Tag: books

Chapman/Hall announces new series on R

Rice University, Houston, Texas, USA - Cohen H...
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R Authors get more choice and variety now-
http://www.mail-archive.com/r-help@r-project.org/msg122965.html
We are pleased to announce the launch of a new series of books on R. 

Chapman & Hall/CRC: The R Series

Aims and Scope
This book series reflects the recent rapid growth in the development and 
application of R, the programming language and software environment for 
statistical computing and graphics. R is now widely used in academic research, 
education, and industry. It is constantly growing, with new versions of the 
core software released regularly and more than 2,600 packages available. It is 
difficult for the documentation to keep pace with the expansion of the 
software, and this vital book series provides a forum for the publication of 
books covering many aspects of the development and application of R.

The scope of the series is wide, covering three main threads:
• Applications of R to specific disciplines such as biology, epidemiology, 
genetics, engineering, finance, and the social sciences.
• Using R for the study of topics of statistical methodology, such as linear 
and mixed modeling, time series, Bayesian methods, and missing data.
• The development of R, including programming, building packages, and graphics.

The books will appeal to programmers and developers of R software, as well as 
applied statisticians and data analysts in many fields. The books will feature 
detailed worked examples and R code fully integrated into the text, ensuring 
their usefulness to researchers, practitioners and students.

Series Editors
John M. Chambers (Department of Statistics, Stanford University, USA; 
j...@stat.stanford.edu)
Torsten Hothorn (Institut für Statistik, Ludwig-Maximilians-Universität, 
München, Germany; torsten.hoth...@stat.uni-muenchen.de)
Duncan Temple Lang (Department of Statistics, University of California, Davis, 
USA; dun...@wald.ucdavis.edu)
Hadley Wickham (Department of Statistics, Rice University, Houston, Texas, USA; 
had...@rice.edu)

Call for Proposals
We are interested in books covering all aspects of the development and 
application of R software. If you have an idea for a book, please contact one 
of the series editors above or one of the Chapman & Hall/CRC statistics 
acquisitions editors below. Please provide brief details of topic, audience, 
aims and scope, and include an outline if possible.

We look forward to hearing from you.

Best regards,Rob Calver (rob.cal...@informa.com)
David Grubbs (david.gru...@taylorandfrancis.com)
John Kimmel (john.kim...@taylorandfrancis.com)

 

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Interview Luis Torgo Author Data Mining with R

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Here is an interview with Prof Luis Torgo, author of the recent best seller “Data Mining with R-learning with case studies”.

Ajay- Describe your career in science. How do you think can more young people be made interested in science.

Luis- My interest in science only started after I’ve finished my degree. I’ve entered a research lab at the University of Porto and started working on Machine Learning, around 1990. Since then I’ve been involved generally in data analysis topics both from a research perspective as well as from a more applied point of view through interactions with industry partners on several projects. I’ve spent most of my career at the Faculty of Economics of the University of Porto, but since 2008 I’m at the department of Computer Science of the Faculty of Sciences of the same university. At the same time I’ve been a researcher at LIAAD / Inesc Porto LA (www.liaad.up.pt).

I like a lot what I do and like science and the “scientific way of thinking”, but I cannot say that I’ve always thought of this area as my “place”. Most of all I like solving challenging problems through data analysis. If that translates into some scientific outcome than I’m more satisfied but that is not my main goal, though I’m kind of “forced” to think about that because of the constraints of an academic career.

That does not mean I’m not passionate about science, I just think there are many more ways of “doing science” than what is reflected in the usual “scientific indicators” that most institutions seem to be more and more obsessed about.

Regards interesting young people in science that is a hard question that I’m not sure I’m qualified to answer. I do tend to think that young people are more sensible to concrete examples of problems they think are interesting and that science helps in solving, as a way of finding a motivation for facing the hard work they will encounter in a scientific career. I do believe in case studies as a nice way to learn and motivate, and thus my book 😉

Ajay- Describe your new book “Data Mining with R, learning with case studies” Why did you choose a case study based approach? who is the target audience? What is your favorite case study from the book

Luis- This book is about learning how to use R for data mining. The book follows a “learn by doing it” approach to data mining instead of the more common theoretical description of the available techniques in this discipline. This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Moreover, the book has an associated web page (www.liaad.up.pt/~ltorgo/DataMiningWithR) where all code inside the book is given so that easy copy-paste is possible for the more lazy readers.

