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)

  • 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.

  • 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.

  • 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

3) Helping with online initiatives like (which is curiously dormant and now largely superseded by and the syntax highlighting tool at 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

Calling #Rstats lovers and bloggers – to work together on “The R Programming wikibook”

so you think u like R, huh. Well it is time to pay it forward.

Message from a dear R blogger, Tal G from Tel Aviv (creator of and

Calling R lovers and bloggers – to work together on “The R Programming wikibook”
Posted: 20 Jun 2011 07:05 AM PDT

This post is a call for both R community members and R-bloggers, to come and help make The R Programming wikibook be amazing:

Dear R community member – please consider giving a visit to The R Programming wikibook. If you wish to contribute your knowledge and editing skills to the project, then you could learn how to write in wiki-markup here, and how to edit a wikibook here (you can even use R syntax highlighting in the wikibook). You could take information into the site from the (soon to be) growing list of available R resources for harvesting.

Dear R blogger, you can help The R Programming wikibook by doing the following:

Write to your readers about the project and invite them to join.
Add your blog’s R content as an available resource for other editors to use for the wikibook. Here is how to do that:
First, make a clear indication on your blog that your content is licensed under cc-by-sa copyrights (*see what it means at the end of the post). You can do this by adding it to the footer of your blog, or by writing a post that clearly states that this is the case (what a great opportunity to write to your readers about the project…).
Next, go and add a link, to where all of your R content is located on your site, to the resource page (also with a link to the license post, if you wrote one). For example, since I write about other things besides R, I would give a link to my R category page, and will also give a link to this post. If you do not know how to add it to the wiki, just e-mail me about it (
If you are an R blogger, besides living up to the spirit of the R community, you will benefit from joining this project in that every time someone will use your content on the wikibook, they will add your post as a resource. In the long run, this is likely to help visitors of the site get to know about you and strengthen your site’s SEO ranking. Which reminds me, if you write about this, I always appreciate a link back to my blog

* Having a cc-by-sa copyrights means that you will agree that anyone may copy, distribute, display, and make derivative works based on your content, only if they give the author (you) the credits in the manner specified by you. And also that the user may distribute derivative works only under a license identical to the license that governs the original work.


Three more points:

1) This post is a result of being contacted by Paul (a.k.a: PAC2), asking if I could help promote “The R Programming wikibook” among R-bloggers and their readers. Paul has made many contributions to the book so far. So thank you Paul for both reaching out and helping all of us with your work on this free open source project.

2) I should also mention that the R wiki exists and is open for contribution. And naturally, every thing that will help the R wikibook will help the R wiki as well.

3) Copyright notice: I hereby release all of the writing material content that is categoriesed in the R category page, under the cc-by-sa copyrights (date: 20.06.2011). Now it’s your turn!


List of R bloggers who have joined: (This list will get updated as this “group writing” project will progress)

R-statistics blog (that’s Tal…) (That’s me)
3) Copyright notice: I hereby release all of the writing material content of this website, under the cc-by-sa copyrights (date: 21.06.2011). Now it’s your turn!

Content Licensing-
This website has all content licensed under
You are free:
to Share — to copy, distribute and transmit the work
to Remix — to adapt the work

RStudio 3- Making R as simple as possible but no simpler

From the nice shiny blog at, a shiny new upgraded software (and I used the Cobalt theme)–this is nice!

awesome coding!!!

Download RStudio v0.94

Diagram desktop

If you run R on your desktop:

Download RStudio Desktop


Diagram server

If you run R on a Linux server and want to enable users to remotely access RStudio using a web browser:

Download RStudio Server


RStudio v0.94 — Release Notes

June 15th, 2011


New Features and Enhancements

Source Editor and Console

  • Run code:
    • Run all lines in source file
    • Run to current line
    • Run from current line
    • Redefine current function
    • Re-run previous region
    • Code is now run line-by-line in the console
  • Brace, paren, and quote matching
  • Improved cursor placement after newlines
  • Support for regex find and replace
  • Optional syntax highlighting for console input
  • Press F1 for help on current selection
  • Function navigation / jump to function
  • Column and line number display
  • Manually set/switch document type
  • New themes: Solarized and Solarized Dark


  • Improved image export:
    • Formats: PNG, JPEG, TIFF, SVG, BMP, Metafile, and Postscript
    • Dynamic resize with preview
    • Option to maintain aspect ratio when resizing
    • Copy to clipboard as bitmap or metafile
  • Improved PDF export:
    • Specify custom sizes
    • Preview before exporting
  • Remove individual plots from history
  • Resizable plot zoom window


  • History tab synced to loaded .Rhistory file
  • New commands:
    • Load and save history
    • Remove individual items from history
    • Clear all history
  • New options:
    • Load history from working directory or global history file
    • Save history always or only when saving .RData
    • Remove duplicate entries in history
  • Shortcut keys for inserting into console or source


  • Check for package updates
  • Filter displayed packages
  • Install multiple packages
  • Remove packages
  • New options:
    • Install from repository or local archive file
    • Target library
    • Install dependencies


