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Here is an interview with JJ Allaire, founder of RStudio. RStudio is the IDE that has overtaken other IDE within the R Community in terms of ease of usage. On the eve of their latest product launch, JJ talks to DecisionStats on RStudio and more.
Ajay- So what is new in the latest version of RStudio and how exactly is it useful for people?
JJ- The initial release of RStudio as well as the two follow-up releases we did last year were focused on the core elements of using R: editing and running code, getting help, and managing files, history, workspaces, plots, and packages. In the meantime users have also been asking for some bigger features that would improve the overall work-flow of doing analysis with R. In this release (v0.95) we focused on three of these features:
Projects. R developers tend to have several (and often dozens) of working contexts associated with different clients, analyses, data sets, etc. RStudio projects make it easy to keep these contexts well separated (with distinct R sessions, working directories, environments, command histories, and active source documents), switch quickly between project contexts, and even work with multiple projects at once (using multiple running versions of RStudio).
Version Control. The benefits of using version control for collaboration are well known, but we also believe that solo data analysis can achieve significant productivity gains by using version control (this discussion on Stack Overflow talks about why). In this release we introduced integrated support for the two most popular open-source version control systems: Git and Subversion. This includes changelist management, file diffing, and browsing of project history, all right from within RStudio.
Code Navigation. When you look at how programmers work a surprisingly large amount of time is spent simply navigating from one context to another. Modern programming environments for general purpose languages like C++ and Java solve this problem using various forms of code navigation, and in this release we’ve brought these capabilities to R. The two main features here are the ability to type the name of any file or function in your project and go immediately to it; and the ability to navigate to the definition of any function under your cursor (including the definition of functions within packages) using a keystroke (F2) or mouse gesture (Ctrl+Click).
Ajay- What’s the product road map for RStudio? When can we expect the IDE to turn into a full fledged GUI?
JJ- Linus Torvalds has said that “Linux is evolution, not intelligent design.” RStudio tries to operate on a similar principle—the world of statistical computing is too deep, diverse, and ever-changing for any one person or vendor to map out in advance what is most important. So, our internal process is to ship a new release every few months, listen to what people are doing with the product (and hope to do with it), and then start from scratch again making the improvements that are considered most important.
Right now some of the things which seem to be top of mind for users are improved support for authoring and reproducible research, various editor enhancements including code folding, and debugging tools.
What you’ll see is us do in a given release is to work on a combination of frequently requested features, smaller improvements to usability and work-flow, bug fixes, and finally architectural changes required to support current or future feature requirements.
While we do try to base what we work on as closely as possible on direct user-feedback, we also adhere to some core principles concerning the overall philosophy and direction of the product. So for example the answer to the question about the IDE turning into a full-fledged GUI is: never. We believe that textual representations of computations provide fundamental advantages in transparency, reproducibility, collaboration, and re-usability. We believe that writing code is simply the right way to do complex technical work, so we’ll always look for ways to make coding better, faster, and easier rather than try to eliminate coding altogether.
Ajay -Describe your journey in science from a high school student to your present work in R. I noticed you have been very successful in making software products that have been mostly proprietary products or sold to companies.
Why did you get into open source products with RStudio? What are your plans for monetizing RStudio further down the line?
JJ- In high school and college my principal areas of study were Political Science and Economics. I also had a very strong parallel interest in both computing and quantitative analysis. My first job out of college was as a financial analyst at a government agency. The tools I used in that job were SAS and Excel. I had a dim notion that there must be a better way to marry computation and data analysis than those tools, but of course no concept of what this would look like.
From there I went more in the direction of general purpose computing, starting a couple of companies where I worked principally on programming languages and authoring tools for the Web. These companies produced proprietary software, which at the time (between 1995 and 2005) was a workable model because it allowed us to build the revenue required to fund development and to promote and distribute the software to a wider audience.
By 2005 it was however becoming clear that proprietary software would ultimately be overtaken by open source software in nearly all domains. The cost of development had shrunken dramatically thanks to both the availability of high-quality open source languages and tools as well as the scale of global collaboration possible on open source projects. The cost of promoting and distributing software had also collapsed thanks to efficiency of both distribution and information diffusion on the Web.
When I heard about R and learned more about it, I become very excited and inspired by what the project had accomplished. A group of extremely talented and dedicated users had created the software they needed for their work and then shared the fruits of that work with everyone. R was a platform that everyone could rally around because it worked so well, was extensible in all the right ways, and most importantly was free (as in speech) so users could depend upon it as a long-term foundation for their work.
