If You are a R Developer, Then You Must Try SAP HANA for Free.

This is a guest blog from Alvaro Tejada Galindo, my colleague and fellow R and SAP HANA enthusiast.  I am thankful to Alvaro for coming and posting on "AllThingsR".

Are you an R developers? Have ever heard of SAP HANA? Would you like to test SAP HANA for free?

SAP HANA is an In-Memory Database Technology allowing developers to analyze big data in real-time.

Processes that took hours now take seconds due to SAP HANA's power to keep everything on RAM memory.

As announced in SAP Sapphire Now event in Orlando, Florida, SAP HANA is free for developers. You just need to download and install both the SAP HANA Client and the SAP HANA Studio, and create an SAP HANA Server on the Amazon Web Services as described in the following document:

Get your own SAP HANA DB server on Amazon Web Services - http://scn.sap.com/docs/DOC-28294

Why should this interest you? Easy...SAP HANA is an agent of change bringing speed to its limits and it can also be integrated with R as described in the following blog:

When SAP HANA met R - First kiss - http://scn.sap.com/community/developer-center/hana/blog/2012/05/21/when-sap-hana-met-r--first-kiss

Want to know more about SAP HANA? Read everything you need here: http://developers.sap.com

You're convinced but don't want to pay for the Amazon Web Services? No problem. Just leave a comment including your name, company and email. We will reach you and send you an Amazon Gift Card so you can get started. Of course, your feedback would be greatly appreciated. Of course, we only a limited set of gift cards, so be quick or be out.

Author Alvaro Tejada Galindo, mostly known as "Blag" is a Development Expert working for the Technology Innovation and Developer Experience team in SAP Labs.  He can be contacted at a.tejada.galindo@sap.com.

Alvaro's background in his own words: I used to be an ABAP Consultant for 11 years. I worked in implementations on Peru and Canada. I’m also a die hard developer using R, Python, Ruby, PHP, Flex and many more languages. Now, I work for SAP Labs and my main roles are evangelize SAP technologies by writing blogs, articles, helping people on the forums, attending SAP events, besides many other “Developer engagement” activities.

I maintain a blog called “Blag’s bag of rants” at blagrants.blogspot.com

Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize in HTML5 Using D3 - Part II

In my last blog, Big Data, R and SAP HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps, I analyzed historical airlines performance data set using R and SAP HANA and put the aggregated analysis on Google Maps.  Undoubtedly, Map is a pretty exciting canvas to view and analyze big data sets. One could draw shapes (circles, polygons) on the map under a marker pin, providing pin-point information and display aggregated information in the info-window when a marker is clicked.  So I enjoyed doing all of that, but I was craving for some old fashion bubble charts and other types of charts to provide comparative information on big data sets.  Ultimately, all big data sets get aggregated into smaller analytical sets for viewing, sharing and reporting.  An old fashioned chart is the best way to tell a visual story!

On bubble charts, one could display four dimensional data for comparative analysis. In this blog analysis, I used the same data-set which had 200M data points and went deeper looking at finer slices of information.  I leveraged D3, R and SAP HANA for this blog post.  Here I am publishing some of this work:  

In this first graphics, the performance of top airlines is compared for 2008.  As expected, Southwest, the largest airlines (when using total number of flights as a proxy), performed well for its size (1.2M flights, 64 destinations but average delay was ~10 mins.)  Some of the other airlines like American and Continental were the worst performers along with Skywest.  Note, I didn't remove outliers from this analysis.  Click here to interact with this example (view source to get D3 code).

In the second analysis, I replaced airlines dimension with airports dimension but kept all the other dimensions the same.  To my disbelief, Newark airport is the worst performing airport when it comes to departure delays.  Chicago O'Hare, SFO and JFK follow.  Atlanta airport is the largest airport but it has the best performance. What are they doing differently at ATL?  Click here to interact with this example (view source to get D3 code).

It was hell of a fun playing with D3, R and HANA, good intellectual stimulation if nothing else!  Happy Analyzing and remember possibilities are endless!

As always, my R modules are fairly simple and straightforward:
###########################################################################################  
#ETL - Read the AIRPORT Information, get major aiport informatoin extracted and upload this 
#transfromed dataset into HANA
###########################################################################################
major.airports <- data.table(read.csv("MajorAirports.csv",  header=TRUE, sep=",", stringsAsFactors=FALSE))
setkey(major.airports, iata)

all.airports <- data.table(read.csv("AllAirports.csv",  header=TRUE, sep=",", stringsAsFactors=FALSE)) 
setkey(all.airports, iata)

airports.2008.hp <- data.table(read.csv("2008.csv",  header=TRUE, sep=",", stringsAsFactors=FALSE)) 
setkey(airports.2008.hp, Origin, UniqueCarrier)

#Merge two datasets
airports.2008.hp <- major.airports[airports.2008.hp,]


###########################################################################################  
# Get airport statisitics for all airports
###########################################################################################
airports.2008.hp.summary <- airports.2008.hp[major.airports,     
    list(AvgDepDelay=round(mean(DepDelay, na.rm=TRUE), digits=2),
    TotalMiles=prettyNum(sum(Distance, na.rm=TRUE), big.mark=","),
    TotalFlights=length(Month),
    TotalDestinations=length(unique(Dest)),
    URL=paste("http://www.fly", Origin, ".com",sep="")), 
                    by=list(Origin)][order(-TotalFlights)]
setkey(airports.2008.hp.summary, Origin)
#merge two data tables
airports.2008.hp.summary <- major.airports[airports.2008.hp.summary, 
                                                     list(Airport=airport, 
                                                          AvgDepDelay, TotalMiles, TotalFlights, TotalDestinations, 
                                                          Address=paste(airport, city, state, sep=", "), 
                                                          Lat=lat, Lng=long, URL)][order(-TotalFlights)]

airports.2008.hp.summary.json <- getRowWiseJson(airports.2008.hp.summary)
writeLines(airports.2008.hp.summary.json, "airports.2008.hp.summary.json")                 
write.csv(airports.2008.hp.summary, "airports.2008.hp.summary.csv", row.names=FALSE)

Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps

Technologies: SAP HANA, R, HTML5, D3, Google Maps, JQuery and JSON

For this fun exercise, I analyzed more than 200 million data points using SAP HANA and R and then brought in the aggregated results in HTML5 using D3, JSON and Google Maps APIs.  The 2008 airlines data is from the data expo and I have been using this entire data set (123 million rows and 29 columns) for quite sometime. See my other blogs

The results look beautiful:

Each airport icon is clickable and when clicked displays an info-window describing the key stats for the selected airport:

I then used D3 to display the aggregated result set in the modal window (light box):

D3 made it looks ridiculously simpler to generate a table from a JSON file. 

Unfortunately, I can't provide the live example due to the restrictions put in by Google Maps APIs and I am approaching my free API limits. 

Fun fact:  The Atlanta airport was the largest airport in 2008 on many dimensions: Total Flights Departed, Total Miles Flew, Total Destinations.  It also experienced lower average departure delay in 2008 than Chicago O'Hare. I always thought Chicago O'Hare is the largest US airport.

As always, I just needed 6 lines of R code including two lines of code to write data in JSON and CSV files:

################################################################################

airports.2008.hp.summary <- airports.2008.hp[major.airports,     

    list(AvgDepDelay=round(mean(DepDelay, na.rm=TRUE), digits=2),

    TotalMiles=prettyNum(sum(Distance, na.rm=TRUE), big.mark=","),

    TotalFlights=length(Month),

    TotalDestinations=length(unique(Dest)),

    URL=paste("http://www.fly", Origin, ".com",sep="")), 

                    by=list(Origin)][order(-TotalFlights)]

setkey(airports.2008.hp.summary, Origin)

#merge the two data tables

airports.2008.hp.summary <- major.airports[airports.2008.hp.summary, 

                                                     list(Airport=airport, 

                                                          AvgDepDelay, TotalMiles, TotalFlights, TotalDestinations, 

                                                          Address=paste(airport, city, state, sep=", "), 

                                                          Lat=lat, Lng=long, URL)][order(-TotalFlights)]

airports.2008.hp.summary.json <- getRowWiseJson(airports.2008.hp.summary)

writeLines(airports.2008.hp.summary.json, "airports.2008.hp.summary.json")                 

write.csv(airports.2008.hp.summary, "airports.2008.hp.summary.csv", row.names=FALSE)

##############################################################################

Happy Coding and remember the possibilities are endless!

Tracking SFO Airport's Performance Using R, HANA and D3

This is my first introduction to D3 and I am simply blown away.  Mike Bostock (@mbostock), you are genius and thanks for creating D3!  With HANA, R, D3, HTML5 and iPad, and you got yourself a KILLER combo!

I have been burning my midnight oil on piecing together my big data story using HANA, R, JSON and HTML5.  If you recall, I did a technical session on R and SAP HANA at DKOM, SAP's Development Kickoff Event last week where I showcased the supreme powers of R and HANA when analyzing 124 million records in real time.  R and SAP HANA: A Highly Potent Combo for Real Time Analytics on Big Data

Since last week, I have been looking for other creative ways to analyze and then visualize this airlines data. I am very fortunate to come across D3.  After spending couple of hours with D3, I decided to build the calendar view for the airlines data I have.  The calendar view is the first example Mike shows on his D3 page. Amazingly awesome!

I created this calendar view capturing the percent of delayed flight from SFO airports that departed daily between 2005-2008.  For this analysis, I used HANA to get the data out for SFO (out of 250 plus airports) over this 4 years period in seconds and then did all the aggregation in R including creating a JSON and .CSV file in seconds again.  Later, I moved to HTML5 and D3 to generate this beautiful calendar view showing SFO's performance.  Graphics is presented below:

As expected, December and January are two notorious months for flights delay.  Have fun with the live example hosted in the Amazon cloud.


Once again, my R code is very simple:

## Depature Delay for SF Airport
ba.hp.sfo <- ba.hp[Origin=="SFO",]

ba.hp.sfo.daily.flights <- ba.hp.sfo[,list(DailyFlights=length(DepDelay)), by=list(Year, Month, DayofMonth)][order(Year,Month,DayofMonth)]
ba.hp.sfo.daily.flights.delayed <- ba.hp.sfo[DepDelay>15,list(DelayedDailyFlights=length(DepDelay)), by=list(Year, Month, DayofMonth)][order(Year,Month,DayofMonth)]
setkey(ba.hp.sfo.daily.flights.delayed, Year, Month, DayofMonth)
response <- ba.hp.sfo.daily.flights.delayed[ba.hp.sfo.daily.flights]
response <- response[,list(Date=as.Date(paste(Year, Month, DayofMonth, sep="-"),"%Y-%m-%d"),
                           #DailyFlights,DelayedDailyFlights,
                           PercentDelayedFlights=round((DelayedDailyFlights/DailyFlights), digits=2))]
objs <- apply(response, 1, toJSON)
res <- paste('{"dailyFlightStats": [', paste(objs, collapse=', '), ']}')
writeLines(res, "dailyFlightStatsForSFO.json")                
write.csv(response, "dailyFlightStatsForSFO.csv", row.names=FALSE)

For D3 and HTML code, please take a look at this example from D3 website.

Happy Analyzing and Keep That Mid Night Oil Burning!