, 0, 0, -0.3, -0.0999999999999996, 0.1, 0.1, 0, -0.1, 0, 0,
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0.12, 0.04, 0.0600000000000001, 0.0499999999999998, -0.26, 0.0600000000000001,
0.0499999999999998, 0.42, 0.02, 0.1, 0.3, 0, 0.32, 0.02, 0.11,
0.0900000000000003, 0.0600000000000001, -0.2, 0.26, -0.14, -0.32,
0.27, -0.24, 0.0300000000000002, 0.0799999999999996, 0.14, 0.59,
0.25, 0.02, 0.11, 0.0500000000000003, 0.13, 0.27, 0.14, 0, 0.0100000000000002,
-0.02, 0, -0.11, -0.12, -0.02, -0.13, -0.02, 0.1, 0, 0.17, 0.11,
-0.14, 0.0500000000000003, 0.00999999999999979, 0.02, -0.0900000000000003,
-0.0599999999999998, 0.04, -0.0899999999999999, -0.0899999999999999,
0.13, 0.32, -0.22, 0.14, 0.00999999999999979, 0.04, -0.11, -0.01,
0.0299999999999998, 0.0800000000000001, -0.34, 0.04, -0.2, 0.04,
0.0799999999999996, 0.0299999999999998, 0.0499999999999998, 0.19,
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0.02, 0.13, -0.0100000000000002, -0.01, 0.0299999999999998, -0.1,
-0.18, -0.11, -0.0899999999999999, -0.11, 0.01, -0.01, 0.02,
0.0199999999999999, -0.01, 0.03, -0.01, 0.03, 0, -0.02, 0, 0,
0.02, -0.04, 0.05, 0, 0.0299999999999999, 0.01, 0.0399999999999999,
0.0499999999999999, -0.0599999999999999, -0.01, -0.01, 0.03,
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0, 0.0499999999999999, -0.05, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, -3,
0, -1, 0, -2, 2, 0, 1, 0, 0, 1, -1, 1, 1, 0, 0, 0, 2, -1, -1,
1, -1, -1, -1, 0, 0, 0.01, 0.05, -0.02, 0.01, 0.02, -0.0299999999999998,
-0.02, 0.03, 0.03, 0.01, 0.04, 0.21, -0.03, -0.0700000000000001,
0.01, -0.0299999999999999, -0.08, -0.0600000000000001, 0.02,
0, -0.0699999999999998, -0.03, 0.03, -0.02, 0, -0.03, 0, -0.05,
0.02, 0.01, -0.0399999999999999, 0, 0.03, 0.01, 0.01, 1, 0, 0,
0, 2, -1, -2, 3, -1, -1, 1, -1, 0, 0, 0, -2, 1, 2, -3, -1, 2,
1, 1, 1, -1, 2, 1, 1, 1, 2, 0, -1, 3, -2, -1, 0.01, 0, -0.02,
0.04, 0, -0.04, 0.03, -0.0299999999999999, -0.01, -0.01, 0.01,
0.01, 0.01, 0.02, 0.03, -0.09, 0.04, -0.0600000000000001, 0.05,
0.05, -0.0499999999999998, -0.0199999999999999, 0.01, 0.05, -0.0599999999999999,
0.0699999999999998, 0, 0.02, -0.01, -0.05, -0.02, 0.02, 0, 0.0399999999999998,
-0.0399999999999998, 0.01, -0.03, -0.02, -0.01, 0.02, 0.0600000000000001,
-0.05, 0, -0.12, -0.13, -0.03, 0, -0.0600000000000001, 0.03,
-0.01, 0, 0.02, 0.04, -0.0600000000000001, 0.035, -0.02, 0.0309999999999999,
0.0599999999999998, 0.01, 0.03, 0.0500000000000003, -0.0399999999999996,
0.0499999999999998, 0, 0.00800000000000001, 0, -0.00900000000000001,
0.14, 0, -0.025), MD_Fuss = c(0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.54, 0.54,
0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54,
0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54,
0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54, 0.54,
6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49,
6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49,
6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49, 6.49,
6.49, 6.49, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66,
0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66,
0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66,
0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.81, 0.81, 0.81, 0.81, 0.81,
0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81,
0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81,
0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84,
12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84,
12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84,
12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84, 12.84,
12.84, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,
0.17, 0.17, 0.17, 0.17, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79,
16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79,
16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79,
16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79, 16.79,
16.79, 16.79, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36,
0.36, 0.36, 0.36, 0.36, 0.36, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29,
0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21,
5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21,
5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21,
5.21, 5.21, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19,
3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19,
