I have the following dataset

structure(list(Var1 = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L), .Label = c("0", "1"), class = "factor"), Var2 = structure(c(1L, 
1L, 2L, 2L, 1L, 1L, 2L, 2L), .Label = c("congruent", "incongruent"
), class = "factor"), Var3 = structure(c(1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L), .Label = c("spoken", "written"), class = "factor"), 
    Freq = c(8L, 2L, 10L, 2L, 10L, 2L, 10L, 2L)), class = "data.frame", row.names = c(NA, 
-8L))

I would like to add another column reporting sum of coupled subsequent rows. I have proceeded like this

Table = as.data.frame(table(data_1$unimodal,data_1$cong_cond, data_1$presentation_mode)) %>% 
  mutate(Var1 = factor(Var1, levels = c('0', '1'))) 


row = Table %>%  #is.factor(Table$Var1)
  summarise(across(where(is.numeric),
                   ~ .[Var1 == '0'] +  .[Var1 == '1'],
                   .names = "{.col}_sum"))

column = c(rbind(row$Freq_sum,rep(NA, 4)))
Table$column = column

But I am looking for the quickest way possible with no scripting separated codes. Here I have used the dplyr package, but if you might know possibly suggest some other ways with map(), for loop, and or the method you deem as the best, please just let me know.