Tag: science

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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PySpread Magic

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Just working with PySpread- and worked on a 1 million by 1 million spreadsheet- Python sure looks promising for the way ahead for stat computing ( you need to

sudo apt-get install python-numpy python-rpy python-scipy python-gmpy wxpython*,

cd to the untarred bz2 file from http://pyspread.sourceforge.net/download.html,  (like

:~/Downloads$ cd pyspread-0.1.2

:~/Downloads/pyspread-0.1.2

sudo python setup.py install

)

http://pyspread.sourceforge.net/

by Martin Manns

 

about Pyspread is a cross-platform Python spreadsheet application. It is based on and written in the programming language Python.

Instead of spreadsheet formulas, Python expressions are entered into the spreadsheet cells. Each expression returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices.

 Pyspread screenshot
features
  • Three dimensional grid with up to 85,899,345 rows and 14,316,555 columns (64 bit systems, depends on row height and column width). Note that a million cells require about 500 MB of memory.
  • Complex data types such as lists, trees or matrices within a single cell.
  • Macros for functionalities that are too complex for a single Python expression.
  • Python module access from each cell, which allows:
    • Arbitrary size rational numbers (via gmpy),
    • Fixed point decimal numbers for business calculations, (via the decimal module from the standard library)
    • Advanced statistics including plotting functions (via RPy)
    • Much more via <your favourite module>.
  • CSV import and export
  • Clipboard access
 Pyspread screenshot
warning The concept of pyspread allows doing everything from each cell that a Python script can do. This powerful feature has its drawbacks. A spreadsheet may very well delete your hard drive or send your data via the Internet. Of course this is a non-issue if you sandbox properly or if you only use self developed spreadsheets.

Since this is not the case for everyone (see discussion at lwn.net), a GPG signature based trust model for spreadsheet files has been introduced. It ensures that only your own trusted files are executed on loading. Untrusted files are displayed in safe mode. You can approve a file manually. Inspect carefully.

 

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The Year 2010

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My annual traffic to this blog was almost 99,000 . Add in additional views on networking sites plus the 400 plus RSS readers- so I can say traffic was 1,20,000 for 2010. Nice. Thanks for reading and hope it was worth your time. (this is a long post and will take almost 440 secs to read but the summary is just given)

My intent is either to inform you, give something useful or atleast something interesting.

see below-

Jan Feb Mar Apr May Jun
2010 6,311 4,701 4,922 5,463 6,493 4,271
Jul Aug Sep Oct Nov Dec Total
5,041 5,403 17,913 16,430 11,723 10,096 98,767

 

 

Sandro Saita from http://www.dataminingblog.com/ just named me for an award on his blog (but my surname is ohRi , Sandro left me without an R- What would I be without R :)) ).

Aw! I am touched. Google for “Data Mining Blog” and Sandro is the best that it is in data mining writing.

”

DMR People Award 2010
There are a lot of active people in the field of data mining. You can discuss with them on forums. You can read their blogs. You can also meet them in events such as PAW or KDD. Among the people I follow on a regular basis, I have elected:

Ajay Ori

He has been very active in 2010, especially on his blog . Good work Ajay and continue sharing your experience with us!”

What did I write in 2010- stuff.

What did you read on this blog- well thats the top posts list.

2009-12-31 to Today

Title Views
Home page More stats 21,150
Top 10 Graphical User Interfaces in Statistical Software More stats 6,237
Wealth = function (numeracy, memory recall) More stats 2,014
Matlab-Mathematica-R and GPU Computing More stats 1,946
The Top Statistical Softwares (GUI) More stats 1,405
About DecisionStats More stats 1,352
Using Facebook Analytics (Updated) More stats 1,313
Test drive a Chrome notebook. More stats 1,170
Top ten RRReasons R is bad for you ? More stats 1,157
Libre Office More stats 1,151
Interview Hadley Wickham R Project Data Visualization Guru More stats 1,007
Using Red R- R with a Visual Interface More stats 854
SAS Institute files first lawsuit against WPS- Episode 1 More stats 790
Interview Professor John Fox Creator R Commander More stats 764
R Package Creating More stats 754
Windows Azure vs Amazon EC2 (and Google Storage) More stats 726
Norman Nie: R GUI and More More stats 716
Startups for Geeks More stats 682
Google Maps – Jet Ski across Pacific Ocean More stats 670
Not so AWkward after all: R GUI RKWard More stats 579
Red R 1.8- Pretty GUI More stats 570
Parallel Programming using R in Windows More stats 569
R is an epic fail or is it just overhyped More stats 559
Enterprise Linux rises rapidly:New Report More stats 537
Rapid Miner- R Extension More stats 518
Creating a Blog Aggregator for free More stats 504
So which software is the best analytical software? Sigh- It depends More stats 473
Revolution R for Linux More stats 465
John Sall sets JMP 9 free to tango with R More stats 460

So how do people come here –

well I guess I owe Tal G for almost 9000 views ( incidentally I withdrew posting my blog from R- Bloggers and Analyticbridge blogs – due to SEO keyword reasons and some spam I was getting see (below))

http://r-bloggers.com is still the CAT’s whiskers and I read it  a lot.