The language used in the book is very informal without many theoretical details on the used data mining techniques. For obtaining these theoretical insights there are already many good data mining books some of which are referred in “further readings” sections given throughout the book. The decision of following this writing style had to do with the intended target audience of the book.

In effect, the objective was to write a monograph that could be used as a supplemental book for practical classes on data mining that exist in several courses, but at the same time that could be attractive to professionals working on data mining in non-academic environments, and thus the choice of this more practically oriented approach.

Regards my favorite case study that is a hard question for an author… still I would probably choose the “Predicting Stock Market Returns” case study (Chapter 3). Not only because I like this challenging problem, but mainly because the case study addresses all aspects of knowledge discovery in a real world scenario and not only the construction of predictive models. It tackles data collection, data pre-processing, model construction, transforming predictions into actions using different trading policies, using business-related performance metrics, implementing a trading simulator for “real-world” evaluation, and laying out grounds for constructing an online trading system.

Obviously, for all these steps there are far too many options to be possible to describe/evaluate all of them in a chapter, still I do believe that for the reader it is important to see the overall picture, and read about the relevant questions on this problem and some possible paths that can be followed at these different steps.

In other words: do not expect to become rich with the solution I describe in the chapter !

Ajay- Apart from R, what other data mining software do you use or have used in the past. How would you compare their advantages and disadvantages with R

Luis- I’ve played around with Clementine, Weka, RapidMiner and Knime, but really only playing with teaching goals, and no serious use/evaluation in the context of data mining projects. For the latter I mainly use R or software developed by myself (either in R or other languages). In this context, I do not think it is fair to compare R with these or other tools as I lack serious experience with them. I can however, tell you about what I see as the main pros and cons of R. The main reason for using R is really not only the power of the tool that does not stop surprising me in terms of what already exists and keeps appearing as contributions of an ever growing community, but mainly the ability of rapidly transforming ideas into prototypes. Regards some of its drawbacks I would probably mention the lack of efficiency when compared to other alternatives and the problem of data set sizes being limited by main memory.

I know that there are several efforts around for solving this latter issue not only from the community (e.g. http://cran.at.r-project.org/web/views/HighPerformanceComputing.html), but also from the industry (e.g. Revolution Analytics), but I would prefer that at this stage this would be a standard feature of the language so the the “normal” user need not worry about it. But then this is a community effort and if I’m not happy with the current status instead of complaining I should do something about it!

Ajay- Describe your writing habit- How do you set about writing the book- did you write a fixed amount daily or do you write in bursts etc

Luis- Unfortunately, I write in bursts whenever I find some time for it. This is much more tiring and time consuming as I need to read back material far too often, but I cannot afford dedicating too much consecutive time to a single task. Actually, I frequently tease my PhD students when they “complain” about the lack of time for doing what they have to, that they should learn to appreciate the luxury of having a single task to complete because it will probably be the last time in their professional life!

Ajay- What do you do to relax or unwind when not working?

Luis- For me, the best way to relax from work is by playing sports. When I’m involved in some game I reset my mind and forget about all other things and this is very relaxing for me. A part from sports I enjoy a lot spending time with my family and friends. A good and long dinner with friends over a good bottle of wine can do miracles when I’m too stressed with work! Finally,I do love traveling around with my family.

Luis Torgo

Short Bio: Luis Torgo has a degree in Systems and Informatics Engineering and a PhD in Computer Science. He is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto. He is also a researcher of the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) belonging to INESC Porto LA. Luis Torgo has been an active researcher in Machine Learning and Data Mining for more than 20 years. He has lead several academic and industrial Data Mining research projects. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. He teaches R at different levels and has given several courses in different countries.

