Summarize and count the number of unique values in grouped df with dplyr

I have this df:

structure(list(CN = c("BR", "BR", "BR", "PL", "PL", "PL", 
"BR", "BR", "BR", "BR", "PL", "PL", "PL"), Year = c(2019, 
2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 
2020), Squad = c("A", "B", "C", "A", "B", "C", "C", "F", "G", 
"I", "D", "E", "F"), X = c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 
1), Y = c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1)), row.names = c(NA, 
-13L), class = c("tbl_df", "tbl", "data.frame"))

I want to summarize (sum of x+y and squad count) grouped by CN and Year; and in the same structure add a column with the count of unique/distinct values for squad grouped by CN only.

It would look like this:

structure(list(CN = c("BR", "BR", "PL", "PL"), Year = c(2019, 
2020, 2019, 2020), Sum = c(12, 14, 12, 12), n_squad = c(3, 4, 
3, 3), n_squad_distinct = c(6, 6, 6, 6)), row.names = c(NA, -4L
), class = c("tbl_df", "tbl", "data.frame"))

Thanks

>Solution :

We may create the ‘n_squad_distinct’ column grouped by ‘CN" by applying n_distinct on the ‘Squad’, then add the ‘Year’ and ‘n_squad_distinct’ also as grouping variables and do the summarise

library(dplyr)
df %>%
   group_by(CN) %>%
   mutate(n_squad_distinct = n_distinct(Squad)) %>% 
   group_by(n_squad_distinct, Year, .add = TRUE) %>%
   summarise(Sum = sum(X + Y), n_squad = n_distinct(Squad), .groups = 'drop')

-output

# A tibble: 4 × 5
  CN    n_squad_distinct  Year   Sum n_squad
  <chr>            <int> <dbl> <dbl>   <int>
1 BR                   6  2019    12       3
2 BR                   6  2020    14       4
3 PL                   6  2019    12       3
4 PL                   6  2020    12       3