The R Imperative

Prof Courtney Brown makes a succinct case for learning the R programming language.



I concur with Prof Brown. The R programming language is real. Much more to follow...

Project R

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Try R Training

For those seeking to get started with the R programming language, O'Reilly Media is sponsoring a free online training course entitled, Try R. Follow the link below to learn more:

The Try R training program is delivered in an easy to follow "learning by doing" format divided into eight chapters as follows:

• Chapter 1: R Syntax
• Chapter 2: Vectors
• Chapter 3: Matrices
• Chapter 4: Summary Statistics
• Chapter 5: Factors
• Chapter 6: Data Frames
• Chapter 7: Working With Real-World Data
• Chapter 8: Installing Additional Packages

I completed the training and was delighted with the "learning by doing" approach of the program. Those new to R will find this offering to be an easy way to get introduced to how R works, as well as the power of the R programming language to perform vector and matrix analytics, which are fundamental to learning and applying advanced statistical methods. Again, Try R is a free training program sponsored by the experts at O'Reilly Media.

Learn More

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R Project is Real

R Project (or simply, R) has become the analytical software of choice for many data scientists around the world. According to the R website:

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes:

• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

For Excel (Microsoft) users, an R interface/add-in called RExcel is available and offers an integration path for analysts who use Excel. I tested RExcel on my personal workstation and found the add-in extensions to be reliable and useful.

R is available as "Free Software" under the terms of the Free Software Foundation's GNU General Public License in source code form. R compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

Analysts and data scientists in all fields who have no yet evaluated R should do so at their earliest convenience. Follow the link below to learn more.

R Project

Programming the Apple //e circa 1980's

The short video below shows the typical user experience that Apple //e programmers followed during the early 1980's.



I was an avid Apple //e user back in the day, so the above video activates personal memories. Even the sounds are very familiar to me. Personal computing has certainly come a long way since the invention of the Apple //e.

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Discovering Statistics Using R

Open source enthusiasts seeking to become statisticians will want to check out, Discovering Statistics Using R (2012) by Prof Andy Field, Prof Jeremy Miles, and Zoë Field. According to the publisher:

Hot on the heels of the award-winning and best selling Discovering Statistics Using SPSS (3rd ed), Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) and Zoë Field to write Discovering Statistics Using R. Keeping the uniquely humouous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible, and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioral sciences throughout the world.

Those seeking to practice statistics while learning to use R (Project R) will find Discovering Statistics Using R to be a useful and instructive guide for learning statistics while also conquering the R programming environment. Follow the link below to learn more about this terrific resource.

Source: Field, A, Miles, J, and Field Z (2012), Discovering Statistics Using R, Thousand Oaks, CA: Sage.

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John Cook: The R Programming Language for Statistics

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ModelRisk 3.0: Best in Class Solution for Excel-Based Risk Analysis

I have been a practicing risk modeler and analyst now for over fifteen years, and during that time, I have worked and trained with a variety of popular spreadsheet-based software tools, including Crystal Ball (Oracle) and @Risk (Palisade). Each of these applications enables users of Excel (Microsoft) to incorporate simulations and optimizations into models. However, neither Crystal Ball nor @Risk offers a comprehensive software solution that combines simulation and optimization with stochastic object modeling, time-series forecasting, and multivariate correlation. ModelRisk 3.0 from Vose Software (Ghent, Brussels) combines all of these features into a single application that works seamlessly with Excel.

The tools and techniques made available in ModelRisk have been developed from Vose Consulting’s experience in assessing risk in a broad range of industries over many years, and goes far beyond the Monte Carlo simulation tools currently available. ModelRisk has been designed to make risk analysis modeling simpler and more intuitive for the novice user, and at the same time provide access to the most advanced risk modeling techniques available. (Press release, Vose Software, May 11, 2009)

The latest version of ModelRisk is the most advanced spreadsheet-based risk-modeling platform ever developed and currently stands as the best in class software solution for quantitative risk analysis, forecasting, simulation, and optimization. ModelRisk enables users to build complex risk analysis models in a fraction of the time required to develop custom-coded applications. Open database connectivity further extends the business intelligence capabilities of this integrated platform by enabling access to essentially any data warehousing system in use today.