3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19,
3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11,
0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16)), class = "data.frame", row.names = c(NA,
-770L))
and this is the plot:
p <- ggplot(data=dat, aes(y = diff_A))+
geom_boxplot(outlier.shape = 1)+
geom_rect(aes(ymin = -MD_Fuss, ymax = MD_Fuss), xmin = -Inf, xmax =Inf, alpha = 0.2)+
theme_bw()+ theme(panel.grid = element_blank())+
xlab('')+ ylab('[mmol/L]') +
scale_y_continuous(expand = c(0.5, 0))+
facet_wrap(.~variable, scales = 'free')
p

geom_rect() draws a rectangle for each row in your data. To get only one rectangle per facet, you need to pass it a data set that contains only one row per faceting variable. Since MD_Fuss seems to be constant within a variable, you can create that data set with unique(dat[, c("variable", "MD_Fuss")]), and then pass it as the data argument to geom_rect():
library(ggplot2)
p <- ggplot(data = dat) +
geom_boxplot(aes(y = diff_A), outlier.shape = 1) +
geom_rect(
data = unique(dat[, c("variable", "MD_Fuss")]),
aes(ymin = -MD_Fuss, ymax = MD_Fuss),
xmin = -Inf, xmax = Inf, alpha = 0.2,
) +
theme_bw() + theme(panel.grid = element_blank()) +
xlab("") + ylab("[mmol/L]") +
scale_y_continuous(expand = c(0.5, 0)) +
facet_wrap(. ~ variable, scales = "free")
p
#> Warning: Removed 2 rows containing missing values (geom_rect).
Created on 2019-07-19 by the reprex package (v0.3.0.9000)

R - ggplot2 - plots and aes depending on multiple variables creates zigzag despite monotone data

I am given a table of prices depending on the year and various parameters.
structure(list(Year = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L), guar = c(0.01, 0.01, 0.01, 0.01, 0.02,
0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02,
0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01,
0.01, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02,
0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02),
vol = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2), fee = c(0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02), value = c(103.490219783939, 104.64694534429,
105.423021866961, 106.003997632507, 103.987761167674, 105.637197779702,
106.901255344043, 107.965567455406, 102.953065656004, 103.545441115507,
103.741845779355, 103.732177003242, 103.490219783939, 104.64694534429,
105.423021866961, 106.003997632507, 102.460474891754, 102.574796992078,
102.307300703979, 101.84752143324, 103.036847936938, 103.785353707651,
104.185436261084, 104.417467321901, 107.438302065026, 110.172519129122,
112.120314291466, 113.657230720516, 107.965567455406, 111.246291601828,
113.750976986408, 115.851941887821, 106.891292109859, 109.043467438409,
110.389127553239, 111.309322271107, 107.438302065026, 110.172519129122,
112.120314291466, 113.657230720516, 106.369273097772, 107.990957085228,
108.806658224208, 109.200666959571, 106.935904609248, 109.17460141814,
110.636502202272, 111.697991480364), rate = c(0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02)), row.names = c(NA, -48L
), class = "data.frame")
Year guar vol fee value rate
1 1 0.01 0.1 0.00 103.4902 0.02
2 2 0.01 0.1 0.00 104.6469 0.02
3 3 0.01 0.1 0.00 105.4230 0.02
4 4 0.01 0.1 0.00 106.0040 0.02
and would like now to plot this with ggplot in such a way that "year" is the x-axis, "value" gives the y-value, color depends on "guar", shape on "vol" and the graph is split in three diagramms according to "fee":
ggplot(df_tot,aes(x=Year,y=value,colour=as.factor(guar),shape=as.factor(vol)))+
geom_line()+geom_point()+ facet_wrap(as.factor(fee))
Yet, the resulting graphs are zig-zagging
despite the data being monotone. For example, we have
> df_tot[df_tot$fee==0 & df_tot$guar==0.01 & df_tot$vol==0.1,5]
[1] 103.4902 104.6469 105.4230 106.0040

I couldn't reproduce the example, got an error.
I changed the facet for facet_wrap(~as.factor(fee)) and got the following:
ggplot(df_tot,
aes(x=Year,y=value,colour=as.factor(guar),shape=as.factor(vol)))+
geom_line()+geom_point() + facet_wrap(as.factor(fee))

R scatterplot loop using two variables

I'm trying to make a for loop to automatically generate ~50 scatterplots comparing two sets of data. It's a quality control analysis, so I'm looking at geochemical values that were analyzed twice (duplicates). So I have a list of 53 elements (periodic table elements) labeled Al1, Ag1, Au1..... and another list of 53 labeled Al2, Ag2....etc.
