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bigdataf1.R
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############### IMPORTS #############################
library('devtools')
library('lattice')
library('ggmap')
library('rmr2')
rmr.options(backend="local")
############### SEGEDFUGGVENYEK ###################
# segedfuggveny hogy belenezhessunk az eredmenyekbe
rread <- function(dset,tt = TRUE) {
if (tt) result = from.dfs(dset)$val
else result = from.dfs(dset)
return(head(result))
}
# NA-val tuzdelt ertekek osszevetese
compareNA <- function(v1,v2) {
# This function returns TRUE wherever elements are the same, including NA's,
# and false everywhere else.
same <- (v1 == v2) | (is.na(v1) & is.na(v2))
same[is.na(same)] <- FALSE
return(same)
}
################# FAJLBEOLVASAS ##################################
# Egy adott konyvtar 4-13. (csv) fajljait osszefuzzuk egy nagy dataframe-be
# es meg hozzaadunk sorfolytonos id es olyan attributumot timestamp alapjan, hogy mely oraban tortent megfigyeles
filenames <- list.files(path = "D:Downloads/bigdata_data", full.names = TRUE)
filenames
d0 <- do.call("rbind", lapply(filenames[4:13], read.csv, col.names = c('ts','line_id','direction','journey_pattern_id','time_frame','vehicle_journey_id','operator','congestion','lon','lat','delay','block_id','vehicle_id','stop_id','at_stop')))
# fajl beolvasas TODO: egyelore csak egy nap
#d0 = read.csv('e:/munka/BME/BigData/siri.20121125.csv',col.names = c('ts','line_id','direction','journey_pattern_id','time_frame','vehicle_journey_id','operator','congestion','lon','lat','delay','block_id','vehicle_id','stop_id','at_stop'))
#subset(d0[ ! duplicated( d0[ c("line_id","operator") ] ) , ], select=c("line_id","operator"))
#summary(d0)
# megfelelo modon sorrendezzuk a rekordokat
d0 = d0[order(d0$time_frame,d0$vehicle_journey_id, d0$ts, d0$journey_pattern_id),]
#sorazonosito generalasa
d0$id <- 1:nrow(d0)
#az idobeli aggregacio alapja, melyik nap melyik orajarol van szo
d0$tst <- paste(format(as.POSIXct(d0$ts/1e6, origin="1970-01-01"),"%Y-%b-%d %H"),'h', sep='')
########### HAVERSINE TAVOLSAG ##################
# haversine tavolsagok szamitasa mapreduce nelkul
# az MR11-es mapreduce kivaltasara
#################################################
# Agi javaslatara mapreduce nelkul is megcsinaltuk a tavolsagszamitast
# oszlopok eltolasa
d0 <- shift.column(data=d0, columns=c("lat","lon","vehicle_journey_id"),up=FALSE)
calc_haversine <- function(lat, lon, plat, plon, vjid, pvjid) {
# radian konverzio
rlat = lat*pi/180
rlon = lon*pi/180
rplat = plat*pi/180
rplon= plon*pi/180
# haversine tavolsag szamitasa
R <- 6371 # Earth mean radius [km]
delta.long <- (rplon - rlon)
delta.lat <- (rplat - rlat)
a <- sin(delta.lat/2)^2 + cos(rlat) * cos(rplat) * sin(delta.long/2)^2
c <- 2 * asin(min(1,sqrt(a)))
# csak ertelmes esetekben adjuk vissza a kalkulalt erteket
if (compareNA(vjid,pvjid)) distdelta = R*c
else distdelta = NA
return(distdelta)
}
# tavolsag szamitasa minden sorra
d0$distdelta <- mapply(calc_haversine, d0$lat, d0$lon, d0$lat.Shifted, d0$lon.Shifted, d0$vehicle_journey_id, d0$vehicle_journey_id.Shifted)
# tovabbiakban felesleges oszlopok torlese, takaritas
d0 <-subset(d0,,-c(lon.Shifted,lat.Shifted,vehicle_journey_id.Shifted))
# lenyomjuk az adatot "hadoop"-ba
hd0 <- to.dfs(d0)
#csinalunk egy pici mintat, csak teszteleshez
#td0 <- to.dfs(head(d0,500))
############# MAPREDUCE kodok###################
################################################
# MR1 utolso mert keses jaratra max timestamp alapjan
end_delay_per_journey <- mapreduce(input = hd0,
map = function(., v){
keyval(v[, c("vehicle_journey_id","time_frame")], v[, c("delay","ts")])
},
reduce = function(k, vv) {
#kikeressuk max timestamp-hez a keses merteket
max_place <- which.max(vv[, c("ts")])
last_delay <- vv[max_place, c("delay")]
# csak azt engedjuk tovabb, ami nem 0 keses, sanszos, hogy az rossz adat (sok jaratnal vegig 0)
if(last_delay!=0)keyval(k, last_delay)
}
)
# MR2 jaratok median kesese naponkent
avg_delay_per_day <- mapreduce(input = end_delay_per_journey,
map = function(k, v){
# A nap lesz a kulcs
day <- weekdays(as.Date(k$time_frame))
keyval(day, v)
},
reduce = function(k, vv) {
keyval(k, median(vv))
}
)
# csinalunk belole dataframe-et
daily <- data.frame(days=from.dfs(avg_delay_per_day)$key, delay=from.dfs(avg_delay_per_day)$val)
daily$days <- factor(daily$days, levels= c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
daily <- daily[order(daily$days), ]
# Ehhez tartozo plot (lattice kell hozza)
plt <- xyplot(daily$delay ~ daily$days, type='b', xlab="Days", ylab="Delay")
update(plt, par.settings = list(fontsize = list(text = 25, points = 20)))
# -----------------------------------------------------------------------------------
# MR3 egyedi line, operator parok kinyerese trukkos aggregacioval
hlines_ops <- mapreduce(input = hd0,
map = function(., v)
keyval(v[, c("line_id","operator")], 1),
reduce = function(k, vv) {
keyval(k, 1)
}
)
# MR4.1 operatorok szama vonalankent
hops_per_line <- mapreduce(input = hlines_ops,
map = function(k, .)
