how to draw a post-hoc probability heatmap using TukeyHSD

I was trying to analyze whether there is a blood pressure difference in different jobs. The number of jobs is exactly 30, and each job has about 50 subjects.

There is a significance blood pressure difference in those jobs by using one-way ANOVA, and post-hoc analysis using TukeyHSD was also performed.

AOV <- aov(Blood_pressure ~ Job, data = myData)
Tukey <- TukeyHSD(AOV)

TukeyHSD() yields the "difference" and "post-hoc p value" as a dataframe. I would like to know how to draw a post-hoc p-value heatmap using TukeyHSD in R.

here is a probability heatmap similar to what I'm expecting:

the heatmap should be triangle and showing different color with different post-hoc p value (<0.05, <0.01, <0.001 and NS).

below is the TukeyHSD result round to 5 digits

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0.9996, 1, 0.6452, 1, 1, 1, 1, 0.99453, 1, 1, 1, 1, 1, 0.99962, 
1, 1, 1, 0.99912, 1, 0.00015, 0.28561, 1, 1, 1, 0.20112, 1, 1, 
1, 1, 0.99999, 0.76547, 0.99946, 1, 0.99715, 1, 0.04619, 0.99994, 
1, 1, 1, 0.67228, 1, 1, 1, 1, 1, 0.9803, 1, 1, 0.99998, 0.00667, 
0.60094, 1, 1, 1, 1, 0.99822, 1, 1, 1, 0.95755, 0.3696, 0.8633, 
1, 0.41242, 1, 1, 1, 1, 0.99981, 1, 1, 1, 1, 1, 0.99558, 1, 1, 
0.04859, 0.08088, 0.99992, 0.99863, 0, 0.96726, 0.8907, 0.25211, 
4e-05, 0.00065, 1, 0.81181, 0.99994, 0.9973, 1, 1, 0, 1, 0.99996, 
0.99991, 0.10484, 0.59105, 0.99993, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 0.91514, 0.9999, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 0.99998, 1, 1, 0.98881, 1, 1, 0.99607, 0, 0.02074, 0.12205, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.99455, 0.99998, 1, 1, 1, 0.97387, 
1, 1, 0.96863, 0.4677, 0.96666, 1, 0.95618, 1, 1, 1, 1, 1), dim = c(406L, 
4L), dimnames = list(c("9-8", "17-8", "18-8", "19-8", "20-8", 
"23-8", "24-8", "25-8", "26-8", "28-8", "31-8", "34-8", "36-8", 
"37-8", "38-8", "43-8", "46-8", "48-8", "66-8", "71-8", "72-8", 
"76-8", "78-8", "83-8", "85-8", "86-8", "88-8", "96-8", "17-9", 
"18-9", "19-9", "20-9", "23-9", "24-9", "25-9", "26-9", "28-9", 
"31-9", "34-9", "36-9", "37-9", "38-9", "43-9", "46-9", "48-9", 
"66-9", "71-9", "72-9", "76-9", "78-9", "83-9", "85-9", "86-9", 
"88-9", "96-9", "18-17", "19-17", "20-17", "23-17", "24-17", 
"25-17", "26-17", "28-17", "31-17", "34-17", "36-17", "37-17", 
"38-17", "43-17", "46-17", "48-17", "66-17", "71-17", "72-17", 
"76-17", "78-17", "83-17", "85-17", "86-17", "88-17", "96-17", 
"19-18", "20-18", "23-18", "24-18", "25-18", "26-18", "28-18", 
"31-18", "34-18", "36-18", "37-18", "38-18", "43-18", "46-18", 
"48-18", "66-18", "71-18", "72-18", "76-18", "78-18", "83-18", 
"85-18", "86-18", "88-18", "96-18", "20-19", "23-19", "24-19", 
"25-19", "26-19", "28-19", "31-19", "34-19", "36-19", "37-19", 
"38-19", "43-19", "46-19", "48-19", "66-19", "71-19", "72-19", 
"76-19", "78-19", "83-19", "85-19", "86-19", "88-19", "96-19", 
"23-20", "24-20", "25-20", "26-20", "28-20", "31-20", "34-20", 
"36-20", "37-20", "38-20", "43-20", "46-20", "48-20", "66-20", 
"71-20", "72-20", "76-20", "78-20", "83-20", "85-20", "86-20", 
