Interview Jason Kuo SAP Analytics #Rstats

Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.

Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.

Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.

Ajay- When did you first start getting interested in R as an analytics platform?

Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.

Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?

Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville

Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.

Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings. 

Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.

About-

Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.

 

R for Business Analytics- Book by Ajay Ohri

So the cover art is ready, and if you are a reviewer, you can reserve online copies of the book I have been writing for past 2 years. Special thanks to my mentors, detractors, readers and students- I owe you a beer!

You can also go here-

http://www.springer.com/statistics/book/978-1-4614-4342-1

 

R for Business Analytics

R for Business Analytics

Ohri, Ajay

2012, 2012, XVI, 300 p. 208 illus., 162 in color.

Hardcover
Information

ISBN 978-1-4614-4342-1

Due: September 30, 2012

(net)

approx. 44,95 €
  • Covers full spectrum of R packages related to business analytics
  • Step-by-step instruction on the use of R packages, in addition to exercises, references, interviews and useful links
  • Background information and exercises are all applied to practical business analysis topics, such as code examples on web and social media analytics, data mining, clustering and regression models

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics.

 

This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.

Content Level » Professional/practitioner

Keywords » Business Analytics – Data Mining – Data Visualization – Forecasting – GUI – Graphical User Interface – R software – Text Mining

Related subjects » Business, Economics & Finance – Computational Statistics – Statistics

TABLE OF CONTENTS

Why R.- R Infrastructure.- R Interfaces.- Manipulating Data.- Exploring Data.- Building Regression Models.- Data Mining using R.- Clustering and Data Segmentation.- Forecasting and Time-Series Models.- Data Export and Output.- Optimizing your R Coding.- Additional Training Literature.- Appendix

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/

 

 

 

 

 

 

 

 

 

 

 

Protected: Converting SAS language code to Java

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Software Review- Google Drive versus Dropbox

Here are some notes from reviewing Google Drive  https://drive.google.com/ vs Dropbox https://www.dropbox.com/.

1) Google Drive gives more free space upfront  than Dropbox.5GB versus 2GB

2) Dropbox has a referral system 500 mb per referral while there is no referral system for Google Drive

3) The sync facility with Google Docs makes Google Drive especially useful for prior users of Google Docs.

4) API access to Google Drive is only for Chrome apps which is intriguing!

https://developers.google.com/drive/apps_overview

Apps will not have any API access to files unless users have first installed the app in Chrome Web Store.

You can use the Dropbox API much more easily –

See the platforms at

https://www.dropbox.com/developers/start/core

Choose your platform:

iOS Android Python Ruby

But-

(though I wonder if you set the R working directory to the local shared drive for Google Drive it should sync up as well but of course be slower –http://scrogster.wordpress.com/2011/01/29/using-dropbox-with-r-2/)

5) Google Drive icon is ugly (seriously, dude!) , but the features in the Windows app is just the same as the Dropbox App. Too similar 😉

 

6) Upgrade space is much more cheaper to Google Drive than Dropbox ( by Google Drive prices being exactly  a quarter of prices on Dropbox and max storage being 16 times as much). This will affect power storage users. I expect to see some slowdown in Dropbox new business unless G Drive has outage (like Gmail) . Existing users at Dropbox probably wont shift for the small dollar amount- though it is quite easy to do so.

 

Install Google Drive on your local workstation and cut and paste your Dropbox local folder to the Google Drive local folder!!

7) Dropbox deserves credit for being first (like Hotmail and AOL) but Google Drive is almost better in all respects!

Google Drive

Free
5 GB of Drive (0% used)
10 GB of Gmail (48% used)
1 GB of Picasa (0% used)

Upgrade:

25 GB
2,49 $ / Month
+25 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

100 GB
4,99 $ / Month
+100 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

 Need more storage?

Up to 16 TB available

Dropbox–

Current account type

Large DropboxDropbox Badge greenFree
Free
Up to 18 GB (2 GB + 500 MB per referral)
Account info 

Other account types

Large DropboxDropbox Badge orange50 GB +
Pro 50
+1 GB per referral, up to +32 GB
$9.99/month or $99.00/year Upgrade to Pro 50
Large DropboxDropbox Badge purple100 GB +
Pro 100
+1 GB per referral, up to +32 GB
$19.99/month or $199.00/year Upgrade to Pro 100
Triple DropboxDropbox For Teams Badge1 TB +
Teams
Plans starting at 1 TB
Large shared quota, centralized admin and billing, and more!

 

 

 

Doing RFM Analysis in R


RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail.


RFM stands for


  • Recency – How recently did the customer purchase?
  • Frequency – How often do they purchase?
  • Monetary Value – How much do they spend?

To create an RFM analysis, one creates categories for each attribute. For instance, the Recency attribute might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be arrived at by applying business rules, or using a data mining technique, such as CHAID, to find meaningful breaks.

from-http://en.wikipedia.org/wiki/RFM

If you are new to RFM or need more step by step help, please read here

https://decisionstats.com/2010/10/03/ibm-spss-19-marketing-analytics-and-rfm/

and here is R code- note for direct marketing you need to compute Monetization based on response rates (based on offer date) as well



##Creating Random Sales Data of the format CustomerId (unique to each customer), Sales.Date,Purchase.Value

sales=data.frame(sample(1000:1999,replace=T,size=10000),abs(round(rnorm(10000,28,13))))

names(sales)=c("CustomerId","Sales Value")

sales.dates <- as.Date("2010/1/1") + 700*sort(stats::runif(10000))

#generating random dates

sales=cbind(sales,sales.dates)

str(sales)

sales$recency=round(as.numeric(difftime(Sys.Date(),sales[,3],units="days")) )

library(gregmisc)

