Jetstrap for builiding websites with Twitter Bootstrap

Twitter Bootstrap is a free collection of tools for creating websites and web applications. It contains HTML and CSS-based design templates for typography, forms, buttons, charts, navigation and other interface components, as well as optional JavaScript extensions.

It is the most popular project in GitHub[2] and is used by NASA and MSNBC among others.

———————-

If you like me, hate to get down and dirty in HTML, CSS , JQuery ( not mentioning the excellent Code Academy HTML/CSS tutorials and  JQuery Track ) and want to create a pretty simple website for yourself- Jetstrap helps you build the popular Twitter Bootstrap design (very minimalistic) for websites.

And it’s free! And click and point and paste your content- and awesome CSS, HTML. Allows you to download the HTML to paste in your existing site!

2

Here is one I created in 5 minutes!

123

So lose your old website! Because not every website needs WordPress!

Try Jetstrap for Bootstrap!

JSS launches special edition for GUI for #Rstats

I love GUIs (graphical user interfaces)- they might be TCL/TK based or GTK based or even QT based. As a researcher they help me with faster coding, as a consultant they help with faster transition of projects from startup to handover stage  and as an R  instructor helps me get people to learn R faster.

I wish Python had some GUIs though 😉

 

from the open access journal of statistical software-

JSS Special Volume 49: Graphical User Interfaces for R

Graphical User Interfaces for R
Pedro M. Valero-Mora, Ruben Ledesma
Vol. 49, Issue 1, Jun 2012
Submitted 2012-06-03, Accepted 2012-06-03
Integrated Degradation Models in R Using iDEMO
Ya-Shan Cheng, Chien-Yu Peng
Vol. 49, Issue 2, Jun 2012
Submitted 2010-12-31, Accepted 2011-06-29
Glotaran: A Java-Based Graphical User Interface for the R Package TIMP
Joris J. Snellenburg, Sergey Laptenok, Ralf Seger, Katharine M. Mullen, Ivo H. M. van Stokkum
Vol. 49, Issue 3, Jun 2012
Submitted 2011-01-20, Accepted 2011-09-16
A Graphical User Interface for R in a Rich Client Platform for Ecological Modeling
Marcel Austenfeld, Wolfram Beyschlag
Vol. 49, Issue 4, Jun 2012
Submitted 2011-01-05, Accepted 2012-02-20
Closing the Gap between Methodologists and End-Users: R as a Computational Back-End
Byron C. Wallace, Issa J. Dahabreh, Thomas A. Trikalinos, Joseph Lau, Paul Trow, Christopher H. Schmid
Vol. 49, Issue 5, Jun 2012
Submitted 2010-11-01, Accepted 2012-12-20
tourrGui: A gWidgets GUI for the Tour to Explore High-Dimensional Data Using Low-Dimensional Projections
Bei Huang, Dianne Cook, Hadley Wickham
Vol. 49, Issue 6, Jun 2012
Submitted 2011-01-20, Accepted 2012-04-16
The RcmdrPlugin.survival Package: Extending the R Commander Interface to Survival Analysis
John Fox, Marilia S. Carvalho
Vol. 49, Issue 7, Jun 2012
Submitted 2010-12-26, Accepted 2011-12-28
Deducer: A Data Analysis GUI for R
Ian Fellows
Vol. 49, Issue 8, Jun 2012
Submitted 2011-02-28, Accepted 2011-09-08
RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R
Stefan Rödiger, Thomas Friedrichsmeier, Prasenjit Kapat, Meik Michalke
Vol. 49, Issue 9, Jun 2012
Submitted 2010-12-28, Accepted 2011-05-06
gWidgetsWWW: Creating Interactive Web Pages within R
John Verzani
Vol. 49, Issue 10, Jun 2012
Submitted 2010-12-17, Accepted 2011-05-11
Oscars and Interfaces
Antony Unwin
Vol. 49, Issue 11, Jun 2012
Submitted 2010-12-08, Accepted 2011-07-15

Interview Kelci Miclaus, SAS Institute Using #rstats with JMP

Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.