"88-20", "96-20", "24-23", "25-23", "26-23", "28-23", "31-23", 
"34-23", "36-23", "37-23", "38-23", "43-23", "46-23", "48-23", 
"66-23", "71-23", "72-23", "76-23", "78-23", "83-23", "85-23", 
"86-23", "88-23", "96-23", "25-24", "26-24", "28-24", "31-24", 
"34-24", "36-24", "37-24", "38-24", "43-24", "46-24", "48-24", 
"66-24", "71-24", "72-24", "76-24", "78-24", "83-24", "85-24", 
"86-24", "88-24", "96-24", "26-25", "28-25", "31-25", "34-25", 
"36-25", "37-25", "38-25", "43-25", "46-25", "48-25", "66-25", 
"71-25", "72-25", "76-25", "78-25", "83-25", "85-25", "86-25", 
"88-25", "96-25", "28-26", "31-26", "34-26", "36-26", "37-26", 
"38-26", "43-26", "46-26", "48-26", "66-26", "71-26", "72-26", 
"76-26", "78-26", "83-26", "85-26", "86-26", "88-26", "96-26", 
"31-28", "34-28", "36-28", "37-28", "38-28", "43-28", "46-28", 
"48-28", "66-28", "71-28", "72-28", "76-28", "78-28", "83-28", 
"85-28", "86-28", "88-28", "96-28", "34-31", "36-31", "37-31", 
"38-31", "43-31", "46-31", "48-31", "66-31", "71-31", "72-31", 
"76-31", "78-31", "83-31", "85-31", "86-31", "88-31", "96-31", 
"36-34", "37-34", "38-34", "43-34", "46-34", "48-34", "66-34", 
"71-34", "72-34", "76-34", "78-34", "83-34", "85-34", "86-34", 
"88-34", "96-34", "37-36", "38-36", "43-36", "46-36", "48-36", 
"66-36", "71-36", "72-36", "76-36", "78-36", "83-36", "85-36", 
"86-36", "88-36", "96-36", "38-37", "43-37", "46-37", "48-37", 
"66-37", "71-37", "72-37", "76-37", "78-37", "83-37", "85-37", 
"86-37", "88-37", "96-37", "43-38", "46-38", "48-38", "66-38", 
"71-38", "72-38", "76-38", "78-38", "83-38", "85-38", "86-38", 
"88-38", "96-38", "46-43", "48-43", "66-43", "71-43", "72-43", 
"76-43", "78-43", "83-43", "85-43", "86-43", "88-43", "96-43", 
"48-46", "66-46", "71-46", "72-46", "76-46", "78-46", "83-46", 
"85-46", "86-46", "88-46", "96-46", "66-48", "71-48", "72-48", 
"76-48", "78-48", "83-48", "85-48", "86-48", "88-48", "96-48", 
"71-66", "72-66", "76-66", "78-66", "83-66", "85-66", "86-66", 
"88-66", "96-66", "72-71", "76-71", "78-71", "83-71", "85-71", 
"86-71", "88-71", "96-71", "76-72", "78-72", "83-72", "85-72", 
"86-72", "88-72", "96-72", "78-76", "83-76", "85-76", "86-76", 
"88-76", "96-76", "83-78", "85-78", "86-78", "88-78", "96-78", 
"85-83", "86-83", "88-83", "96-83", "86-85", "88-85", "96-85", 
"88-86", "96-86", "96-88"), c("diff", "lwr", "upr", "p adj")))

You can use geom_tile from ggplot2 after creating the data frame from the output of TukeyHSD (the data you provided).

library(dplyr)

dd <- data.frame(p=Tukey[,4]) |>
  rownames_to_column() |>
  mutate(x=as.factor(as.numeric(sub('.*-(.*)', '\\1', rowname))),
         y=as.factor(as.numeric(sub('(.*)-.*', '\\1', rowname))),
         pValue=cut(p, breaks=c(-1, 0.0001, 0.001, 0.01, 0.05, 1), 
                       right=FALSE,
                    labels=c('<0.0001', '<0.001', '<0.01', '<0.05', 'NS'))) 

library(ggplot2)

ggplot(dd, aes(x, y, fill=pValue)) +
  geom_tile(col="grey50") +
  scale_fill_manual(values=c("purple3","blue","cyan","lightblue","grey70")) +
  scale_y_discrete(limits=rev) +
  theme_classic()