Tag: stack overflow

So many R Packages Everywhere, which one do I use? #rstats

Some thoughts on R Packages

  • CRAN is no longer the sole repository for many useful R packages. This includes R Forge, Google Code and increasingly Github
  • CRAN lacks the flexibility and social aspect of Github.
  • CRAN Views is the only thing that lists subject wide listing of R packages. The categorization is however done more on methods than on use cases or business domains.
  • Multiple R packages for the same thing. Which one do I use? Only Stack Overflow helps with that. No rating , no recommendation system
  • The packages suggested by R package feature needs better and automatic association analysis . Right now it is manual and dependent on package author and maintainer.

  • Quis custodiet ipsos custodes? Who guards the guardians of R packages. In an era of cyber security, we need better transparency on security measures within R packages especially given the international nature of the project.  I am very sure I ( or anyone) can create R code to communicate discretely especially on Windows

  • I would rather not install anything on my local machine, and read the package directly from the CRAN . CRAN was designed in an era of low bandwidth- this needs to be upgraded.
  • Note I am refraining respectfully from the atrocious nature of aesthetics in the home website. Many statisticians feel no use of making R user friendly. My professors at U tenn (from which I dropped out in 2 sems) were horrified when I took courses in graphic design as I wanted to know more on the A and B, which make the A/B testing of statistical design. Now that I am getting older, I get horrified by the lack of HTML, CSS and JQuery by some of the brightest programmers in this project.
  • Please comment below.

 

Who made Who in #Rstats

While Bob M, my old mentor and fellow TN man maintains the website http://r4stats.com/ how popular R is across various forums, I am interested in who within R community of 3 million (give or take a few) is contributing more. I am very sure by 2014, we can have a new fork of R called Hadley R, in which all packages would be made by Hadley Wickham and you wont need anything else.

But jokes apart, since I didnt have the time to

1) scrape CRAN for all package authors

2) scrape for lines of code across all packages

3) allocate lines of code (itself a dubious software productivity metric) to various authors of R packages-

OR

1) scraping the entire and 2011’s R help list

2) determine who is the most frequent r question and answer user (ala SAS-L’s annual MVP and rookie of the year awards)

I did the following to atleast who is talking about R across easily scrapable Q and A websites

Stack Overflow still rules over all.

http://stackoverflow.com/tags/r/topusers shows the statistics on who made whom in R on Stack Overflow

All in all, initial ardour seems to have slowed for #Rstats on Stack Overflow ? or is it just summer?

No the answer- credit to Rob J Hyndman is most(?) activity is shifting to Stats Exchange

http://stats.stackexchange.com/tags/r/topusers


You could also paste this in Notepad and some graphs on Average Score / Answer or even make a social network graph if you had the time.

Do NOT (Go/Bi) search for Stack Overflow API or web scraping stack overflow- it gives you all the answers on the website but 0 answers on how to scrape these websites.

I have added a new website called Meta Optimize to this list based on Tal G’s interview of Joseph Turian,  at http://www.r-statistics.com/2010/07/statistical-analysis-qa-website-did-stackoverflow-just-lose-it-to-metaoptimize-and-is-it-good-or-bad/

http://metaoptimize.com/qa/tags/r/?sort=hottest

There are only 17 questions tagged R but it seems a lot of views is being generated.

I also decided to add views from Quora since it is Q and A site (and one which I really like)

http://www.quora.com/R-software

Again very few questions but lot many followers

Saving Output in R for Presentations

While SAS language has a beautifully designed ODS (Output Delivery System) for saving output from certain analysis in excel files (and html and others), in R one can simply use the object, put it in a write.table and save it a csv file using the file parameter within write.table.

As a business analytics consultant, the output from a Proc Means, Proc Freq (SAS) or a summary/describe/table command (in R) is to be presented as a final report. Copying and pasting is not feasible especially for large amounts of text, or remote computers.

Using the following we can simple save the output  in R

 

> getwd()
[1] “C:/Users/KUs/Desktop/Ajay”
> setwd(“C:\Users\KUs\Desktop”)

#We shifted the directory, so we can save output without putting the entire path again and again for each step.

#I have found the summary command most useful for initial analysis and final display (particularly during the data munging step)

nams=summary(ajay)

# I assigned a new object to the analysis step (summary), it could also be summary,names, describe (HMisc) or table (for frequency analysis),
> write.table(nams,sep=”,”,file=”output.csv”)

Note: This is for basic beginners in R using it for business analytics dealing with large number of variables.

 

pps: Note

If you have a large number of files in a local directory to be read in R, you can avoid typing the entire path again and again by modifying the file parameter in the read.table and changing the working directory to that folder

 

setwd(“C:/Users/KUs/Desktop/”)
ajayt1=read.table(file=”test1.csv”,sep=”,”,header=T)

ajayt2=read.table(file=”test2.csv”,sep=”,”,header=T)

 

and so on…

maybe there is a better approach somewhere on Stack Overflow or R help, but this will work just as well.

you can then merge the objects created ajayt1 and ajayt2… (to be continued)

Interview JJ Allaire Founder, RStudio

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.

About-

http://rstudio.org/docs/about

Jjallaire

JJ Allaire

JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion,Windows Live Writer, Lose It!, and RStudio.

From http://en.wikipedia.org/wiki/Joseph_J._Allaire
In 1995 Joseph J. (JJ) Allaire co-founded Allaire Corporation with his brother Jeremy Allaire, creating the web development tool ColdFusion.[1] 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-

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.