I've successfully gotten my loop to work for generating graphs that only need one variable, with the x axis being fixed, like below.
for(i in colNames){
plt <- ggplot(YGS_Dupes, mapping = aes_string(x=Dup_Num, y = i)) +
geom_bar() + theme_calc() + ggtitle(paste(i, "Duplicate Comparison", sep=" -
"))
print(plt)
ggsave(paste0(i,".png"))
Sys.sleep(2)
}
I set colNames to be the element columns, and the function runs through the different elements and generates a bar plot for each, where it's just showing Sample 1 or Sample 2 as the X axis (so it produces two bar plots side by side).
What I need to make now is a scatterplot where I compare the data from Al1 to Al2 or Fe1 to Fe2, so I need the for loop to run using two parallel sets of changing variables. I made the function for a single graph like so:
ggplot(YGS_Dup_Scatter, mapping = aes(x = Fe_pct1, y =
Fe_pct2))+geom_point()
and it looks like this:
Fe vs Fe Scatterplot
So what I have done is made a similar set of colNames groups, like below:
colNames_scatter_dup <- names(YGS_Dup_Scatter)[4:56]
colNames_scatter_dup2 <- names(YGS_Dup_Scatter)[57:109]
Where 4-56 are all the element 1 set and 57-109 are the element 2 set. They are ordered the same so I want 4/57, 5/58....etc to be pairs.
How do I set up my for loop equation to do this?
Thank you for any help
Edit: Adding the dput data for people to try. I had too many observations and variables so I cut most of them out:
Edit 2: Ok, so I made a nested loop and it makes what I want, but it also makes way too many graphs, shown below:
for (j in colNames_scatter_dup2) {
for(i in colNames_scatter_dup){
plt <- ggplot(YGS_Dup_Scatter, mapping = aes_string(x=j, y = i)) +
geom_point()
print(plt)
ggsave(paste0(i,".png"))
Sys.sleep(2)
}
}
The issue I have now is that it does Al1 vs Al2, then Ag1 vs Al2, ......then gets to Al1 vs Ag2.....and make hundreds of graphs. I only want to make the actual 53 element pairs, and I can't figure out how to restrict it to just those.
thanks
structure(list(DUP_COMP_ID = structure(c(1L, 12L, 23L, 34L, 45L,
56L, 67L, 78L, 89L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 68L, 69L,
70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L,
98L, 99L), .Label = c("DCI_1", "DCI_10", "DCI_11", "DCI_12",
"DCI_13", "DCI_14", "DCI_15", "DCI_16", "DCI_17", "DCI_18", "DCI_19",
"DCI_2", "DCI_20", "DCI_21", "DCI_22", "DCI_23", "DCI_24", "DCI_25",
"DCI_26", "DCI_27", "DCI_28", "DCI_29", "DCI_3", "DCI_30", "DCI_31",
"DCI_32", "DCI_33", "DCI_34", "DCI_35", "DCI_36", "DCI_37", "DCI_38",
"DCI_39", "DCI_4", "DCI_40", "DCI_41", "DCI_42", "DCI_43", "DCI_44",
