#Rstats for Business Intelligence

This is a short list of several known as well as lesser known R ( #rstats) language codes, packages and tricks to build a business intelligence application. It will be slightly Messy (and not Messi) but I hope to refine it someday when the cows come home.

It assumes that BI is basically-

a Database, a Document Database, a Report creation/Dashboard pulling software as well unique R packages for business intelligence.

What is business intelligence?

Seamless dissemination of data in the organization. In short let it flow- from raw transactional data to aggregate dashboards, to control and test experiments, to new and legacy data mining models- a business intelligence enabled organization allows information to flow easily AND capture insights and feedback for further action.

BI software has lately meant to be just reporting software- and Business Analytics has meant to be primarily predictive analytics. the terms are interchangeable in my opinion -as BI reports can also be called descriptive aggregated statistics or descriptive analytics, and predictive analytics is useless and incomplete unless you measure the effect in dashboards and summary reports.

Data Mining- is a bit more than predictive analytics- it includes pattern recognizability as well as black box machine learning algorithms. To further aggravate these divides, students mostly learn data mining in computer science, predictive analytics (if at all) in business departments and statistics, and no one teaches metrics , dashboards, reporting  in mainstream academia even though a large number of graduates will end up fiddling with spreadsheets or dashboards in real careers.

Using R with

1) Databases-

I created a short list of database connectivity with R here at https://rforanalytics.wordpress.com/odbc-databases-for-r/ but R has released 3 new versions since then.

The RODBC package remains the package of choice for connecting to SQL Databases.

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

Details on creating DSN and connecting to Databases are given at  https://rforanalytics.wordpress.com/odbc-databases-for-r/

For document databases like MongoDB and CouchDB

( what is the difference between traditional RDBMS and NoSQL if you ever need to explain it in a cocktail conversation http://dba.stackexchange.com/questions/5/what-are-the-differences-between-nosql-and-a-traditional-rdbms

Basically dispensing with the relational setup, with primary and foreign keys, and with the additional overhead involved in keeping transactional safety, often gives you extreme increases in performance

NoSQL is a kind of database that doesn’t have a fixed schema like a traditional RDBMS does. With the NoSQL databases the schema is defined by the developer at run time. They don’t write normal SQL statements against the database, but instead use an API to get the data that they need.

instead relating data in one table to another you store things as key value pairs and there is no database schema, it is handled instead in code.)

I believe any corporation with data driven decision making would need to both have atleast one RDBMS and one NoSQL for unstructured data-Ajay. This is a sweeping generic statement 😉 , and is an opinion on future technologies.

  • Use RMongo

From- http://tommy.chheng.com/2010/11/03/rmongo-accessing-mongodb-in-r/

http://plindenbaum.blogspot.com/2010/09/connecting-to-mongodb-database-from-r.html

Connecting to a MongoDB database from R using Java

http://nsaunders.wordpress.com/2010/09/24/connecting-to-a-mongodb-database-from-r-using-java/

Also see a nice basic analysis using R Mongo from

http://pseudofish.com/blog/2011/05/25/analysis-of-data-with-mongodb-and-r/

For CouchDB

please see https://github.com/wactbprot/R4CouchDB and

http://digitheadslabnotebook.blogspot.com/2010/10/couchdb-and-r.html

  • First install RCurl and RJSONIO. You’ll have to download the tar.gz’s if you’re on a Mac. For the second part, we’ll need to installR4CouchDB,

2) External Report Creating Software-

Jaspersoft- It has good integration with R and is a certified Revolution Analytics partner (who seem to be the only ones with a coherent #Rstats go to market strategy- which begs the question – why is the freest and finest stats software having only ONE vendor- if it was so great lots of companies would make exclusive products for it – (and some do -see https://rforanalytics.wordpress.com/r-business-solutions/ and https://rforanalytics.wordpress.com/using-r-from-other-software/)

