For this exercise, I combined following four separate blogs that I did on BigData, R and SAP HANA. Historical airlines and weather data were used for the underlying analysis. The aggregated output of this analysis was outputted in JSON which was visualized in HTML5, D3 and Google Maps. The previous blogs on this series are:
- Big Data, R and SAP HANA: Analyze 200 Million Data Points and Later Visualize in HTML5 Using D3 – Part II
- Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps
- Getting Historical Weather Data in R and SAP HANA
- Tracking SFO Airport’s Performance Using R, HANA and D3
In this blog, I wanted to mash-up disparate data sources in R and HANA by combining airlines data with weather data to understand the reasons behind the airport/airlines delay. Why weather – because weather is one of the commonly cited reasons in the airlines industry for flight delays. Fortunately, the airlines data breaks up the delay by weather, security, late aircraft etc., so weather related delays can be isolated and then the actual weather data can be mashed-up to validate the airlines’ claims. However, I will not be doing this here, I will just be displaying the mashed-up data.
I have intentionally focused on the three bay-area airports and have used last 4 years of historical data to visualize the airport’s performance using a HTML5 calendar built from scratch using D3.js. One can use all 20 years of data and for all the airports to extend this example. I had downloaded historical weather data for the same 2005-2008 period for SFO and SJC airports as shown in my previous blog (For some strange reasons, there is no weather data for OAK, huh?). Here is how the final result will look like in HTML5:
Click here to interact with the live example. Hover over any cell in the live example and a tool tip with comprehensive analytics will show the break down of the performance delay for the selected cell including weather data and correct icons* – result of a mash-up. Choose a different airport from the drop-down to change the performance calendar.
* Weather icons are properties of Weather Underground.
As anticipated, SFO airport had more red on the calendar than SJC and OAK. SJC definitely is the best performing airport in the bay-area. Contrary to my expectation, weather didn’t cause as much havoc on SFO as one would expect, strange?
Creating a mash-up in R for these two data-sets was super easy and a CSV output was produced to work with HTML5/D3. Here is the R code and if it not clear from all my previous blogs: I just love data.table package.
###########################################################################################
# Percent delayed flights from three bay area airports, a break up of the flights delay by various reasons, mash-up with weather data
###########################################################################################
baa.hp.daily.flights <- baa.hp[,list( TotalFlights=length(DepDelay), CancelledFlights=sum(Cancelled, na.rm=TRUE)),
by=list(Year, Month, DayofMonth, Origin)]
setkey(baa.hp.daily.flights,Year, Month, DayofMonth, Origin)
baa.hp.daily.flights.delayed <- baa.hp[DepDelay>15,
list(DelayedFlights=length(DepDelay),
WeatherDelayed=length(WeatherDelay[WeatherDelay>0]),
AvgDelayMins=round(sum(DepDelay, na.rm=TRUE)/length(DepDelay), digits=2),
CarrierCaused=round(sum(CarrierDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),
WeatherCaused=round(sum(WeatherDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),
NASCaused=round(sum(NASDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),
SecurityCaused=round(sum(SecurityDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),
LateAircraftCaused=round(sum(LateAircraftDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2)), by=list(Year, Month, DayofMonth, Origin)]
setkey(baa.hp.daily.flights.delayed, Year, Month, DayofMonth, Origin)
# Merge two data-tables
baa.hp.daily.flights.summary <- baa.hp.daily.flights.delayed[baa.hp.daily.flights,list(Airport=Origin,
TotalFlights, CancelledFlights, DelayedFlights, WeatherDelayed,
PercentDelayedFlights=round(DelayedFlights/(TotalFlights-CancelledFlights), digits=2),
AvgDelayMins, CarrierCaused, WeatherCaused, NASCaused, SecurityCaused, LateAircraftCaused)]
setkey(baa.hp.daily.flights.summary, Year, Month, DayofMonth, Airport)
# Merge with weather data
baa.hp.daily.flights.summary.weather <-baa.weather[baa.hp.daily.flights.summary]
baa.hp.daily.flights.summary.weather$Date <- as.Date(paste(baa.hp.daily.flights.summary.weather$Year,
baa.hp.daily.flights.summary.weather$Month,
baa.hp.daily.flights.summary.weather$DayofMonth,
sep=”-“),”%Y-%m-%d”)
# remove few columns
baa.hp.daily.flights.summary.weather <- baa.hp.daily.flights.summary.weather[,
which(!(colnames(baa.hp.daily.flights.summary.weather) %in% c(“Year”, “Month”, “DayofMonth”, “Origin”))), with=FALSE]
#Write the output in both JSON and CSV file formats
objs <- baa.hp.daily.flights.summary.weather[, getRowWiseJson(.SD), by=list(Airport)]
# You have now (Airportcode, JSONString), Once again, you need to attach them together.
row.json <- apply(objs, 1, function(x) paste('{\"AirportCode\":"', x[1], '","Data\":', x[2], '}', sep=""))
json.st <- paste('[', paste(row.json, collapse=', '), ']')
writeLines(json.st, “baa-2005-2008.summary.json”)
write.csv(baa.hp.daily.flights.summary.weather, “baa-2005-2008.summary.csv”, row.names=FALSE)
Happy Coding!