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Inspired by David Smith ‘s blog post at http://blog.revolutionanalytics.com/2012/10/r-user-group-sponsorship-applications-open-for-2013.html I set up a meetup group for New Delhi at http://www.meetup.com/New-Delhi-R-UseR-Group/ ( India to my surprise has only 1 R user meetup group before this in Bangalore). The first meeting was awesome, we met in a cafe, and the plan going forward is to cover cross domain learning and collaboration on tools, startups, mashups and training.
Hopefully we can reach out to analytics enthusiasts in Mumbai and Chennai to help kickstart the R User groups. Indian companies like Mu Sigma have been using R more and more in analytics (offshoring). You can even use the sponsorship from Revolution Analytics to start your meetup group , Meetup.com gives you a 50% discount if you pay 6 months in advance, and given Oracle’s and IBM/Google\s big Indian presence I hope they lend a hand to User groups for R in India as well.
Ajay- Why did you choose Rapid Miner and R? What were the other software alternatives you considered and discarded?
Analyst- We considered most of the other major players in statistics/data mining or enterprise BI. However, we found that the value proposition for an open source solution was too compelling to justify the premium pricing that the commercial solutions would have required. The widespread adoption of R and the variety of packages and algorithms available for it, made it an easy choice. We liked RapidMiner as a way to design structured, repeatable processes, and the ability to optimize learner parameters in a systematic way. It also handled large data sets better than R on 32-bit Windows did. The GUI, particularly when 5.0 was released, made it more usable than R for analysts who weren’t experienced programmers.
Ajay- What analytics do you do think Rapid Miner and R are best suited for?
Analyst- We use RM+R mainly for sports analysis so far, rather than for more traditional business applications. It has been quite suitable for that, and I can easily see how it would be used for other types of applications.
Ajay- Any experiences as an enterprise customer? How was the installation process? How good is the enterprise level support?
Analyst- Rapid-I has been one of the most responsive tech companies I’ve dealt with, either in my current role or with previous employers. They are small enough to be able to respond quickly to requests, and in more than one case, have fixed a problem, or added a small feature we needed within a matter of days. In other cases, we have contracted with them to add larger pieces of specific functionality we needed at reasonable consulting rates. Those features are added to the mainline product, and become fully supported through regular channels. The longer consulting projects have typically had a turnaround of just a few weeks.
Ajay- What challenges if any did you face in executing a pure open source analytics bundle ?
Analyst- As Rapid-I is a smaller company based in Europe, the availability of training and consulting in the USA isn’t as extensive as for the major enterprise software players, and the time zone differences sometimes slow down the communications cycle. There were times where we were the first customer to attempt a specific integration point in our technical environment, and with no prior experiences to fall back on, we had to work with Rapid-I to figure out how to do it. Compared to the what traditional software vendors provide, both R and RM tend to have sparse, terse, occasionally incomplete documentation. The situation is getting better, but still lags behind what the traditional enterprise software vendors provide.
Ajay- What are the things you can do in R ,and what are the things you prefer to do in Rapid Miner (comparison for technical synergies)
Analyst- Our experience has been that RM is superior to R at writing and maintaining structured processes, better at handling larger amounts of data, and more flexible at fine-tuning model parameters automatically. The biggest limitation we’ve had with RM compared to R is that R has a larger library of user-contributed packages for additional data mining algorithms. Sometimes we opted to use R because RM hadn’t yet implemented a specific algorithm. The introduction the R extension has allowed us to combine the strengths of both tools in a very logical and productive way.
In particular, extending RapidMiner with R helped address RM’s weakness in the breadth of algorithms, because it brings the entire R ecosystem into RM (similar to how Rapid-I implemented much of the Weka library early on in RM’s development). Further, because the R user community releases packages that implement new techniques faster than the enterprise vendors can, this helps turn a potential weakness into a potential strength. However, R packages tend to be of varying quality, and are more prone to go stale due to lack of support/bug fixes. This depends heavily on the package’s maintainer and its prevalence of use in the R community. So when RapidMiner has a learner with a native implementation, it’s usually better to use it than the R equivalent.
The Google Visualization API is a great way for people to make dashboards with slick graphics based on data without getting into the fine print of the scripting language itself. It utilizes the same tools as Google itself does, and makes visualizing data using API calls to the Visualization API. Thus a real-time customizable dashboard that is publishable to the internet can be created within minutes, and more importantly insights can be much more easily drawn from graphs than from looking at rows of tables and numbers.