 

Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?

Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.

One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.

I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.

(see http://www.decisionstats.com/jmp-and-r-rstats/)

Ajay- Are there plans to extend JMP integration with other languages like Python?

Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.

 Ajay- How is R a complimentary fit to JMP’s technical capabilities?

Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.

JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.

Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.

Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?

Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.

Ajay-  While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.

 Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included.  Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.

We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.

About- Kelci Miclaus

Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.

.

 

Product Review – Revolution R 5.0

So I got the email from Revolution R. Version 5.0 is ready for download, and unlike half hearted attempts by many software companies they make it easy for the academics and researchers to get their free copy. Free as in speech and free as in beer.

Some thoughts-

1) R ‘s memory problem is now an issue of marketing and branding. Revolution Analytics has definitely bridged this gap technically  beautifully and I quote from their documentation-

The primary advantage 64-bit architectures bring to R is an increase in the amount of memory available to a given R process.
The first benefit of that increase is an increase in the size of data objects you can create. For example, on most 32-bit versions of R, the largest data object you can create is roughly 3GB; attempts to create 4GB objects result in errors with the message “cannot allocate vector of length xxxx.”
On 64-bit versions of R, you can generally create larger data objects, up to R’s current hard limit of 231 􀀀 1 elements in a vector (about 2 billion elements). The functions memory.size and memory.limit help you manage the memory used byWindows versions of R.
In 64-bit Revolution R Enterprise, R sets the memory limit by default to the amount of physical RAM minus half a gigabyte, so that, for example, on a machine with 8GB of RAM, the default memory limit is 7.5GB:

2) The User Interface is best shown as below or at  https://docs.google.com/presentation/pub?id=1V_G7r0aBR3I5SktSOenhnhuqkHThne6fMxly_-4i8Ag&start=false&loop=false&delayms=3000

-(but I am still hoping for the GUI ,Revolution Analytics promised us for Christmas)

3) The partnership with Microsoft HPC is quite awesome given Microsoft’s track record in enterprise software penetration

but I am also interested in knowing more about the Oracle version of R and what it will do there.

Creating Pages on Google Plus for some languages

So I decided to create Pages on Google Plus for my favorite programming languages.

a programming language that lets you work more quickly and integrate your systems more effectively

Add to circles

  –  Comment  –  Share

Ajay Ohri

Ajay Ohri's profile photo

Ajay Ohri  –
Ajay Ohri shared a Google+ page with you.
Structured Query Language
Leading statistical language since 1960’s especially in sociology and market research
The leading statistical language in the world
The leading statistical language since 1970’s

Python

https://plus.google.com/107930407101060924456/posts

 

These are in accordance with Google’s Policies http://www.google.com/intl/en/+/policy/pagesterm.html  Continue reading “Creating Pages on Google Plus for some languages”

Google Plus API- statistical text mining anyone

For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.

And it would also be  a nice benchmark to do sentiment analysis across multiple social networks.

Some interesting use cases of using Twitter that have been used in R.

  • Using R to search Twitter for analysis
http://www.franklincenterhq.org/2429/using-r-to-search-twitter-for-analysis/
  • Text Data Mining With Twitter And R
  • TWITTER FROM R… SURE, WHY NOT!
  • A package called TwitteR
  • slides from my R tutorial on Twitter text mining #rstats
  • Generating graphs of retweets and @-messages on Twitter using R and Gephi
But with Google Plus API now active

The Console lets you see and manage the following project information:

