Author: Ajay Ohri

http://about.me/ajayohri

Interview Alvaro Tejada Galindo, SAP Labs Montreal, Using SAP Hana with #Rstats

Here is a brief interview with Alvaro Tejada Galindo aka Blag who is a developer working with SAP Hana and R at SAP Labs, Montreal. SAP Hana is SAP’s latest offering in BI , it’s also a database and a computing environment , and using R and HANA together on the cloud can give major productivity gains in terms of both speed and analytical ability, as per preliminary use cases.

Ajay- Describe how you got involved with databases and R language.
Blag-  I used to work as an ABAP Consultant for 11 years, but also been involved with programming since the last 13 years, so I was in touch with SQLServer, Oracle, MySQL and SQLite. When I joined SAP, I heard that SAP HANA was going to use an statistical programming language called “R”. The next day I started my “R” learning.

Ajay- What made the R language a fit for SAP HANA. Did you consider other languages? What is your view on Julia/Python/SPSS/SAS/Matlab languages

Blag- I think “R” is a must for SAP HANA. As the fastest database in the market, we needed a language that could help us shape the data in the best possible way. “R” filled that purpose very well. Right now, “R” is not the only language as “L” can be used as well (http://wiki.tcl.tk/17068) …not forgetting “SQLScript” which is our own version of SQL (http://goo.gl/x3bwh) . I have to admit that I tried Julia, but couldn’t manage to make it work. Regarding Python, it’s an interesting question as I’m going to blog about Python and SAP HANA soon. About Matlab, SPSS and SAS I haven’t used them, so I got nothing to say there.

Ajay- What is your view on some of the limitations of R that can be overcome with using it with SAP HANA.

Blag-  I think mostly the ability of SAP HANA to work with big data. Again, SAP HANA and “R” can work very nicely together and achieve things that weren’t possible before.

Ajay-  Have you considered other vendors of R including working with RStudio, Revolution Analytics, and even Oracle R Enterprise.

Blag-  I’m not really part of the SAP HANA or the R groups inside SAP, so I can’t really comment on that. I can only say that I use RStudio every time I need to do something with R. Regarding Oracle…I don’t think so…but they can use any of our products whenever they want.

Ajay- Do you have a case study on an actual usage of R with SAP HANA that led to great results.

Blag-   Right now the use of “R” and SAP HANA is very preliminary, I don’t think many people has start working on it…but as an example that it works, you can check this awesome blog entry from my friend Jitender Aswani “Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps “ (http://allthingsr.blogspot.com/#!/2012/04/big-data-r-and-hana-analyze-200-million.html)

Ajay- Does your group in SAP plan to give to the R ecosystem by attending conferences like UseR 2012, sponsoring meets, or package development etc

Blag- My group is in charge of everything developers, so sure, we’re planning to get more in touch with R developers and their ecosystem. Not sure how we’re going to deal with it, but at least I’m going to get myself involved in the Montreal R Group.

 

About-

http://scn.sap.com/people/alvaro.tejadagalindo3

Name: Alvaro Tejada Galindo
Email: a.tejada.galindo@sap.com
Profession: Development
Company: SAP Canada Labs-Montreal
Town/City: Montreal
Country: Canada
Instant Messaging Type: Twitter
Instant Messaging ID: Blag
Personal URL: http://blagrants.blogspot.com
Professional Blog URL: http://www.sdn.sap.com/irj/scn/weblogs?blog=/pub/u/252210910
My Relation to SAP: employee
Short Bio: Development Expert for the Technology Innovation and Developer Experience team.Used to be an ABAP Consultant for the last 11 years. Addicted to programming since 1997.

http://www.sap.com/solutions/technology/in-memory-computing-platform/hana/overview/index.epx

and from

http://en.wikipedia.org/wiki/SAP_HANA

SAP HANA is SAP AG’s implementation of in-memory database technology. There are four components within the software group:[1]

  • SAP HANA DB (or HANA DB) refers to the database technology itself,
  • SAP HANA Studio refers to the suite of tools provided by SAP for modeling,
  • SAP HANA Appliance refers to HANA DB as delivered on partner certified hardware (see below) as anappliance. It also includes the modeling tools from HANA Studio as well replication and data transformation tools to move data into HANA DB,[2]
  • SAP HANA Application Cloud refers to the cloud based infrastructure for delivery of applications (typically existing SAP applications rewritten to run on HANA).