  • Find text within help topic
  • Sort file listing by name, type, size, or modified
  • Set working directory based on source file, files pane, or browsed for directory.
  • Console titlebar button to view current working directory in files pane
  • Source file menu command
  • Replace space and dash with dot (.) in import dataset generated variable names
  • Add decimal separator preference for import dataset
  • Added .tar.gz (Linux) and .zip (Windows) distributions for non-admin installs
  • Read /etc/paths.d on OS X to ensure RStudio has the same path as terminal sessions do
  • Added manifest to rsession.exe to prevent unwanted program files and registry virtualization


  • Break PAM auth into its own binary for improved compatibility with 3rd party PAM authorization modules.
  • Ensure that AppArmor profile is enforced even after reboot
  • Ability to add custom LD library path for all sessions
  • Improved R discovery:
    • Use which R then fallback to scanning for R script
    • Run R discovery unconfined then switch into restricted profile
  • Default to uncompressed save.image output if the administrator or user hasn’t specified their own options (improved suspend/resume performance)
  • Ensure all running sessions are automatically updated during server version upgrade
  • Added verify-installation command to rstudio-server utility for easily capturing configuration and startup related errors


Bug Fixes

Source Editor

  • Undo to unedited state clears now dirty bit
  • Extract function now captures free variables used on lhs
  • Selected variable highlight now visible in all themes
  • Syncing to source file updates made outside of RStudio now happens immediately at startup and does not cause a scroll to the bottom of the document.
  • Fixed various issues related to copying and pasting into word processors
  • Fixed incorrect syntax highlighting issues in .Rd files
  • Make sure font size for printed source files matches current editor setting
  • Eliminate conflict with Ctrl+F shortcut key on OS X
  • Zoomed Google Chrome browser no longer causes cursor position to be off
  • Don’t prevent opening of unknown file types in the editor


  • Fixed sporadic missing underscores (and other bottom clipping of text) in console
  • Make sure console history is never displayed offscreen
  • Page Up and Page Down now work properly in the console
  • Substantially improved console performance for both rapid output and large quantities of output


  • Install successfully on Windows with special characters in home directory name
  • make install more tolerant of configurations where it can’t write into /usr/share
  • Eliminate spurious stderr output in forked children of multicore package
  • Ensure that file modified times always update in the files pane after a save
  • Always default to installing packages into first writeable path of .libPaths()
  • Ensure that LaTeX log files are always preserved after compilePdf
  • Fix conflicts with zap function from epicalc package
  • Eliminate shortcut key conflicts with Ubuntu desktop workspace switching shortcuts
  • Always prompt when attempting to save files of the same name
  • Maximized main window now properly restored when reopening RStudio
  • PAM authorization works correctly even if account has password expiration warning
  • Correct display of manipulate panel when Plots pane is on the left


Previous Release Notes


Using Code Editors in R

Using Enhanced Code Editors

Advantages of using enhanced code editors

1) Readability- Features like syntax coloring helps make the code more readable for documentation as well as debugging and improvement. Example functions may be colored in blue, input parameters in green, and simple default code syntax in black. Especially for lengthy programs or tweaking auto generated code by GUI, this readability comes in handy.

2) Automatic syntax error checking- Enhanced editors can prompt you if certain errors in syntax (like brackets not closed, commas misplaced)- and errors may be highlighted in color (red mostly). This helps a lot in correcting code especially if you are either new to R programming or your main focus is business insights and not just coding. Syntax debugging is thus simplified.

3) Speed of writing code- Most programmers report an increase in writing code speed when using an enhanced editor.

4) Point Breaks- You can insert breaks at certain parts of code to run some lines of code together, or debug a program. This is a big help given that default code editor makes it very cumbersome and you have to copy and paste lines of code again and again to run selectively. On an enhanced editor you can submit lines as well as paragraphs of code.

5) Auto-Completion- Auto completion enables or suggests options you to complete the syntax even when you have typed part of the function name.

Some commonly used code editors are –
Notepad++ -It supports R and also has a plugin called NPP to R.
It can be used  for a wide variety of other languages as well, and has all the features mentioned above.

Revolution R Productivity Environment (RPE)-While Revolution R has announced a new GUI to be launched in 2011- the existing enhancements to their software include a code editor called RPE.

Syntax color highlighting is already included. Code Snippets work in a fairly simply way.
Right click-
Click on Insert Code Snippet.

You can get a drop down of tasks to do- (like Analysis)
Selecting Analysis we get another list of sub-tasks (like Clustering).
Once you click on Clustering you get various options.
Like clicking clara will auto insert the code for clara clustering.

Now even if you are averse to using a GUI /or GUI creators don’t have your particular analysis you can basically type in code at an extremely fast pace.
It is useful to even experienced people who do not have to type in the entire code, but it is a boon to beginners as the parameters in function inserted by code snippet are automatically selected in multiple colors. And it can help you modify the auto generated code by your R GUI at a much faster pace.

TinnR -The most popular and a very easy to use code editor. It is available at
It’s disadvantage is it supports Windows operating system only.
Recommended as the beginner’s chose fore code editor.

Eclipse with R plugin This is recommended especially to people working with Eclipse and on Unix systems. It enables you to do most of the productivity enhancement featured in other text editors including submitting code the R session.

Gvim ( along Vim-R-plugin2
( should be
cited. The Vim-R-plugin developer recently added windows support to a
lean cross-platform package that works well. It can be suited as a niche text editor to people who like less features in the software. It is not as good as Eclipse or Notepad++ but is probably the simplest to use.