  • Activated APIs – Activate one or more APIs to enable traffic monitoring, filtering, and billing, and API-specific pages for your project. Read more about activating APIs here.
  • Traffic information – The Console reports traffic information for each activated API. Additionally, you can cap or filter usage by API. Read more about traffic reporting and request filtering here.
  • Billing information – When you activate billing, your activated APIs can exceed the courtesy usage quota. Usage fees are billed to the Google Checkout account that you specify. Read more about billing here.
  • Project keys – Each project is identified by either an API key or an OAuth 2.0 token. Use this key/token in your API requests to identify the project, in order to record usage data, enforce your filtering restrictions, and bill usage to the proper project. You can use the Console to generate or revoke API keys or OAuth 2.0 certificates to use in your application. Read more about keys here.
  • Team members – You can specify additional members with read, write, or ownership access to this project’s Console page. Read more about team members here.
Google+ API Courtesy limit: 1,000 queries/day

Effective limits:

API Per-User Limit Used Courtesy Limit
Google+ API 5.0 requests/second/user 0% 1,000 queries/day
API Calls
Most of the Google+ API follows a RESTful API design, meaning that you use standard HTTP methods to retrieve and manipulate resources. For example, to get the profile of a user, you might send an HTTP request like:

GET https://www.googleapis.com/plus/v1/people/userId

Common Parameters

Different API methods require parameters to be passed either as part of the URL path or as query parameters. Additionally, there are a few parameters that are common to all API endpoints. These are all passed as optional query parameters.

Parameter Name

Value

Description

callback

string

Specifies a JavaScript function that will be passed the response data for using the API with JSONP.

fields

string

Selector specifying which fields to include in a partial response.

key

string

API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.

access_token

string

OAuth 2.0 token for the current user. Learn more about OAuth.

prettyPrint

boolean

If set to “true”, data output will include line breaks and indentation to make it more readable. If set to “false”, unnecessary whitespace is removed, reducing the size of the response. Defaults to “true”.

userIp

string

Identifies the IP address of the end user for whom the API call is being made. This allows per-user quotas to be enforced when calling the API from a server-side application. Learn more about Capping Usage.

Data Formats

Resources in the Google+ API are represented using JSON data formats. For example, retrieving a user’s profile may result in a response like:

{
  "kind": "plus#person",
  "id": "118051310819094153327",
  "displayName": "Chirag Shah",
  "url": "https://plus.google.com/118051310819094153327",
  "image": {
    "url": "https://lh5.googleusercontent.com/-XnZDEoiF09Y/AAAAAAAAAAI/AAAAAAAAYCI/7fow4a2UTMU/photo.jpg"
  }
}

Common Properties

While each type of resource will have its own unique representation, there are a number of common properties that are found in almost all resource representations.

Property Name

Value

Description

displayName

string

This is the name of the resource, suitable for displaying to a user.

id

string

This property uniquely identifies a resource. Every resource of a given kind will have a unique id. Even though an id may sometimes look like a number, it should always be treated as a string.

kind

string

This identifies what kind of resource a JSON object represents. This is particularly useful when programmatically determining how to parse an unknown object.

url

string

This is the primary URL, or permalink, for the resource.

Pagination

In requests that can respond with potentially large collections, such as Activities list, each response contains a limited number of items, set by maxResults(default: 20). Each response also contains a nextPageToken property. To obtain the next page of items, you pass this value of nextPageToken to the pageTokenproperty of the next request. Repeat this process to page through the full collection.

For example, calling Activities list returns a response with nextPageToken:

{
  "kind": "plus#activityFeed",
  "title": "Plus Public Activities Feed",
  "nextPageToken": "CKaEL",
  "items": [
    {
      "kind": "plus#activity",
      "id": "123456789",
      ...
    },
    ...
  ]
  ...
}

To get the next page of activities, pass the value of this token in with your next Activities list request:

https://www.googleapis.com/plus/v1/people/me/activities/public?pageToken=CKaEL

As before, the response to this request includes nextPageToken, which you can pass in to get the next page of results. You can continue this cycle to get new pages — for the last page, “nextPageToken” will be absent.

