Creating ROC curve with GGPLOT - r

I have the following data:
df <- structure(list(TPR = c(0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14,
0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36,
0.38, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58,
0.6, 0.62, 0.64, 0.64, 0.64, 0.66, 0.68, 0.7, 0.72, 0.74, 0.76,
0.78, 0.8, 0.8, 0.82, 0.82, 0.84, 0.84, 0.84, 0.86, 0.86, 0.86,
0.86, 0.88, 0.88, 0.9, 0.92, 0.92, 0.92, 0.92, 0.94, 0.94, 0.96,
0.96, 0.96, 0.96, 0.96, 0.96, 0.98, 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, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.02, 0.04, 0.06, 0.08, 0.1,
0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.24, 0.26, 0.28, 0.3,
0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.42, 0.42, 0.44, 0.46, 0.48,
0.5, 0.52, 0.54, 0.56, 0.58, 0.6, 0.6, 0.6, 0.6, 0.62, 0.62,
0.62, 0.64, 0.66, 0.66, 0.68, 0.68, 0.68, 0.7, 0.72, 0.74, 0.76,
0.78, 0.8, 0.8, 0.8, 0.82, 0.82, 0.84, 0.84, 0.84, 0.86, 0.86,
0.86, 0.86, 0.86, 0.88, 0.88, 0.88, 0.9, 0.9, 0.9, 0.9, 0.9,
0.9, 0.9, 0.92, 0.94, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96,
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, 0.02, 0.04, 0.06, 0.08, 0.1, 0.1, 0.1, 0.12,
0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.24, 0.26, 0.28, 0.28, 0.3,
0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.42, 0.42, 0.42, 0.44, 0.44,
0.44, 0.46, 0.48, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58, 0.58, 0.6,
0.62, 0.62, 0.62, 0.64, 0.66, 0.68, 0.68, 0.7, 0.72, 0.72, 0.72,
0.72, 0.74, 0.74, 0.74, 0.76, 0.76, 0.78, 0.78, 0.8, 0.82, 0.84,
0.84, 0.84, 0.86, 0.88, 0.88, 0.9, 0.9, 0.92, 0.92, 0.92, 0.92,
0.92, 0.92, 0.92, 0.92, 0.94, 0.94, 0.96, 0.96, 0.96, 0.96, 0.98,
0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98,
1, 1, 1, 1, 0.02, 0.04, 0.06, 0.06, 0.06, 0.08, 0.08, 0.1, 0.12,
0.14, 0.16, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.28, 0.3,
0.32, 0.32, 0.34, 0.34, 0.36, 0.38, 0.4, 0.42, 0.42, 0.44, 0.46,
0.46, 0.46, 0.48, 0.48, 0.5, 0.52, 0.54, 0.56, 0.56, 0.58, 0.6,
0.62, 0.64, 0.64, 0.64, 0.64, 0.64, 0.66, 0.68, 0.68, 0.7, 0.7,
0.7, 0.7, 0.7, 0.72, 0.74, 0.76, 0.76, 0.78, 0.78, 0.78, 0.8,
0.8, 0.82, 0.82, 0.84, 0.86, 0.86, 0.86, 0.86, 0.88, 0.9, 0.92,
0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.94, 0.94, 0.94,
0.94, 0.94, 0.94, 0.96, 0.98, 0.98, 0.98, 0.98, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.06, 0.06, 0.08, 0.08,
0.1, 0.12, 0.12, 0.14, 0.16, 0.18, 0.18, 0.2, 0.2, 0.2, 0.22,
0.24, 0.26, 0.26, 0.28, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.38,
0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58, 0.58,
0.6, 0.62, 0.64, 0.66, 0.68, 0.7, 0.72, 0.74, 0.76, 0.78, 0.8,
0.82, 0.84, 0.86, 0.88, 0.9, 0.92, 0.94, 0.96, 0.98, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.04, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.08, 0.1, 0.12, 0.12, 0.14, 0.16, 0.16,
0.16, 0.18, 0.18, 0.2, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22,
0.24, 0.26, 0.26, 0.28, 0.28, 0.3, 0.32, 0.32, 0.34, 0.36, 0.38,
0.4, 0.4, 0.42, 0.44, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56,
0.56, 0.56, 0.56, 0.58, 0.6, 0.62, 0.64, 0.66, 0.68, 0.68, 0.7,
0.72, 0.74, 0.76, 0.78, 0.8, 0.82, 0.84, 0.84, 0.86, 0.88, 0.9,
0.92, 0.94, 0.96, 0.98, 1, 0, 0, 0, 0, 0, 0.02, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.06, 0.06, 0.06, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.1, 0.12, 0.14, 0.14, 0.16, 0.18,
0.18, 0.18, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.22, 0.22, 0.22, 0.24,
0.26, 0.26, 0.26, 0.26, 0.28, 0.28, 0.28, 0.3, 0.32, 0.34, 0.34,
0.36, 0.38, 0.38, 0.4, 0.4, 0.42, 0.42, 0.42, 0.42, 0.44, 0.46,
0.46, 0.46, 0.48, 0.48, 0.5, 0.5, 0.52, 0.54, 0.56, 0.58, 0.6,
0.62, 0.64, 0.64, 0.66, 0.66, 0.68, 0.7, 0.72, 0.72, 0.74, 0.76,
0.78, 0.8, 0.82, 0.84, 0.86, 0.88, 0.9, 0.92, 0.94, 0.94, 0.96,
