Tag: statistics

England rule India- again

If you type the words “business intelligence expert” in Google. you may get the top ranked result as http://goo.gl/pCqUh or Peter James Thomas, a profound name as it can be as it spans three of the most important saints in the church.

The current post for this is very non business -intelligence topic called Wager. http://peterjamesthomas.com/2011/07/20/wager/

It details how Peter, a virtual friend whom I have never met, and who looks suspiciously like Hugh Grant with the hair, and Ajay Ohri (myself) waged a wager on which cricket team would emerge victorious in the ongoing test series . It was a 4 match series, and India needed to win atleast the series or avoid losing it by a difference of 2, to retain their world cricket ranking (in Tests) as number 1.

Sadly at the end of the third test, the Indian cricket team have lost the series, the world number 1 ranking, and some serious respect by 3-0.

What is a Test Match? It is a game of cricket played over 5 days.
Why was Ajay so confident India would win. Because India won the one day world championship this April 2011. The one day series is a one day match of cricket.

There lies the problem. From an analytic point of view, I had been lulled into thinking that past performance was an indicator of future performance, indeed the basis of most analytical assumptions. Quite critically, I managed to overlook the following cricketing points-

1) Cricket performance is different from credit performance. It is the people and their fitness.

India’s strike bowler Zaheer Khan was out due to injury, we did not have any adequate replacement for him. India’s best opener Virender Sehwag was out due to shoulder injury in the first two tests.

Moral – Statistics can be misleading if you do not apply recent knowledge couple with domain expertise (in this case cricket)

2) What goes up must come down. Indeed if a team has performed its best two months back, it is a good sign that cyclicality will ensure performance will go down.

Moral- Do not depend on regression or time series with ignoring cyclical trends.

3) India’s cricket team is aging. England ‘s cricket team is youthful.

I should have gotten this one right. One of the big and understated reasons that the Indian economy is booming -is because we have the youngest population in the world with a median age of 28.

or as http://en.wikipedia.org/wiki/Demographics_of_India

India has more than 50% of its population below the age of 25 and more than 65% hovers below the age of 35. It is expected that, in 2020, the average age of an Indian will be 29 years, compared to 37 for China and 48 for Japan; and, by 2030, India’s dependency ratio should be just over 0.4

India’s population is 1.21 billion people, so potentially a much larger pool of athletes , once we put away our laptops that is.

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

 

the total population of the United Kingdom was 58,789,194 (I dont have numbers for average age)

 

Paradoxically India have the oldest cricket team in the world . This calls for detailed investigation and some old timers should give way to new comers after this drubbing.

Moral- Demographics matters. It is the people who vary more than any variable.

4) The Indian cricket team has played much less Test cricket and much more 20:20 and one day matches. 20:20 is a format in which only twenty overs are bowled per side. In Test Matches 90 overs are bowled every day for 5 days.

Stamina is critical in sports.

Moral- Context is important in extrapolating forecasts.

Everything said and done- the English cricket team played hard and fair and deserve to be number ones. I would love to say more on the Indian cricket team, but I now intend to watch Manchester United play soccer.

 

 

 

 

 

Interview Mike Boyarski Jaspersoft

Here is an interview with Mike Boyarski , Director Product Marketing at Jaspersoft

.

 

the largest BI community with over 14 million downloads, nearly 230,000 registered members, representing over 175,000 production deployments, 14,000 customers, across 100 countries.

