Use Group by with mutate, case_when, any() and all() function in R

Question

I have a status_df with id and status at each stage:

id	stage	status
15	1	Pending
15	2	Not Sent
16	1	Approved
16	2	Rejected
16	3	Not Sent
16	4	Not Sent
20	1	Approved
20	2	Approved
20	3	Approved

I am trying to do a group_by ID and apply the following logic:

• if any stage for an ID has 'Pending' status, final_status column is 'Pending'
• if any stage for an ID has 'Rejected' status, final_status column is 'Rejected'
• if all stages for an ID are approved, final_status column is 'Approved'

I am trying this (not working):

final_status_df = status_df %>% select(id, status) %>% group_by(id) %>%
mutate(final_status = case_when(any(status)=="Pending" ~ "Pending",
any(status)=="Rejected" ~ "Rejected", 
all(status)=="Approved" ~ "Approved"))

Expected output (final_status_df):

id	final_status
15	Pending
16	Rejected
20	Approved

Answer 1

You were in the right direction with your attempt however, you closed any/all brackets early before comparison (==). Also since you only want 1 row for every id you can use summarise instead of mutate which will also avoid the use of select.

library(dplyr)

status_df %>% 
  group_by(id) %>%
  summarise(final_status = case_when(any(status == "Pending") ~ "Pending",
                                     any(status == "Rejected") ~ "Rejected", 
                                     all(status == "Approved") ~ "Approved"))

#    id final_status
#* <int> <chr>       
#1    15 Pending     
#2    16 Rejected    
#3    20 Approved