0.98, 1, 0, 0, 0, 0.02, 0.04, 0.04, 0.06, 0.06, 0.06, 0.06, 0.06,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.1, 0.1, 0.1, 0.12,
0.12, 0.14, 0.14, 0.14, 0.14, 0.14, 0.16, 0.16, 0.16, 0.18, 0.2,
0.2, 0.22, 0.22, 0.22, 0.22, 0.22, 0.24, 0.24, 0.24, 0.24, 0.24,
0.26, 0.28, 0.3, 0.32, 0.32, 0.32, 0.34, 0.34, 0.36, 0.38, 0.4,
0.42, 0.42, 0.42, 0.42, 0.44, 0.44, 0.46, 0.48, 0.48, 0.5, 0.5,
0.52, 0.52, 0.52, 0.54, 0.56, 0.58, 0.58, 0.58, 0.58, 0.6, 0.62,
0.64, 0.66, 0.68, 0.7, 0.72, 0.74, 0.74, 0.76, 0.78, 0.8, 0.82,
0.84, 0.84, 0.84, 0.86, 0.88, 0.9, 0.9, 0.92, 0.94, 0.96, 0.98,
1), GeneSet = 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, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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("Distort = 1", "Distort = 1.5",
"Distort = 2", "Distort = 2.5"), class = "factor")), .Names = c("TPR",
"FPR", "GeneSet"), row.names = c(NA, -400L), class = "data.frame")
But why the following code fail to create the desired plot?
library(ggplot2)
library(RColorBrewer)
p <- qplot(FPR, TPR, data = df, geom = "blank", main = "ROC curve", xlab = "False Positive Rate (1-Specificity)", ylab = "True Positive Rate (Sensitivity)" )
p <- p + geom_line(aes(x = FPR, y = TPR, data = data, colour = GeneSet), size = 2, alpha = 0.7) + scale_colour_manual(values=colors)
p
I got this error message:
Don't know how to automatically pick scale for object of type data.frame. Defaulting to continuous
Error: Aesthetics must either be length one, or the same length as the dataProblems:data
The desired plot is this:


You don't need to plot geom="blank" and geom_line() - it can be done just by geom_line(). Only colors can't be reproduced because variable colors isn't provided in question.
ggplot(df,aes(FPR,TPR,color=GeneSet))+geom_line(size = 2, alpha = 0.7)+
labs(title= "ROC curve",
x = "False Positive Rate (1-Specificity)",
y = "True Positive Rate (Sensitivity)")

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).

How to show the results of a Tukey test with boxplots showing CLD letters

I have collected data on 216 individuals. I measured the concentration of the same 7 Substances in each individual, represented by Sub1:Sub7. The concentration of these Substances may be different in individuals from different Locations. I am interested in the level of refinement at which these individuals can be classified into groups based on their concentrations of these substances. I am also interested in seeing how these Substances may be correlated with each other, as the concentration of some may effect the concentration of others. Each Individual in my data set is represented by a unique ID number. Three "nested" grouping variables (Location, State, and Region) can be used to separate these individuals. Multiple Locations are in each State, and multiple States are part of larger Regions. For instance, the individuals in the Locations: APNG, BLEA, and NEAR are all in FL, while the individuals in the Locations: CACT, OYLE, and PIY are all in GA. The states FL and GA are both in Region A. I used this function to conduct an anova:
library(tidyverse)
library(multicomp)
library(multicompView)
tests <- list()
Groups <- c(1:3)
Variables <- 6:12
for(i in Groups){
Group <- as.factor(data[[i]])
for(j in Variables)
{
test_name <- paste0(names(data)[j], "_by_", names(data[i]))
Response <- data[[j]]
sublist <- list()
sublist$aov <- aov(Response ~ Group)
sublist$tukey <- TukeyHSD(sublist$aov)
sublist$multcomp <- multcompLetters(extract_p(sublist$tukey$Group))
tests[[test_name]] <- sublist
}
}
#i can access the results like this:
lapply(tests, function(x) summary(x$aov))
#and access the compact letter display results like this:
lapply(tests, function(x) x$multcomp)
using the object tests, how can I tell R to create boxplots of the TukeyHSD results and show the CLD letters and paste the plots onto a pdf?