Ajay- Describe your career in science from Biology to marketing great software.
Mike- I studied Biology with the assumption I’d pursue a career in medicine. It took about 2 weeks during an internship at a Los Angeles hospital to determine I should do something else.  I enjoyed learning about life science, but the whole health care environment was not for me.  I was initially introduced to enterprise-level software while at Applied Materials within their Microcontamination group.  I was able to assist with an internal application used to collect contamination data.  I later joined Oracle to work on an Oracle Forms application used to automate the production of software kits (back when documentation and CDs had to be physically shipped to recognize revenue). This gave me hands on experience with Oracle 7, web application servers, and the software development process.
I then transitioned to product management for various products including application servers, software appliances, and Oracle’s first generation SaaS based software infrastructure. In 2006, with the Siebel and PeopleSoft acquisitions underway, I moved on to Ingres to help re-invigorate their solid yet antiquated technology. This introduced me to commercial open source software and the broader Business Intelligence market.  From Ingres I joined Jaspersoft, one of the first and most popular open source Business Intelligence vendors, serving as head of product marketing since mid 2009.
Ajay- Describe some of the new features in Jaspersoft 4.1 that help differentiate it from the rest of the crowd. What are the exciting product features we can expect from Jaspersoft down the next couple of years.
Mike- Jaspersoft 4.1 was an exciting release for our customers because we were able to extend the latest UI advancements in our ad hoc report designer to the data analysis environment. Now customers can use a unified intuitive web-based interface to perform several powerful and interactive analytic functions across any data source, whether its relational, non-relational, or a Big Data source.
 The reality is that most (roughly 70%) of todays BI adoption is in the form of reports and dashboards. These tools are used to drive and measure an organizations business, however, data analysis presents the most strategic opportunity for companies because it can identify new opportunities, efficiencies, and competitive differentiation.  As more data comes online, the difference between those companies that are successful and those that are not will likely be attributed to their ability to harness data analysis techniques to drive and improve business performance. Thus, with Jaspersoft 4.1, and our improved ad hoc reporting and analysis UI we can effectively address a broader set of BI requirements for organizations of all sizes.
Ajay-  What do you think is a good metric to measure influence of an open source software product – is it revenue or is it number of downloads or number of users. How does Jaspersoft do by these counts.
Mike- History has shown that open source software is successful as a “bottoms up” disrupter within IT or the developer market.  Today, many new software projects and startup ventures are birthed on open source software, often initiated with little to no budget. As the organization achieves success with a particular project, the next initiative tends to be larger and more strategic, often displacing what was historically solved with a proprietary solution. These larger deployments strengthen the technology over time.
Thus, the more proven and battle tested an open source solution is, often measured via downloads, deployments, community size, and community activity, usually equates to its long term success. Linux, Tomcat, and MySQL have plenty of statistics to model this lifecycle. This model is no different for open source BI.
The success to date of Jaspersoft is directly tied to its solid proven technology and the vibrancy of the community.  We proudly and openly claim to have the largest BI community with over 14 million downloads, nearly 230,000 registered members, representing over 175,000 production deployments, 14,000 customers, across 100 countries.  Every day, 30,000 developers are using Jaspersoft to build BI applications.  Behind Excel, its hard to imagine a more widely used BI tool in the market.  Jaspersoft could not reach these kind of numbers with crippled or poorly architected software.
Ajay- What are your plans for leveraging cloud computing, mobile and tablet platforms and for making Jaspersoft more easy and global  to use.

The Top Statisticians in the World

 

 

 

 

 

 

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

 

John Tukey

From Wikipedia, the free encyclopedia
John Tukey

John Wilder Tukey
Born June 16, 1915
New Bedford, Massachusetts, USA
Died July 26, 2000 (aged 85)
New Brunswick, New Jersey
Residence United States
Nationality American
Fields Mathematician
Institutions Bell Labs
Princeton University
Alma mater Brown University
Princeton University
Doctoral advisor Solomon Lefschetz
Doctoral students Frederick Mosteller
Kai Lai Chung
Known for FFT algorithm
Box plot
Coining the term ‘bit’
Notable awards Samuel S. Wilks Award (1965)
National Medal of Science (USA) in Mathematical, Statistical, and Computational Sciences (1973)
Shewhart Medal (1976)
IEEE Medal of Honor (1982)
Deming Medal (1982)
James Madison Medal (1984)
Foreign Member of the Royal Society(1991)

John Wilder Tukey ForMemRS[1] (June 16, 1915 – July 26, 2000) was an American statistician.

Contents

[hide]

[edit]Biography

Tukey was born in New Bedford, Massachusetts in 1915, and obtained a B.A. in 1936 and M.Sc.in 1937, in chemistry, from Brown University, before moving to Princeton University where he received a Ph.D. in mathematics.[2]

During World War II, Tukey worked at the Fire Control Research Office and collaborated withSamuel Wilks and William Cochran. After the war, he returned to Princeton, dividing his time between the university and AT&T Bell Laboratories.

Among many contributions to civil society, Tukey served on a committee of the American Statistical Association that produced a report challenging the conclusions of the Kinsey Report,Statistical Problems of the Kinsey Report on Sexual Behavior in the Human Male.