So I started RStudio with the aim of making useful contributions to the R community. We started with building an IDE because it seemed like a first-rate development environment for R that was both powerful and easy to use was an unmet need. Being aware that many other companies had built successful businesses around open-source software, we were also convinced that we could make RStudio available under a free and open-source license (the AGPLv3) while still creating a viable business. At this point RStudio is exclusively focused on creating the best IDE for R that we can. As the core product gets where it needs to be over the next couple of years we’ll then also begin to sell other products and services related to R and RStudio.
In 1995 Joseph J. (JJ) Allaire co-founded Allaire Corporation with his brother Jeremy Allaire, creating the web development tool ColdFusion. In March 2001, Allaire was sold to Macromedia where ColdFusion was integrated into the Macromedia MX product line. Macromedia was subsequently acquired by Adobe Systems, which continues to develop and market ColdFusion.
After the sale of his company, Allaire became frustrated at the difficulty of keeping track of research he was doing using Google. To address this problem, he co-founded Onfolio in 2004 with Adam Berrey, former Allaire co-founder and VP of Marketing at Macromedia.
On March 8, 2006, Onfolio was acquired by Microsoft where many of the features of the original product are being incorporated into the Windows Live Toolbar. On August 13, 2006, Microsoft released the public beta of a new desktop blogging client called Windows Live Writer that was created by Allaire’s team at Microsoft.
Starting in 2009, Allaire has been developing a web-based interface to the widely used R technical computing environment. A beta version of RStudio was publicly released on February 28, 2011.
JJ Allaire received his B.A. from Macalester College (St. Paul, MN) in 1991.
RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. Like R, RStudio is available under a free software license. RStudio is designed to be as straightforward and intuitive as possible to provide a friendly environment for new and experienced R users alike. RStudio is also a company, and they plan to sell services (support, training, consulting, hosting) related to the open-source software they distribute.
I use the simple-tags plugin in WordPress for automatically creating and posting tags. I am hoping this makes the site better to navigate. Given the fact that I had not been a very efficient tagger before, this plugin can really be useful for someone in creating tags for more than 100 (or 1000 posts) especially WordPress based blog aggregators.
The plugin is available here -
Simple Tags is the successor of Simple Tagging Plugin This is THE perfect tool to manage perfectly your WP terms for any taxonomy
It was written with this philosophy : best performances, more secured and brings a lot of new functions
This plugin is developped on WordPress 3.3, with the constant WP_DEBUG to TRUE.
- Tags suggestion from Yahoo! Term Extraction API, OpenCalais, Alchemy, Zemanta, Tag The Net, Local DB with AJAX request
- Compatible with TinyMCE, FCKeditor, WYMeditor and QuickTags
- tags management (rename, delete, merge, search and add tags, edit tags ID)
- Edit mass tags (more than 50 posts once)
- Auto link tags in post content
- Auto tags !
- Type-ahead input tags / Autocompletion Ajax
- Click tags
- Possibility to tag pages (not only posts) and include them inside the tags results
- Easy configuration ! (in WP admin)
Ajay-You can also combine this plugin with RSS auto post blog aggregator (read instructions here) and create SEO optimized Blog Aggregation or Curation
While Six Sigma was initially a quality control system, it has also been very succesful in managing projects. The various stages of an analytical project can be divided using the DMAIC methodology.
DMAIC stands for
Related to this is DMADV, ( “Design For Six Sigma”)
- Measure and identify CTQs
CRISP-DM stands for Cross Industry Standard Process for Data Mining
CRISP-DM breaks the process of data mining into six major phases- and these can be used for business analytics projects as well.
- Business Understanding
- Data Understanding
- Data Preparation
SEMMA stands for
4) ISO 9001
ISO 9001 is a certification as well as a philosophy for making a Quality Management System to measure , reduce and eliminate error and customer complaints. Any customer complaint or followup has to be treated as an error, logged, and investigated for control.
LEAN is a philosophy to eliminate Wastage in a process. Applying LEAN principles to analytics projects helps a lot in eliminating project bottlenecks, technology compatibility issues and data quality resolution. I think LEAN would be great in data quality issues, and IT infrastructure design because that is where the maximum waste is observed in analytics projects.
6) Demings Plan Do Check Act cycle.
Here is an interview with Dr Ingo Mierswa , CEO of Rapid -I and Dr Simon Fischer, Head R&D. Rapid-I makes the very popular software Rapid Miner – perhaps one of the earliest leading open source software in business analytics and business intelligence. It is quite easy to use, deploy and with it’s extensions and innovations (including compatibility with R )has continued to grow tremendously through the years.
