Bay Area R User Group 2009 Kickoff Video

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Ron Fredericks writes: In February I attended the Bay Area R User Group meeting recently held at Predictive Analytics World 2009. Michael E. Driscoll, one of the two kickoff meeting co-chairs, was gracious enough to let LectureMaker capture the video for the event as a technical marketing “lighthouse” project.

Editor’s Note: New Bay Area useR Group Video can be found here:
RHIPE: An Interface Between Hadoop and R for Large and Complex Data Analysis

Today I am happy to present this video to my readers.

If you manage the marketing, feature roll-out, or web site design, for a social network or professional ecosystem, then you need the techniques presented in this video.

Watch this video to learn about:

  1. The open-source analytics programming language called R
  2. How Google and Facebook approach analytics to predict their web user community’s behavior
  3. Where to download R and get enterprise level support
  4. How the meeting co-chairs use R

The R and Science of Predictive Analytics: Four Case Studies in R – the Video
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Panel of four recognized R users from industry:

  • Bo Cowgill, Google
  • Itamar Rosenn, Facebook
  • David Smith, Revolution Computing
  • Jim Porzak, The Generations Network

Moderator and co-chair of Bay Area R User Group:

  • Michael E. Driscoll, Dataspora LLC

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A live R demo
The co-chairs, Michael and Jim, presented a great overview of the R language. Here at LectureMaker, source code highlighting is supported with automatic links back to language documentation for 132 languages plus my own R highlighter.

For example, here is a short R program:

x <- rnorm(100)            # 100 random numbers from a normal(0,1) distribution
y <- exp(x) + rnorm(100)   # an exponential function with error
result <- lm(y ~ x)        # regress x on y and store the results
summary(result)            # print the regression results
plot(x,y)                  # pretty obvious what this does
abline(result)             # add the regression line to the plot
lines(lowess(x,y), col=2)  # add a nonparametric regression line (a smoother)
hist(result$residuals)     # histogram of the residuals from the regression
# source code reference:

You can click on the highlighted methods, or copy the code above and paste it into a web version of R called RWeb

How I developed the video
The LectureMaker video capture, editing, publishing, and technical marketing service starts with the on location video capture of a live event. Druing the process I become emersed in the presenter(s) and the audience participation. My empathy acquired during the project combined with my own professional background allows me to turn video into technical marketing.

I watched the video presented here myself several times: Starting with the content through the lens of my Canon XL-H1S 3CCD HDV High Definition Professional Camcorder with 20x HD Video Zoom Lens III
prosumer digital video camera during the event, followed by several editing iterations on my eVGA e-GeForce GTX280 1GB DDR3 PCI-Express 2.0 Graphics Card-Lifetime Warranty with Free Special Edition EVGA Precision Overclocking Utility
graphics powered PC, ending with comprehensive testing from several Linux web servers connected to Mac, Linux, and Windows client PCs.

To create the video I took the following steps to edit and publish the video, primarily with Adobe Creative Suite 4 Master Collection

  • Edit the video with Adobe After Effects and Premier Pro CS4. I incorporated the features of the Neat Video Pro noise reduction plug-in to correct for low light high gain raw camera issues
  • Edit and optimize the sound track separately using Adobe Soundbooth CS4 along with brightening of the speech itself as only Toastmasters International experience can provide
  • Design and develop a state of the art Video Player with custom hot spot navigation dots, intelligent preloader, and client-server progressive content management using Adobe Flash Professional CS4 ActionScript 3.0
  • Customize the LectureMaker Video Player with external preloader graphic image, personalized playback faceplate, selection of a playback control skin, and custom buttons using Adobe Illustrator and Photoshop CS4 Extended
  • Compress the 75 minute 200 GB high definition 1440 x 1080 mpeg video down to a 85 MB 800 x 600 flash video using both Adobe Media Encoder CS4 and Sorenson Squeeze 5 Pro
  • Lastly, I hosted the video inside this blog package currently at version 2.71 along with KFE SWFObject flash movie publisher plug-in
  • Some thoughts for next time: I did not have ideal placement for the video camera so I struggled with noise, light, who is talking when, and tripod stability. I made a few mistakes with sound and white balance during live experiments with the 100 or so buttons on this new camera too. I have since solved these issues with more experience, an after-market Sony XEL-1 11-Inch OLED Digital TV
    and wired remote control. I apologize to you the viewer, the R presenters, and attendees who participated with their questions. Hopefully these errors will seem small thanks to the excellent delivery of great material during the R User Group event itself. This was one of the best panel discussions I have attended in either engineering or science.

Contact LectureMaker to develop a video enhanced technical marketing program
This blog post’s content and its longtail of moderated comments over time demonstrates LectureMaker’s live video capture, video editing, social networking outreach, and technical marketing service. It might be based on a single live event, or a series of events integrated into a year-long community outreach program. What’s more, most of the cost for the LectureMaker service is already built into your current editorial events calender! Yet adding LectureMaker service to your existing events program could make all the difference in meeting your goals around ecosystem or sales development. You can get a brief overview here:

LectureMaker targets this service to exceptional people, brands, and products. Turn your high value live but localized events into reusable global marketing and education programs

How the panelists use R for predictive analytics
Each panelist came prepared to discuss R’s strengths and weaknesses as a tool, along with example case studies. Mike has a great blog post summarizing this meeting. So I invite those who would like to learn more about the techniques presented in this video to jump over to read it now:

How Google and Facebook are using R
by Michael E. Driscoll | February 19, 2009

At the end of this process I learned a great deal about the material presented: including R packages, where to download it, who to contract as project mentors, who to lead group training, and some additional open-source packages such as Octave and R’s interface with Python. I Developed a passion for the techniques discussed in this video, and every video product I put together. But in this case I also plan to get started with R along with some of the integrated tools that were discussed in the near future.

Want to learn R? Here is a link to the book recommended by the R User Group:
Software for Data Analysis: Programming with R (Statistics and Computing)

17 Replies to “Bay Area R User Group 2009 Kickoff Video”

  1. Hi Ron – Many thanks for putting this together – the video looks great! I particularly like the way you’ve allowed browsing of the content with the red status bars below. Look forward to more great stuff in the future.


  2. I was particularly impressed with how common the power law distribution is used to characterize and predict online user behavior within a well defined ecosystem such as facebook.

    power law distribution



    P(x) right size of an event, such as users who stay on facebook more than three weeks
    x right property of an event, such as number of users who join facebook per day
    alpha right some parameter of the distribution, such as the percent of users who enter their gender during new user registration
    K right a scaling constant

    So the size of a community event, P(x), can be predicted with the power law distribution. The problem might reduce down to the selection of the right parameter alpha and scaling constant K to apply to the properties characterized by x. The correlation measured from historical data can then be used to determine if further on-site changes could be made to improve performance from an online community.

    R then becomes the statistics tool to crunch data, calculate, and plot distributions. Then the results from R can be used to predict and improve future online behavior with new community policies or a change in applications. Neat.

    Management of large amounts of data was discussed throughout the lecture. One solution is in the open-source cloud initiative called Hadoop.


    1. LectureMaker supports math publishing, both in its blog posts and in its comments. See my wordpress plugin to learn more, called wpmathpub
    2. Itamar made reference to Hadoop as one way the facebook data team manages large amounts of data for R projects
    3. I had to look up “what is a power law distribution” as it applies to social networks. Here is the reference I choose to rely upon Power Law Distributions in Real and Virtual Worlds by Narushige SHIODE and Michael BATTY, University College London
  3. Thanks for this Nice post, Really usefull all of us. just bookmarked this post in my digg profile, hope you will update more post soon.
    I really liked your blog!


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