He was awarded the IEEE Medal of Honor in 1982 “For his contributions to the spectral analysis of random processes and the fast Fourier transform (FFT) algorithm.”

Tukey retired in 1985. He died in New Brunswick, New Jersey on July 26, 2000.

[edit]Scientific contributions

His statistical interests were many and varied. He is particularly remembered for his development with James Cooley of the Cooley–Tukey FFT algorithm. In 1970, he contributed significantly to what is today known as the jackknife estimation—also termed Quenouille-Tukey jackknife. He introduced the box plot in his 1977 book,”Exploratory Data Analysis“.

Tukey’s range test, the Tukey lambda distribution, Tukey’s test of additivity and Tukey’s lemma all bear his name. He is also the creator of several little-known methods such as the trimean andmedian-median line, an easier alternative to linear regression.

In 1974, he developed, with Jerome H. Friedman, the concept of the projection pursuit.[3]

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

Sir Ronald Aylmer Fisher FRS (17 February 1890 – 29 July 1962) was an English statistician,evolutionary biologist, eugenicist and geneticist. Among other things, Fisher is well known for his contributions to statistics by creating Fisher’s exact test and Fisher’s equation. Anders Hald called him “a genius who almost single-handedly created the foundations for modern statistical science”[1] while Richard Dawkins named him “the greatest biologist since Darwin“.[2]

 

contacts.xls

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

William Sealy Gosset (June 13, 1876–October 16, 1937) is famous as a statistician, best known by his pen name Student and for his work on Student’s t-distribution.

Born in Canterbury, England to Agnes Sealy Vidal and Colonel Frederic Gosset, Gosset attendedWinchester College before reading chemistry and mathematics at New College, Oxford. On graduating in 1899, he joined the Dublin brewery of Arthur Guinness & Son.

Guinness was a progressive agro-chemical business and Gosset would apply his statistical knowledge both in the brewery and on the farm—to the selection of the best yielding varieties ofbarley. Gosset acquired that knowledge by study, trial and error and by spending two terms in 1906–7 in the biometric laboratory of Karl Pearson. Gosset and Pearson had a good relationship and Pearson helped Gosset with the mathematics of his papers. Pearson helped with the 1908 papers but he had little appreciation of their importance. The papers addressed the brewer’s concern with small samples, while the biometrician typically had hundreds of observations and saw no urgency in developing small-sample methods.

Another researcher at Guinness had previously published a paper containing trade secrets of the Guinness brewery. To prevent further disclosure of confidential information, Guinness prohibited its employees from publishing any papers regardless of the contained information. However, after pleading with the brewery and explaining that his mathematical and philosophical conclusions were of no possible practical use to competing brewers, he was allowed to publish them, but under a pseudonym (“Student”), to avoid difficulties with the rest of the staff.[1] Thus his most famous achievement is now referred to as Student’s t-distribution, which might otherwise have been Gosset’s t-distribution.

#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)
Related articles

Heritage prize= 3mill now open

I am still angry with THE netflix for 1 mill I lost out. No sweat! this time the money is 3 times as much, it is legit, and yes baby you can change the world, make it a better place and get rich.! see details below-http://www.heritagehealthprize.com/c/hhp/Data

HERITAGE HEALTH PRIZE DATA FILES

You must accept this competition’s rules before you’ll be able to download data files.

IMPORTANT NOTE: The information provided below is intended only to provide general guidance to participants in the Heritage Health Prize Competition and is subject to the Competition Official Rules. Any capitalized term not defined below is defined in the Competition Official Rules. Please consult the Competition Official Rules for complete details.

Heritage Provider Network is providing Competition Entrants with deidentified member data collected during a forty-eight month period that is allocated among three data sets (the “Data Sets”). Competition Entrants will use the Data Sets to develop and test their algorithms for accurately predicting the number of days that the members will spend in a hospital (inpatient or emergency room visit) during the 12-month period following the Data Set cut-off date.

HHP_release2.zip contains the latest files, so you can ignore HHP_release1.zip. SampleEntry.CSV shows you how an entry should look.