R is integrated in HANA DB via TCP/IP. HANA uses SQL-SHM, a shared memory-based data exchange to incorporate R’s vertical data structure. HANA also introduces R scripts equivalent to native database operations like join or aggregation.[20] HANA developers can write R scripts in SQL and the types are automatically converted in HANA. R scripts can be invoked with HANA tables as both input and output in the SQLScript. R environments need to be deployed to use R within SQLScript

More blog posts on using SAP and R together

Dealing with R and HANA

http://scn.sap.com/community/in-memory-business-data-management/blog/2011/11/28/dealing-with-r-and-hana
R meets HANA

http://scn.sap.com/community/in-memory-business-data-management/blog/2012/01/29/r-meets-hana

HANA meets R

http://scn.sap.com/community/in-memory-business-data-management/blog/2012/01/26/hana-meets-r
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

 

Using RODBC with SAP HANA DB-

SAP HANA: My experiences on using SAP HANA with R

http://scn.sap.com/community/in-memory-business-data-management/blog/2012/02/21/sap-hana-my-experiences-on-using-sap-hana-with-r

and of course the blog that started it all-

Jitender Aswani’s http://allthingsr.blogspot.in/

 

 

How big is R on CRAN #rstats

3.87 GB and 3786 packages. Thats what you need to install the whole of R as on CRAN

( Note- Many IT administrators /Compliance Policies in enterprises forbid installing from the Internet in work offices.

Which is where the analytics,$$, and people are)

As downloaded from the CRAN Mirror at UCLA.

Takes 3 hours to download at 1 mbps (I was on an Amazon Ec2 instance)

See screenshot.

Next question- who is the man responsible in the R project for deleting old /depreciated/redundant packages if the authors dont do it.

 

Data Quality in R #rstats

Many Data Quality Formats give problems when importing in your statistical software.A statistical software is quite unable to distingush between $1,000, 1000% and 1,000 and 1000 and will treat the former three as character variables while the third as a numeric variable by default. This issue is further compounded by the numerous ways we can represent date-time variables.

The good thing is for specific domains like finance and web analytics, even these weird data input formats are fixed, so we can fix up a list of handy data quality conversion functions in R for reference.

 

After much muddling about with coverting internet formats (or data used in web analytics) (mostly time formats without date like 00:35:23)  into data frame numeric formats, I found that the way to handle Date-Time conversions in R is

Dataset$Var2= strptime(as.character(Dataset$Var1),”%M:%S”)

The problem with this approach is you will get the value as a Date Time format (02/31/2012 04:00:45-  By default R will add today’s date to it.)  while you are interested in only Time Durations (4:00:45 or actually just the equivalent in seconds).

this can be handled using the as.difftime function

dataset$Var2=as.difftime(paste(dataset$Var1))

or to get purely numeric values so we can do numeric analysis (like summary)

dataset$Var2=as.numeric(as.difftime(paste(dataset$Var1)))

(#Maybe there is  a more elegant way here- but I dont know)

The kind of data is usually one we get in web analytics for average time on site , etc.

 

 

 

 

 

and

for factor variables

Dataset$Var2= as.numeric(as.character(Dataset$Var1))

 

or

Dataset$Var2= as.numeric(paste(Dataset$Var1))

 

Slight problem is suppose there is data like 1,504 – it will be converted to NA instead of 1504

The way to solve this is use the nice gsub function ONLy on that variable. Since the comma is also the most commonly used delimiter , you dont want to replace all the commas, just only the one in that variable.

 

dataset$Variable2=as.numeric(paste(gsub(“,”,””,dataset$Variable)))

 

Now lets assume we have data in the form of % like 0.00% , 1.23%, 3.5%

again we use the gsub function to replace the % value in the string with  (nothing).

 

dataset$Variable2=as.numeric(paste(gsub(“%”,””,dataset$Variable)))

 

 

If you simply do the following for a factor variable, it will show you the level not the value. This can create an error when you are reading in CSV data which may be read as character or factor data type.

Dataset$Var2= as.numeric(Dataset$Var1)

An additional way is to use substr (using substr( and concatenate (using paste) for manipulating string /character variables.

 

iris$sp=substr(iris$Species,1,3) –will reduce the famous Iris species into three digits , without losing any analytical value.

The other issue is with missing values, and na.rm=T helps with getting summaries of numeric variables with missing values, we need to further investigate how suitable, na.omit functions are for domains which have large amounts of missing data and need to be treated.

 

 

Analytics 2012 Conference

A nice conference from the grand old institution of Analytics,  SAS  Institute’s annual analytic pow-wow.

I especially like some of the trainings- and wonder if they could be stored as e-learning modules for students/academics to review

in SAS’s extensive and generous Online Education Program.

Sunday Morning Workshop

SAS Sentiment Analysis Studio: Introduction to Building Models

This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
View outline
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250

Sunday Afternoon Workshops

Business Analytics Consulting Workshops

This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
View outline
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200

Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand

This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
View outline
Sunday, Oct. 7; 1-5 p.m.

Forecast Value Added Analysis

Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
View outline
Sunday, Oct. 7; 1-5 p.m.