- There are 41 gadgets (including made by both Google and third-party developers ) available in the Gadget Gallery ( https://developers.google.com/chart/interactive/docs/gadgetgallery)
- There are 12 kinds of charts available in the Chart Gallery (https://developers.google.com/chart/interactive/docs/gallery) .
- However there 26 additional charts in the charts page at https://developers.google.com/chart/interactive/docs/more_charts )
Building and embedding charts is simplified to a few steps
- Load the AJAX API
- Load the Visualization API and the appropriate package (like piechart or barchart from the kinds of chart)
- Set a callback to run when the Google Visualization API is loaded
- Within the Callback – It creates and populates a data table, instantiates the particular chart type chosen, passes in the data and draws it.
- Create the data table with appropriately named columns and data rows.
- Set chart options with Title, Width and Height
- Instantiate and draw the chart, passing in some options including the name and id
- Finally write the HTML/ Div that will hold the chart
You can simply copy and paste the code directly from https://developers.google.com/chart/interactive/docs/quick_start without getting into any details, and tweak them according to your data, chart preference and voila your web dashboard is ready!
That is the beauty of working with API- you can create and display genius ideas without messing with the scripting languages and code (too much). If you like to dive deeper into the API, you can look at the various objects at https://developers.google.com/chart/interactive/docs/reference
First launched in Mar 2008, Google Visualization API has indeed come a long way in making dashboards easier to build for people wanting to utilize advanced data visualization . It came about directly as a result of Google’s 2007 acquisition of GapMinder (of Hans Rosling fame).
As invariably and inevitably computing shifts to the cloud, visualization APIs will be very useful. Tableau Software has been a pioneer in selling data visualizing to the lucrative business intelligence and business dashboards community (you can see the Tableau Software API at http://onlinehelp.tableausoftware.com/v7.0/server/en-us/embed_api.htm ), and Google Visualization can do the same and capture business dashboard and visualization market , if there is more focus on integrating it from Google in it’s multiple and often confusing API offerings.
However as of now, this is quite simply the easiest way to create a web dashboard for your personal needs. Google guarantees 3 years of backward compatibility with this API and it is completely free.
Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.
Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.
Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.
Ajay- When did you first start getting interested in R as an analytics platform?
Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.
Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?
Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville
Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.
Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings.
Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.
Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.
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.
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.
Here is an interview with JJ Allaire, founder of RStudio. RStudio is the IDE that has overtaken other IDE within the R Community in terms of ease of usage. On the eve of their latest product launch, JJ talks to DecisionStats on RStudio and more.
Ajay- So what is new in the latest version of RStudio and how exactly is it useful for people?
JJ- The initial release of RStudio as well as the two follow-up releases we did last year were focused on the core elements of using R: editing and running code, getting help, and managing files, history, workspaces, plots, and packages. In the meantime users have also been asking for some bigger features that would improve the overall work-flow of doing analysis with R. In this release (v0.95) we focused on three of these features:
Projects. R developers tend to have several (and often dozens) of working contexts associated with different clients, analyses, data sets, etc. RStudio projects make it easy to keep these contexts well separated (with distinct R sessions, working directories, environments, command histories, and active source documents), switch quickly between project contexts, and even work with multiple projects at once (using multiple running versions of RStudio).
Version Control. The benefits of using version control for collaboration are well known, but we also believe that solo data analysis can achieve significant productivity gains by using version control (this discussion on Stack Overflow talks about why). In this release we introduced integrated support for the two most popular open-source version control systems: Git and Subversion. This includes changelist management, file diffing, and browsing of project history, all right from within RStudio.
Code Navigation. When you look at how programmers work a surprisingly large amount of time is spent simply navigating from one context to another. Modern programming environments for general purpose languages like C++ and Java solve this problem using various forms of code navigation, and in this release we’ve brought these capabilities to R. The two main features here are the ability to type the name of any file or function in your project and go immediately to it; and the ability to navigate to the definition of any function under your cursor (including the definition of functions within packages) using a keystroke (F2) or mouse gesture (Ctrl+Click).
Ajay- What’s the product road map for RStudio? When can we expect the IDE to turn into a full fledged GUI?