"DCI_45", "DCI_46", "DCI_47", "DCI_48", "DCI_49", "DCI_5", "DCI_50",
"DCI_51", "DCI_52", "DCI_53", "DCI_54", "DCI_55", "DCI_56", "DCI_57",
"DCI_58", "DCI_59", "DCI_6", "DCI_60", "DCI_61", "DCI_62", "DCI_63",
"DCI_64", "DCI_65", "DCI_66", "DCI_67", "DCI_68", "DCI_69", "DCI_7",
"DCI_70", "DCI_71", "DCI_72", "DCI_73", "DCI_74", "DCI_75", "DCI_76",
"DCI_77", "DCI_78", "DCI_79", "DCI_8", "DCI_80", "DCI_81", "DCI_82",
"DCI_83", "DCI_84", "DCI_85", "DCI_86", "DCI_87", "DCI_88", "DCI_89",
"DCI_9", "DCI_90", "DCI_91", "DCI_92", "DCI_93", "DCI_94", "DCI_95",
"DCI_96", "DCI_97", "DCI_98", "DCI_99"), class = "factor"), Dup_Code = 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), .Label = "Sample 1", class = "factor"), Dup_Code.1 = 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), .Label = "Sample 2", class = "factor"), Ag_ppb1 = c(56L,
58L, 52L, 59L, 68L, 318L, 50L, 70L, 398L, 114L, 38L, 52L, 63L,
64L, 65L, 81L, 66L, 62L, 86L, 146L, 67L, 70L, 49L, 69L, 74L,
55L, 55L, 47L, 109L, 41L, 78L, 115L, 65L, 373L, 59L, 47L, 85L,
72L, 86L, 72L, 77L, 554L, 68L, 85L, 105L, 70L, 67L, 127L, 69L,
67L, 38L, 59L, 284L, 94L, 57L, NA, 92L, 88L, 74L, 73L, 50L, NA,
63L, 57L, 111L, 71L, 47L, 69L, 81L, 45L, 52L, 42L, 34L, 176L,
73L, 140L, 87L, 41L, 36L, 204L, 272L, 52L, 37L, 45L, 187L, 180L,
100L, 60L, 39L, 71L, 92L, 29L, 308L, 157L, 78L, 91L, NA, 60L,
217L), As_ppm1 = c(4.3, 4.8, 4.6, 5, 1.9, 14.3, 3, 5.8, 49.7,
9.2, 3.8, 3.1, 5.9, 5.4, 5, 4.3, 5.3, 4.2, 3.8, 35, 5.8, 6.6,
3.3, 11.2, 3.5, 3.8, 3.8, 4.4, 8.8, 4.9, 3.6, 18.3, 3.6, 6.1,
4.2, 4.4, 9, 7.3, 3.7, 3.4, 13.7, 21.9, 3.9, 5.8, 3.6, 4.4, 2.9,
5.2, 4.9, 5.4, 4.4, 4.3, 5.5, 8.3, 3.4, NA, 6.2, 4.2, 3.5, 5.5,
5, NA, 3.4, 4.2, 7.1, 5.1, 3.8, 6.9, 6.7, 3.2, 4.8, 4.3, 2.6,
4.6, 4.8, 9.3, 7.5, 2.8, 4.2, 4.9, 17, 3.1, 3.9, 4.7, 9.7, 883.2,
7.8, 5.1, 2.4, 10.4, 7.2, 2.9, 6.7, 9.3, 3.7, 7.3, NA, 4.8, 21.5
), Au_ppb1 = c(0.7, 4.6, 1.5, 0.6, 11.9, 2.4, 0.8, 0.8, 2.2,
3.5, 0.4, 0.8, 0.9, 1.7, 1.2, 3.5, 1.4, 1.4, 2.2, 2.6, 3, 0.9,
0.6, 1.5, 0.9, 0.7, 1.4, 3.5, 8.7, 0.4, 0.6, 2.4, 1.1, 1.7, 1.5,
1.3, 0.1, 0.1, 4.5, 44.5, 0.8, 6.6, 48.7, 1.5, 0.7, 0.3, 0.8,
1.1, 1.2, 5.5, 1.4, 1.4, 2.7, 1.9, 1, NA, 0.4, 1, 1.6, 0.3, 0.4,
NA, 0.8, 1.8, 1.9, 0.1, 0.5, 1.4, 0.8, 0.2, 0.8, 0.6, 0.3, 1.1,
1, 2.1, 0.8, 0.4, 0.9, 0.9, 1.2, 1.2, 1.2, 1.3, 1.2, 1.6, 1.8,
0.5, 1.4, 1.3, 1.4, 0.1, 0.6, 1.9, 0.8, 1.5, NA, 0.6, 3.4), B_ppm1 = c(10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 21L, NA, 10L, 10L, 10L, 10L, 10L, NA, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, NA, 10L, 10L), Ba_ppm1 = c(141, 124.2, 171.9,
171, 246.8, 359.3, 96, 205.4, 187.4, 195.3, 115.2, 134.9, 162.9,
