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AggCalc.R
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AggCalc.R
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AggCalc <- function (f, fname, region) { #calculates all agregates of a given scenario
#get file parameters
if (fname=='baseline') { #baseline special case
MS <- 0
equity <- 0
ebike <- 0
} else { #other scenarios
params <- regmatches(fname, gregexpr("\\d+(?:\\.\\d+)?", fname))[[1]]
MS <- as.numeric(params[1])
ebike <- as.integer(params[2])
equity <- as.integer(params[3])
} #rest of files
#1-MILES
carMiles <-sum(f[(f$MainMode_B04ID %in% c(3,4,5,12)) & (f$now_cycle==0 & f$Cycled==0),'TripDisIncSW'])
carMilesR <- round(aggr[region+1,'carMiles0'] - carMiles,2)
# !!!!!
carMiles.pers <- round(carMiles/(aggr[region+1, 'nopeople'] ),1)
carMilesR.pers <- round((aggr[region+1, 'carMiles0'] - carMiles)/(aggr[region+1, 'nopeople'] ),1)
carMilesCycled <- sum(f[(f$MainMode_B04ID %in% c(3,4,5,12)) & (f$now_cycle==1 | f$Cycled==1),'TripDisIncSW']) #no need for Cycled=1!
carMilesCycled <-round(carMilesCycled,1)
# !!!!!
#!!!!!
milesCycled <- (sum(f[(f$now_cycle==1|f$Cycled==1),'TripDisIncSW']))/(aggr[region+1, 'nopeople'] )
milesCycled <- round(milesCycled,2)
milesCycled.male <- round((sum(f[(f$now_cycle==1|f$Cycled==1) & f$Sex=='Male','TripDisIncSW']))/(aggr[region+1,'no.males'] ),2)
milesCycled.female <- round((sum(f[(f$now_cycle==1|f$Cycled==1) & f$Sex=='Female','TripDisIncSW']))/( aggr[region+1,'no.females'] ),2)
# !!!!!
# !!!!!
milesCycled.white <- round((sum(f[(f$now_cycle==1|f$Cycled==1) & f$EthGroupTS_B02ID==1,'TripDisIncSW']))/( aggr[region+1,'no.white'] ),2)
milesCycled.nonwhite <- round( (sum(f[(f$now_cycle==1|f$Cycled==1) & f$EthGroupTS_B02ID==2,'TripDisIncSW']))/(aggr[region+1,'no.nonwhite'] ),2)
milesCycled.caraccess <- round((sum(f[(f$now_cycle==1|f$Cycled==1) & f$CarAccess_B01ID %in% c(1,2,3,4),'TripDisIncSW']))/( aggr[region+1, 'no.caraccess'] ),2)
milesCycled.noncaraccess <- round((sum(f[(f$now_cycle==1|f$Cycled==1) & f$CarAccess_B01ID %in% c(5,6),'TripDisIncSW']))/(aggr[region+1, 'no.noncaraccess'] ),2)
# !!!!!
#2-co2 !!!!!!
CO2 <- round(carMiles * 1.61 * 3.1 * 1e-1,2) #in Kg
CO2R <- round(100 * (CO20-CO2)/CO20,1)
CO2.pers <- CO2/(aggr[region+1, 'nopeople'] )
#3-METs
METh<-round(sum(f$METh),1)
METhincr <- round(METh - METh0,1)
MMETh <- round(sum(f$MMETh),1)
MMETh.pers <- round(MMETh/ aggr[region+1, 'nopeople'] ,1) #!!
MMEThincr <- round(MMETh - MMETh0,1)
#4-TIMES &DISTANCES !!!