This website: r-graph-gallery.com/84-tukey-test.html explains how to do this, but I cannot get it to work with the object tests.
here is my data:
> dput(data)
structure(list(Region = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 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, 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, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L), .Label = c("A", "B", "C", "D", "E"), class = "factor"),
State = structure(c(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, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 10L, 10L, 10L,
10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L), .Label = c("DE", "FL", "GA", "MA",
"MD", "ME", "NC", "NH", "NY", "SC", "VA", "VT"), class = "factor"),
Location = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 20L, 20L, 20L, 20L, 20L, 20L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L
), .Label = c("APNG", "BATO", "BLEA", "CACT", "CHAG", "CHOG",
"COTR", "DTU", "HAB", "LOP", "MASV", "NEAR", "NGUP", "OYLE",
"PIRT", "PIY", "PKE", "PONO", "PPP", "ROG", "VONG", "YENQ"
), class = "factor"), Sex = structure(c(1L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
2L), .Label = c("F", "M"), class = "factor"), ID = 1:216,
Sub1 = c(0.03, 0.03, 0.03, 0.04, 0.04, 0.03, 0.03, 0.03,
0.03, 0.03, 0.04, 0.03, 0.04, 0.03, 0.03, 0.03, 0.02, 0.04,
0.03, 0.03, 0.03, 0.02, 0.04, 0.04, 0.02, 0.03, 0.02, 0.03,
0.05, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.04, 0.03, 0.04, 0.06, 0.03, 0.03, 0.03, 0.03,
0.02, 0.03, 0.03, 0.03, 0.04, 0.03, 0.02, 0.02, 0.04, 0.03,
0.04, 0.03, 0.03, 0.03, 0.05, 0.03, 0.03, 0.04, 0.03, 0.02,
0.04, 0.02, 0.03, 0.02, 0.02, 0.04, 0.03, 0.02, 0.03, 0.03,
0.05, 0.04, 0.03, 0.02, 0.03, 0.05, 0.02, 0.04, 0.03, 0.05,
0.03, 0.04, 0.02, 0.03, 0.02, 0.03, 0.03, 0.03, 0.02, 0.05,
0.03, 0.03, 0.04, 0.02, 0.02, 0.04, 0.05, 0.03, 0.03, 0.02,
2.03, 2.03, 2.03, 2.04, 2.04, 2.03, 2.03, 2.03, 2.03, 2.03,
2.04, 2.03, 2.04, 2.03, 2.03, 2.03, 2.02, 2.04, 2.03, 2.03,
2.03, 2.02, 2.04, 2.04, 2.02, 2.03, 2.02, 2.03, 2.05, 2.03,
2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03,
2.04, 2.03, 2.04, 2.06, 2.03, 2.03, 2.03, 2.03, 2.02, 2.03,
2.03, 2.03, 2.04, 2.03, 2.02, 2.02, 2.04, 2.03, 2.04, 2.03,
2.03, 2.03, 2.05, 2.03, 2.03, 2.04, 2.03, 2.02, 2.04, 2.02,
2.03, 2.02, 2.02, 2.04, 2.03, 2.02, 2.03, 2.03, 2.05, 2.04,
2.03, 2.02, 2.03, 2.05, 2.02, 2.04, 2.03, 2.05, 2.03, 2.04,
2.02, 2.03, 2.02, 2.03, 2.03, 2.03, 2.02, 2.05, 2.03, 2.03,
2.04, 2.02, 2.02, 2.04, 2.05, 2.03, 2.03, 2.02), Sub2 = c(0.69,