156.9, 186.7, 148.4, 164.9, 165.5, 329.1, 106.8, 137.3, 150.7,
180.9, 123.4, 150.6, 122.7, 230.4, 176.1, 208.9, 154.5, 147.2,
242.2, 184.2, 465.5, 217.2, 171.3, 286.6, 248, 243.1, 265.9,
273.3, 317.4, 150.7, 272.7, 332.1, 293.1, 185.7, 262.9, 203.4,
333, 185.2, 203.4, 300.8, 227.3, 193.2, NA, 328, 293.2, 225.7,
286.9, 237.6, NA, 193.5, 293.8, 294.5, 252.2, 160.5, 277, 349.2,
184.5, 231.3, 251.4, 150, 372.4, 237.7, 227.9, 271.8, 66.6, 92.8,
53.4, 112.5, 172.6, 188.5, 177, 315.5, 193.8, 300.2, 132.9, 199.4,
221.4, 375.6, 128.7, 82.7, 157.4, 175.5, 297.9, NA, 190.9, 206.4
), Be_ppm1 = c(0.3, 0.5, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.6, 0.3,
0.4, 0.4, 0.3, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.3, 0.9, 0.4, 0.6,
0.3, 0.5, 0.3, 0.3, 0.2, 0.3, 0.3, 0.4, 0.6, 0.3, 0.2, 0.3, 0.3,
0.3, 0.2, 0.6, 0.4, 0.4, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4,
0.2, 0.3, 0.3, 0.2, 0.3, 0.3, NA, 0.3, 0.05, 0.3, 0.3, 0.2, NA,
0.3, 0.5, 0.3, 0.5, 0.3, 0.3, 0.3, 0.3, 0.2, 0.3, 0.2, 0.4, 0.3,
0.5, 0.4, 0.2, 0.1, 1.8, 1.8, 0.4, 0.2, 0.2, 0.8, 35.9, 0.3,
0.4, 0.2, 0.4, 0.2, 0.4, 0.2, 0.4, 0.3, 0.4, NA, 0.4, 1.2), Bi_ppm1 = c(0.24,
0.29, 0.21, 0.19, 0.13, 0.28, 0.15, 0.16, 0.73, 0.14, 0.12, 0.39,
0.1, 0.12, 0.4, 0.42, 0.13, 0.13, 0.11, 6.67, 0.14, 0.22, 0.15,
0.18, 0.09, 0.06, 0.09, 0.1, 0.18, 0.08, 0.08, 0.14, 0.06, 0.23,
0.1, 0.09, 0.08, 0.14, 0.13, 0.06, 0.08, 0.13, 0.08, 0.15, 0.11,
0.1, 0.07, 0.11, 0.1, 0.06, 0.11, 0.08, 0.11, 0.11, 0.08, NA,
0.12, 0.22, 0.1, 0.13, 0.08, NA, 0.06, 0.18, 0.13, 0.1, 0.16,
0.15, 0.13, 0.07, 0.09, 0.08, 0.06, 0.14, 0.07, 0.21, 0.17, 0.01,
0.05, 2.07, 0.35, 0.13, 0.08, 0.09, 0.23, 0.55, 0.17, 1.1, 0.06,
0.07, 0.14, 0.04, 0.06, 0.15, 0.08, 0.12, NA, 0.09, 0.97), Ca_pct1 = c(0.69,
0.58, 0.46, 0.46, 0.42, 0.41, 0.51, 0.5, 0.6, 0.83, 0.42, 0.34,
0.69, 0.98, 0.51, 0.43, 0.78, 0.44, 0.38, 0.56, 1.07, 0.46, 0.72,
0.77, 1.08, 0.64, 0.46, 0.57, 0.5, 0.5, 0.88, 0.65, 0.67, 0.28,
0.75, 0.59, 0.49, 0.72, 0.31, 0.42, 0.71, 0.14, 0.42, 0.69, 0.29,
0.39, 0.31, 0.94, 0.7, 0.47, 0.71, 0.38, 0.31, 0.5, 0.47, NA,
0.47, 0.37, 0.67, 0.68, 0.32, NA, 0.64, 0.31, 0.83, 0.52, 0.33,
0.71, 0.91, 0.49, 0.58, 0.35, 0.34, 0.5, 0.54, 0.92, 0.4, 3.74,
1.69, 0.21, 0.4, 0.45, 0.66, 0.49, 0.56, 0.88, 0.41, 0.41, 0.31,
0.53, 0.96, 1.13, 0.35, 0.58, 0.33, 0.56, NA, 0.68, 0.32), Cd_ppm1 = c(0.13,
0.22, 0.12, 0.15, 0.09, 0.99, 0.13, 0.19, 0.88, 0.34, 0.1, 0.15,
0.17, 0.16, 0.14, 0.2, 0.14, 0.11, 0.15, 0.2, 0.14, 0.17, 0.1,
0.17, 0.18, 0.13, 0.11, 0.13, 0.2, 0.12, 0.13, 0.27, 0.13, 0.37,
0.21, 0.12, 0.18, 0.08, 0.14, 0.11, 0.15, 0.41, 0.19, 0.3, 0.23,