From

http://www.jaspersoft.com/sites/default/files/downloads/events/Analytics%20-Jaspersoft-SEP2010.pdf

we see

http://jasperforge.org/projects/rrevodeployrbyrevolutionanalytics

RevoConnectR for JasperReports Server

RevoConnectR for JasperReports Server RevoConnectR for JasperReports Server is a Java library interface between JasperReports Server and Revolution R Enterprise’s RevoDeployR, a standardized collection of web services that integrates security, APIs, scripts and libraries for R into a single server. JasperReports Server dashboards can retrieve R charts and result sets from RevoDeployR.

http://jasperforge.org/plugins/esp_frs/optional_download.php?group_id=409

 

Using R and Pentaho
Extending Pentaho with R analytics”R” is a popular open source statistical and analytical language that academics and commercial organizations alike have used for years to get maximum insight out of information using advanced analytic techniques. In this twelve-minute video, David Reinke from Pentaho Certified Partner OpenBI provides an overview of R, as well as a demonstration of integration between R and Pentaho.
and from
R and BI – Integrating R with Open Source Business
Intelligence Platforms Pentaho and Jaspersoft
David Reinke, Steve Miller
Keywords: business intelligence
Increasingly, R is becoming the tool of choice for statistical analysis, optimization, machine learning and
visualization in the business world. This trend will only escalate as more R analysts transition to business
from academia. But whereas in academia R is often the central tool for analytics, in business R must coexist
with and enhance mainstream business intelligence (BI) technologies. A modern BI portfolio already includes
relational databeses, data integration (extract, transform, load – ETL), query and reporting, online analytical
processing (OLAP), dashboards, and advanced visualization. The opportunity to extend traditional BI with
R analytics revolves on the introduction of advanced statistical modeling and visualizations native to R. The
challenge is to seamlessly integrate R capabilities within the existing BI space. This presentation will explain
and demo an initial approach to integrating R with two comprehensive open source BI (OSBI) platforms –
Pentaho and Jaspersoft. Our efforts will be successful if we stimulate additional progress, transparency and
innovation by combining the R and BI worlds.
The demonstration will show how we integrated the OSBI platforms with R through use of RServe and
its Java API. The BI platforms provide an end user web application which include application security,
data provisioning and BI functionality. Our integration will demonstrate a process by which BI components
can be created that prompt the user for parameters, acquire data from a relational database and pass into
RServer, invoke R commands for processing, and display the resulting R generated statistics and/or graphs
within the BI platform. Discussion will include concepts related to creating a reusable java class library of
commonly used processes to speed additional development.

If you know Java- try http://ramanareddyg.blog.com/2010/07/03/integrating-r-and-pentaho-data-integration/

 

and I like this list by two venerable powerhouses of the BI Open Source Movement

http://www.openbi.com/demosarticles.html

Open Source BI as disruptive technology

http://www.openbi.biz/articles/osbi_disruption_openbi.pdf

Open Source Punditry

TITLE AUTHOR COMMENTS
Commercial Open Source BI Redux Dave Reinke & Steve Miller An review and update on the predictions made in our 2007 article focused on the current state of the commercial open source BI market. Also included is a brief analysis of potential options for commercial open source business models and our take on their applicability.
Open Source BI as Disruptive Technology Dave Reinke & Steve Miller Reprint of May 2007 DM Review article explaining how and why Commercial Open Source BI (COSBI) will disrupt the traditional proprietary market.