keyval(k[, c("line_id")], 1),
reduce = function(k, vv) {
keyval(k, length(vv))
}
)
# MR4.2 peratorok szama vonalankent alternativ megoldas
hops_per_line <- mapreduce(hlines_ops,
map = function(k, v)
keyval(k$line_id, 1),
reduce = function(k, v)
cbind(line = k, no_ops = sum(v, na.rm = TRUE)))
# MR5 vonalak szama operatoronkent
hlines_per_op <- mapreduce(input = hlines_ops,
map = function(k, .)
keyval(k[, c("operator")], 1),
reduce = function(k, vv) {
keyval(k, length(vv))
}
)
# MR6.1 atlagos keses vonalankent
hdelay_per_line <- mapreduce(input = hd0,
map = function(., v)
keyval(v[, c("line_id")],v[, c("delay")]),
reduce = function(k, vv) {
keyval(k, mean(vv))
}
)
# MR6.2 atlagos keses vonalankent 2. szamu megoldas
hdelay_per_line <- mapreduce(hd0,
map = function(k, v)
keyval(v$line_id, v$delay),
reduce = function(k, v)
cbind(line = k, mean = mean(v, na.rm = TRUE)))
rread(hdelay_per_line,FALSE)
# Eredm?nyek kinyer?se
result <- from.dfs(hdelay_per_line)
d1 = data.frame(result)
colnames(d1) <- c('line_id','mean_delay')
result <- from.dfs(hops_per_line)
d2 = data.frame(result)
colnames(d2) <- c('line_id','operator')
# tablak kombinalasa
d3 = merge(x = d1, y = d2, by = "line_id", all = TRUE)
# Ehhez plotok
# Operatorok es kesesek scatter plot
p1 <- ggplot(d3, aes(x = operator, y = mean_delay))
p1 <- p1 + geom_point(color="blue", size = 4)
p1 + theme(axis.title=element_text(face="bold",size="20"), axis.text = element_text(size = 20), legend.position="top") + labs(x = "Number of Operators", y = "Delay")
# Operatorszam es iranyitott vonalszam eloszlas
bar <- barchart(d2$operator, horizontal=FALSE, xlab="Number of Operators", ylab="Number of Lines")
update(bar, par.settings = list(fontsize = list(text = 20)))
# Lines per operators
barplot(height=from.dfs(hlines_per_op)$val, names.arg=from.dfs(hlines_per_op)$key, xlab="Operators", ylab="Lines")
# MR7 atlagos keses orankent
hhourly_meandelay <- mapreduce(input = hd0,
map = function(., v)
keyval(v[, c("vehicle_journey_id","time_frame","tst")], v[, c("delay")]),
reduce = function(k, vv)
keyval(k, mean(vv))
)
#rread(hhourly_meandelay)
# MR8 szumma atlagos keses orankent
hhourly_totaldelay <- mapreduce(input = hhourly_meandelay,
map = function(k, v)
keyval(k[, c("tst")], v),
reduce = function(k, vv)
keyval(k, sum(vv))
)
#rread(hhourly_totaldelay,FALSE)
#ddelay = data.frame(from.dfs(hhourly_meandelay))
#colnames(ddelay) <- c("vehicle_journey_id","time_frame","hour","total_delay")
#head(ddelay,10)
#ddelay = ddelay[order(ddelay$hour),]
#ddelay$date = as.Date(ddelay$hour, format="%Y-%b-%d %H")
ddelay = data.frame(from.dfs(hhourly_totaldelay))
colnames(ddelay) <- c('hour','total_delay')
ddelay = ddelay[order(ddelay$hour),]
#write.csv(ddelay, file='c:/Users/gergo/Documents/bigdatahf/ddelay.csv', sep=',', row.names=FALSE, quote=FALSE)
# Ehhez plot
plot <- ggplot( data = ddelay, aes( strptime(hour, "%Y-%B-%d %Hh"), total_delay /3600 )) + geom_line()
plot + theme(axis.title=element_text(face="bold",size="20"), axis.text = element_text(size = 20), legend.position="top") + labs(x = "Time (Hours)", y = "Delay (Hours)")
