How do you remove duplicate rows while conditionally keeping the observation with a larger value?

[r_q]

When there are duplicate rows with one column being different (Rate), how do you remove the duplicates that are smaller - keeping the higher value Rate and not altering the other data. I want to conditionally select, when there are duplicates, the higher value in the Rate column. Any tips? I did a group_by and summarised a count to find duplicates. I filtered down the df to the duplicates and removed the smaller values with a filter using & and was going to rbind it back but there has to be a quicker way?

Town	Occupation	Rate
Chicago	Teacher	20
Chicago	Teacher	25
Madison	Nurse	0
Madison	Nurse	35
Madison	Doctor	45
Madison	Custodian	33
Chicago	Dental Hygienist	22

Town	Occupation	Rate
Chicago	Teacher	25
Madison	Nurse	35
Madison	Doctor	45
Madison	Custodian	33
Chicago	Dental Hygienist	22

[FJCC]

I think you want to group by Town and Occupation and summarize to find the maximum Rate.

[jonspring]

This should work:

df %>% 
  group_by(Town, Occupation) %>%
  slice_max(Rate) %>%
  ungroup()