Pairwise comparison of proportion with R

I am new to R. So here is my problem, I would like to perform a pairwise comparison of proportion. In my data, I have three sessions and time slots per session. I added the count per time slot knowing that sample size for M1 session = 181, M2 = 67 and M3 = 106. I want to compare the proportion of each session, per time slots. Like for time slot 18h-19h I have p-value from M1 vs M2 , M2 vs M3 and M1 vs M3.

data <- tibble::tribble(
  ~Horaire, ~SessionM1, ~NbM1, ~SessionM2, ~NbM2, ~SessionM3, ~NbM3,
"18h-19h","M1", 6, "M2", 7, "M3", 6,
"19h-20h","M1", 6, "M2", 10, "M3", 17,
"20h-21h","M1", 14, "M2", 10, "M3", 16,
"21h-22h","M1", 23, "M2", 5, "M3", 9,
"22h-23h","M1", 15, "M2", 9, "M3", 4,
"23h-00h","M1", 19, "M2", 4, "M3", 6,
"00h-01h","M1", 19, "M2", 5, "M3", 10,
"01h-02h","M1", 21, "M2", 3, "M3", 7,
"02h-03h","M1", 8, "M2", 5, "M3", 7,
"03h-04h","M1", 15, "M2", 4, "M3", 10,
"04h-05h","M1", 15, "M2", 4, "M3", 9,
"05h-06h","M1", 20, "M2", 1, "M3", 5)

PS: I edited the data.

Can someone help, please? Thanks

I expect to obtain p-values for each combination of sessions within each time slot. Thanks

Solution

You can use pairwise.prop.test() for each row. Different methods for adjusting p-values can be used by p.adjust.method argument. The default is p.adjust.method = "holm". For more choices, see ?p.adjust.

t(apply(data[grep("NbM", names(data))], 1, \(m) {
  p <- pairwise.prop.test(x = m, n = c(181, 67, 106))$p.value
  p[!is.na(p)]
})) |>
  as.data.frame() |>
  setNames(combn(1:3, 2, \(x) paste0('M', x, collapse = 'vs')))

Its tidyverse version:

library(tidyverse)

data %>%
  pivot_longer(starts_with("Nb"), names_prefix = "Nb") %>%
  reframe(as.data.frame.table(
            pairwise.prop.test(setNames(value, name), c(181, 67, 106))$p.value
          ), .by = Horaire) %>%
  drop_na(Freq) %>% 
  pivot_wider(names_from = c(Var2, Var1), names_sep = "vs", values_from = Freq)

Output

#    M1vsM2 M1vsM3 M2vsM3
# 1   0.166  0.771  0.771
# 2   0.005  0.001  1.000
# 3   0.289  0.232  1.000
# 4   1.000  1.000  1.000
# 5   0.431  0.431  0.121
# 6   0.796  0.707  1.000
# 7   1.000  1.000  1.000
# 8   0.447  0.483  0.803
# 9   1.000  1.000  1.000
# 10  1.000  1.000  1.000
# 11  1.000  1.000  1.000
# 12  0.096  0.211  0.482