gistfile1.txt

occupancy_fct <- function(start_date = as.Date("2016-06-01", tz = "Europe/London"),
                          end_date = as.Date("2016-06-08", tz = "Europe/London"),
                          data){

  dt_los <- data %>%
    dplyr::select(spell_number, spell_start, spell_end) %>%
    dplyr::mutate(Same_day_discharge = as.numeric(difftime(spell_end, spell_start, unit = c("min"))))


  dt_calc <- dt_los %>%
    dplyr::mutate(Same_day_discharge = dplyr::if_else(as.Date(spell_start) == as.Date(spell_end),  TRUE, FALSE),
                  Los = as.numeric(difftime(spell_end, spell_start, unit = c("hour"))),
                  Discharged_24hr = dplyr::if_else(Los < 24, TRUE, FALSE)) %>%
    dplyr::filter(Discharged_24hr == TRUE & Los <= 24) %>%
    dplyr::filter(spell_start > start_date & spell_end < end_date)


  # using gather function to create a new column with date
  occupancy <-  dt_calc %>%
    tidyr::gather(key = type, time, spell_start:spell_end) %>%
    dplyr::mutate(change = dplyr::if_else(type == "spell_start", 1, -1)) %>%
    dplyr::group_by(time_hr = lubridate::floor_date(time, "1 hour")) %>%
    dplyr::summarise(change = sum(change),
                     arrivals = sum(type == "spell_start")) %>%
    padr::pad(start_val = start_date, end_val = end_date) %>%
    tidyr::replace_na(list(arrivals = 0, change = 0)) %>%
    dplyr::mutate(occupancy = cumsum(change)) %>%
    tidyr::drop_na()

}