How to apply chi squared test on a series of count tables?

I have these datasets: df as the main dataframe (but let's imagine all of them as very big datasets).

df = data.frame(x = seq(1,20,2),
y = c('a','a','b','c','a','a','b','c','a','a'),
z = c('d','e','e','d','f','e','e','d','e','f') )

stage1 = data.frame(xx = c(2,3,4,5,7,8,9) )

stage2 = data.frame(xx = c(3,5,7,8,9) )

stage3 = data.frame(xx = c(2,3,6,8) )

stage4 = data.frame(xx = c(1,3,6) )

And then creating count tables as follows:

library(dplyr)
library(purrr)
map(lst(stage1 , stage2 ,stage3 ,stage4 ), 
   ~ inner_join(df, .x, by = c("x" = "xx")) %>%      
       count(y, name = 'Count'))

I wish to apply a chi squared test to study if the difference between each two consecutive tables is significant or not.

library(dplyr)
library(purrr)

df = data.frame(x = seq(1, 20, 2),
                y = c('a', 'a', 'b', 'c', 'a', 'a', 'b', 'c', 'a', 'a'),
                z = c('d', 'e', 'e', 'd', 'f', 'e', 'e', 'd', 'e', 'f') )
stage1 = data.frame(xx = c(2, 3, 4, 5, 7, 8, 9) )
stage2 = data.frame(xx = c(3, 5, 7, 8, 9) )
stage3 = data.frame(xx = c(2, 3, 6, 8))
stage4 = data.frame(xx = c(1, 3, 6))

tbls <- map(lst(stage1 , stage2 ,stage3 ,stage4 ), 
            ~ inner_join(df, .x, by = c("x" = "xx")) %>%      
              count(y, name = 'Count'))

results <- cbind(seq(1, length(tbls), by = 2),
                 seq(2, length(tbls), by = 2)) |> 
  apply(1, function(x) {
    result <- list(test_result = NA, 
                   table_idx = NA)
    result$table_idx <- c(x[ 1 ], x[ 2 ])
    test_result <- chisq.test(tbls[[ x[ 1 ] ]]$Count, 
                              tbls[[ x[ 2 ] ]]$Count, 
                              correct = FALSE) |>
      try()
    if ('try-error' %in% class(test_result)) {
      return(result)
    }
    result$test_result <- test_result 
    return(result)
  })

print(results)