Spotlight on R

TITLE AUTHOR COMMENTS
R You Ready for Open Source Statistics? Steve Miller R has become the “lingua franca” for academic statistical analysis and modeling, and is now rapidly gaining exposure in the commercial world. Steve examines the R technology and community and its relevancy to mainstream BI.
R and BI (Part 1): Data Analysis with R Steve Miller An introduction to R and its myriad statistical graphing techniques.
R and BI (Part 2): A Statistical Look at Detail Data Steve Miller The usage of R’s graphical building blocks – dotplots, stripplots and xyplots – to create dashboards which require little ink yet tell a big story.
R and BI (Part 3): The Grooming of Box and Whiskers Steve Miller Boxplots and variants (e.g. Violin Plot) are explored as an essential graphical technique to summarize data distributions by categories and dimensions of other attributes.
R and BI (Part 4): Embellishing Graphs Steve Miller Lattices and logarithmic data transformations are used to illuminate data density and distribution and find patterns otherwise missed using classic charting techniques.
R and BI (Part 5): Predictive Modelling Steve Miller An introduction to basic predictive modelling terminology and techniques with graphical examples created using R.
R and BI (Part 6) :
Re-expressing Data
Steve Miller How do you deal with highly skewed data distributions? Standard charting techniques on this “deviant” data often fail to illuminate relationships. This article explains techniques to re-express skewed data so that it is more understandable.
The Stock Market, 2007 Steve Miller R-based dashboards are presented to demonstrate the return performance of various asset classes during 2007.
Bootstrapping for Portfolio Returns: The Practice of Statistical Analysis Steve Miller Steve uses the R open source stats package and Monte Carlo simulations to examine alternative investment portfolio returns…a good example of applied statistics using R.
Statistical Graphs for Portfolio Returns Steve Miller Steve uses the R open source stats package to analyze market returns by asset class with some very provocative embedded trellis charts.
Frank Harrell, Iowa State and useR!2007 Steve Miller In August, Steve attended the 2007 Internation R User conference (useR!2007). This article details his experiences, including his meeting with long-time R community expert, Frank Harrell.
An Open Source Statistical “Dashboard” for Investment Performance Steve Miller The newly launched Dashboard Insight web site is focused on the most useful of BI tools: dashboards. With this article discussing the use of R and trellis graphics, OpenBI brings the realm of open source to this forum.
Unsexy Graphics for Business Intelligence Steve Miller Utilizing Tufte’s philosophy of maximizing the data to ink ratio of graphics, Steve demonstrates the value in dot plot diagramming. The R open source statistical/analytics software is showcased.
I think that the report generation package Brew would also qualify as a BI package, but large scale implementation remains to be seen in
a commercial business environment
  • brew: Creating Repetitive Reports
 brew: Templating Framework for Report Generation

brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. http://bit.ly/jINmaI
  • Yarr- creating reports in R
to be continued ( when I have more time and the temperature goes down from 110F in Delhi, India)

JMP Genomics 5 released

Animation of the structure of a section of DNA...
Image via Wikipedia

Close to the launch of JMP9 with it’s R integration comes the announcement of JMP Genomics 5 released. The product brief is available here http://jmp.com/software/genomics/pdf/103112_jmpg5_prodbrief.pdf and it has an interesting mix of features. If you want to try out the features you can see http://jmp.com/software/license.shtml

As per me, I snagged some “new”stuff in this release-

  • Perform enrichment analysis using functional information from Ingenuity Pathways Analysis.+
  • New bar chart track allows summarization of reads or intensities.
  • New color map track displays heat plots of information for individual subjects.
  • Use a variety of continuous measures for summarization.
  • Using a common identifier, compare list membership for up tofive groups and display overlaps with Venn diagrams.
  • Filter or shade segments by mean intensity, with an optionto display segment mean intensity and set a reference valuefor shading.
  • Adjust intensities or counts for experimental samples using paired or grouped control samples.
  • Screen paired DNA and RNA intensities for allele-specific expression.
  • Standardize using a shifting factor and perform log2transformation after standardization.
  • Use kernel density information in loess and quantile normalization.
  • Depict partition tree information graphically for standard models with new Tree Viewer
  • Predictive modeling for survival analysis with Harrell’s assessment method and integration with Cross-Validation Model Comparison.

That’s right- that is incorporating the work of our favorite professor from R Project himself- http://biostat.mc.vanderbilt.edu/wiki/Main/FrankHarrell

Apparently Prof Frank E was quite a SAS coder himself (see http://biostat.mc.vanderbilt.edu/wiki/Main/SasMacros)

Back to JMP Genomics 5-

The JMP software platform provides:

• New integration capabilities let R users leverage JMP’s interactivegraphics to display analytic results.

• Tools for R programmers to build and package user interfaces that let them share customized R analytics with a broader audience.•

A new add-in infrastructure that simplifies the integration of external analytics into JMP.