TripTotalTime1 <-sum(f$TripTotalTime1) #time in scenario
TripTravelTime <- sum(f$TripTravelTime) #original time
timeSaved <- round((aggr[region+1, 'TripTotalTime0'] - TripTotalTime1)/60,1) #total h, all trips
TripTotalTime1.cyclist <-sum(f$TripTotalTime1[f$now_cycle==1]) #new time
TripTotalTime.cyclist <-sum(f$TripTotalTime[f$now_cycle==1]) #old time
timeSavedCyclists <- round(100 * TripTotalTime1.cyclist / TripTotalTime.cyclist,1)
#total in h, saved by cyclists
TripDisIncSW <- round(sum(f$TripDisIncSW),0)
# #5-CYCLISTS & RATES
nocyclists <-length(unique(f$ID[(f$now_cycle==1 | f$Cycled==1)])) #real cyclists
newcyclists <-length(unique(f$ID[f$now_cycle==1]))
#
# cyclists.white <- length(unique(f$ID[ (f$now_cycle==1| f$Cycled==1) & f$EthGroupTS_B02ID==1]))
# newcyclists.white <-length(unique(f$ID[f$now_cycle==1 & f$EthGroupTS_B02ID==1]))
#
# cyclists.nonwhite <- length(unique(f$ID[ (f$now_cycle==1| f$Cycled==1) & f$EthGroupTS_B02ID==2]))
# newcyclists.nonwhite <-length(unique(f$ID[f$now_cycle==1 & f$EthGroupTS_B02ID==2]))
#
# cyclist.caraccess <- length(unique(f$ID [ (f$now_cycle==1 | f$Cycled==1) & (f$CarAccess_B01ID %in% c(1,2,3,4))]))
# cyclist.noncaraccess <- length(unique(f$ID [(f$now_cycle==1 | f$Cycled==1) & (f$CarAccess_B01ID %in% c(5,6) )]))
#
# cyclist.potential <- length(unique(f$ID[f$cyclist==1])) #potential cyclists
# cyclist.male <-length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$Sex_B01ID==1]))
# cyclist.female <- length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$Sex_B01ID==2]))
#
#
# cyclist.male.perc <- round(100*cyclist.male/(no.males+no.males.wotrips),1)
# cyclist.female.perc <- round(100*cyclist.female/(no.females+no.females.wotrips),1)
# ##########
# !!
cyclist.perc <- round(100 * nocyclists/(aggr[region+1, 'nopeople'] ) ,1)
cyclist.incr <- round(100 * (nocyclists- aggr[region+1, 'ncyclists0'])/ aggr[region+1, 'ncyclists0'] ,1)
# ######## NOT USED
# cyclist.white.perc <- round(100 * cyclists.white/(no.white+no.white.wotrips),1)
# cyclist.nonwhite.perc <- round(100 * cyclists.nonwhite/(no.nonwhite+no.nonwhite.wotrips),1)
#
# cyclist.caraccess.perc <- round(100 * cyclist.caraccess/(no.caraccess+no.caraccess.wotrips),1)
# cyclist.noncaraccess.perc <- round(100 * cyclist.noncaraccess/(no.noncaraccess+no.noncaraccess.wotrips),1)
#
# cyclist.nssec1 <- length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==1]))
# cyclist.nssec2 <- length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==2]))
# cyclist.nssec3 <- length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==3]))
# cyclist.nssec4 <- length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==4]))
# cyclist.nssec5 <- length(unique(f$ID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==5]))
#
# cyclist.nssec1.perc <- round(100 * cyclist.nssec1 / (no.nssec1 + no.nssec1.wotrips),1)
# cyclist.nssec2.perc <- round(100 * cyclist.nssec2 / (no.nssec2 + no.nssec2.wotrips),1)
# cyclist.nssec3.perc <- round(100 * cyclist.nssec3 / (no.nssec3 + no.nssec3.wotrips),1)
# cyclist.nssec4.perc <- round(100 * cyclist.nssec4 / (no.nssec4 + no.nssec4.wotrips),1)
# cyclist.nssec5.perc <- round(100 * cyclist.nssec5 / (no.nssec5 + no.nssec5.wotrips),1)
#6.0 -BIKE % SHARE WHOLE POPUL. !!!!