In an extensive interview Ingo and Simon talk about algorithms marketplace, extensions , big data analytics, hadoop, mobile computing and use of the graphical user interface in analytics.
Special Thanks to Nadja from Rapid I communication team for helping coordinate this interview.( Statuary Blogging Disclosure- Rapid I is a marketing partner with Decisionstats as per the terms in http://decisionstats.com/privacy-3/)
Ajay- Describe your background in science. What are the key lessons that you have learnt while as scientific researcher and what advice would you give to new students today.
Ingo: My time as researcher really was a great experience which has influenced me a lot. I have worked at the AI lab of Prof. Dr. Katharina Morik, one of the persons who brought machine learning and data mining to Europe. Katharina always believed in what we are doing, encouraged us and gave us the space for trying out new things. Funnily enough, I never managed to use my own scientific results in any real-life project so far but I consider this as a quite common gap between science and the “real world”. At Rapid-I, however, we are still heavily connected to the scientific world and try to combine the best of both worlds: solving existing problems with leading-edge technologies.
Simon: In fact, during my academic career I have not worked in the field of data mining at all. I worked on a field some of my colleagues would probably even consider boring, and that is theoretical computer science. To be precise, my research was in the intersection of game theory and network theory. During that time, I have learnt a lot of exciting things, none of which had any business use. Still, I consider that a very valuable experience. When we at Rapid-I hire people coming to us right after graduating, I don’t care whether they know the latest technology with a fancy three-letter acronym – that will be forgotten more quickly than it came. What matters is the way you approach new problems and challenges. And that is also my recommendation to new students: work on whatever you like, as long as you are passionate about it and it brings you forward.
Ajay- How is the Rapid Miner Extensions marketplace moving along. Do you think there is a scope for people to say create algorithms in a platform like R , and then offer that algorithm as an app for sale just like iTunes or Android apps.
Simon: Well, of course it is not going to be exactly like iTunes or Android apps are, because of the more business-orientated character. But in fact there is a scope for that, yes. We have talked to several developers, e.g., at our user conference RCOMM, and several people would be interested in such an opportunity. Companies using data mining software need supported software packages, not just something they downloaded from some anonymous server, and that is only possible through a platform like the new Marketplace. Besides that, the marketplace will not only host commercial extensions. It is also meant to be a platform for all the developers that want to publish their extensions to a broader community and make them accessible in a comfortable way. Of course they could just place them on their personal Web pages, but who would find them there? From the Marketplace, they are installable with a single click.
Ingo: What I like most about the new Rapid-I Marketplace is the fact that people can now get something back for their efforts. Developing a new algorithm is a lot of work, in some cases even more that developing a nice app for your mobile phone. It is completely accepted that people buy apps from a store for a couple of Dollars and I foresee the same for sharing and selling algorithms instead of apps. Right now, people can already share algorithms and extensions for free, one of the next versions will also support selling of those contributions. Let’s see what’s happening next, maybe we will add the option to sell complete RapidMiner workflows or even some data pools…
Ajay- What are the recent features in Rapid Miner that support cloud computing, mobile computing and tablets. How do you think the landscape for Big Data (over 1 Tb ) is changing and how is Rapid Miner adapting to it.
Simon: These are areas we are very active in. For instance, we have an In-Database-Mining Extension that allows the user to run their modelling algorithms directly inside the database, without ever loading the data into memory. Using analytic databases like Vectorwise or Infobright, this technology can really boost performance. Our data mining server, RapidAnalytics, already offers functionality to send analysis processes into the cloud. In addition to that, we are currently preparing a research project dealing with data mining in the cloud. A second project is targeted towards the other aspect you mention: the use of mobile devices. This is certainly a growing market, of course not for designing and running analyses, but for inspecting reports and results. But even that is tricky: When you have a large screen you can display fancy and comprehensive interactive dashboards with drill downs and the like. On a mobile device, that does not work, so you must bring your reports and visualizations very much to the point. And this is precisely what data mining can do – and what is hard to do for classical BI.
Ingo: Then there is Radoop, which you may have heard of. It uses the Apache Hadoop framework for large-scale distributed computing to execute RapidMiner processes in the cloud. Radoop has been presented at this year’s RCOMM and people are really excited about the combination of RapidMiner with Hadoop and the scalability this brings.
Ajay- Describe the Rapid Miner analytics certification program and what steps are you taking to partner with academic universities.