 

+ For people in life sciences who like new stats software you can also download a trial version of IPA here at http://www.ingenuity.com/products/IPA/Free-Trial-Software.html

JMP 9 releasing on Oct 12

JMP 9 releases on Oct 12- it is a very good reliable data visualization and analytical tool ( AND available on Mac as well)

AND IT is advertising R Graphics as well (lol- I can visualize the look on some ahem SAS fans in the R Project)

Updated Pricing- note I am not sure why they are charging US academics 495$ when SAS On Demand is free for academics. Shouldnt JMP be free to students- maybe John Sall and his people can do a tradeoff analysis for this given JMP’s graphics are better than Base SAS (which is under some pressure from WPS and R)

http://www.sas.com/govedu/edu/programs/soda-account-setup.html

and http://www.enterpriseinnovation.net/content/sas-delivers-free-data-management-and-analytics-solutions-academe

*Offer good in the U.S. only.

OFFER PRICING DETAILS
New Corporate Customer

$1,595

Save $300.

No special requirements.
ORDER NOW (WIN) ORDER NOW (MAC)
Corporate Upgrade

$795

Save $155.

Complete the form below or call 1-877-594-6567. Requires valid JMP® 8 serial number.
New Academic

$495

Save $100.

Complete the form below or call 1-877-594-6567. Requires campus street address and campus e-mail address.
Academic Upgrade

$250

Save $45.

Complete the form below or call 1-877-594-6567. Requires campus street address and campus e-mail address.

From- the mailer-

Be First in Line for JMP® 9
Save up to $300 when you pre-order a
single-user license by Oct. 11

Pre-Order JMP 9

Make JMP your analytic hub for visual data discovery with this special offer, good through Oct. 11, 2010. Pre-order a single-user license of JMP 9 – for a discount of up to $300 – and get ready for a leap in data interactivity.

Order now and enjoy the compelling new features of JMP 9 when the software is released Oct. 12. New capabilities in JMP 9 let you:

  • Optimize and simulate using your Microsoft Excel spreadsheets.
  • Use maps to find patterns in your geographic data.
  • Enjoy the updated look and flexibility of JMP 9 on Microsoft Windows.
  • Create and share custom add-ins that extend JMP.
  • Leverage an expanded array of advanced statistical methodologies.
  • Display analytic results from R using interactive graphics.

PRE-ORDER JMP 9

What if I already have a JMP 8 single-user license?
Great news! You can upgrade to JMP 9 for less than half the regular price.

What if I’m an annual license customer?
Don’t worry, we’ve got you covered. Annual license customers enjoy priority access to all the latest JMP releases as soon as they become available. JMP 9 will be shipped to you automatically.

What if I work or study in the academic world?
Call 1-877-594-6567 to learn about significant discounts for students and professors through the JMP Academic Program.

Please feel free to forward this offer to interested colleagues.


Got two or more users?
A JMP® annual license is the way to go. Call for details.
1-877-594-6567

Remember: Act by Oct. 11!

JMP runs on Macintosh and Windows

Towards better analytical software

Here are some thoughts on using existing statistical software for better analytics and/or business intelligence (reporting)-

1) User Interface Design Matters- Most stats software have a legacy approach to user interface design. While the Graphical User Interfaces need to more business friendly and user friendly- example you can call a button T Test or You can call it Compare > Means of Samples (with a highlight called T Test). You can call a button Chi Square Test or Call it Compare> Counts Data. Also excessive reliance on drop down ignores the next generation advances in OS- namely touchscreen instead of mouse click and point.

Given the fact that base statistical procedures are the same across softwares, a more thoughtfully designed user interface (or revamped interface) can give softwares an edge over legacy designs.

2) Branding of Software Matters- One notable whine against SAS Institite products is a premier price. But really that software is actually inexpensive if you see other reporting software. What separates a Cognos from a Crystal Reports to a SAS BI is often branding (and user interface design). This plays a role in branding events – social media is often the least expensive branding and marketing channel. Same for WPS and Revolution Analytics.