SAS Enterprise Content Categorization: An Introduction

This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
View outline
Sunday, Oct. 7; 1-5 p.m.

 

 
You can see more on this yourself at –

http://www.sas.com/events/analytics/us/

 

 

 

 

 

 

 

 

 

 

 

Webscraping using iMacros

The noted Diamonds dataset in the ggplot2 package of R is actually culled from the website http://www.diamondse.info/diamond-prices.asp

However it has ~55000 diamonds, while the whole Diamonds search engine has almost ten times that number. Using iMacros – a Google Chrome Plugin, we can scrape that data (or almost any data). The iMacros chrome plugin is available at  https://chrome.google.com/webstore/detail/cplklnmnlbnpmjogncfgfijoopmnlemp while notes on coding are at http://wiki.imacros.net

Imacros makes coding as easy as recording macro and the code is automatcially generated for whatever actions you do. You can set parameters to extract only specific parts of the website, and code can be run into a loop (of 9999 times!)

Here is the iMacros code-Note you need to navigate to the web site http://www.diamondse.info/diamond-prices.asp before running it

VERSION BUILD=5100505 RECORDER=CR
FRAME F=1
SET !EXTRACT_TEST_POPUP NO
SET !ERRORIGNORE YES
TAG POS=6 TYPE=TABLE ATTR=TXT:* EXTRACT=TXT
TAG POS=1 TYPE=DIV ATTR=CLASS:paginate_enabled_next
SAVEAS TYPE=EXTRACT FOLDER=* FILE=test+3

 

 

 

 

 

 

 

 

 

and voila- all the diamonds you need to analyze!

The returning data can be read using the standard delimiter data munging in the language of SAS or R.

More on IMacros from

https://chrome.google.com/webstore/detail/cplklnmnlbnpmjogncfgfijoopmnlemp/details

Description

Automate your web browser. Record and replay repetitious work

If you encounter any problems with iMacros for Chrome, please let us know in our Chrome user forum at http://forum.iopus.com/viewforum.php?f=21

Our forum is also the best place for new feature suggestions :-)
----

iMacros was designed to automate the most repetitious tasks on the web. If there’s an activity you have to do repeatedly, just record it in iMacros. The next time you need to do it, the entire macro will run at the click of a button! With iMacros, you can quickly and easily fill out web forms, remember passwords, create a webmail notifier, and more. You can keep the macros on your computer for your own use, use them within bookmark sync / Xmarks or share them with others by embedding them on your homepage, blog, company Intranet or any social bookmarking service as bookmarklet. The uses are limited only by your imagination!

Popular uses are as web macro recorder, form filler on steroids and highly-secure password manager (256-bit AES encryption).


FaceBook IPO- Who hacked whom?

Some thoughts on the FB IPO-

1) Is Zuck reading emails on his honeymoon? Where is he?

2) In 3 days FB lost 34 billion USD in market valuation. Thats enough to buy AOL,Yahoo, LinkedIn and Twitter (combined)

3) People are now shorting FB based on 3-4 days of trading performance. Maybe they know more ARIMA !

4) Who made money on the over-pricing in terms on employees who sold on 1 st day, financial bankers who did the same?

5) Who lost money on the first three days due to Nasdaq’s problems?

6) What is the exact technical problem that Nasdaq had?

7) The much deplored FaceBook Price/Earnings ratio (99) is still comparable to AOL’s (85) and much less than LI (620!). see http://www.google.com/finance?cid=296878244325128

8) Maybe FB can stop copying Google’s ad model (which Google invented) and go back to the drawing table. Like a FB kind of Paypal

9) There are more experts on the blogosphere than experts in Wall Street.

10) No blogger is willing to admit that they erred in the optimism on the great white IPO hope.

I did. Mea culpa. I thought FB is a good stock. I would buy it still- but the rupee tanked by 10% since past 1 week against the dollar.

 

I am now waiting for Chinese social network market to open with IPO’s. Thats walled gardens within walled gardens of Jade and Bamboo.

Related- Art Work of Another 100 billion dollar company (2006)

Visualizing Bigger Data in R using Tabplot

The amazing tabplot package creates the tableplot feature for visualizing huge chunks of data. This is a great example of creative data visualization that is resource lite and extremely fast in a first look at the data. (note- The tabplot package is being used and table plot function is being used . The TABLEPLOT package is different and is NOT being used here).

library(ggplot2)
data(diamonds)
library(tabplot)
tableplot(diamonds)
system.time(tableplot(diamonds))

visualizing a 50000 row by 10 variable dataset in 0.7 s is fast !!

click on screenshot to see it

and some say R is slow 😉

 

Note I used a free Windows Amazon EC2 Instance for it-

See screenshot for hardware configuration

 

the best thing is there is a handy GTK GUI for this package. You can check it out at