For reading “Data Mining with R” – you can visit this site, also to avail of a 20% discount the publishers have generously given (message below)-

For more information and to place an order, visit us at http://www.crcpress.com.  Order online and apply 20% Off discount code 907HM at checkout.  CRC is pleased to offer free standard shipping on all online orders!

link to the book page  http://www.crcpress.com/product/isbn/9781439810187

Price: $79.95
Cat. #: K10510
ISBN: 9781439810187
ISBN 10: 1439810184
Publication Date: November 09, 2010
Number of Pages: 305
Availability: In Stock
Binding(s): Hardback 

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Interview Ajay Ohri Decisionstats.com with DMR

From-

http://www.dataminingblog.com/data-mining-research-interview-ajay-ohri/

Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!

Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?

Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.

All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).

My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).

DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?

AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.

I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.

I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.

I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.

SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.

I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.

If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.

I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.

Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.

Also I have worked with a lot of KXEN, SQL, SPSS.

DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?

AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.

In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.

Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys :) – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.

The more questions I asked around the internet, the more people responded, and I wrote it all down.

I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.

I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.

Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.

DMR: There is a rumor about a R book you plan to publish in 2011 :-) Can you confirm the rumor and tell us more?

AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.

Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.

Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
The target audience however is business analysts- or people working in corporate environments.

Brief Bio-
Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.

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How to balance your online advertising and your offline conscience

Google in 1998, showing the original logo
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I recently found an interesting example of  a website that both makes a lot of money and yet is much more efficient than any free or non profit. It is called ECOSIA

If you see a website that wants to balance administrative costs  plus have a transparent way to make the world better- this is a great example.

  • http://ecosia.org/how.php
  • HOW IT WORKS
    You search with Ecosia.
  • Perhaps you click on an interesting sponsored link.
  • The sponsoring company pays Bing or Yahoo for the click.
  • Bing or Yahoo gives the bigger chunk of that money to Ecosia.
  • Ecosia donates at least 80% of this income to support WWF’s work in the Amazon.
  • If you like what we’re doing, help us spread the word!

    • Key facts about the park:

    • World’s largest tropical forest reserve (38,867 square kilometers, or about the size of Switzerland)
    • Home to about 14% of all amphibian species and roughly 54% of all bird species in the Amazon – not to mention large populations of at least eight threatened species, including the jaguar
    • Includes part of the Guiana Shield containing 25% of world’s remaining tropical rainforests – 80 to 90% of which are still pristine
    • Holds the last major unpolluted water reserves in the Neotropics, containing approximately 20% of all of the Earth’s water
    • One of the last tropical regions on Earth vastly unaltered by humans
    • Significant contributor to climatic regulation via heat absorption and carbon storage

     

    http://ecosia.org/statistics.php

    They claim to have donated 141,529.42 EUR !!!

    http://static.ecosia.org/files/donations.pdf

     

     

     

     

     

     

     

     

     

     

    Well suppose you are the Web Admin of a very popular website like Wikipedia or etc

    One way to meet server costs is to say openly hey i need to balance my costs so i need some money.

    The other way is to use online advertising.

    I started mine with Google Adsense.

    Click per milli (or CPM)  gives you a very low low conversion compared to contacting ad sponsor directly.

    But its a great data experiment-

    as you can monitor which companies are likely to be advertised on your site (assume google knows more about their algols than you will)

    which formats -banner or text or flash have what kind of conversion rates

    what are the expected pay off rates from various keywords or companies (like business intelligence software, predictive analytics software and statistical computing software are similar but have different expected returns (if you remember your eco class)

     

    NOW- Based on above data, you know whats your minimum baseline to expect from a private advertiser than a public, crowd sourced search engine one (like Google or Bing)

    Lets say if you have 100000 views monthly. and assume one out of 1000 page views will lead to a click. Say the advertiser will pay you 1 $ for every 1 click (=1000 impressions)

    Then your expected revenue is $100.But if your clicks are priced at 2.5$ for every click , and your click through rate is now 3 out of 1000 impressions- (both very moderate increases that can done by basic placement optimization of ad type, graphics etc)-your new revenue is  750$.

    Be a good Samaritan- you decide to share some of this with your audience -like 4 Amazon books per month ( or I free Amazon book per week)- That gives you a cost of 200$, and leaves you with some 550$.

