WordPress.com Analytics

The Analytics (or stats) dashboard at WordPress.com continues to disappoint, and is a major reason for people to move out of WordPress.com hosting (since they need better analytics like that by Google Analytics which cant be enabled on the default mode)

Its not really beautiful unlike the rest of WordPress Universe!

It can be made better if people try harder! Analytics matters

Here are some points

1) Bar charts and Histograms are not really the best way to visualize trends across time

2) Location Analytics is limited to just country level analysis and the heatmap (?) is aweful in terms of distinguishing gradients 

3) Referrers Tab needs to do a better job on distinguishing between mobile and non mobile traffic, social and non social traffic (and there are better ways to visualize than just a simple list)!

4)  I cant even export my traffic stats (and forget an api !) so I am stuck with the bad data viz here

Google Visualization Tools Can Help You Build a Personal Dashboard

The Google Visualization API is a great way for people to make dashboards with slick graphics based  on data without getting into the fine print of the scripting language  itself.  It utilizes the same tools as Google itself does, and makes visualizing data using API calls to the Visualization API. Thus a real-time customizable dashboard that is publishable to the internet can be created within minutes, and more importantly insights can be much more easily drawn from graphs than from looking at rows of tables and numbers.

  1. There are 41 gadgets (including made by both Google and third-party developers ) available in the Gadget  Gallery ( https://developers.google.com/chart/interactive/docs/gadgetgallery)
  2. There are 12 kinds of charts available in the Chart Gallery (https://developers.google.com/chart/interactive/docs/gallery) .
  3. However there 26 additional charts in the charts page at https://developers.google.com/chart/interactive/docs/more_charts )

Building and embedding charts is simplified to a few steps

  • Load the AJAX API
  • Load the Visualization API and the appropriate package (like piechart or barchart from the kinds of chart)
  • Set a callback to run when the Google Visualization API is loaded
    • Within the Callback – It creates and populates a data table, instantiates the particular chart type chosen, passes in the data and draws it.
    • Create the data table with appropriately named columns and data rows.
    • Set chart options with Title, Width and Height
  • Instantiate and draw the chart, passing in some options including the name and id
  • Finally write the HTML/ Div that will hold the chart

You can simply copy and paste the code directly from https://developers.google.com/chart/interactive/docs/quick_start without getting into any details, and tweak them according to your data, chart preference and voila your web dashboard is ready!
That is the beauty of working with API- you can create and display genius ideas without messing with the scripting languages and code (too much). If you like to dive deeper into the API, you can look at the various objects at https://developers.google.com/chart/interactive/docs/reference

First launched in Mar 2008, Google Visualization API has indeed come a long way in making dashboards easier to build for people wanting to utilize advanced data visualization . It came about directly as a result of Google’s 2007 acquisition of GapMinder (of Hans Rosling fame).
As invariably and inevitably computing shifts to the cloud, visualization APIs will be very useful. Tableau Software has been a pioneer in selling data visualizing to the lucrative business intelligence and business dashboards community (you can see the Tableau Software API at http://onlinehelp.tableausoftware.com/v7.0/server/en-us/embed_api.htm ), and Google Visualization can do the same and capture business dashboard and visualization market , if there is more focus on integrating it from Google in it’s multiple and often confusing API offerings.
However as of now, this is quite simply the easiest way to create a web dashboard for your personal needs. Google guarantees 3 years of backward compatibility with this API and it is completely free.

Interview Kelci Miclaus, SAS Institute Using #rstats with JMP

Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.

 

Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?

Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.

One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.

I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.

(see http://www.decisionstats.com/jmp-and-r-rstats/)

Ajay- Are there plans to extend JMP integration with other languages like Python?

Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.

 Ajay- How is R a complimentary fit to JMP’s technical capabilities?

Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.

JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.

Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.

Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?

Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.

Ajay-  While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.

 Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included.  Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.

We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.

About- Kelci Miclaus

Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.

.

 

The all new Blogging in Blogger

I had given up on Blogspot ever having a makeover in favor of the nice themes at

wordpress, but man, the new CEO at google is really shaking some stuff here.

Check out the nice features for customizing the themes at Blogspot

Continue reading “The all new Blogging in Blogger”

Updated Interview Elissa Fink -VP Tableau Software

Here is an interview with Elissa Fink, VP Marketing of that new wonderful software called Tableau that makes data visualization so nice and easy to learn and work with.

Elissa Fink, VP, Marketing

Ajay-  Describe your career journey from high school to over 20 plus years in marketing. What are the various trends that you have seen come and go in marketing.