# MR9 szumma km orankent
hhourly_totaldist <- mapreduce(input = hd0,
map = function(., v)
keyval(v[, c("tst")], v[, c("distdelta")]),
reduce = function(k, vv)
keyval(k, sum(vv,na.rm = TRUE))
)
#rread(hhourly_totaldist,FALSE)
dkm = data.frame(from.dfs(hhourly_totaldist))
colnames(dkm) <- c('tst','distdelta')
dkm = dkm[order(dkm$tst),]
# Plot az orankenti osszes kilometerhez
plot <- ggplot( data = dkm, aes( strptime(tst, "%Y-%B-%d %Hh"), distdelta)) + geom_line()
plot + theme(axis.title=element_text(face="bold",size="20"), axis.text = element_text(size = 20), legend.position="top") + labs(x = "Time (Hours)", y = "Total distance (km)")
# MR10 szumma jarmuvek orankent
hhourly_count <- mapreduce(input = hd0,
map = function(., v)
keyval(v[, c("tst")], v[, c("vehicle_id")]),
reduce = function(k, vv)
keyval(k, length(unique(vv)))
)
#rread(hhourly_count,FALSE)
dcount = data.frame(from.dfs(hhourly_count))
colnames(dcount) <- c('tst','vehicle_count')
dcount = dcount[order(dcount$tst),]
# Plot orankenti jarmumennyiseghez
plot2 <- ggplot( data = dcount, aes( strptime(tst, "%Y-%B-%d %Hh"), vehicle_count)) + geom_line()
plot2 + theme(axis.title=element_text(face="bold",size="20"), axis.text = element_text(size = 20), legend.position="top") + labs(x = "Time (Hours)", y = "Number of Vehicles on Journey")
# MR11 haversine tavolsag trukkos mapreduce-szal
haversines <- mapreduce(hd0,
map = function(k, v)
keyval(c(v$id,v$id+1),cbind(v$lat,v$lon,v$vehicle_journey_id,v$delay)),
reduce = function(k, v) {
# kinyerjuk a koordinatakat a reduce set-bol
if (length(v) <= 4){
lat = v[1]
lon = v[2]
plat = NA
plon= NA
vjid = v[3]
pvjid = NA
del = v[4]
pdel = NA}
if (length(v) > 4){
lat = v[1]
lon = v[3]
plat = v[2]
plon= v[4]
vjid = v[5]
pvjid = v[6]
del = v[7]
pdel = v[8]}
# radian konverzio
rlat = lat*pi/180
rlon = lon*pi/180
rplat = plat*pi/180
rplon= plon*pi/180
# haversine tavolsag szamitasa
R <- 6371 # Earth mean radius [km]
delta.long <- (rplon - rlon)
delta.lat <- (rplat - rlat)
a <- sin(delta.lat/2)^2 + cos(rlat) * cos(rplat) * sin(delta.long/2)^2
c <- 2 * asin(min(1,sqrt(a)))
if (compareNA(vjid,pvjid)) distdelta = R*c
else distdelta = NA
cbind(id=k,lat=lat,lon=lon,vjid=vjid,del=del,plat=plat,plon=plon,pvjid=pvjid,pdel=pdel,distdelta = distdelta)})
rread(haversines)
#################### TERKEPES plotok ################
#####################################################
# sima plot
DublinMap <- qmap('dublin', zoom = 11,color = 'bw', legend = 'topleft')
DublinMap +geom_point(aes(x = lon, y = lat), data = subset(d0, at_stop == 1) )
# map line id alapjan
DublinMap <- qmap('dublin', zoom = 11,color = 'bw', legend = 'topleft')
DublinMap +geom_point(aes(x = lon, y = lat), size = 4, data = subset(d0, line_id == "54A") )
# map line id alapjan, zoomolva
DublinMap <- qmap('dublin', zoom = 15,color = 'bw', legend = 'topleft')
DublinMap +geom_point(aes(x = lon, y = lat), size = 4, data = subset(d0, line_id == "54A") )
# map operator alapjan szinezve
DublinMap <- qmap('dublin', zoom = 11,color = 'bw', legend = 'topleft')
DublinMap +geom_point(aes(x = lon, y = lat, colour = operator), data = subset(d0, at_stop == 1) )