1.28, 1.27, 2.25, 1.05, 1.76, 1.57, 1.09, 0.68, 1.35, 0.85,
1.55, 0.12, 0, 0.58, 1.13, 0.1, 1.9, 0.54, 1.48, 0.8, 0.52,
1.76, 1.77, 1.24, 0.63, 0.63, 0.57, 0.63, 0.53, 1.32, 1.79,
1.16, 1.11, 1.1, 1.92, 1.06, 1.18, 0.43, 0.67, 0.75, 2.37,
3.93, 0.3, 2.8, 1.25, 0.9, 1.32, 0.5, 0.4, 0.72, 0.34, 0.12,
0.89, 0.69, 1.13, 1.22, 0.88, 4.13, 1.27, 0.62, 2.9, 2.42,
0.9, 0.4, 1.29, 1.61, 0.3, 1.47, 0.36, 1.27, 0.84, 1.81,
0.18, 0.47, 1.01, 0.85, 0.59, 1.73, 0.72, 0.5, 0.83, 0.9,
0.81, 0.59, 2.84, 2.24, 2.68, 1.18, 1.36, 0.84, 1.79, 1.01,
0.34, 0.41, 2.22, 0.51, 0.42, 1.26, 2.26, 1.79, 1.43, 1.3,
1.8, 2.21, 1.65, 2.39, 0.31, 2.69, 3.28, 3.27, 4.25, 3.05,
3.76, 3.57, 3.09, 2.68, 3.35, 2.85, 3.55, 2.12, 2, 2.58,
3.13, 2.1, 3.9, 2.54, 3.48, 2.8, 2.52, 3.76, 3.77, 3.24,
2.63, 2.63, 2.57, 2.63, 2.53, 3.32, 3.79, 3.16, 3.11, 3.1,
3.92, 3.06, 3.18, 2.43, 2.67, 2.75, 4.37, 5.93, 2.3, 4.8,
3.25, 2.9, 3.32, 2.5, 2.4, 2.72, 2.34, 2.12, 2.89, 2.69,
3.13, 3.22, 2.88, 6.13, 3.27, 2.62, 4.9, 4.42, 2.9, 2.4,
3.29, 3.61, 2.3, 3.47, 2.36, 3.27, 2.84, 3.81, 2.18, 2.47,
3.01, 2.85, 2.59, 3.73, 2.72, 2.5, 2.83, 2.9, 2.81, 2.59,
4.84, 4.24, 4.68, 3.18, 3.36, 2.84, 3.79, 3.01, 2.34, 2.41,
4.22, 2.51, 2.42, 3.26, 4.26, 3.79, 3.43, 3.3, 3.8, 4.21,
3.65, 4.39, 2.31), Sub3 = c(1.32, 0.19, 0.27, 0.73, 0.41,
0.37, 0.89, 1.35, 0.49, 1.32, 0.69, 0, 0.57, 0.24, 0.23,
0.71, 0, 0, 0, 0.58, 0.32, 1.1, 0.45, 0.61, 0.38, 0.3, 0.01,
0.06, 0.48, 0.62, 0.64, 1.96, 0.61, 0.43, 0.25, 0.34, 0.17,
0.57, 0.1, 0.6, 1.07, 0.44, 0.12, 0.55, 0.08, 0.56, 0.59,
0.66, 0.44, 0.58, 0.75, 0.99, 0.77, 0.57, 0.35, 0.18, 0.16,
0.31, 0.04, 0.17, 0.46, 0.19, 0.8, 0.61, 1.14, 0.3, 0.08,
0.25, 0.78, 1.07, 0.38, 0.17, 0.42, 0.48, 0.55, 0.74, 2.98,
1.96, 0.51, 0.63, 0, 0.52, 0.32, 0.23, 0.31, 0.09, 0.06,
0.26, 0.23, 0.58, 1.49, 0.46, 0.33, 0.37, 1.16, 0.91, 0.41,
0.72, 0.2, 0.84, 0.71, 0.56, 0.34, 0.68, 0.81, 0.52, 0.78,
0.19, 3.32, 2.19, 2.27, 2.73, 2.41, 2.37, 2.89, 3.35, 2.49,
3.32, 2.69, 2, 2.57, 2.24, 2.23, 2.71, 2, 2, 2, 2.58, 2.32,
3.1, 2.45, 2.61, 2.38, 2.3, 2.01, 2.06, 2.48, 2.62, 2.64,
3.96, 2.61, 2.43, 2.25, 2.34, 2.17, 2.57, 2.1, 2.6, 3.07,
2.44, 2.12, 2.55, 2.08, 2.56, 2.59, 2.66, 2.44, 2.58, 2.75,
2.99, 2.77, 2.57, 2.35, 2.18, 2.16, 2.31, 2.04, 2.17, 2.46,
2.19, 2.8, 2.61, 3.14, 2.3, 2.08, 2.25, 2.78, 3.07, 2.38,
2.17, 2.42, 2.48, 2.55, 2.74, 4.98, 3.96, 2.51, 2.63, 2,
2.52, 2.32, 2.23, 2.31, 2.09, 2.06, 2.26, 2.23, 2.58, 3.49,
2.46, 2.33, 2.37, 3.16, 2.91, 2.41, 2.72, 2.2, 2.84, 2.71,
2.56, 2.34, 2.68, 2.81, 2.52, 2.78, 2.19), Sub4 = c(0.63,
0.05, 0.2, 0.41, 0.43, 0.54, 0.26, 0.78, 0.13, 0.8, 0.47,
