diff --git a/R/stop_times_to_frequencies.R b/R/stop_times_to_frequencies.R
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+++ b/R/stop_times_to_frequencies.R
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+# PURPOSE: convert a stop_times based feed to a frequency based feed
+
+stop_times_to_frequencies <- function(gtfs,
+                                      time_ranges = tibble(start_time = c("00:00:00", "09:00:00", "12:00:00", "19:00:00"),
+                                                           end_time =   c("09:00:00", "12:00:00", "19:00:00", "23:59:00"))){
+  # PURPOSE: convert a stop_times based feed to a frequency based feed
+  # INPUT:
+  # gtfs: the gtfs feed you want to edit
+  # time_ranges: the day is split into multiple time slots. We calculate the frequency of trip in each of these slots
+  #              format: a tibble with columns "start_time" and "end_time"
+  # OUTPUT
+  # a frequency based gtfs feed
+
+  message(" ... converting time ranges to hms ... ")
+  # ----- convert time ranges to hms
+  time_ranges <- time_ranges %>%
+    mutate(across(everything(), hms::as_hms))
+
+
+  # --- Calculate the headway
+
+  # 1. identify trips with the same itinerary. Trip IDs are unique for every departting vehicle,
+  #    so we group vehicles that have the same stop sequence
+
+  # Create a column to identify same trips (trips with the same stop sequence)
+  message("... identifying trips with same stop sequence ...")
+
+  trips_stop_sequence <- gtfs$stop_times %>% group_by(trip_id) %>%
+    mutate(stop_id_order = paste0(stop_id, collapse = '-')) %>%
+    ungroup()
+
+  # keep only one row per unique trip
+  trips_stop_sequence <- trips_stop_sequence %>%
+    filter(stop_sequence == 0) %>%
+    # some arrival times are bigger than 24 - these cause errors when converting to time
+    filter(as.character(arrival_time) <= "23:59:59") %>%
+    mutate(arrival_time = hms::as_hms(arrival_time))
+
+  # 2. Assign a time range to each trip based on the departure from the first stop
+  message("... assigning trips to time ranges ... ")
+
+  trips_time_ranges <- trips_stop_sequence %>%
+    inner_join(time_ranges,
+               join_by(arrival_time >= start_time, arrival_time < end_time))
+
+  # 3. Get the headway of each trip
+
+  # calculate number of buses for each unique trip + time range combination
+  message("... calculating headways ... ")
+
+  trips_headways <- trips_time_ranges %>%
+    group_by(stop_id_order, start_time, end_time) %>%
+    summarise(vehicles = n(),
+              # we don't need all of the trip IDs (all trips in the same group have the same itinerary)
+              trip_id = first(trip_id)) %>%
+    ungroup()
+
+  # get the headway (time between buses = time period / no. of buses)
+  trips_headways <- trips_headways %>%
+    mutate(headway_secs = round(as.numeric(end_time - start_time) / vehicles)) %>%
+    # keep necessary columns only
+    select(trip_id, start_time, end_time, headway_secs)
+
+
+  # 4. edit the gtfs feed to produce a frequency-based feed
+  message("... replacing stop_times with frequencies ... ")
+
+  # remove stop_times file
+  gtfs_edited <- gtfs[names(gtfs) != "stop_times"]
+
+  # add "frequencies" file to the gtfs feed
+  gtfs_edited$frequencies <- trips_headways
+
+  # filter feed to only keep the trip ids in the new "frequencies" file
+  gtfs_edited <- gtfs_edited %>%
+    #gtfstools::filter_by_trip_id(trip_id =  .$frequencies$trip_id)
+    tidytransit::filter_feed_by_trips(trip_ids = .$frequencies$trip_id)
+
+  return(gtfs_edited)
+
+}