I still dont know who linked my blog to a free sex movie site with 400 views but I have a few suspects.

2009-12-31 to Today

Referrer Views
r-bloggers.com 9,131
Reddit 3,829
rattle.togaware.com 1,500
Twitter 1,254
Google Reader 1,215
linkedin.com 717
freesexmovie.irwanaf.com 422
analyticbridge.com 341
Google 327
coolavenues.com 322
Facebook 317
kdnuggets.com 298
dataminingblog.com 278
en.wordpress.com 185
google.co.in 151
xianblog.wordpress.com 130
inside-r.org 124
decisionstats.com 119
ifreestores.com 117
bits.blogs.nytimes.com 108

–

Still reading this post- gosh let me sell you some advertising. It is only $100 a month (yes its a recession)

Advertisers are treated on First in -Last out (FILO)

I have been told I am obsessed with SEO , but I dont care much for search engines apart from Google, and yes SEO is an interesting science (they should really re name it GEO or Google Engine Optimization)

Apparently Hadley Wickham and Donald Farmer are big keywords for me so I should be more respectful I guess.

Search Terms for 365 days ending 2010-12-31 (Summarized)

2009-12-31 to Today

Search Views
libre office 925
facebook analytics 798
test drive a chrome notebook 467
test drive a chrome notebook. 215
r gui 203
data mining 163
wps sas lawsuit 158
wordle.net 133
wps sas 123
google maps jet ski 123
test drive chrome notebook 96
sas wps 89
sas wps lawsuit 85
chrome notebook test drive 83
decision stats 83
best statistics software 74
hadley wickham 72
google maps jetski 72
libreoffice 70
doug savage 65
hive tutorial 58
funny india 56
spss certification 52
donald farmer microsoft 51
best statistical software 49

What about outgoing links? Apparently I need to find a way to ask Google to pay me for the free advertising I gave their chrome notebook launch. But since their search engine and browser is free to me, guess we are even steven.

Clicks for 365 days ending 2010-12-31 (Summarized)

2009-12-31 to Today

URL Clicks
rattle.togaware.com 378
facebook.com/Decisionstats 355
rapid-i.com/content/view/182/196 319
services.google.com/fb/forms/cr48basic 313
red-r.org 228
decisionstats.wordpress.com/2010/05/07/the-top-statistical-softwares-gui 199
teamwpc.co.uk/products/wps 162
r4stats.com/popularity 148
r-statistics.com/2010/04/r-and-the-google-summer-of-code-2010-accepted-students-and-projects 138
socserv.mcmaster.ca/jfox/Misc/Rcmdr 138
spss.com/certification 116
learnr.wordpress.com 114
dudeofdata.com/decisionstats 108
r-project.org 107
documentfoundation.org/faq 104
goo.gl/maps/UISY 100
inside-r.org/download 96
en.wikibooks.org/wiki/R_Programming 92
nytimes.com/external/readwriteweb/2010/12/07/07readwriteweb-report-google-offering-chrome-notebook-test-11919.html 92
sourceforge.net/apps/mediawiki/rkward/index.php?title=Main_Page 92
analyticdroid.togaware.com 88
yeroon.net/ggplot2 87

so in 2010,

SAS remained top daddy in business analytics,

R made revolutionary strides in terms of new packages,

JMP  launched a new version,

SPSS got integrated with Cognos,

Oracle sued Google and did build a great Data Mining GUI,

Libre Office gave you a non Oracle Open office ( or open even more office)

2011 looks like  a fun year. Have safe partying .

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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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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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Interview Jamie Nunnelly NISS

An interview with Jamie Nunnelly, Communications Director of National Institute of Statistical Sciences

Ajay– What does NISS do? And What does SAMSI do?

Jamie– The National Institute of Statistical Sciences (NISS) was established in 1990 by the national statistics societies and the Research Triangle universities and organizations, with the mission to identify, catalyze and foster high-impact, cross-disciplinary and cross-sector research involving the statistical sciences.

NISS is dedicated to strengthening and serving the national statistics community, most notably by catalyzing community members’ participation in applied research driven by challenges facing government and industry. NISS also provides career development opportunities for statisticians and scientists, especially those in the formative stages of their careers.

The Institute identifies emerging issues to which members of the statistics community can make key contributions, and then catalyzes the right combinations of researchers from multiple disciplines and sectors to tackle each problem. More than 300 researchers from over 100 institutions have worked on our projects.

The Statistical and Applied Mathematical Sciences Institute (SAMSI) is a partnership of Duke University,  North Carolina State University, The University of North Carolina at Chapel Hill, and NISS in collaboration with the William Kenan Jr. Institute for Engineering, Technology and Science and is part of the Mathematical Sciences Institutes of the NSF.

SAMSI focuses on 1-2 programs of research interest in the statistical and/or applied mathematical area and visitors from around the world are involved with the programs and come from a variety of disciplines in addition to mathematics and statistics.

Many come to SAMSI to attend workshops, and also participate in working groups throughout the academic year. Many of the working groups communicate via WebEx so people can be involved with the research remotely. SAMSI also has a robust education and outreach program to help undergraduate and graduate students learn about cutting edge research in applied mathematics and statistics.