0.15, 0.1, 0.34, 0.13, 0.13, 0.09, 0.15, 0.25, 0.17, 0.12, NA,
0.17, 0.22, 0.14, 0.21, 0.11, NA, 0.1, 0.16, 0.27, 0.19, 0.13,
0.22, 0.26, 0.05, 0.17, 0.15, 0.1, 0.39, 0.16, 0.47, 0.21, 0.17,
0.14, 0.59, 1.11, 0.12, 0.13, 0.1, 0.63, 0.47, 0.33, 0.2, 0.11,
0.26, 0.28, 0.11, 0.1, 0.55, 0.37, 0.29, NA, 0.18, 0.82), Ag_ppb2 = c(59L,
73L, 69L, 75L, 85L, 319L, 43L, 73L, 405L, 121L, 33L, 45L, 71L,
67L, 67L, 80L, 50L, 45L, 68L, 140L, 56L, 69L, 51L, 71L, 79L,
51L, 36L, 52L, 93L, 31L, 98L, 134L, 67L, 386L, 47L, 46L, 90L,
63L, 86L, 54L, 59L, 478L, 61L, 114L, 108L, 74L, 72L, 147L, 60L,
74L, 40L, 56L, 256L, 112L, 62L, 87L, 71L, 104L, 109L, 55L, 45L,
84L, 69L, 63L, 107L, 70L, 57L, 73L, 100L, 45L, 43L, 36L, 39L,
161L, 108L, 100L, 93L, 32L, 45L, 187L, 267L, 68L, 37L, 57L, 228L,
74L, 69L, 47L, 65L, 101L, 33L, 32L, 139L, 77L, 78L, NA, 59L,
214L, 410L), As_ppm2 = c(3.9, 3.8, 4.4, 5.4, 1.7, 14.4, 3.1,
5.9, 52.3, 9.7, 3.5, 2.7, 6.7, 5.2, 5, 4.3, 4.8, 4, 3.9, 31.9,
5.3, 6.5, 3.6, 10.4, 3.5, 3.9, 3.6, 4.3, 8.9, 5.3, 3.8, 16.7,
3.7, 6.1, 3.7, 4, 9.6, 6.4, 4, 3.1, 13.2, 22.1, 4.3, 6.9, 3.6,
4.9, 3.4, 4.8, 4.1, 4.8, 4.2, 3.8, 5.3, 9.2, 3.3, 12.5, 5.3,
4.4, 4.8, 5.7, 5, 5.5, 3.4, 4.4, 6.5, 4.8, 4, 6.5, 6.2, 3.4,
4.5, 3.8, 2.6, 4.7, 8, 8.5, 7.6, 2.6, 4.7, 5.2, 15.8, 4, 3.1,
5.3, 343.7, 7.4, 5.1, 3, 11, 7.3, 3, 6.8, 21.1, 4.1, 9.1, NA,
4.4, 21, 122.1), Au_ppb2 = c(0.9, 1.6, 0.1, 1.3, 0.7, 1.8, 0.6,
0.8, 1.6, 2.7, 0.4, 0.9, 0.9, 1.8, 1.5, 1.6, 1.5, 0.9, 2, 1.3,
0.3, 3, 0.8, 2.5, 1.5, 0.4, 1.2, 1.4, 1, 1.1, 0.4, 113.3, 0.6,
2.2, 1.9, 0.7, 0.5, 0.1, 1.8, 0.9, 1.4, 4.3, 1.6, 0.8, 0.7, 0.9,
0.6, 2.4, 5.6, 1.2, 0.9, 1.1, 2.1, 1.1, 0.9, 0.8, 0.9, 1, 4,
0.3, 1.5, 0.5, 1.2, 1, 1.5, 0.1, 1.2, 19.8, 32.8, 0.1, 0.7, 0.7,
1, 0.5, 2.3, 1.6, 1.6, 0.6, 0.9, 1.7, 1.9, 1.3, 1.1, 1.1, 0.9,
4.8, 0.5, 0.4, 1.6, 1, 0.1, 0.9, 1.3, 0.8, 2.7, NA, 0.8, 4, 3.6
), B_ppm2 = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 22L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 23L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, NA, 10L, 10L, 10L), Ba_ppm2 = c(137.5,
128, 175, 205.6, 262.7, 356.1, 91.2, 212.8, 207, 217.4, 111,
132.4, 179.4, 139.8, 188.9, 164.4, 136, 158.7, 348.9, 96.6, 141.3,
143.7, 187, 121.2, 166.9, 131, 235.9, 189.5, 201.4, 158.7, 148.3,
227, 190, 415.9, 197.2, 178, 268, 221.1, 251.5, 243.3, 260.4,
310, 165.8, 308.2, 342.8, 317, 185, 241.7, 189.2, 291.4, 199.4,
214.7, 312.2, 273, 197.8, 265, 255, 315.2, 281.7, 326, 236.5,
229.7, 197.8, 308.4, 277.2, 258.7, 185.7, 261.2, 354.7, 177.7,
213.2, 226.7, 159.2, 369.5, 359.1, 224.9, 275.4, 54, 106.7, 53.4,
100.9, 194.7, 188.4, 187.4, 162.9, 237.7, 146.9, 189, 214.9,