0.65, 0, 0.22, 0.45, 0.85, 0.47, 0, 0.62, 0.59, 0.14, 0.8,
0.9, 0.88, 0.56, 0.56, 0.47, 0.24, 0.62, 1.77, 0.56, 0.99,
0.21, 0.9, 0.62, 0.58, 0.41, 0.97, 0.2, 0.9, 0.68, 0.52,
0.14, 1.27, 0.63, 0.51, 0.12, 0.61, 0.31, 0.43, 0.62, 1.18,
0.95, 0.59, 0.39, 0.26, 0.53, 0.77, 0.4, 0.39, 0, 0.19, 0.82,
1.1, 0.46, 0.25, 0.29, 0.2, 2.01, 0.36, 0.62, 0.54, 0.48,
0.87, 0.66, 1.46, 2.59, 1.37, 1.28, 0.99, 0.71, 0.32, 0.64,
0.66, 0.47, 0.48, 0.38, 0.67, 0.18, 1.02, 0.54, 0.53, 0.25,
0.43, 1.02, 0.58, 0.58, 0.48, 0.2, 0.7, 0.38, 0.28, 0.65,
1.21, 1.03, 0.38, 0.6, 0.44, 2.63, 2.05, 2.2, 2.41, 2.43,
2.54, 2.26, 2.78, 2.13, 2.8, 2.47, 2.65, 2, 2.22, 2.45, 2.85,
2.47, 2, 2.62, 2.59, 2.14, 2.8, 2.9, 2.88, 2.56, 2.56, 2.47,
2.24, 2.62, 3.77, 2.56, 2.99, 2.21, 2.9, 2.62, 2.58, 2.41,
2.97, 2.2, 2.9, 2.68, 2.52, 2.14, 3.27, 2.63, 2.51, 2.12,
2.61, 2.31, 2.43, 2.62, 3.18, 2.95, 2.59, 2.39, 2.26, 2.53,
2.77, 2.4, 2.39, 2, 2.19, 2.82, 3.1, 2.46, 2.25, 2.29, 2.2,
4.01, 2.36, 2.62, 2.54, 2.48, 2.87, 2.66, 3.46, 4.59, 3.37,
3.28, 2.99, 2.71, 2.32, 2.64, 2.66, 2.47, 2.48, 2.38, 2.67,
2.18, 3.02, 2.54, 2.53, 2.25, 2.43, 3.02, 2.58, 2.58, 2.48,
2.2, 2.7, 2.38, 2.28, 2.65, 3.21, 3.03, 2.38, 2.6, 2.44),
Sub5 = c(1.14, 1.38, 1.5, 1.43, 1.65, 1.34, 1.29, 1.72, 1.32,
1.17, 1.19, 1.35, 1.34, 1.06, 1.24, 1.33, 1.2, 1.31, 1.29,
1.37, 1.42, 1.08, 1.77, 1.32, 1.2, 1.14, 1.48, 0.98, 1.33,
1.65, 1.24, 1.43, 1.41, 1.2, 1.42, 1.09, 1.04, 1.57, 0.78,
1.37, 0.99, 1.4, 1.13, 1.34, 1.35, 1.23, 0.93, 0.94, 1.02,
1.16, 1.08, 0.96, 1.33, 1.19, 1.25, 1.44, 1.62, 1.27, 1.4,
1.4, 1.29, 1.53, 1.43, 1.33, 1.25, 1.82, 1.45, 1.36, 1.38,
1.34, 1.29, 1.86, 1.15, 1.31, 1.21, 1.23, 1.42, 1.57, 1.23,
0.99, 1.33, 1.74, 1.03, 1.33, 1.41, 1.01, 0.97, 1.46, 1.55,
1.04, 1.22, 1.19, 1.74, 1.64, 1.35, 1.34, 1.21, 1.55, 1.31,
1.5, 1.45, 1.21, 0.83, 1.17, 1.25, 1.54, 1.5, 1.11, 3.14,
3.38, 3.5, 3.43, 3.65, 3.34, 3.29, 3.72, 3.32, 3.17, 3.19,
3.35, 3.34, 3.06, 3.24, 3.33, 3.2, 3.31, 3.29, 3.37, 3.42,
3.08, 3.77, 3.32, 3.2, 3.14, 3.48, 2.98, 3.33, 3.65, 3.24,
3.43, 3.41, 3.2, 3.42, 3.09, 3.04, 3.57, 2.78, 3.37, 2.99,
3.4, 3.13, 3.34, 3.35, 3.23, 2.93, 2.94, 3.02, 3.16, 3.08,
2.96, 3.33, 3.19, 3.25, 3.44, 3.62, 3.27, 3.4, 3.4, 3.29,
3.53, 3.43, 3.33, 3.25, 3.82, 3.45, 3.36, 3.38, 3.34, 3.29,
3.86, 3.15, 3.31, 3.21, 3.23, 3.42, 3.57, 3.23, 2.99, 3.33,
3.74, 3.03, 3.33, 3.41, 3.01, 2.97, 3.46, 3.55, 3.04, 3.22,
3.19, 3.74, 3.64, 3.35, 3.34, 3.21, 3.55, 3.31, 3.5, 3.45,
3.21, 2.83, 3.17, 3.25, 3.54, 3.5, 3.11), Sub6 = c(0.2, 0.15,
0.16, 0.14, 0.19, 0.12, 0.14, 0.35, 0.29, 0.25, 0.06, 0.16,
