R/total_mmet.R

In danielgils/cobeneficioswwf: Evaluación de Cobeneficios WWF

#' Calculate total mMETs per person
#'
#' Calculate total mMETs per person based on PA and active travel
#'
#' @param trip_scen_sets data frame of all trips from all scenarios
#'
#' @return total mMETs per week per person
#'
#' @export
total_mmet <- function(trip_scen_sets){

  synth_pop <- setDT(SYNTHETIC_POPULATION)
  rd_pa <- setDT(trip_scen_sets)[trip_scen_sets$stage_mode %in%
                                   c('pedestrian','walk_to_pt','cycle') &
                                   trip_scen_sets$participant_id > 0,]
  rd_pa$stage_mode[rd_pa$stage_mode == 'walk_to_pt'] <- 'pedestrian'
  # Convert baseline's trip duration from mins to hours and multiply this value by 7
  # assuming the person makes this same trip every single day of the week
  rd_pa$stage_duration_hrs <- rd_pa$stage_duration / 60 * DAY_TO_WEEK_TRAVEL_SCALAR
  # Get total individual level pedestrian and cycling and sport mmets
  for (i in 1:length(SCEN)) {
    synth_pop[[paste0(SCEN_SHORT_NAME[i],'_mmet')]] <- synth_pop$work_ltpa_marg_met *
      BACKGROUND_PA_SCALAR
    scen_trips <- rd_pa[rd_pa$scenario == SCEN[i],]

    individual_data <- scen_trips[,.(cycling_mmet_base = sum(stage_duration_hrs[stage_mode == 'cycle']),
                                     walking_mmet_base = sum(stage_duration_hrs[stage_mode == 'pedestrian']) ),
                                  by = 'participant_id']

    part_id <- match(individual_data$participant_id, synth_pop$participant_id)
    synth_pop[[paste0(SCEN_SHORT_NAME[i],'_mmet')]][part_id] <-
      synth_pop[[paste0(SCEN_SHORT_NAME[i],'_mmet')]][part_id] +
      individual_data$cycling_mmet_base * MMET_CYCLING +
      individual_data$walking_mmet_base * MMET_WALKING
  } # End for

  name_indices <- which(colnames(synth_pop) %in%
                          c('participant_id', 'sex', 'age', 'age_cat',
                            paste0(SCEN_SHORT_NAME,'_mmet')))
  mmets <- as.data.frame(synth_pop)[,name_indices]
  mmets

}