Data Sets will be released to Entrants after registration on the Website according to the following schedule:

April 4, 2011 Claims Table – Y1 and DaysInHospital Table – Y2

May 4, 2011

 All other Data Sets except Labs Table and Rx Table

From https://www.kaggle.com/

The $3 million Heritage Health Prize opens to entries

It’s been one month since the launch of the Heritage Health Prize. The prize has attracted some great publicity, receiving coverage from the Wall Street Journal, The Economist, Slate andForbes.

By now, people have had a good chance to poke around the first portion of the data. Now the fun starts! HPN have released two more years’-worth of data, set the accuracy threshold and are opening up the competition to entries. The data are available from the Heritage Health Prize page. Good luck to all participants!

The Deloitte/FIDE Chess Ratings Competition results

The Deloitte/FIDE Chess Ratings Competition attracted one of the strongest fields ever seen in a Kaggle Competition. The competition attracted 189 teams, ranging from chess ratings  experts to Netflix Prize winners. As Jeff Sonas wrote on the Kaggle blog last week, the  competition has far exceeded his expectations. A big congratulations the provisional winner, Tim Salimans, an econometrician at Erasmus University in Rotterdam. We look forward to reading about the approaches used by top performers on the Kaggle blog. We also look forward to the results of the FIDE prize, which could see the introduction of a new chess ratings system.

ICDAR 2011 Competition Results

The ICDAR 2011 competition also finished recently. The competiiton required participants to develop an algorithm that correctly matched handwriting samples. The winners were Lewis Griffin and Andrew Newell from the University College London who achieved Kaggle’s first ever perfect score by managing to match every sample correctly! Andrew and Lewis have posted a description of their winning method on the Kaggle blog.

Revolution R Enterprise

Since R is the most popular language used by Kaggle members, the Revolution Analytics team is making Revolution R Enterprise (the pre-eminent commercial version of R) available free of charge to Kaggle members. Revolution R Enterprise has several advantages over standard R, including the ability to seemlessly handle larger datasets. To get your free copy, visit http://info.revolutionanalytics.com/Kaggle.html.
Kaggle-in-Class

As many of you know, Kaggle offers a free platform, Kaggle-in-Class, for instructors who want to host competitions for their students. For those interested in hearing more about the use of Kaggle-in-Class as a teaching tool, Susan Holmes and Nelson Ray from Stanford University share their experience in a webinar organized by the Consortium for the Advancement of Undergraduate Statistics Education.

Related Articles

Scholarships for students via #rstatsjobs and R-lings

A vector drawing of the University of York coa...
Image via Wikipedia

Outstandingly attractive scholarships are available for students willing to travel to Yorkshire. Thats where the Battle of Roses was fought by the British Royal Family.

see http://en.wikipedia.org/wiki/Wars_of_the_Roses

Emphasis  and spaces in email above are made by me.

Message from Dr Top i   bell ow-


It is not New York but very old York, in the North of England.

The scholarships carry a tax-free stipend and financial assistance will be
given for travel expenses to and from York. Accommodation for successful
students is available on the University of York Campus.

For information about the tax-free stipend please write to
scholarships@yccsa.org.

Continue reading “Scholarships for students via #rstatsjobs and R-lings”

Using Views in R and comparing functions across multiple packages

Some RDF hacking relating to updating probabil...
Image via Wikipedia

R has almost 2923 available packages

This makes the task of searching among these packages and comparing functions for the same analytical task across different packages a bit tedious and prone to manual searching (of reading multiple Pdfs of help /vignette of packages) or sending an email to the R help list.

However using R Views is a slightly better way of managing all your analytical requirements for software rather than the large number of packages (see Graphics view below).

CRAN Task Views allow you to browse packages by topic and provide tools to automatically install all packages for special areas of interest. Currently, 28 views are available. http://cran.r-project.org/web/views/

Bayesian Bayesian Inference
ChemPhys Chemometrics and Computational Physics
ClinicalTrials Clinical Trial Design, Monitoring, and Analysis
Cluster Cluster Analysis & Finite Mixture Models
Distributions Probability Distributions
Econometrics Computational Econometrics
Environmetrics Analysis of Ecological and Environmental Data
ExperimentalDesign Design of Experiments (DoE) & Analysis of Experimental Data
Finance Empirical Finance
Genetics Statistical Genetics
Graphics Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
gR gRaphical Models in R
HighPerformanceComputing High-Performance and Parallel Computing with R
MachineLearning Machine Learning & Statistical Learning
MedicalImaging Medical Image Analysis
Multivariate Multivariate Statistics
NaturalLanguageProcessing Natural Language Processing
OfficialStatistics Official Statistics & Survey Methodology
Optimization Optimization and Mathematical Programming
Pharmacokinetics Analysis of Pharmacokinetic Data
Phylogenetics Phylogenetics, Especially Comparative Methods
Psychometrics Psychometric Models and Methods
ReproducibleResearch Reproducible Research
Robust Robust Statistical Methods
SocialSciences Statistics for the Social Sciences
Spatial Analysis of Spatial Data
Survival Survival Analysis
TimeSeries Time Series Analysis