368.1, 134.8, 82.4, 130.4, 187.8, 291.2, NA, 171.9, 209.5, 318.5
), Be_ppm2 = c(0.2, 0.3, 0.4, 0.3, 0.3, 0.4, 0.1, 0.3, 0.6, 0.4,
0.4, 0.5, 0.4, 0.3, 0.5, 0.7, 0.3, 0.3, 0.2, 0.4, 0.7, 0.4, 0.4,
0.3, 0.4, 0.2, 0.3, 0.3, 0.5, 0.6, 0.5, 0.4, 0.3, 0.3, 0.3, 0.2,
0.2, 0.2, 0.5, 0.2, 0.3, 0.3, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3,
0.2, 0.3, 0.2, 0.3, 0.5, 0.3, 0.4, 0.3, 0.3, 0.2, 0.3, 0.1, 0.5,
0.2, 0.6, 0.3, 0.4, 0.4, 0.2, 0.4, 0.3, 0.3, 0.2, 0.2, 0.3, 0.5,
0.3, 0.3, 0.2, 0.2, 1.6, 1.8, 0.5, 0.2, 0.6, 33.1, 0.1, 0.6,
0.05, 0.2, 0.3, 0.7, 0.2, 1.5, 0.3, 0.3, NA, 0.3, 1.2, 1.4),
Bi_ppm2 = c(0.23, 0.28, 0.23, 0.21, 0.12, 0.26, 0.14, 0.16,
0.69, 0.16, 0.12, 0.34, 0.11, 0.11, 0.41, 0.36, 0.12, 0.11,
0.11, 2.86, 0.14, 0.23, 0.19, 0.18, 0.1, 0.05, 0.08, 0.11,
0.15, 0.08, 0.09, 0.15, 0.06, 0.24, 0.08, 0.09, 0.09, 0.12,
0.14, 0.07, 0.07, 0.12, 0.09, 0.18, 0.1, 0.1, 0.09, 0.09,
0.11, 0.06, 0.1, 0.07, 0.1, 0.12, 0.08, 0.09, 0.1, 0.2, 0.09,
0.1, 0.09, 0.17, 0.06, 0.15, 0.12, 0.1, 0.17, 0.13, 0.12,
0.05, 0.08, 0.08, 0.07, 0.17, 0.12, 0.21, 0.17, 0.01, 0.05,
1.93, 0.33, 0.15, 0.05, 0.08, 0.68, 0.12, 0.3, 0.06, 0.06,
0.14, 0.05, 0.08, 0.4, 0.09, 0.12, NA, 0.07, 0.98, 2.21),
Ca_pct2 = c(0.6, 0.56, 0.48, 0.53, 0.4, 0.41, 0.47, 0.51,
0.58, 0.86, 0.41, 0.33, 0.7, 0.9, 0.51, 0.45, 0.67, 0.44,
0.39, 0.56, 1.05, 0.48, 1.21, 0.83, 1.1, 0.66, 0.45, 0.62,
0.5, 0.47, 1.04, 0.66, 0.64, 0.3, 0.74, 0.58, 0.49, 0.65,
0.31, 0.42, 0.62, 0.13, 0.42, 0.84, 0.29, 0.4, 0.32, 1.01,
0.6, 0.46, 0.71, 0.41, 0.3, 0.58, 0.5, 1.02, 0.4, 0.39, 0.87,
0.79, 0.34, 0.44, 0.67, 0.31, 0.79, 0.47, 0.33, 0.67, 0.86,
0.5, 0.49, 0.29, 0.35, 0.5, 0.87, 0.8, 0.39, 3.36, 1.78,
0.22, 0.36, 0.5, 0.57, 0.53, 0.58, 0.37, 0.43, 0.3, 0.46,
1.03, 1.12, 0.36, 0.48, 0.38, 0.52, NA, 0.52, 0.33, 1.21),
Cd_ppm2 = c(0.13, 0.19, 0.12, 0.15, 0.1, 0.97, 0.1, 0.21,
0.92, 0.35, 0.1, 0.09, 0.16, 0.18, 0.16, 0.17, 0.11, 0.11,
0.2, 0.16, 0.11, 0.16, 0.13, 0.17, 0.2, 0.13, 0.14, 0.15,
0.25, 0.05, 0.18, 0.28, 0.09, 0.3, 0.22, 0.09, 0.18, 0.12,
0.1, 0.1, 0.15, 0.3, 0.17, 0.33, 0.2, 0.15, 0.1, 0.59, 0.16,
0.16, 0.1, 0.13, 0.24, 0.21, 0.11, 0.46, 0.12, 0.24, 0.23,
0.17, 0.11, 0.22, 0.13, 0.18, 0.24, 0.16, 0.17, 0.18, 0.23,
0.09, 0.12, 0.1, 0.1, 0.35, 0.37, 0.43, 0.24, 0.16, 0.17,
0.62, 1, 0.13, 0.12, 0.11, 0.56, 0.23, 0.22, 0.15, 0.23,
0.28, 0.12, 0.1, 0.97, 0.36, 0.3, NA, 0.19, 0.89, 3.59)), class = "data.frame", row.names = c(NA,
-99L))

Consider Map (wrapper to mapply) which is the iteration function to run elementwise between equal length lists and saves output into a list. Doing so, you avoid extraneous looping as seen with nested for loops approach.