Elissa- I studied literature and linguistics in college and didn’t discover analytics until my first job selling advertising for the Wall Street Journal. Oddly enough, the study of linguistics is not that far from decision analytics: they both are about taking a structured view of information and trying to see and understand common patterns. At the Journal, I was completely captivated analyzing and comparing readership data. At the same time, the idea of using computers in marketing was becoming more common. I knew that the intersection of technology and marketing was going to radically change things – how we understand consumers, how we market and sell products, and how we engage with customers. So from that point on, I’ve always been focused on technology and marketing, whether it’s working as a marketer at technology companies or applying technology to marketing problems for other types of companies.  There have been so many interesting trends. Taking a long view, a key trend I’ve noticed is how marketers work to understand, influence and motivate consumer behavior. We’ve moved marketing from where it was primarily unpredictable, qualitative and aimed at talking to mass audiences, where the advertising agency was king. Now it’s a discipline that is more data-driven, quantitative and aimed at conversations with individuals, where the best analytics wins. As with any trend, the pendulum swings far too much to either side causing backlashes but overall, I think we are in a great place now. We are using data-driven analytics to understand consumer behavior. But pure analytics is not the be-all, end-all; good marketing has to rely on understanding human emotions, intuition and gut feel – consumers are far from rational so taking only a rational or analytical view of them will never explain everything we need to know.

Ajay- Do you think technology companies are still predominantly dominated by men . How have you seen diversity evolve over the years. What initiatives has Tableau taken for both hiring and retaining great talent.

Elissa- The thing I love about the technology industry is that its key success metrics – inventing new products that rapidly gain mass adoption in pursuit of making profit – are fairly objective. There’s little subjective nature to the counting of dollars collected selling a product and dollars spent building a product. So if a female can deliver a better product and bigger profits faster and better, then that female is going to get the resources, jobs, power and authority to do exactly that. That’s not to say that the technology industry is gender-blind, race-blind, etc. It isn’t – technology is far from perfect. For example, the industry doesn’t have enough diversity in positions of power. But I think overall, in comparison to a lot of other industries, it’s pretty darn good at giving people with great ideas the opportunities to realize their visions regardless of their backgrounds or characteristics.

At Tableau, we are very serious about bringing in and developing talented people – they are the key to our growth and success. Hiring is our #1 initiative so we’ve spent a lot of time and energy both on finding great candidates and on making Tableau a place that they want to work. This includes things like special recruiting events, employee referral programs, a flexible work environment, fun social events, and the rewards of working for a start-up. Probably our biggest advantage is the company itself – working with people you respect on amazing, cutting-edge products that delight customers and are changing the world is all too rare in the industry but a reality at Tableau. One of our senior software developers put it best when he wrote “The emphasis is on working smarter rather than longer: family and friends are why we work, not the other way around. Tableau is all about happy, energized employees executing at the highest level and delivering a highly usable, high quality, useful product to our customers.” People who want to be at a place like that should check out our openings at http://www.tableausoftware.com/jobs.

Ajay- What are most notable features in tableau’s latest edition. What are the principal software that competes with Tableau Software products and how would you say Tableau compares with them.

Elissa- Tableau 6.1 will be out in July and we are really excited about it for 3 reasons.

First, we’re introducing our mobile business intelligence capabilities. Our customers can have Tableau anywhere they need it. When someone creates an interactive dashboard or analytical application with Tableau and it’s viewed on a mobile device, an iPad in particular, the viewer will have a native, touch-optimized experience. No trying to get your fingertips to act like a mouse. And the author didn’t have to create anything special for the iPad; she just creates her analytics the usual way in Tableau. Tableau knows the dashboard is being viewed on an iPad and presents an optimized experience.

Second, we’ve take our in-memory analytics engine up yet another level. Speed and performance are faster and now people can update data incrementally rapidly. Introduced in 6.0, our data engine makes any data fast in just a few clicks. We don’t run out of memory like other applications. So if I build an incredible dashboard on my 8-gig RAM PC and you try to use it on your 2-gig RAM laptop, no problem.

And, third, we’re introducing more features for the international markets – including French and German versions of Tableau Desktop along with more international mapping options.  It’s because we are constantly innovating particularly around user experience that we can compete so well in the market despite our relatively small size. Gartner’s seminal research study about the Business Intelligence market reported a massive market shift earlier this year: for the first time, the ease-of-use of a business intelligence platform was more important than depth of functionality. In other words, functionality that lots of people can actually use is more important than having sophisticated functionality that only specialists can use. Since we focus so heavily on making easy-to-use products that help people rapidly see and understand their data, this is good news for our customers and for us.