no.trips <- length(unique(f$TripID))
trips.bike <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1)]))
trips.bike.perc <- round(100 * trips.bike / no.trips ,1)
# ######### NOT USED
# #6-TRIPS % SHARE NS-SEC
# no.trips.nssec1 <- length(unique(f$TripID[f$NSSec_B03ID==1]))
# no.trips.nssec2 <- length(unique(f$TripID[f$NSSec_B03ID==2]))
# no.trips.nssec3 <- length(unique(f$TripID[f$NSSec_B03ID==3]))
# no.trips.nssec4 <- length(unique(f$TripID[f$NSSec_B03ID==4]))
# no.trips.nssec5 <- length(unique(f$TripID[f$NSSec_B03ID==5]))
#
# trips.nssec1 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==1]))
# trips.nssec2 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==2]))
# trips.nssec3 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==3]))
# trips.nssec4 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==4]))
# trips.nssec5 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & f$NSSec_B03ID==5]))
#
# trips.nssec1.perc <- round( 100 * trips.nssec1 / no.trips.nssec1 ,1)
# trips.nssec2.perc <- round( 100 * trips.nssec2 / no.trips.nssec2 ,1)
# trips.nssec3.perc <- round( 100 * trips.nssec3 / no.trips.nssec3 ,1)
# trips.nssec4.perc <- round( 100 * trips.nssec4 / no.trips.nssec4 ,1)
# trips.nssec5.perc <- round( 100 * trips.nssec5 / no.trips.nssec5 ,1)
#
# # 7-TRIPS % SHARE BY AGE
# no.trips.age20.39 <- length(unique(f$TripID[f$Age_B01ID>=10 & f$Age_B01ID<=13]))
# no.trips.age40.59 <- length(unique(f$TripID[f$Age_B01ID>=14 & f$Age_B01ID<=15]))
# no.trips.age60plus <- length(unique(f$TripID[f$Age_B01ID>=16]))
#
# trips.age20.39 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & (f$Age_B01ID>=10 & f$Age_B01ID<=13) ]))
# trips.age40.59 <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & (f$Age_B01ID>=14 & f$Age_B01ID<=15)]))
# trips.age60plus <- length(unique(f$TripID[(f$now_cycle==1 |f$Cycled==1) & f$Age_B01ID>=16 ]))
#
# trips.age20.39.perc <- round( 100 * trips.age20.39 / no.trips.age20.39 ,1)
# trips.age40.59.perc <- round( 100 * trips.age40.59 / no.trips.age40.59 ,1)
# trips.age60plus.perc <- round( 100 * trips.age60plus / no.trips.age60plus,1)
#
# #8. PEOPLE WITH NO CAR TRIPS
# f$MainMode_B04ID[f$now_cycle==1] <- 2
#
# bltrips <- transform(f, car_trip = as.numeric(MainMode_B04ID == 3
# | MainMode_B04ID == 4| MainMode_B04ID == 5 | MainMode_B04ID == 12))
#
# bltrips <- sqldf("Select ID,Sex,CarAccess_B01ID,EthGroupTS_B02ID,
# sum(car_trip) as [car] from bltrips Group by ID")
#
# nocar.people <- nrow(subset(bltrips,car==0))
# nocar.people.perc <- round(100 * (nocar.people+notripspeople) / (nopeople + notripspeople) ,1)
#
# nocar.males <- nrow(subset(bltrips,car==0 & Sex=='Male'))
# nocar.females <-nrow(subset(bltrips,car==0 & Sex=='Female'))
#
# nocar.males.perc <- round(100* (nocar.males+no.males.wotrips) /(no.males+no.males.wotrips),1)
# nocar.females.perc <- round(100* (nocar.females+no.females.wotrips) /(no.females+no.females.wotrips),1)
#
# nocar.white <- nrow(subset(bltrips,car==0 & EthGroupTS_B02ID==1))
# nocar.nonwhite <- nrow(subset(bltrips,car==0 & EthGroupTS_B02ID==2))
#
# nocar.white.perc <- round(100 * (nocar.white+no.white.wotrips)/(no.white + no.white.wotrips),1)
# nocar.nonwhite.perc <- round(100 *(nocar.nonwhite+no.nonwhite.wotrips) /(no.nonwhite + no.nonwhite.wotrips),1)
#
# nocar.caraccess <- nrow(subset(bltrips,car==0 & CarAccess_B01ID %in% c(1,2,3,4)) )
# nocar.noncaraccess <- nrow(subset(bltrips,car==0 & CarAccess_B01ID %in% c(5,6)) )
#
# nocar.caraccess.perc <- round(100*(nocar.caraccess+no.caraccess.wotrips) / (no.caraccess+no.caraccess.wotrips),1)
# nocar.noncaraccess.perc <- round(100* (nocar.noncaraccess+ no.noncaraccess.wotrips) / (no.noncaraccess + no.noncaraccess.wotrips),1)
# WRAPPING
info <- c(fname,MS,ebike,equity,
carMiles.pers,carMilesR.pers,carMilesCycled,
milesCycled, milesCycled.male, milesCycled.female,
milesCycled.white, milesCycled.nonwhite,
milesCycled.caraccess, milesCycled.noncaraccess,
MMETh.pers,
CO2.pers,
timeSavedCyclists,
cyclist.perc, trips.bike.perc,
region
)
info
}