Category: Analytics

Understanding OAuth 1.0 for #rstats

The lovely lovely diagram at  https://developer.linkedin.com/documents/oauth-overview   is worth a thousand words and errors.

Very useful if you are trying to coax rCurl to do the job for you.

Credits-Idan Gazit

 

 

Also a great slideshare in Japanese (no! Google Translate didnt work on pdf’s and slideshares and scribds (why!!) but still very lucid on using OAuth with R for Twitter.

Why use OAuth- you get 350 calls per hour for authenticated sessions than 150 calls .

I tried but failed using registerTwitterOAuth

There is a real need for a single page where you can go and see which social netowork /website is using what kind of oAuth, which url within that website has your API keys, and the accompanying R Code for the same. Google Plus,LinkedIn, Twitter, Facebook all can be scraped better by OAuth. Something like this-

 

New Free Online Book by Rob Hyndman on Forecasting using #Rstats

From the creator of some of the most widely used packages for time series in the R programming language comes a brand new book, and its online!

This time the book is free, will be updated and 7 chapters are ready (to read!)

. If you do forecasting professionally, now is the time to suggest your own use cases to be featured as the book gets ready by end- 2012. The book is intended as a replace­ment for Makri­dakis, Wheel­wright and Hyn­d­man (Wiley 1998).

http://otexts.com/fpp/

The book is writ­ten for three audi­ences:

(1) people find­ing them­selves doing fore­cast­ing in busi­ness when they may not have had any for­mal train­ing in the area;

(2) undergraduate stu­dents study­ing busi­ness;

(3) MBA stu­dents doing a fore­cast­ing elec­tive.

The book is dif­fer­ent from other fore­cast­ing text­books in sev­eral ways.

  • It is free and online, mak­ing it acces­si­ble to a wide audience.
  • It is con­tin­u­ously updated. You don’t have to wait until the next edi­tion for errors to be removed or new meth­ods to be dis­cussed. We will update the book frequently.
  • There are dozens of real data exam­ples taken from our own con­sult­ing prac­tice. We have worked with hun­dreds of busi­nesses and orga­ni­za­tions help­ing them with fore­cast­ing issues, and this expe­ri­ence has con­tributed directly to many of the exam­ples given here, as well as guid­ing our gen­eral phi­los­o­phy of forecasting.
  • We empha­sise graph­i­cal meth­ods more than most fore­cast­ers. We use graphs to explore the data, analyse the valid­ity of the mod­els fit­ted and present the fore­cast­ing results.

A print ver­sion and a down­load­able e-version of the book will be avail­able to pur­chase on Ama­zon, but not until a few more chap­ters are written.

Contents

(Ajay-Support the open textbook movement!)

If you’ve found this book helpful, please consider helping to fund free, open and online textbooks. (Donations via PayPal.)

Look for yourself at http://otexts.com/fpp/

 

Little Book of R For Time Series #rstats

I loved this book. Only 75 pages and very lucidly written and available on Github for free. Nice job by Avril Coghlan a.coghlan@ucc.ie

.Of course My usual suspects for Time Series Readings are –

1) The seminal pdf (2008!!) by  a certain Prof Hyndman

Click to access Rtimeseries-ohp.pdf

 

2) JSS Paper -Automatic Time Series Forecasting: The forecast
Package for R http://www.jstatsoft.org/v27/i03/paper

3) The CRAN View http://cran.r-project.org/web/views/TimeSeries.html

This is cluttered and getting more and more cluttered. Some help on helping recent converts to R, especially in the field of corporate forecasting or time series for business analytics would really help.

Avril does an awesome job with this curiously named ( 😉 ) booklet  at http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/src/timeseries.html

RevoDeployR and commercial BI using R and R based cloud computing using Open CPU

Revolution Analytics has of course had RevoDeployR, and in a  webinar strive to bring it back to center spotlight.

BI is a good lucrative market, and visualization is a strength in R, so it is matter of time before we have more R based BI solutions. I really liked the two slides below for explaining RevoDeployR better to newbies like me (and many others!)

Integrating R into 3rd party and Web applications using RevoDeployR

Please click here to download the PDF.

Here are some additional links that may be of interest to you:

 

( I still think someone should make a commercial version of Jeroen Oom’s web interfaces and Jeff Horner’s web infrastructure (see below) for making customized Business Intelligence (BI) /Data Visualization solutions , UCLA and Vanderbilt are not exactly Stanford when it comes to deploying great academic solutions in the startup-tech world). I kind of think Google or someone at Revolution  should atleast dekko OpenCPU as a credible cloud solution in R.

I still cant figure out whether Revolution Analytics has a cloud computing strategy and Google seems to be working mysteriously as usual in broadening access to the Google Compute Cloud to the rest of R Community.

Open CPU  provides a free and open platform for statistical computing in the cloud. It is meant as an open, social analysis environment where people can share and run R functions and objects. For more details, visit the websit: www.opencpu.org

and esp see

https://public.opencpu.org/userapps/opencpu/opencpu.demo/runcode/

Jeff Horner’s

http://rapache.net/

Jerooen Oom’s

Big Noise on Big Data

Increasingly Big Data is used in writing where Business Analytics was used, and data mining is thrown in as a word just to keep liberal art majors happy that they are reading a scientific article.

Some Big Words I have noticed in my Short life-

Big Data? High Performance Analytics? High Performance Computing ? Cloud Computing? Time Sharing? Data Mining? SEMMA? CRISP-DM? KDD? Business Intelligence? Business Analytics and Optimization? (pick a card and any card)

(or Just Moore’s Law catching up with the analytics)

Some examples-

Replace Big Data with Analytics in these articles and let me know if you can make out much of a difference

  • Big Data on Campus

http://www.nytimes.com/2012/07/22/education/edlife/colleges-awakening-to-the-opportunities-of-data-mining.html

  • From the man who famously said BI is dead, is now burying Business Analytics within the new buzzword , SAS CMO Jim Davis

How to transform big data from an obstacle into an asset

http://blogs.sas.com/content/corneroffice/2012/07/22/how-to-transform-big-data-from-an-obstacle-into-an-asset/

(Related- Is big data over hyped? by Jim Davis

http://www.sas.com/knowledge-exchange/business-analytics/featured/is-big-data-over-hyped/index.html )

I am sure by 2015, Jim Davis, NYT and the merry men of analytics will find some other buzzwords to rally the troops. In the meantime, let me throw out the flag and call it Big  .