Ajay-  Cloud computing is the next big thing with everyone having a cloud version of their software. So how would you run Cloud versions of Tableau Server (say deploying it on an Amazon Ec2  or a private cloud)

Elissa- In addition to the usual benefits espoused about Cloud computing, the thing I love best is that it makes data and information more easily accessible to more people. Easy accessibility and scalability are completely aligned with Tableau’s mission. Our free product Tableau Public and our product for commercial websites Tableau Digital are two Cloud-based products that deliver data and interactive analytics anywhere. People often talk about large business intelligence deployments as having thousands of users. With Tableau Public and Tableau Digital, we literally have millions of users. We’re serving up tens of thousands of visualizations simultaneously – talk about accessibility and scalability!  We have lots of customers connecting to databases in the Cloud and running Tableau Server in the Cloud. It’s actually not complex to set up. In fact, we focus a lot of resources on making installation and deployment easy and fast, whether it’s in the cloud, on premise or what have you. We don’t want people to have spend weeks or months on massive roll-out projects. We want it to be minutes, hours, maybe a day or 2. With the Cloud, we see that people can get started and get results faster and easier than ever before. And that’s what we’re about.

Ajay- Describe some of the latest awards that Tableau has been wining. Also how is Tableau helping universities help address the shortage of Business Intelligence and Big Data professionals.

Elissa-Tableau has been very fortunate. Lately, we’ve been acknowledged by both Gartner and IDC as the fastest growing business intelligence software vendor in the world. In addition, our customers and Tableau have won multiple distinctions including InfoWorld Technology Leadership awards, Inc 500, Deloitte Fast 500, SQL Server Magazine Editors’ Choice and Community Choice awards, Data Hero awards, CODiEs, American Business Awards among others. One area we’re very passionate about is academia, participating with professors, students and universities to help build a new generation of professionals who understand how to use data. Data analysis should not be exclusively for specialists. Everyone should be able to see and understand data, whatever their background. We come from academic roots, having been spun out of a Stanford research project. Consequently, we strongly believe in supporting universities worldwide and offer 2 academic programs. The first is Tableau For Teaching, where any professor can request free term-length licenses of Tableau for academic instruction during his or her courses. And, we offer a low-cost Student Edition of Tableau so that students can choose to use Tableau in any of their courses at any time.

Elissa Fink, VP Marketing,Tableau Software

 

Elissa Fink is Tableau Software’s Vice President of Marketing. With 20+ years helping companies improve their marketing operations through applied data analysis, Elissa has held executive positions in marketing, business strategy, product management, and product development. Prior to Tableau, Elissa was EVP Marketing at IXI Corporation, now owned by Equifax. She has also served in executive positions at Tele Atlas (acquired by TomTom), TopTier Software (acquired by SAP), and Nielsen/Claritas. Elissa also sold national advertising for the Wall Street Journal. She’s a frequent speaker and has spoken at conferences including the DMA, the NCDM, Location Intelligence, the AIR National Forum and others. Elissa is a graduate of Santa Clara University and holds an MBA in Marketing and Decision Systems from the University of Southern California.

Elissa first discovered Tableau late one afternoon at her previous company. Three hours later, she was still “at play” with her data. “After just a few minutes using the product, I was getting answers to questions that were taking my company’s programmers weeks to create. It was instantly obvious that Tableau was on a special mission with something unique to offer the world. I just had to be a part of it.”

To know more – read at http://www.tableausoftware.com/

and existing data viz at http://www.tableausoftware.com/learn/gallery

Storm seasons: measuring and tracking key indicators
What’s happening with local real estate prices?
How are sales opportunities shaping up?
Identify your best performing products
Applying user-defined parameters to provide context
Not all tech companies are rocket ships
What’s really driving the economy?
Considering factors and industry influencers
The complete orbit along the inside, or around a fixed circle
How early do you have to be at the airport?
What happens if sales grow but so does customer churn?
What are the trends for new retail locations?
How have student choices changed?
Do patients who disclose their HIV status recover better?
Closer look at where gas prices swing in areas of the U.S.
U.S. Census data shows more women of greater age
Where do students come from and how does it affect their grades?
Tracking customer service effectiveness
Comparing national and local test scores
What factors correlate with high overall satisfaction ratings?
Fund inflows largely outweighed outflows well after the bubble
Which programs are competing for federal stimulus dollars?
Oil prices and volatility
A classic candlestick chart
How do oil, gold and CPI relate to the GDP growth rate?

 

#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)