    Wait! it doesnt end there- Adam Smith‘s invisible hand moves on .

    You say hmm let me put 100 $ for an annual paper writing contest of $1000, donate $200 to one laptop per child ( or to Amazon rain forests or to Haiti etc etc etc), pay $100 to your upgraded server hosting, and put 350$ in online advertising. say $200 for search engines and $150 for Facebook.

    Woah!

    Month 1 would should see more people  visiting you for the first time. If you have a good return rate (returning visitors as a %, and low bounce rate (visits less than 5 secs)- your traffic should see atleast a 20% jump in new arrivals and 5-10 % in long term arrivals. Ignoring bounces- within  three months you will have one of the following

    1) An interesting case study on statistics on online and social media advertising, tangible motivations for increasing community response , and some good data for study

    2) hopefully better cost management of your server expenses

    3)very hopefully a positive cash flow

     

    you could even set a percentage and share the monthly (or annually is better actions) to your readers and advertisers.

    go ahead- change the world!

    the key paradigms here are sharing your traffic and revenue openly to everyone

    donating to a suitable cause

    helping increase awareness of the suitable cause

    basing fixed percentages rather than absolute numbers to ensure your site and cause are sustained for years.

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    Book Reviews- Hindu Myths- Mere Christianity

    A statue of Hindu deity Shiva in a temple in B...
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    Over the month long break I took, I was helping firm up my ideas for R for Analytics , I also took a break and read some books. Here are brief reviews of two, three of them-

    1) Hindu Myths

    This is a classical book translated from original Sanskrit written by Professor Wendy O Flaherty of University of Chicago. I found some of the older myths very interesting in terms of contradictions, retelling the same story in a modified way by another classic, the beautiful poetic and fantastic imagery evoked by Hindu myths. Some stories are as relevant in prayers, fasts and religious ceremonies as they were around 11000 years while most have morphed , edited or even distorted.

    It should help the non Indian reader understand why hundreds of millions of conservative Indians worship Shiv Ling ( or literally an idol of the Phallus of Shiva), the Hindu two cents of creation of the universe, and the somewhat fantastic stories on super heroes /gods/ in the ancient world.

    The book suffers from a few drawbacks in my opinion-

    1) Sanskrit is a bit like Latin- you can lose not just the flavor but original meaning of words and situational context. Some of the stories made better sense when i read a more recent Hindi translation.

    2) An excessive emphasis on sexual imagery rather than emotional imagery. The author seems wonder struck to read and translate ancient indians were so matter of fact about physical relationships. However the words were always written in discrete poetic than crass soft pornography.

    3) Almost no drawings or figures. This makes the book a bit dense to read at 300 pages.

    I liked another book on Hindu Myths (Myth= Mithya which I read in 2009) and you can see if you can read it if you find the topic interesting.

    A Handbook of Hindu Mythology

    Hindus have one God.
    They also have 330 million gods: male gods, female gods, personal gods, family gods, household gods, village gods, gods of space and time, gods for specific castes and particular professions, gods who reside in trees, in animals, in minerals, in geometrical patterns and in man-made objects.
    Then there are a whole host of demons.
    But no Devil.


    Mere Christianity by C S Lewis is a classic book on reinterpreting Christianity in modern times. However the author wrote this when World War 2 was on and it seems more like a British or Anglo Saxon interpretation of beliefs of Christ Jesus– who was actually a Jewish teacher born in Middle East Asia.

    While the language and reading makes it much easier to read- it is recommended more at Western audiences, than Eastern ones, as it seems some of the parables are a more palatable re interpretation of the New Testament. The Bible is a deceptively easy book to read, the language is short and beautiful-and the original parables in the Gospels remain powerful easy to understand.

    C S Lewis tends to emphasize morality than religiosity or faith, and there is not much comparison with any other faith or alternative morality. Dumbing down the Bible so as to market it better to reluctant consumers seems to be Mr Lewis intention and it is not as scholarly a work as an exercise in pure prose.

    However it is quite good as a self improvement book and is quite better than the “You Can Win” kind of books or even business concept books.