JJ- Linus Torvalds has said that “Linux is evolution, not intelligent design.” RStudio tries to operate on a similar principle—the world of statistical computing is too deep, diverse, and ever-changing for any one person or vendor to map out in advance what is most important. So, our internal process is to ship a new release every few months, listen to what people are doing with the product (and hope to do with it), and then start from scratch again making the improvements that are considered most important.
Right now some of the things which seem to be top of mind for users are improved support for authoring and reproducible research, various editor enhancements including code folding, and debugging tools.
What you’ll see is us do in a given release is to work on a combination of frequently requested features, smaller improvements to usability and work-flow, bug fixes, and finally architectural changes required to support current or future feature requirements.
While we do try to base what we work on as closely as possible on direct user-feedback, we also adhere to some core principles concerning the overall philosophy and direction of the product. So for example the answer to the question about the IDE turning into a full-fledged GUI is: never. We believe that textual representations of computations provide fundamental advantages in transparency, reproducibility, collaboration, and re-usability. We believe that writing code is simply the right way to do complex technical work, so we’ll always look for ways to make coding better, faster, and easier rather than try to eliminate coding altogether.
Ajay -Describe your journey in science from a high school student to your present work in R. I noticed you have been very successful in making software products that have been mostly proprietary products or sold to companies.
Why did you get into open source products with RStudio? What are your plans for monetizing RStudio further down the line?
JJ- In high school and college my principal areas of study were Political Science and Economics. I also had a very strong parallel interest in both computing and quantitative analysis. My first job out of college was as a financial analyst at a government agency. The tools I used in that job were SAS and Excel. I had a dim notion that there must be a better way to marry computation and data analysis than those tools, but of course no concept of what this would look like.
From there I went more in the direction of general purpose computing, starting a couple of companies where I worked principally on programming languages and authoring tools for the Web. These companies produced proprietary software, which at the time (between 1995 and 2005) was a workable model because it allowed us to build the revenue required to fund development and to promote and distribute the software to a wider audience.
By 2005 it was however becoming clear that proprietary software would ultimately be overtaken by open source software in nearly all domains. The cost of development had shrunken dramatically thanks to both the availability of high-quality open source languages and tools as well as the scale of global collaboration possible on open source projects. The cost of promoting and distributing software had also collapsed thanks to efficiency of both distribution and information diffusion on the Web.
When I heard about R and learned more about it, I become very excited and inspired by what the project had accomplished. A group of extremely talented and dedicated users had created the software they needed for their work and then shared the fruits of that work with everyone. R was a platform that everyone could rally around because it worked so well, was extensible in all the right ways, and most importantly was free (as in speech) so users could depend upon it as a long-term foundation for their work.
So I started RStudio with the aim of making useful contributions to the R community. We started with building an IDE because it seemed like a first-rate development environment for R that was both powerful and easy to use was an unmet need. Being aware that many other companies had built successful businesses around open-source software, we were also convinced that we could make RStudio available under a free and open-source license (the AGPLv3) while still creating a viable business. At this point RStudio is exclusively focused on creating the best IDE for R that we can. As the core product gets where it needs to be over the next couple of years we’ll then also begin to sell other products and services related to R and RStudio.
In 1995 Joseph J. (JJ) Allaire co-founded Allaire Corporation with his brother Jeremy Allaire, creating the web development tool ColdFusion. In March 2001, Allaire was sold to Macromedia where ColdFusion was integrated into the Macromedia MX product line. Macromedia was subsequently acquired by Adobe Systems, which continues to develop and market ColdFusion.
After the sale of his company, Allaire became frustrated at the difficulty of keeping track of research he was doing using Google. To address this problem, he co-founded Onfolio in 2004 with Adam Berrey, former Allaire co-founder and VP of Marketing at Macromedia.
On March 8, 2006, Onfolio was acquired by Microsoft where many of the features of the original product are being incorporated into the Windows Live Toolbar. On August 13, 2006, Microsoft released the public beta of a new desktop blogging client called Windows Live Writer that was created by Allaire’s team at Microsoft.
Starting in 2009, Allaire has been developing a web-based interface to the widely used R technical computing environment. A beta version of RStudio was publicly released on February 28, 2011.
JJ Allaire received his B.A. from Macalester College (St. Paul, MN) in 1991.
RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. Like R, RStudio is available under a free software license. RStudio is designed to be as straightforward and intuitive as possible to provide a friendly environment for new and experienced R users alike. RStudio is also a company, and they plan to sell services (support, training, consulting, hosting) related to the open-source software they distribute.