Contribution to #Rstats by Revolution

I have been watching for Revolution Analytics product almost since the inception of the company. It has managed to sail over storms, naysayers and critics with simple and effective strategy of launching good software, making good partnerships and keeping up media visibility with white papers, joint webinars, blogs, conferences and events.

However this is a listing of all technical contributions made by Revolution Analytics products to the #rstats project.

1) Useful Packages mostly in parallel processing or more efficient computing like

 

2) RevoScaler package to beat R’s memory problem (this is probably the best in my opinion as it is yet to be replicated by the open source version and is a clear cut reason for going in for the paid version)

http://www.revolutionanalytics.com/products/enterprise-big-data.php

  • Efficient XDF File Format designed to efficiently handle huge data sets.
  • Data Step Functionality to quickly clean, transform, explore, and visualize huge data sets.
  • Data selection functionality to store huge data sets out of memory, and select subsets of rows and columns for in-memory operation with all R functions.
  • Visualize Large Data sets with line plots and histograms.
  • Built-in Statistical Algorithms for direct analysis of huge data sets:
    • Summary Statistics
    • Linear Regression
    • Logistic Regression
    • Crosstabulation
  • On-the-fly data transformations to include derived variables in models without writing new data files.
  • Extend Existing Analyses by writing user- defined R functions to “chunk” through huge data sets.
  • Direct import of fixed-format text data files and SAS data sets into .xdf format

 

3) RevoDeploy R for  API based R solution – I somehow think this feature will get more important as time goes on but it seems a lower visibility offering right now.

http://www.revolutionanalytics.com/products/enterprise-deployment.php

  • Collection of Web services implemented as a RESTful API.
  • JavaScript and Java client libraries, allowing users to easily build custom Web applications on top of R.
  • .NET Client library — includes a COM interoperability to call R from VBA
  • Management Console for securely administrating servers, scripts and users through HTTP and HTTPS.
  • XML and JSON format for data exchange.
  • Built-in security model for authenticated or anonymous invocation of R Scripts.
  • Repository for storing R objects and R Script execution artifacts.

 

4) Revolutions IDE (or Productivity Environment) for a faster coding environment than command line. The GUI by Revolution Analytics is in the works. – Having used this- only the Code Snippets function is a clear differentiator from newer IDE and GUI. The code snippets is awesome though and even someone who doesnt know much R can get analysis set up quite fast and accurately.

http://www.revolutionanalytics.com/products/enterprise-productivity.php

  • Full-featured Visual Debugger for debugging R scripts, with call stack window and step-in, step-over, and step-out capability.
  • Enhanced Script Editor with hover-over help, word completion, find-across-files capability, automatic syntax checking, bookmarks, and navigation buttons.
  • Run Selection, Run to Line and Run to Cursor evaluation
  • R Code Snippets to automatically generate fill-in-the-blank sections of R code with tooltip help.
  • Object Browser showing available data and function objects (including those in packages), with context menus for plotting and editing data.
  • Solution Explorer for organizing, viewing, adding, removing, rearranging, and sourcing R scripts.
  • Customizable Workspace with dockable, floating, and tabbed tool windows.
  • Version Control Plug-in available for the open source Subversion version control software.

 

Marketing contributions from Revolution Analytics-

1) Sponsoring R sessions and user meets

2) Evangelizing R at conferences  and partnering with corporate partners including JasperSoft, Microsoft , IBM and others at http://www.revolutionanalytics.com/partners/

3) Helping with online initiatives like http://www.inside-r.org/ (which is curiously dormant and now largely superseded by R-Bloggers.com) and the syntax highlighting tool at http://www.inside-r.org/pretty-r. In addition Revolution has been proactive in reaching out to the community

4) Helping pioneer blogging about R and Twitter Hash tag discussions , and contributing to Stack Overflow discussions. Within a short while, #rstats online community has overtaken a lot more established names- partly due to decentralized nature of its working.

 

Did I miss something out? yes , they share their code by GPL.

 

Let me know by feedback

Free and Open Source cannot get basic economics correct

Nutch robots
Image via Wikipedia

Before you rev up those keyboards, and shoot off a snarky comment- consider this statement- there are many ways to run (and ruin economies). But they still have not found a replacement for money. Yes Happiness is important. Search Engine is good.

So unless they start a new branch of economics with lots more motivational theory and psychology and lot less quant especially for open source projects, money ,revenue, sales is the only true measure of success in enterprise software. Particularly if you have competitors who are making more money selling the same class of software.

Popularity contests are for high school quarterbacks —so even if your open source software is popular in downloads, email discussions, stack overflow or Continue reading “Free and Open Source cannot get basic economics correct”

High Performance Analytics

Marry Big Data Analytics to High Performance Computing, and you get the buzzword of this season- High Performance Analytics.

It basically consists of Parallelized code to run in parallel on custom hardware, in -database analytics for speed, and cloud computing /high performance computing environments. On an operational level, it consists of software (as in analytics) partnering with software (as in databases, Map reduce, Hadoop) plus some hardware (HP or IBM mostly). It is considered a high margin , highly profitable, business with small number of deals compared to say desktop licenses.

As per HPC Wire- which is a great tool/newsletter to keep updated on HPC , SAS Institute has been busy on this front partnering with EMC Greenplum and TeraData (who also acquired  SAS Partner AsterData to gain a much needed foot in the MR/SQL space) Continue reading “High Performance Analytics”