3) Alliances matter- The alliances of parent companies are reflected in the sales of bundled software. For a complete solution , you need a database plus reporting plus analytical software. If you are not making all three of the above, you need to partner and cross sell. Technically this means that software (either DB, or Reporting or Analytics) needs to talk to as many different kinds of other softwares and formats. This is why ODBC in R is important, and alliances for small companies like Revolution Analytics, WPS and Netezza are just as important as bigger companies like IBM SPSS, SAS Institute or SAP. Also tie-ins with Hadoop (like R and Netezza appliance)  or  Teradata and SAS help create better usage.

4) Cloud Computing Interfaces could be the edge- Maybe cloud computing is all hot air. Prudent business planing demands that any software maker in analytics or business intelligence have an extremely easy to load interface ( whether it is a dedicated on demand website) or an Amazon EC2 image. Easier interfaces win and with the cloud still in early stages can help create an early lead. For R software makers this is critical since R is bad in PC usage for larger sets of data in comparison to counterparts. On the cloud that disadvantage vanishes. An easy to understand cloud interface framework is here ( its 2 years old but still should be okay) http://knol.google.com/k/data-mining-through-cloud-computing#

5) Platforms matter- Softwares should either natively embrace all possible platforms or bundle in middle ware themselves.

Here is a case study SAS stopped supporting Apple OS after Base SAS 7. Today Apple OS is strong  ( 3.47 million Macs during the most recent quarter ) and the only way to use SAS on a Mac is to do either

http://goo.gl/QAs2

or do a install of Ubuntu on the Mac ( https://help.ubuntu.com/community/MacBook ) and do this

http://ubuntuforums.org/showthread.php?t=1494027

Why does this matter? Well SAS is free to academics and students  from this year, but Mac is a preferred computer there. Well WPS can be run straight away on the Mac (though they are curiously not been able to provide academics or discounted student copies 😉 ) as per

http://goo.gl/aVKu

Does this give a disadvantage based on platform. Yes. However JMP continues to be supported on Mac. This is also noteworthy given the upcoming Chromium OS by Google, Windows Azure platform for cloud computing.

Top 10 Graphical User Interfaces in Statistical Software

Here is a list of top 10 GUIs in Statistical Software. The overall criterion is based on-

  • User Friendly Nature for a New User to begin click and point and learn.
  • Cleanliness of Automated Code or Log generated.
  • Practical application in consulting and corporate world.
  • Cost and Ease of Ownership (including purchase,install,training,maintainability,renewal)
  • Aesthetics (or just plain pretty)

However this list is not in order of ranking- ( as beauty (of GUI) lies in eyes of the beholder). For a list of top 10 GUI in R language only please see –

https://rforanalytics.wordpress.com/graphical-user-interfaces-for-r/

This is only a GUI based list so it excludes notable command line or text editor submit commands based softwares which are also very powerful and user friendly.

  1. JMP –

While critics of SAS Institute often complain on the premium pricing of the basic model (especially AFTER the entry of another SAS language software WPS from http://www.teamwpc.co.uk/products/wps – they should try out JMP from http://jmp.com – it has a 1 month free evaluation, is much less expensive and the GUI makes it very very easy to do basic statistical analysis and testing. The learning curve is surprisingly fast to pick it up (as it should be for well designed interfaces) and it allows for very good quality output graphics as well.

2.SPSS

The original GUI in this class of softwares- it has now expanded to a big portfolio of products. However SPSS 18 is nice with the increasing focus on Python and an early adoptee of R compatible interfaces, SPSS does offer a much affordable solution as well with a free evaluation. See especially http://www.spss.com/statistics/ and http://www.spss.com/software/modeling/modeler-pro/

the screenshot here is of SPSS Modeler

3. WPS

While it offers an alternative to Base SAS and SAS /Access software , I really like the affordability (1 Month Free Evaluation and overall lower cost especially for multiple CPU servers ), speed (on the desktop but not on the IBM OS version ) and the intuitive design as well as extensibility of the Workbench. It may look like an integrated development environment and not a proper GUI, but with all the menu features it does qualify as a GUI in my opinion. Continue reading “Top 10 Graphical User Interfaces in Statistical Software”