0.18, 0.65, 0.18, 0.12, 0.42, 0.09, 0.13, 0.12, 0.22, 0.49,
0.18, 0.11, 0.29, 0.16, 0.18, 0.15, 0.46, 0.19, 0.15, 0.19,
0.1, 0.09, 0.11, 0.14, 0.1, 0.31, 0.53, 0.32, 0.23, 0.18,
0.14, 0.38, 0.19, 0.1, 0.14, 0.08, 0.21, 0.13, 0.08, 0.08,
0.26, 0.14, 0.17, 0.09, 0.09, 0.22, 0.26, 0.09, 0.3, 0.16,
0.17, 0.09, 0.12, 0.17, 0.14, 0.34, 0.12, 0.21, 0.1, 0.27,
0.11, 0.13, 0.15, 0.17, 0.21, 0.16, 0.12, 0.36, 0.16, 0.17,
0.27, 0.32, 0.15, 0.13, 0.14, 0.15, 0.1, 0.26, 0.25, 0.08,
0.25, 0.19, 0.38, 0.08, 0.64, 0.71, 0.1, 0.18, 0.12, 0.13,
0.1, 1.17, 0.14, 0.19, 0.14, 0.24, 2.2, 2.15, 2.16, 2.14,
2.19, 2.12, 2.14, 2.35, 2.29, 2.25, 2.06, 2.16, 2.18, 2.65,
2.18, 2.12, 2.42, 2.09, 2.13, 2.12, 2.22, 2.49, 2.18, 2.11,
2.29, 2.16, 2.18, 2.15, 2.46, 2.19, 2.15, 2.19, 2.1, 2.09,
2.11, 2.14, 2.1, 2.31, 2.53, 2.32, 2.23, 2.18, 2.14, 2.38,
2.19, 2.1, 2.14, 2.08, 2.21, 2.13, 2.08, 2.08, 2.26, 2.14,
2.17, 2.09, 2.09, 2.22, 2.26, 2.09, 2.3, 2.16, 2.17, 2.09,
2.12, 2.17, 2.14, 2.34, 2.12, 2.21, 2.1, 2.27, 2.11, 2.13,
2.15, 2.17, 2.21, 2.16, 2.12, 2.36, 2.16, 2.17, 2.27, 2.32,
2.15, 2.13, 2.14, 2.15, 2.1, 2.26, 2.25, 2.08, 2.25, 2.19,
2.38, 2.08, 2.64, 2.71, 2.1, 2.18, 2.12, 2.13, 2.1, 3.17,
2.14, 2.19, 2.14, 2.24), Sub7 = c(0.01, 0, 0, 0.01, 0, 0,
0.01, 0.01, 0.02, 0.03, 0.01, 0, 0.03, 0, 0.02, 0, 0, 0,
0.01, 0.03, 0.03, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0,
0, 0.05, 0.02, 0.04, 0.02, 0, 0.02, 0.02, 0.02, 0.04, 0.01,
0.02, 0.04, 0.02, 0.01, 0.01, 0.01, 0.01, 0.03, 0.02, 0,
0.02, 0.05, 0.14, 0, 0.01, 0, 0.01, 0.01, 0, 0.01, 0.02,
0.01, 0.02, 0.01, 0.03, 0.05, 0.06, 0.03, 0.02, 0.11, 0.05,
0.02, 0.02, 0, 0.01, 0, 0.01, 0.06, 0.04, 0.02, 0.02, 0,
0.02, 0.01, 0.02, 0.01, 0, 0.01, 0.01, 0.02, 0.01, 0.02,
0.01, 0, 0.01, 0.06, 0.01, 0.02, 0.01, 0.01, 0.03, 0.02,
0.03, 0.03, 0.02, 0.09, 0, 0.19, 0.02, 2.01, 2, 2, 2.01,
2, 2, 2.01, 2.01, 2.02, 2.03, 2.01, 2, 2.03, 2, 2.02, 2,
2, 2, 2.01, 2.03, 2.03, 2.02, 2.02, 2.02, 2.01, 2.01, 2.01,
2, 2, 2.05, 2.02, 2.04, 2.02, 2, 2.02, 2.02, 2.02, 2.04,
2.01, 2.02, 2.04, 2.02, 2.01, 2.01, 2.01, 2.01, 2.03, 2.02,
2, 2.02, 2.05, 2.14, 2, 2.01, 2, 2.01, 2.01, 2, 2.01, 2.02,
2.01, 2.02, 2.01, 2.03, 2.05, 2.06, 2.03, 2.02, 2.11, 2.05,
2.02, 2.02, 2, 2.01, 2, 2.01, 2.06, 2.04, 2.02, 2.02, 2,
2.02, 2.01, 2.02, 2.01, 2, 2.01, 2.01, 2.02, 2.01, 2.02,
2.01, 2, 2.01, 2.06, 2.01, 2.02, 2.01, 2.01, 2.03, 2.02,
2.03, 2.03, 2.02, 2.09, 2, 2.19, 2.02)), class = "data.frame", row.names = c(NA,
-216L))

I think the issue with your tests object is that it holds too much informations to figure out how to plot it.