To automatically install these views, the ctv package needs to be installed, e.g., via

install.packages("ctv")
library("ctv")
Created by Pretty R at inside-R.org


and then the views can be installed via install.views or update.views (which first assesses which of the packages are already installed and up-to-date), e.g.,

install.views("Econometrics")
 update.views("Econometrics")
 Created by Pretty R at inside-R.org

CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization

Maintainer: Nicholas Lewin-Koh
Contact: nikko at hailmail.net
Version: 2009-10-28

R is rich with facilities for creating and developing interesting graphics. Base R contains functionality for many plot types including coplots, mosaic plots, biplots, and the list goes on. There are devices such as postscript, png, jpeg and pdf for outputting graphics as well as device drivers for all platforms running R. lattice and grid are supplied with R’s recommended packages and are included in every binary distribution. lattice is an R implementation of William Cleveland’s trellis graphics, while grid defines a much more flexible graphics environment than the base R graphics.

R’s base graphics are implemented in the same way as in the S3 system developed by Becker, Chambers, and Wilks. There is a static device, which is treated as a static canvas and objects are drawn on the device through R plotting commands. The device has a set of global parameters such as margins and layouts which can be manipulated by the user using par() commands. The R graphics engine does not maintain a user visible graphics list, and there is no system of double buffering, so objects cannot be easily edited without redrawing a whole plot. This situation may change in R 2.7.x, where developers are working on double buffering for R devices. Even so, the base R graphics can produce many plots with extremely fine graphics in many specialized instances.

One can quickly run into trouble with R’s base graphic system if one wants to design complex layouts where scaling is maintained properly on resizing, nested graphs are desired or more interactivity is needed. grid was designed by Paul Murrell to overcome some of these limitations and as a result packages like lattice, ggplot2, vcd or hexbin (on Bioconductor ) use grid for the underlying primitives. When using plots designed with grid one needs to keep in mind that grid is based on a system of viewports and graphic objects. To add objects one needs to use grid commands, e.g., grid.polygon() rather than polygon(). Also grid maintains a stack of viewports from the device and one needs to make sure the desired viewport is at the top of the stack. There is a great deal of explanatory documentation included with grid as vignettes.

The graphics packages in R can be organized roughly into the following topics, which range from the more user oriented at the top to the more developer oriented at the bottom. The categories are not mutually exclusive but are for the convenience of presentation:

  • Plotting : Enhancements for specialized plots can be found in plotrix, for polar plotting, vcd for categorical data, hexbin (on Bioconductor ) for hexagon binning, gclus for ordering plots and gplots for some plotting enhancements. Some specialized graphs, like Chernoff faces are implemented in aplpack, which also has a nice implementation of Tukey’s bag plot. For 3D plots lattice, scatterplot3d and misc3d provide a selection of plots for different kinds of 3D plotting. scatterplot3d is based on R’s base graphics system, while misc3d is based on rgl. The package onion for visualizing quaternions and octonions is well suited to display 3D graphics based on derived meshes.
  • Graphic Applications : This area is not much different from the plotting section except that these packages have tools that may not for display, but can aid in creating effective displays. Also included are packages with more esoteric plotting methods. For specific subject areas, like maps, or clustering the excellent task views contributed by other dedicated useRs is an excellent place to start.
    • Effect ordering : The gclus package focuses on the ordering of graphs to accentuate cluster structure or natural ordering in the data. While not for graphics directly cba and seriation have functions for creating 1 dimensional orderings from higher dimensional criteria. For ordering an array of displays, biclust can be useful.
    • Large Data Sets : Large data sets can present very different challenges from moderate and small datasets. Aside from overplotting, rendering 1,000,000 points can tax even modern GPU’s. For univariate datalvplot produces letter value boxplots which alleviate some of the problems that standard boxplots exhibit for large data sets. For bivariate data ash can produce a bivariate smoothed histogram very quickly, and hexbin, on Bioconductor , can bin bivariate data onto a hexagonal lattice, the advantage being that the irregular lines and orientation of hexagons do not create linear artifacts. For multivariate data, hexbin can be used to create a scatterplot matrix, combined with lattice. An alternative is to use scagnostics to produce a scaterplot matrix of “data about the data”, and look for interesting combinations of variables.
    • Trees and Graphs : ape and ade4 have functions for plotting phylogenetic trees, which can be used for plotting dendrograms from clustering procedures. While these packages produce decent graphics, they do not use sophisticated algorithms for node placement, so may not be useful for very large trees. igraph has the Tilford-Rheingold algorithm implementead and is useful for plotting larger trees. diagram as facilities for flow diagrams and simple graphs. For more sophisticated graphs Rgraphviz and igraph have functions for plotting and layout, especially useful for representing large networks.
  • Graphics Systems : lattice is built on top of the grid graphics system and is an R implementation of William Cleveland’s trellis system for S-PLUS. lattice allows for building many types of plots with sophisticated layouts based on conditioning. ggplot2 is an R implementation of the system described in “A Grammar of Graphics” by Leland Wilkinson. Like lattice, ggplot (also built on top of grid) assists in trellis-like graphics, but allows for much more. Since it is built on the idea of a semantics for graphics there is much more emphasis on reshaping data, transformation, and assembling the elements of a plot.
  • Devices : Whereas grid is built on top of the R graphics engine, many in the R community have found the R graphics engine somewhat inflexible and have written separate device drivers that either emphasize interactivity or plotting in various graphics formats. R base supplies devices for PostScript, PDF, JPEG and other formats. Devices on CRAN include cairoDevice which is a device based libcairo, which can actually render to many device types. The cairo device is desgned to work with RGTK2, which is an interface to the Gimp Tool Kit, similar to pyGTK2. GDD provides device drivers for several bitmap formats, including GIF and BMP. RSvgDevice is an SVG device driver and interfaces well with with vector drawing programs, or R web development packages, such as Rpad. When SVG devices are for web display developers should be aware that internet explorer does not support SVG, but has their own standard. Trust Microsoft. rgl provides a device driver based on OpenGL, and is good for 3D and interactive development. Lastly, the Augsburg group supplies a set of packages that includes a Java-based device, JavaGD.
  • Colors : The package colorspace provides a set of functions for transforming between color spaces and mixcolor() for mixing colors within a color space. Based on the HCL colors provided in colorspace, vcdprovides a set of functions for choosing color palettes suitable for coding categorical variables ( rainbow_hcl()) and numerical information ( sequential_hcl(), diverge_hcl()). Similar types of palettes are provided in RColorBrewer and dichromat is focused on palettes for color-impaired viewers.
  • Interactive Graphics : There are several efforts to implement interactive graphics systems that interface well with R. In an interactive system the user can interactively query the graphics on the screen with the mouse, or a moveable brush to zoom, pan and query on the device as well as link with other views of the data. rggobi embeds the GGobi interactive graphics system within R, so that one can display a data frame or several in GGobi directly from R. The package has functions to support longitudinal data, and graphs using GGobi’s edge set functionality. The RoSuDA repository maintained and developed by the University of Augsburg group has two packages, iplots and iwidgets as well as their Java development environment including a Java device, JavaGD. Their interactive graphics tools contain functions for alpha blending, which produces darker shading around areas with more data. This is exceptionally useful for parallel coordinate plots where many lines can quickly obscure patterns. playwith has facilities for building interactive versions of R graphics using the cairoDevice and RGtk2. Lastly, the rgl package has mechanisms for interactive manipulation of plots, especially 3D rotations and surfaces.
  • Development : For development of specialized graphics packages in R, grid should probably be the first consideration for any new plot type. rgl has better tools for 3D graphics, since the device is interactive, though it can be slow. An alternative is to use Java and the Java device in the RoSuDA packages, though Java has its own drawbacks. For porting plotting code to grid, using the package gridBase presents a nice intermediate step to embed base graphics in grid graphics and vice versa.

 

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