basic assignment loop over last name and case load

assignment loop.r

##### Your code leading up to this point ######

# initialize active cases
SWDetail$active_cases <- 0
SWDetail[1,"active_cases"] <- 1
# initialize most recent cases
SWDetail$last_case_given <- 0
SWDetail[1,"last_case_given"] <- 1
# resorting this by date
cps_test_assignment <- cps_test_assignment[order(cps_test_assignment$assignment_date),]
bind_ass <- NULL
for(i in seq_len(nrow(cps_test_assignment))){
# for(i in 1:100){
  cat(i,"\n")
  ## For trying to simulate a history, this should be sorted by date, then we can track 
  assign_date <- cps_test_assignment[i,"assignment_date"]
  # outputs a list of SW working at time of case assignment
  SW_subset <- subset(SWDetail, Start <= assign_date & End >= assign_date) 
  # make sure it is sorted correctly
  SW_subset <- SW_subset[order(SW_subset$Last),]
  
  ###### This is where the action is
  # takes simply the first (add complexity here)
  # 1) exclude person with last case
  # 2) make sure they have fewer active cases
  # 3) arrange those by last name
  # 4) take the top of the list
  # 5) set their active cases + 1

  Optimal_SW <- SW_subset %>% 
    filter(last_case_given != 1) %>% 
           # trick to go back to top when everyone has the same number of cases
    filter(active_cases <= mean(.$active_cases)) %>% 
    arrange(Last) %>% 
    slice(1L) %>% 
    mutate(active_cases = active_cases + 1)
  
  # reset last_case_given and advance active cases
  SWDetail$last_case_given <- 0
  SWDetail[SWDetail$Assessment.SW == Optimal_SW$Assessment.SW, "last_case_given"] <- 1 
  SWDetail[SWDetail$Assessment.SW == Optimal_SW$Assessment.SW, "active_cases"] <-  SWDetail[SWDetail$Assessment.SW == Optimal_SW$Assessment.SW, "active_cases"] + 1
  
  # bind assigned SW to assigned case 
  # (this assumes cps_test_assignment is sorted well, could do it a bit better)
  first_ass <- cbind(cps_test_assignment[i,17:21], Optimal_SW) 
  # quick way to keep your output
  bind_ass <- rbind(bind_ass, first_ass)
  
  caseloads <- bind_ass %>% #new DF of caseloads.. 
    dplyr::group_by(Assessment.SW) %>% 
    dplyr::summarise(n= sum(predictions_rf))
}