Here, I focused only on Regions columns, but you can apply the same workflow to other categorical columns of your dataset.
1) We need to obtain the label (letters) associated to each region for each substance, so recycling your loop, I did this:
library(multcomp)
library(multcompView)
Labels_box = NULL
Group <- as.factor(data[,"Region"])
for(j in 6:12)
{
Response <- data[, j]
TUKEY <- TukeyHSD(aov(lm(Response ~ Group)))
MultComp <- multcompLetters(extract_p(TUKEY$Group))
Region <- names(MultComp$Letters)
Labels <- MultComp$Letters
df <- data.frame(Region, Labels)
df$Substance <- colnames(data)[j]
if(j == 1){Labels_box = df}
else{Labels_box = rbind(Labels_box,df)}
}
Now, the dataset Labels_box should look like:
head(Labels_box)
Region Labels Substance
B B a Sub1
C C b Sub1
D D b Sub1
E E b Sub1
A A a Sub1
B1 B a Sub2
2) Next, in order to add them on the top of each boxplot, we will have to define the y position for each labels. So, we are going to calculate the max value of each region for each substance using dplyr and tidyr:
library(tidyverse)
Max_Val <- data %>% pivot_longer(., cols = starts_with("Sub"), names_to = "Substance", values_to = "Value") %>%
group_by(Region, Substance) %>% summarise(MAX = max(Value)+0.2)
# A tibble: 6 x 3
# Groups: Region [1]
Region Substance MAX
<fct> <chr> <dbl>
1 A Sub1 0.26
2 A Sub2 4.13
3 A Sub3 1.55
4 A Sub4 2.21
5 A Sub5 2.06
6 A Sub6 0.85
And we combine both Labels_box and Max_Val datasets using left_join:
Labels_box <- left_join(Labels_box, Max_Val, by = c("Region" = "Region", "Substance" = "Substance"))
Region Labels Substance MAX
1 B a Sub1 0.25
2 C b Sub1 2.25
3 D b Sub1 2.26
4 E b Sub1 2.25
5 A a Sub1 0.26
6 B a Sub2 4.33
3) Finally, we need to reshape in a long format all values for each substances from your data to match the grammar used by ggplot. For that, we can re-use the pivot_longer function seen in 2):
library(tidyverse)
data_box <- data %>% pivot_longer(., cols = starts_with("Sub"), names_to = "Substance", values_to = "Value")
# A tibble: 6 x 7
Region State Location Sex ID Substance Value
<fct> <fct> <fct> <fct> <int> <chr> <dbl>
1 A FL APNG F 1 Sub1 0.03
2 A FL APNG F 1 Sub2 0.69
3 A FL APNG F 1 Sub3 1.32
4 A FL APNG F 1 Sub4 0.63
5 A FL APNG F 1 Sub5 1.14
6 A FL APNG F 1 Sub6 0.2
We are almost ready but in order to set a color matching group identified by Tukey test, we need to add the label on our data_box.
For that, we can do a left_join:
data_box <- left_join(data_box,Labels_box, by = c("Region" = "Region", "Substance" = "Substance"))
# A tibble: 6 x 9
Region State Location Sex ID Substance Value Labels MAX
<fct> <fct> <fct> <fct> <int> <chr> <dbl> <fct> <dbl>
1 A FL APNG F 1 Sub1 0.03 a 0.26
2 A FL APNG F 1 Sub2 0.69 a 4.13
3 A FL APNG F 1 Sub3 1.32 a 1.55
4 A FL APNG F 1 Sub4 0.63 a 2.21
5 A FL APNG F 1 Sub5 1.14 a 2.06
6 A FL APNG F 1 Sub6 0.2 a 0.85
4) Now, we are ready to plot everything:
library(ggplot2)
ggplot(data_box, aes(x = Region, y = Value, fill = Labels))+
geom_boxplot()+
geom_text(data = Labels_box,aes( x = Region, y = MAX, label = Labels))+
facet_grid(.~Substance, scales = "free")
And you get this:
Does it look satisfying for you ?

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!

Rotate a faceted, grouped bar plot

**UPDATED BELOW
I have created a plot, I literally need it horizontal, but the coord_flip() leaves the facets on the bottom instead of having nested groups on the left.