 

it would be interesting the first wave of analysis on this new social network and see if it is any different from others, if at all.
After all, an API is only as good as the analysis and applications  that can be done on the data it provides

 

Interview Rapid-I -Ingo Mierswa and Simon Fischer

Here is an interview with Dr Ingo Mierswa , CEO of Rapid -I and Dr Simon Fischer, Head R&D. Rapid-I makes the very popular software Rapid Miner – perhaps one of the earliest leading open source software in business analytics and business intelligence. It is quite easy to use, deploy and with it’s extensions and innovations (including compatibility with R )has continued to grow tremendously through the years.

In an extensive interview Ingo and Simon talk about algorithms marketplace, extensions , big data analytics, hadoop, mobile computing and use of the graphical user interface in analytics.

Special Thanks to Nadja from Rapid I communication team for helping coordinate this interview.( Statuary Blogging Disclosure- Rapid I is a marketing partner with Decisionstats as per the terms in https://decisionstats.com/privacy-3/)

Ajay- Describe your background in science. What are the key lessons that you have learnt while as scientific researcher and what advice would you give to new students today.

Ingo: My time as researcher really was a great experience which has influenced me a lot. I have worked at the AI lab of Prof. Dr. Katharina Morik, one of the persons who brought machine learning and data mining to Europe. Katharina always believed in what we are doing, encouraged us and gave us the space for trying out new things. Funnily enough, I never managed to use my own scientific results in any real-life project so far but I consider this as a quite common gap between science and the “real world”. At Rapid-I, however, we are still heavily connected to the scientific world and try to combine the best of both worlds: solving existing problems with leading-edge technologies.

Simon: In fact, during my academic career I have not worked in the field of data mining at all. I worked on a field some of my colleagues would probably even consider boring, and that is theoretical computer science. To be precise, my research was in the intersection of game theory and network theory. During that time, I have learnt a lot of exciting things, none of which had any business use. Still, I consider that a very valuable experience. When we at Rapid-I hire people coming to us right after graduating, I don’t care whether they know the latest technology with a fancy three-letter acronym – that will be forgotten more quickly than it came. What matters is the way you approach new problems and challenges. And that is also my recommendation to new students: work on whatever you like, as long as you are passionate about it and it brings you forward.

Ajay-  How is the Rapid Miner Extensions marketplace moving along. Do you think there is a scope for people to say create algorithms in a platform like R , and then offer that algorithm as an app for sale just like iTunes or Android apps.

 Simon: Well, of course it is not going to be exactly like iTunes or Android apps are, because of the more business-orientated character. But in fact there is a scope for that, yes. We have talked to several developers, e.g., at our user conference RCOMM, and several people would be interested in such an opportunity. Companies using data mining software need supported software packages, not just something they downloaded from some anonymous server, and that is only possible through a platform like the new Marketplace. Besides that, the marketplace will not only host commercial extensions. It is also meant to be a platform for all the developers that want to publish their extensions to a broader community and make them accessible in a comfortable way. Of course they could just place them on their personal Web pages, but who would find them there? From the Marketplace, they are installable with a single click.

Ingo: What I like most about the new Rapid-I Marketplace is the fact that people can now get something back for their efforts. Developing a new algorithm is a lot of work, in some cases even more that developing a nice app for your mobile phone. It is completely accepted that people buy apps from a store for a couple of Dollars and I foresee the same for sharing and selling algorithms instead of apps. Right now, people can already share algorithms and extensions for free, one of the next versions will also support selling of those contributions. Let’s see what’s happening next, maybe we will add the option to sell complete RapidMiner workflows or even some data pools…

Ajay- What are the recent features in Rapid Miner that support cloud computing, mobile computing and tablets. How do you think the landscape for Big Data (over 1 Tb ) is changing and how is Rapid Miner adapting to it.