Ajay– What successes have you had in 2010- and what do you need to succeed in 2011. Whats planned for 2011 anyway

Jamie– NISS has had a very successful collaboration with the National Agricultural Statistical Service (NASS) over the past two years that was just renewed for the next two years. NISS & NASS had three teams consisting of a faculty researcher in statistics, a NASS researcher, a NISS mentor, a postdoctoral fellow and a graduate student working on statistical modeling and other areas of research for NASS.

NISS is also working on a syndromic surveillance project with Clemson University, Duke University, The University of Georgia, The University of South Carolina. The group is currently working with some hospitals to test out a model they have been developing to help predict disease outbreak.

SAMSI had a very successful year with two programs ending this past summer, which were the Stochastic Dynamics program and the Space-time Analysis for Environmental Mapping, Epidemiology and Climate Change. Several papers were written and published and many presentations have been made at various conferences around the world regarding the work that was conducted as SAMSI last year.

Next year’s program is so big that the institute has decided to devote all it’s time and energy around it, which is uncertainty quantification. The opening workshop, in addition to the main methodological theme, will be broken down into three areas of interest under this broad umbrella of research: climate change, engineering and renewable energy, and geosciences.

Ajay– Describe your career in science and communication.

Jamie– I have been in communications since 1985, working for large Fortune 500 companies such as General Motors and Tropicana Products. I moved to the Research Triangle region of North Carolina after graduate school and got into economic development and science communications first working for the Research Triangle Regional Partnership in 1994.

From 1996-2005 I was the communications director for the Research Triangle Park, working for the Research Triangle Foundation of NC. I published a quarterly magazine called The Park Guide for awhile, then came to work for NISS and SAMSI in 2008.

I really enjoy working with the mathematicians and statisticians. I always joke that I am the least educated person working here and that is not far from the truth! I am honored to help get the message out about all of the important research that is conducted here each day that is helping to improve the lives of so many people out there.

Ajay– Research Triangle or Silicon Valley– Which is better for tech people and why? Your opinion

Jamie– Both the Silicon Valley and Research Triangle are great regions for tech people to locate, but of course, I have to be biased and choose Research Triangle!

Really any place in the world that you find many universities working together with businesses and government, you have an area that will grow and thrive, because the collaborations help all of us generate new ideas, many of which blossom into new businesses, or new endeavors of research.

The quality of life in places such as the Research Triangle is great because you have people from around the world moving to a place, each bringing his/her culture, food, and uniqueness to this place, and enriching everyone else as a result.

Two advantages the Research Triangle has over Silicon Valley are that the Research Triangle has a bigger diversity of industries, so when the telecommunications industry busted back in 2001-02, the region took a hit, but the biotechnology industry was still growing, so unemployment rose, but not to the extent that other areas might have experienced.

The latest recession has hit us all very hard, so even this strategy has not made us immune to having high unemployment, but the Research Triangle region has been pegged by experts to be one of the first regions to emerge out of the Great Recession.

The other advantage I think we have is that our cost of living is still much more reasonable than Silicon Valley. It’s still possible to get a nice sized home, some land and not break the bank!

Ajay– How do you manage an active online social media presence, your job and your family. How important is balance in professional life and when young professional should realize this?

Jamie– Balance is everything, isn’t it? When I leave the office, I turn off my iPhone and disconnect from Twitter/Facebook etc.

I know that is not recommended by some folks, but I am a one person communications department and I love my family and friends and feel its important to devote time to them as well as to my career.

I think it is very important for young people to establish this early in their careers because if they don’t they will fall victim to working way too many hours and really, who loves you at the end of the day?

Your company may appreciate all you do for them, but if you leave, or you get sick and cannot work for them, you will be replaced

. Lee Iacocca, former CEO of Chrystler, said, “No matter what you’ve done for yourself or for humanity, if you can’t look back on having given love and attention to your own family, what have you really accomplished?” I think that is what is really most important in life.

About-

Jamie Nunnelly has been in communications for 25 years. She is currently on the board of directors for Chatham County Economic Development Corporation and Leadership Triangle & is a member of the International Association of Business Communicators and the Public Relations Society of America. She earned a bachelor’s degree in interpersonal and public communications at Bowling Green State University and a master’s degree in mass communications at the University of South Florida.

You can contact Jamie at http://niss.org/content/jamie-nunnelly or on twitter at

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STEM is cool

Lady Gaga holding a speech at National Equalit...
Image via Wikipedia

A good video created by my favorite social media people from a company in North Carolina.

STEM is cool (Science Technology Engineering Maths?)

No, Science is not kool aid- it is just COOL. and better paying than watching Justin Bieber or Lady Gaga videos. Get those lazy teenagers out of Glee clubs and back into Science clubs.

The video itself-

Disclaimer- I have no direct or indirect  financial relationship with the creators of this video. I think it is cool people express creativity in positive ways to help their favorite software,company, and even the world. Blah Blah Blah 🙂

Yeah, STEM is cool again.

 

 

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