The data:
srvc_data <- structure(list(dept = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("Distribution Centre Services",
"IT", "Marketing", "Merchandise & Inventory", "Operations and Communication"
), class = "factor"), label = c("test5", "test7", "test3", "test10",
"test4", "test6", "test2", "test1", "test11", "test12", "test9",
"test8", "test18", "test19", "test15", "test17", "test13", "test16",
"test20", "test14", "test22", "test21", "test25", "test23", "test24",
"test27", "test26", "test28", "test29", "test31", "test33", "test30",
"test32", "test38", "test36", "test37", "test43", "test34", "test35",
"test40", "test39", "test42", "test41", "test5", "test7", "test3",
"test10", "test4", "test6", "test2", "test1", "test11", "test12",
"test9", "test8", "test18", "test19", "test15", "test17", "test13",
"test16", "test20", "test14", "test22", "test21", "test25", "test23",
"test24", "test27", "test26", "test28", "test29", "test31", "test33",
"test30", "test32", "test38", "test36", "test37", "test43", "test34",
"test35", "test40", "test39", "test42", "test41"), Gap = c(-0.07,
-0.13, -0.15, -0.16, -0.16, -0.21, -0.22, -0.24, -0.24, -0.25,
-0.3, -0.3, -0.18, -0.19, -0.24, -0.29, -0.3, -0.34, -0.36, -0.41,
-0.46, -0.63, -0.16, -0.18, -0.21, -0.22, -0.27, -0.29, -0.31,
-0.31, -0.35, -0.39, -0.42, -0.15, -0.15, -0.2, -0.21, -0.22,
-0.27, -0.29, -0.29, -0.31, -0.36, -0.07, -0.13, -0.15, -0.16,
-0.16, -0.21, -0.22, -0.24, -0.24, -0.25, -0.3, -0.3, -0.18,
-0.19, -0.24, -0.29, -0.3, -0.34, -0.36, -0.41, -0.46, -0.63,
-0.16, -0.18, -0.21, -0.22, -0.27, -0.29, -0.31, -0.31, -0.35,
-0.39, -0.42, -0.15, -0.15, -0.2, -0.21, -0.22, -0.27, -0.29,
-0.29, -0.31, -0.36), impeff = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("Importance", "Effectiveness"), class = "factor"),
score = c(0.87, 0.79, 0.78, 0.82, 0.81, 0.81, 0.92, 0.92,
0.78, 0.81, 0.86, 0.91, 0.79, 0.79, 0.87, 0.93, 0.9, 0.9,
0.82, 0.95, 0.91, 0.95, 0.77, 0.79, 0.82, 0.8, 0.83, 0.9,
0.91, 0.94, 0.89, 0.94, 0.91, 0.82, 0.74, 0.78, 0.81, 0.83,
0.85, 0.82, 0.81, 0.8, 0.83, 0.8, 0.66, 0.63, 0.66, 0.65,
0.6, 0.7, 0.68, 0.54, 0.56, 0.56, 0.61, 0.61, 0.6, 0.63,
0.64, 0.6, 0.56, 0.46, 0.54, 0.45, 0.32, 0.61, 0.61, 0.61,
0.58, 0.56, 0.61, 0.6, 0.63, 0.54, 0.55, 0.49, 0.67, 0.59,
0.58, 0.6, 0.61, 0.58, 0.53, 0.52, 0.49, 0.47)), row.names = c(NA,
-86L), .Names = c("dept", "label", "Gap", "impeff", "score"), class = "data.frame")
And the code:
ggplot(data = srvc_data, aes(x = label, y = score)) +
geom_bar( aes(fill = impeff),stat = "identity", position = "dodge",width = 1) +
facet_grid(~dept, switch = "x", scales = "free", space = "free") +
#coord_flip()+
The plot (without the flip) looks like the below, I need it horizontal, with the facet categories on the far left. How does the coord_flip() work? Why wouldn't it also flip/move the facet strips? Please ignore the crammed formatting!
**UPDATE
So thanks to #neilfws I have fixed the plot, by switching the order of the data.
ggplot(data = srvc_data, aes(x = label, y = score)) +
geom_bar( aes(fill = impeff),stat = "identity", position = "dodge",width = 1) +
facet_grid(dept~., switch = "y", scales = "free_y", space = "free") +
coord_flip()
Now I have the correctly oriented plot, but there is lots of unused space for all the labels that are unused in each facet. Within the facet_grid call, setting scales = "free" doesn't work, nor does drop = T. Any ideas? Plot below for reference.

If you coord_flip, you also need to reverse the faceting relationship (~), to place it on the side, and the switch, to place it on the y-axis. Does this get you close to what you want?
ggplot(srvc_data, aes(label, score)) +
geom_bar( aes(fill = impeff), stat = "identity", position = "dodge", width = 1) +
facet_grid(dept ~ ., switch = "y", scales = "free", space = "free") + coord_flip()

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