Filter row based on max & unique value in one column

Question

Its a bit tricky to explain, Ill try my best, query below. I have a df as below. I need to filter rows by group based on maximum pop in country column but which has not already occurred in the above groups. (As per output (image), the reason why A didnt feature in group2 because it had already occured in Group 1)

In short, I need to get unique values in country column at the same time get maximum value in pop (on a group level). I hope picture can convey what I could not. (Tidyverse solution preferred)

df<- structure(list(Group = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), country = c("A", "B", "C", "A", "E", "F", "A", "E", "G"), pop = c(200L, 100L, 50L, 200L, 150L, 120L, 200L, 150L, 
100L)), class = "data.frame", row.names = c(NA, -9L))

Answer 1

I think this will do. Explanation of syntax

• split the data into list for each group
• leave first group (as it will be used as .init in next step but after filtering for the max of pop value.
• use purrr::reduce here which will reduce the list of tibbles to a single tibble
• iterations used in reduce
  • .init used as filtered first group
  • thereafter countries in previous groups removed through anti_join
  • this data filtered for max pop again
  • added the previously filtered countries by bind_rows()
• Thus, in the end we will have desired tibble.

df %>% group_split(Group) %>% .[-1] %>%
  reduce(.init =df %>% group_split(Group) %>% .[[1]] %>% 
               filter(pop == max(pop)), 
             ~ .y  %>%
               anti_join(.x, by = c("country" = "country")) %>% 
               filter(pop == max(pop)) %>%
               bind_rows(.x) %>% arrange(Group)) 

# A tibble: 3 x 3
  Group country   pop
  <int> <chr>   <int>
1     1 A         200
2     2 E         150
3     3 G         100