rboxplotanovatukey
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,
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0, 0.05, 0.02, 0.04, 0.02, 0, 0.02, 0.02, 0.02, 0.04, 0.01,
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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))
Solution
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 ?
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