looking for a better way to manipulate data in a gather mutate spread pattern

gather_mutate_spread.r

I have categorical data spread across multiple columns that I would like to aggregate.

    library(tidyverse)
    
    data <- data_frame(var1 = sample(LETTERS[1:2], 50, replace = TRUE), # categorical A/B
                       var2 = sample(LETTERS[1:2], 50, replace = TRUE),
                       var3 = sample(LETTERS[1:2], 50, replace = TRUE),
                       var4 = sample(LETTERS[3:4], 50, replace = TRUE), # categorical C/D
                       var5 = sample(LETTERS[3:4], 50, replace = TRUE),
                       var6 = sample(LETTERS[3:4], 50, replace = TRUE)) %>%
      mutate(id = row_number())

This solves my problem:
    
    data %>%
      group_by(id) %>%
      gather(key, val, paste0("var", 1:3)) %>%
      mutate(numA = sum(val == "A")) %>%
      spread(key, val) %>%
      gather(key2, val2, paste0("var", 4:6)) %>%
      mutate(numC = sum(val2 == "C")) %>%
      spread(key2, val2)

Is there a way to do this with less boilerplate? After mutation, the var1:var6 columns are no longer necessary.

data %>% 
  group_by(id) %>% 
  gather(j, v,  -id) %>% 
  count(v) %>% 
  spread(v, n) %>% 
  mutate_all(coalesce, 0L) %>% 
  ungroup()