##if you have existing sales data you need to just shape it in this format

rename.vars(sales, from="Sales Value", to="Purchase.Value")#Renaming Variable Names

## Creating Total Sales(Monetization),Frequency, Last Purchase date for each customer

salesM=aggregate(sales[,2],list(sales$CustomerId),sum)

names(salesM)=c("CustomerId","Monetization")

salesF=aggregate(sales[,2],list(sales$CustomerId),length)

names(salesF)=c("CustomerId","Frequency")

salesR=aggregate(sales[,4],list(sales$CustomerId),min)

names(salesR)=c("CustomerId","Recency")

##Merging R,F,M

test1=merge(salesF,salesR,"CustomerId")

salesRFM=merge(salesM,test1,"CustomerId")

##Creating R,F,M levels 

salesRFM$rankR=cut(salesRFM$Recency, 5,labels=F) #rankR 1 is very recent while rankR 5 is least recent

salesRFM$rankF=cut(salesRFM$Frequency, 5,labels=F)#rankF 1 is least frequent while rankF 5 is most frequent

salesRFM$rankM=cut(salesRFM$Monetization, 5,labels=F)#rankM 1 is lowest sales while rankM 5 is highest sales

##Looking at RFM tables
table(salesRFM[,5:6])
table(salesRFM[,6:7])
table(salesRFM[,5:7])

Code Highlighted by Pretty R at inside-R.org

Note-you can also use quantile function instead of cut function. This changes cut to equal length instead of equal interval. or  see other methods for finding breaks for categories.

 

send email by R

For automated report delivery I have often used send email options in BASE SAS. For R, for scheduling tasks and sending me automated mails on completion of tasks I have two R options and 1 Windows OS scheduling option. Note red font denotes the parameters that should be changed. Anything else should NOT be changed.

Option 1-

Use the mail package at

http://cran.r-project.org/web/packages/mail/mail.pdf

> library(mail)

Attaching package: ‘mail’

The following object(s) are masked from ‘package:sendmailR’:

sendmail

>
> sendmail(“ohri2007@gmail.com“, subject=”Notification from R“,message=“Calculation finished!”, password=”rmail”)
[1] “Message was sent to ohri2007@gmail.com! You have 19 messages left.”

Disadvantage- Only 20 email messages by IP address per day. (but thats ok!)

Option 2-

use sendmailR package at http://cran.r-project.org/web/packages/sendmailR/sendmailR.pdf

install.packages()
library(sendmailR)
from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
to <- “<ohri2007@gmail.com>”
subject <- “Hello from R
body <- list(“It works!”, mime_part(iris))
sendmail(from, to, subject, body,control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))

 

 

BiocInstaller version 1.2.1, ?biocLite for help
> install.packages(“sendmailR”)
Installing package(s) into ‘/home/ubuntu/R/library’
(as ‘lib’ is unspecified)
also installing the dependency ‘base64’

trying URL ‘http://cran.at.r-project.org/src/contrib/base64_1.1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 61109 bytes (59 Kb)
opened URL
==================================================
downloaded 59 Kb

trying URL ‘http://cran.at.r-project.org/src/contrib/sendmailR_1.1-1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 6399 bytes
opened URL
==================================================
downloaded 6399 bytes

BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘base64’ …
** package ‘base64’ successfully unpacked and MD5 sums checked
** libs
gcc -std=gnu99 -I/usr/local/lib64/R/include -I/usr/local/include -fpic -g -O2 -c base64.c -o base64.o
gcc -std=gnu99 -shared -L/usr/local/lib64 -o base64.so base64.o -L/usr/local/lib64/R/lib -lR
installing to /home/ubuntu/R/library/base64/libs
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (base64)
BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘sendmailR’ …
** package ‘sendmailR’ successfully unpacked and MD5 sums checked
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (sendmailR)

The downloaded packages are in
‘/tmp/RtmpsM222s/downloaded_packages’
> library(sendmailR)
Loading required package: base64
> from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
> to <- “<ohri2007@gmail.com>”
> subject <- “Hello from R”
> body <- list(“It works!”, mime_part(iris))
> sendmail(from, to, subject, body,
+ control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))
$code
[1] “221”

$msg
[1] “2.0.0 closing connection ff2si17226764qab.40”

Disadvantage-This worked when I used the Amazon Cloud using the BioConductor AMI (for free 2 hours) at http://www.bioconductor.org/help/cloud/

It did NOT work when I tried it use it from my Windows 7 Home Premium PC from my Indian ISP (!!) .

It gave me this error

or in wait_for(250) :
SMTP Error: 5.7.1 [180.215.172.252] The IP you’re using to send mail is not authorized

 

PAUSE–

ps Why do this (send email by R)?

Note you can add either of the two programs of the end of the code that you want to be notified automatically. (like daily tasks)

This is mostly done for repeated business analytics tasks (like reports and analysis that need to be run at specific periods of time)

pps- What else can I do with this?

Can be modified to include sms or tweets  or even blog by email by modifying the   “to”  location appropriately.

3) Using Windows Task Scheduler to run R codes automatically (either the above)

or just sending an email

got to Start>  All Programs > Accessories >System Tools > Task Scheduler ( or by default C:Windowssystem32taskschd.msc)

Create a basic task

Now you can use this to run your daily/or scheduled R code  or you can send yourself email as well.

and modify the parameters- note the SMTP server (you can use the ones for google in example 2 at ASPMX.L.GOOGLE.COM)

and check if it works!

 

Related

 Geeky Things , Bro

Configuring IIS on your Windows 7 Home Edition-

note path to do this is-

Control Panel>All Control Panel Items> Program and Features>Turn Windows features on or off> Internet Information Services

and

http://stackoverflow.com/questions/709635/sending-mail-from-batch-file