Tag: Language

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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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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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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SAS X

0o0 0O

Tal G, creator of the rbloggers.com website, has created a new blog aggregator for SAS language users at http://sas-x.com/

With almost 26 blogs joining there (I suspect many more should join , it seems like a good website to use for analytics users and students.  My favorite SAS Blog is http://statcompute.spaces.live.com/ – its pure code- little anything else.

Related-

SAS MACRO TO CALCULATE PDO (Points to Double Odds) OF A SCORECARD

A SAS MACRO FOR DECISION STUMP

A DEMO OF VECTOR AUTOREGRESSIVE FORECASTING MODEL

 

 

 

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How to Analyze Wikileaks Data – R SPARQL

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Drew Conway- one of the very very few Project R voices I used to respect until recently. declared on his blog http://www.drewconway.com/zia/

Why I Will Not Analyze The New WikiLeaks Data

and followed it up with how HE analyzed the post announcing the non-analysis.

“If you have not visited the site in a week or so you will have missed my previous post on analyzing WikiLeaks data, which from the traffic and 35 Comments and 255 Reactions was at least somewhat controversial. Given this rare spotlight I thought it would be fun to use the infochimps API to map out the geo-location of everyone that visited the blog post over the last few days. Unfortunately, after nearly two years with the same web hosting service, only today did I realize that I was not capturing daily log files for my domain”

Anyways – non American users of R Project can analyze the Wikileaks data using the R SPARQL package I would advise American friends not to use this approach or attempt to analyze any data because technically the data is still classified and it’s possession is illegal (which is the reason Federal employees and organizations receiving federal funds have advised not to use this or any WikiLeaks dataset)

https://code.google.com/p/r-sparql/

Overview

R is a programming language designed for statistics.

R Sparql allows you to run SPARQL Queries inside R and store it as a R data frame.

The main objective is to allow the integration of Ontologies with Statistics.

It requires Java and rJava installed.

Example (in R console):

> library(sparql)> data <- query("SPARQL query>","RDF file or remote SPARQL Endpoint")

and the data in a remote SPARQL  http://www.ckan.net/package/cablegate

SPARQL is an easy language to pick  up, but dammit I am not supposed to blog on my vacations.

http://code.google.com/p/r-sparql/wiki/GettingStarted

Getting Started

1. Installation

1.1 Make sure Java is installed and is the default JVM:

$ sudo apt-get install sun-java6-bin sun-java6-jre sun-java6-jdk$ sudo update-java-alternatives -s java-6-sun

1.2 Configure R to use the correct version of Java

$ sudo R CMD javareconf

1.3 Install the rJava library

$ R> install.packages("rJava")> q()

1.4 Download and install the sparql library

Download: http://code.google.com/p/r-sparql/downloads/list

$ R CMD INSTALL sparql-0.1-X.tar.gz

2. Executing a SPARQL query

2.1 Start R

#Load the librarylibrary(sparql)#Run the queryresult <- query("SELECT ... ", "http://...")#Print the resultprint(result)

3. Examples

3.1 The Query can be a string or a local file:

query("SELECT ?date ?number ?season WHERE {  ... }", "local-file.rdf")
query("my-query.rq", "local-file.rdf")

The package will detect if my-query.rq exists and will load it from the file.

3.3 The uri can be a file or an url (for remote queries):

query("SELECT ... ","local-file.db")
query("SELECT ... ","http://dbpedia.org/sparql")

3.4 Get some examples here: http://code.google.com/p/r-sparql/downloads/list

SPARQL Tutorial-

http://openjena.org/ARQ/Tutorial/index.html

Also read-

http://webr3.org/blog/linked-data/virtuoso-6-sparqlgeo-and-linked-data/

and from the favorite blog of Project R- Also known as NY Times

http://bits.blogs.nytimes.com/2010/11/15/sorting-through-the-government-data-explosion/?twt=nytimesbits

In May 2009, the Obama administration started putting raw 
government data on the Web. 
It started with 47 data sets. Today, there are more than
 270,000 government data sets, spanning every imaginable 
category from public health to foreign aid.

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Sugar CRM: Forrester Webinar

Analytické CRM
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https://sugarcrmevents.webex.com/mw0306lb/mywebex/default.do?nomenu=true&siteurl=sugarcrmevents&service=6&main_url=https://sugarcrmevents.webex.com/ec0605lb/eventcenter/event/eventAction.do%3FtheAction%3Ddetail%26confViewID%3D279191911%26siteurl%3Dsugarcrmevents%26%26%26

Date and time:

Thursday, December 2, 2010 11:00 am 
Pacific Standard Time (San Francisco, GMT-08:00) 
Change time zone
Thursday, December 2, 2010 2:00 pm 
Eastern Standard Time (New York, GMT-05:00)
Thursday, December 2, 2010 7:00 pm
Western European Time (London, GMT)
Thursday, December 2, 2010 8:00 pm
Europe Time (Berlin, GMT+01:00)
Duration: 1 hour
Description:
Every organization wants to improve the way they manage their customer relationships. But until recently, adding robust CRM tools to your organization was a time consuming and cost prohibitive endeavor for many resources-constrained organizations. Until Now. On December 2 join us to learn how new developments in technology like open source, cloud computing and web 2.0 – are making it easier than ever to add a top notch CRM system to your operations. 

 

This live webinar hosted by SugarCRM will feature Forrester Research, Inc. Vice President William Band, named one CRM Magazine’s 2007 Influential Leaders. Mr. Band will discuss the current state of the market, review the major trends affecting the CRM landscape, and discuss some criteria you can use to ensure your next CRM decision is the right one.

In addition, all attendees of the live webinar will receive a complimentary download a recent Forrester Wave™ Report! Register today!

Speakers:

William Band, Vice President, Forrester Research
Martin Schneider, Sr. Director Communications, SugarCRM

Who Should Attend:
VP Sales, VP Marketing, CIO’s, Head of Customer Support and other technical decision makers
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Using SAS/IML with R

Analyze That
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SAS just released an updated documentation to SAS/IML language with a special chapter devoted to using R

Here is an example-

CALL EXPORTMATRIXTOR( IMLMatrix, RMatrix ) ;

CALL IMPORTMATRIXFROMR( IMLMatrix, RExpr ) ;

If you have existing SAS licences and existing hardware and loots of data -this may be the best of both worlds- without getting into the mess of technically learning MKL threads/BLAS/Premium Packages/Cloud

Another thought- its a good professional looking help book, which is what more R packages can do (work on improving ease of their help/update vignettes)

 

Link-http://support.sas.com/documentation/cdl/en/imlug/63541/HTML/default/viewer.htm#r_toc.htm

 

Calling Functions in the R Language

[continuerule]

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