Simon: These are areas we are very active in. For instance, we have an In-Database-Mining Extension that allows the user to run their modelling algorithms directly inside the database, without ever loading the data into memory. Using analytic databases like Vectorwise or Infobright, this technology can really boost performance. Our data mining server, RapidAnalytics, already offers functionality to send analysis processes into the cloud. In addition to that, we are currently preparing a research project dealing with data mining in the cloud. A second project is targeted towards the other aspect you mention: the use of mobile devices. This is certainly a growing market, of course not for designing and running analyses, but for inspecting reports and results. But even that is tricky: When you have a large screen you can display fancy and comprehensive interactive dashboards with drill downs and the like. On a mobile device, that does not work, so you must bring your reports and visualizations very much to the point. And this is precisely what data mining can do – and what is hard to do for classical BI.

Ingo: Then there is Radoop, which you may have heard of. It uses the Apache Hadoop framework for large-scale distributed computing to execute RapidMiner processes in the cloud. Radoop has been presented at this year’s RCOMM and people are really excited about the combination of RapidMiner with Hadoop and the scalability this brings.

 Ajay- Describe the Rapid Miner analytics certification program and what steps are you taking to partner with academic universities.

Ingo: The Rapid-I Certification Program was created to recognize professional users of RapidMiner or RapidAnalytics. The idea is that certified users have demonstrated a deep understanding of the data analysis software solutions provided by Rapid-I and how they are used in data analysis projects. Taking part in the Rapid-I Certification Program offers a lot of benefits for IT professionals as well as for employers: professionals can demonstrate their skills and employers can make sure that they hire qualified professionals. We started our certification program only about 6 months ago and until now about 100 professionals have been certified so far.

Simon: During our annual user conference, the RCOMM, we have plenty of opportunities to talk to people from academia. We’re also present at other conferences, e.g. at ECML/PKDD, and we are sponsoring data mining challenges and grants. We maintain strong ties with several universities all over Europe and the world, which is something that I would not want to miss. We are also cooperating with institutes like the ITB in Dublin during their training programmes, e.g. by giving lectures, etc. Also, we are leading or participating in several national or EU-funded research projects, so we are still close to academia. And we offer an academic discount on all our products 🙂

Ajay- Describe the global efforts in making Rapid Miner a truly international software including spread of developers, clients and employees.

Simon: Our clients already are very international. We have a partner network in America, Asia, and Australia, and, while I am responding to these questions, we have a training course in the US. Developers working on the core of RapidMiner and RapidAnalytics, however, are likely to stay in Germany for the foreseeable future. We need specialists for that, and it would be pointless to spread the development team over the globe. That is also owed to the agile philosophy that we are following.

Ingo: Simon is right, Rapid-I already is acting on an international level. Rapid-I now has more than 300 customers from 39 countries in the world which is a great result for a young company like ours. We are of course very strong in Germany and also the rest of Europe, but also concentrate on more countries by means of our very successful partner network. Rapid-I continues to build this partner network and to recruit dynamic and knowledgeable partners and in the future. However, extending and acting globally is definitely part of our strategic roadmap.

Biography

Dr. Ingo Mierswa is working as Chief Executive Officer (CEO) of Rapid-I. He has several years of experience in project management, human resources management, consulting, and leadership including eight years of coordinating and leading the multi-national RapidMiner developer team with about 30 developers and contributors world-wide. He wrote his Phd titled “Non-Convex and Multi-Objective Optimization for Numerical Feature Engineering and Data Mining” at the University of Dortmund under the supervision of Prof. Morik.

Dr. Simon Fischer is heading the research & development at Rapid-I. His interests include game theory and networks, the theory of evolutionary algorithms (e.g. on the Ising model), and theoretical and practical aspects of data mining. He wrote his PhD in Aachen where he worked in the project “Design and Analysis of Self-Regulating Protocols for Spectrum Assignment” within the excellence cluster UMIC. Before, he was working on the vtraffic project within the DFG Programme 1126 “Algorithms for large and complex networks”.

http://rapid-i.com/content/view/181/190/ tells you more on the various types of Rapid Miner licensing for enterprise, individual and developer versions.

(Note from Ajay- to receive an early edition invite to Radoop, click here http://radoop.eu/z1sxe)