    Note- I find reading books on religion as good exercises in reading the fountain source of philosophies. As a polytheist- I tend to read more than one faith.

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    Google Books Ngram Viewer

    Here is a terrific data visualization from Google based on their digitized books collection. How does it work, basically you can test the frequency of various words across time periods from 1700s to 2010.

    Like the frequency and intensity of kung fu vs yoga, or pizza versus hot dog. The basic datasets scans millions /billions of words.

    Here is my yoga vs kung fu vs judo graph.

    http://ngrams.googlelabs.com/info

    What’s all this do?

    When you enter phrases into the Google Books Ngram Viewer, it displays a graph showing how those phrases have occurred in a corpus of books (e.g., “British English”, “English Fiction”, “French”) over the selected years. Let’s look at a sample graph:

    This shows trends in three ngrams from 1950 to 2000: “nursery school” (a 2-gram or bigram), “kindergarten” (a 1-gram or unigram), and “child care” (another bigram). What the y-axis shows is this: of all the bigrams contained in our sample of books written in English and published in the United States, what percentage of them are “nursery school” or “child care”? Of all the unigrams, what percentage of them are “kindergarten”? Here, you can see that use of the phrase “child care” started to rise in the late 1960s, overtaking “nursery school” around 1970 and then “kindergarten” around 1973. It peaked shortly after 1990 and has been falling steadily since.

    (Interestingly, the results are noticeably different when the corpus is switched to British English.)

    Corpora

    Below are descriptions of the corpora that can be searched with the Google Books Ngram Viewer. All of these corpora were generated in July 2009; we will update these corpora as our book scanning continues, and the updated versions will have distinct persistent identifiers.

    Informal corpus name Persistent identifier Description
    American English googlebooks-eng-us-all-20090715 Same filtering as the English corpus but further restricted to books published in the United States.
    British English googlebooks-eng-gb-all-20090715 Same filtering as the English corpus but further restricted to books published in Great Britain.
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    Choosing R for business – What to consider?

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    Additional features in R over other analytical packages-

    1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

    2) Wide literature of training material in the form of books is available for the R analytical platform.

    3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

    4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

    5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

     

    6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

    Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

    Specific to the following domains R has the following costs and benefits

    • Business Analytics
      • R is free per license and for download
      • It is one of the few analytical platforms that work on Mac OS
      • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
      • It has open source code for customization as per GPL
      • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
      • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
      • Huge library of packages for regression, time series, finance and modeling
      • High quality data visualization packages
      • Data Mining
        • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
        • Flexibility in tweaking a standard algorithm by seeing the source code
        • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
        • Business Dashboards and Reporting
        • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
          • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
          • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

    Additional factors to consider in your R installation-

    There are some more choices awaiting you now-
    1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

    2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

    3) Operating system sub choice- 32- bit or 64 bit.

    4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

    5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

    6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

    7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

    1) Licensing Choices-
    You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

    Commercial Vendors of R Language Products-
    1) Revolution Analytics http://www.revolutionanalytics.com/
    2) XL Solutions- http://www.experience-rplus.com/
    3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
    4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

    1. Choosing Operating System
        1. Windows

     

    Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

          1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
        2. MacOS

     

    Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

        1. Linux

     

          1. Ubuntu
          2. Red Hat Enterprise Linux
          3. Other versions of Linux

     

    Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

    Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

        1. Multiple operating systems-
          1. Virtualization vs Dual Boot-

     

    You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

    1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

     

    1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

    Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
    Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

      1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
        1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
        2. Workstation
      2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
        1. Amazon
        2. Google
        3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
      3. How much resources
        1. RAM-Hard Disk-Processors- for workstation computing
        2. Instances or API calls for cloud computing
    2. Interface Choices
      1. Command Line
      2. GUI
      3. Web Interfaces
    3. Software Component Choices
      1. R dependencies
      2. Packages to install
      3. Recommended Packages
    4. Additional software choices
      1. Additional legacy software
      2. Optimizing your R based computing
      3. Code Editors
        1. Code Analyzers
        2. Libraries to speed up R

    citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

    (Note- this is a draft in progress)

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