“Good risk analysis modeling doesn’t have to be hard, but the tools just weren’t there to make it easy and intuitive. So we asked, “If we could start from the beginning, what would the ideal risk analysis tool be like?” says David Vose, Technical Director of Vose Software. “ModelRisk is the result. Users of competing spreadsheet risk analysis tools will find all the features they are familiar with in ModelRisk, but ModelRisk throws open the doors to a far richer world of risk analysis modeling. Better still, ModelRisk has many visual tools that really help the user understand what they are modeling so they can be confident in what they do, and ModelRisk costs no more than the older tools available. We also have a training program second-to-none: the people teaching our courses are risk analysts with years of real-world experience, not just software trainers.”

ModelRisk 3.0 now includes:

• Over 100 distribution types
• Stochastic ‘objects’ for more powerful and intuitive modeling
• Time-series forecasting tools such as ARMA, ARCH, GARCH, and more
• Advanced correlations via copulas
• Distribution fitting of time-series data, including correlation structures
• Probability measures and reporting
• Integrated optimization using the most advanced, proven methods available
• Multiple visual interfaces for ModelRisk functions
• User library for organizing models, assumptions, references, simulation results, and more
• Direct linking to external databases
• Extreme-value modeling
• Advanced data visualization tools
• Expert elicitation tools
• Mathematical tools fors numerical integration, series summation, and matrix analysis
• Comprehensive statistical analytics
• World class help file
• Developers’ kit for programming using ModelRisk’s technology

Currently, no other competing software package on the market offers the same comprehensive list or range of features found in ModelRisk 3.0, which is now my primary risk modeling, forecasting, and business intelligence platform. For more information, follow the link below.

Learn More

Statisticians in Demand

We live in a world where data is the raw material that analysts use to produce information, also known as knowledge. However, without analysis, data remains data in raw form. This might explain the recent upsurge in hiring for statisticians. Steve Lohr (“For Today’s Graduate, Just One Word: Statistics,” NYT, Aug 5, 2009) argues that statistics may be the password for hiring in the coming years:

The rising stature of statisticians, who can earn $125,000 at top companies in their first year after getting a doctorate, is a byproduct of the recent explosion of digital data. In field after field, computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.

The demand for statisticians is consistent with a larger trend toward competing on analytics in enterprise. This trend has also given impetus to the need for other experts, especially in computer programming.

Though at the fore, statisticians are only a small part of an army of experts using modern statistical techniques for data analysis. Computing and numerical skills, experts say, matter far more than degrees. So the new data sleuths come from backgrounds like economics, computer science and mathematics.

Over the past several decades, firms have invested heavily into data management technology, including server and data-warehousing systems. These investments have created massive amounts of raw data that are begging to be analyzed by people trained and skilled in descriptive and inferential statistics, stochastic modeling, linear and non-linear forecasting, and so forth. The creation of so much raw data in recent years makes statistical analysis of that data a vital value-adding activity that enables competing on analytics.

“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”

The Benefits of R

Prof John Verzani (2001) summarizes some of the major benefits of using R (Project R) for learning and teaching statistics as follows:

1. R is free.
2. R is open-source and runs on Linux, Windows, and Apple.
3. R has an excellent built-in help system.
4. R has excellent graphing capabilities.
5. R is a computer programming language.

I will be talking more about each of the above benefits in future posts. For now, let's focus on the first benefit, which is that R is free software. Follow the link below to download and install a free copy of R.

Source: Verzani, J (2001), SimpleR: Using R for Introductory Statistics.

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Well Said...

"Interfaces keep things tidy, but don’t accelerate growth: Functions do."

~ Prof Alan Jay Perlis, Epigrams on Programming (1982)

Prof Alan Jay Perlis (1922-1990)

Lessig: Code is Law

Programmers and non-programmers alike will be interested in Prof Lawrence "Larry" Lessig's views on programming code as law.

Well Said...

"Design and programming are human activities; forget that and all is lost."

~ Bjarne Stroustrup

Prof Bjarne Stroustrup (1951- )

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Education of a Computer Programmer

I came across a photogragh of my first personal computer purchased back in 1983 -- the Apple //e -- and suddenly found myself reminiscing about how I became a computer user. The Apple //e was powered by the venerable MOS Technology 6502 8-bit microprocessor running at 1.023 MHz, and boasted 128 kilobytes of RAM. I recall taking a $2,500 personal loan to make the purchase, which included the hardware shown below along with an Apple Imagewriter dot-matrix printer.

I spent many hours programming and otherwise "playing" on my Apple //e during the mid-1980's. What I did not know at the the time was how important computer technology would become for me and my career. I essentially taught myself to program on the Apple //e at home, and would later build upon what I had learned as I migrated onto more powerful machines in subsequent years. However, my education about personal computers definitely started on the Apple //e computer, and my life has not been the same since.

My how computer technology has evolved.