Ingo: The Rapid-I Certification Program was created to recognize professional users of RapidMiner or RapidAnalytics. The idea is that certified users have demonstrated a deep understanding of the data analysis software solutions provided by Rapid-I and how they are used in data analysis projects. Taking part in the Rapid-I Certification Program offers a lot of benefits for IT professionals as well as for employers: professionals can demonstrate their skills and employers can make sure that they hire qualified professionals. We started our certification program only about 6 months ago and until now about 100 professionals have been certified so far.
Simon: During our annual user conference, the RCOMM, we have plenty of opportunities to talk to people from academia. We’re also present at other conferences, e.g. at ECML/PKDD, and we are sponsoring data mining challenges and grants. We maintain strong ties with several universities all over Europe and the world, which is something that I would not want to miss. We are also cooperating with institutes like the ITB in Dublin during their training programmes, e.g. by giving lectures, etc. Also, we are leading or participating in several national or EU-funded research projects, so we are still close to academia. And we offer an academic discount on all our products :-)
Ajay- Describe the global efforts in making Rapid Miner a truly international software including spread of developers, clients and employees.
Simon: Our clients already are very international. We have a partner network in America, Asia, and Australia, and, while I am responding to these questions, we have a training course in the US. Developers working on the core of RapidMiner and RapidAnalytics, however, are likely to stay in Germany for the foreseeable future. We need specialists for that, and it would be pointless to spread the development team over the globe. That is also owed to the agile philosophy that we are following.
Ingo: Simon is right, Rapid-I already is acting on an international level. Rapid-I now has more than 300 customers from 39 countries in the world which is a great result for a young company like ours. We are of course very strong in Germany and also the rest of Europe, but also concentrate on more countries by means of our very successful partner network. Rapid-I continues to build this partner network and to recruit dynamic and knowledgeable partners and in the future. However, extending and acting globally is definitely part of our strategic roadmap.
Dr. Ingo Mierswa is working as Chief Executive Officer (CEO) of Rapid-I. He has several years of experience in project management, human resources management, consulting, and leadership including eight years of coordinating and leading the multi-national RapidMiner developer team with about 30 developers and contributors world-wide. He wrote his Phd titled “Non-Convex and Multi-Objective Optimization for Numerical Feature Engineering and Data Mining” at the University of Dortmund under the supervision of Prof. Morik.
Dr. Simon Fischer is heading the research & development at Rapid-I. His interests include game theory and networks, the theory of evolutionary algorithms (e.g. on the Ising model), and theoretical and practical aspects of data mining. He wrote his PhD in Aachen where he worked in the project “Design and Analysis of Self-Regulating Protocols for Spectrum Assignment” within the excellence cluster UMIC. Before, he was working on the vtraffic project within the DFG Programme 1126 “Algorithms for large and complex networks”.
http://rapid-i.com/content/view/181/190/ tells you more on the various types of Rapid Miner licensing for enterprise, individual and developer versions.
(Note from Ajay- to receive an early edition invite to Radoop, click here http://radoop.eu/z1sxe)
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
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.
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
Connecting to a MongoDB database from R using Java
Also see a nice basic analysis using R Mongo from
please see https://github.com/wactbprot/R4CouchDB and
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/)
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.
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
Open Source BI as disruptive technology
Open Source Punditry
|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
|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) :
|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.|
- 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
- the formidable Dirk with awesome stock reports
(dedicated to all the intelligence agencies in the world. All of them except those that kill their own countrymen)
The Ethics of a Spy
is never to question Why
Instead pause and wait
Act now, before it is too late
We wait and watch
with the worst kind of homo sapiens
hoping our soul is not as corrupted
We are the watchers, the perpetual legal aliens
The ethics of a cop
May be to who dun it or stop
But the ethics of spy
Is to act now before people die
The Flotilla 13, The Alpha Team, The Seals, The Cobras
We are all brothers from the other mothers
We destroy our souls so we can save
Humanity from destroying itself.
Every man we killed
Haunts us in our dreams
Every woman we loved
was the one and truly love it seems
Those who live by the sword
Shall die by the sword too
But if that is any excuse for not doing
Then you must be a bigger foo
The ethics of a spy
is never to ask why
But to find and search
Protect the sheep from stumbling in the lurch
And when it is all over
The lucky ones are already dead
Old spies never die
We just wait for another op till the end.
- An Exotic Tool for Espionage: Moral Compass http://www.nytimes.com/2006/01/28/politics/28ethics.html
- Ethics of Spying: A Reader for the Intelligence Professional [Paperback]
Image: Spy vs. Spy is the property of Mad Magazine.
|This book just won an international award
producing graphs alongside results. In most cases, each page or two-page spread completes a JMP task, which maximizes the book’s utility as a reference.