```r library(dplyr)
df %>% mutate(across(col1:col2, ~ if_else(n_distinct(.x) == 1, paste(.x, collapse = ':'), NA), .names = "match_extract_{.col}"), match_extract_col1_2 = if_else(col1[1] == col2[2], paste(col1[1], col2[2], sep = ':'), NA), .by = ID)
# ID col1 col2 match_extract_col1 match_extract_col2 match_extract_col1_2 # 1 AB 1 3 <NA> <NA> 1:1 # 2 AB 0 1 <NA> <NA> 1:1 # 3 CD 2 4 2:2 <NA> <NA> # 4 CD 2 3 2:2 <NA> <NA> ```

You can use `n_distinct() == 1` to know if there is a mismatch in each column by `ID` groups.
```r library(dplyr)
df %>% mutate(across(col1:col2, ~ if_else(n_distinct(.x) == 1, NA, toString(.x)), .names = "mismatch_extract_{.col}"), .by = ID)
# # A tibble: 4 × 5 # ID col1 col2 mismatch_extract_col1 mismatch_extract_col2 # <chr> <int> <int> <lgl> <chr> # 1 AB 1 3 NA NA # 2 AB 1 3 NA NA # 3 CD 2 4 NA 4, 3 # 4 CD 2 3 NA 4, 3 ```