# EXTRACT NEEDED NAMES
samples1 <- names(YGS_Dupes)[grep("1$", names(YGS_Dupes))][-1] # -1 TO REMOVE Dupe_Code.1
samples2 <- names(YGS_Dupes)[grep("2$", names(YGS_Dupes))]
# SET UP LOOPING FUNCTION
plot_fct <- function(s1, s2) {
s_title <- gsub("1", "", s1)
p <- ggplot(YGS_Dupes, aes_string(x=s1, y=s2)) + geom_point(color="#0072B2") +
ggtitle(paste(s_title, "Duplicate Comparison", sep=" - ")) +
theme(plot.title = element_text(hjust = 0.5), legend.position="top",
axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5))
ggsave(paste0(s_title,".png"))
return(p)
}
# BUILD LIST LOOPING ELEMENTWISE
plot_list2 <- Map(plot_fct, samples1, samples2)
# OUTPUT PLOTS BY NAME
plot_list2$Ag_ppb1
plot_list2$As_ppm1
plot_list2$Au_ppb1
Output (first three plots)

As a general solution to plot scatterplots using for loop, you can use following flow.
Step 1: Create a plotting function
In the following code, I have explicitly provided my dataframe and the x-axis target variable. For the variable on the y-axis I pass the column number in the function so that it would be to run a for-loop later.
sct_plot_function <- function(dataset = car.c2.num, target_x = car.c2.num$price, target_y_num){
ggplot(dataset, aes(x = target_x, y = car.c2.num[,target_y_num])) +
geom_point() +
geom_smooth(level = 0.95) +
theme_bw() +
labs(title = paste("Scatter plot of Price Vs ", colnames(car.c2.num)[target_y_num]), y = colnames(car.c2.num)[target_y_num], x = "Price") +
theme(plot.title = element_text(hjust = 0.5))
}
Step 2: Use a for loop to plot multiple scatter plots in one go.
Using dim(car.c2.num)[2] - 1 to extract the number of columns minus from the dataframe and loop it using i in 1:(dim(car.c2.num)[2] - 1)
The reason I have done is that the 14 variable for me is the target variable which is fixed for the x-axis.
for(i in 1:(dim(car.c2.num)[2] - 1) ){
plot(sct_plot_function(target_y_num = i))
}
you can use this as a basic structure to re-define for your multiple x and y axes.
Can further use nested for loop if you are aiming to plot all variable combinations on x and y axes respectively.
Sample Image:
Scatter plot of Price Vs compression_ratio for Automobile dataset UCI

Try this:
For (i in 1:length(colNames_scatter_dup)){
print(ggplot(YGS_Dup_Scatter, mapping = aes(x = YGS_Dup_Scatter[,names(YGS_Dup_Scatter) %in% colNames_scatter_dup[i]], y = YGS_Dup_Scatter[,names(YGS_Dup_Scatter) %in% colNames_scatter_dup2[i]]))+geom_point())
}

ok Parfait, thank you for helping, discussing your answer with a colleague got me to where I needed to be.
The final result was the following:
YGS_Dup_Scatter = read.csv(file.choose(), header=TRUE, sep=",")
colNames_scatter_dup <- names(YGS_Dup_Scatter)[4:56]
colNames_scatter_dup2 <- names(YGS_Dup_Scatter)[57:109]
for (j in 1:length(colNames_scatter_dup)) {
plt <- ggplot(YGS_Dup_Scatter, mapping = aes_string(x=colNames_scatter_dup[j], y =colNames_scatter_dup2[j])) +
geom_point() + theme_calc() + ggtitle(paste(colNames_scatter_dup[j], "Duplicate Comparison", sep=" - ")) + theme(plot.title = element_text(face = "bold", hjust = 0.5, size = 16), axis.text.x = element_text(face = "bold", size = "14"), axis.text.y = element_text(face = "bold", size = "12"), plot.margin = margin(10, 30, 2, 2), axis.title.y=element_text(face = "bold", size = "14"), plot.background = element_rect(fill = "lightskyblue2"))
print(plt)
ggsave(paste0(i,".png"))
Sys.sleep(2)
}
The key was using the length function and structuring my columns so that it went A1, A2...A53, then B1, B2....etc.
Being the same length allowed the length function to keep them paired.
Thanks for the help everyone!

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