1 / 39

Open Source Analytics Visualization and Predictive Modeling of Big Data with R

Open Source Analytics Visualization and Predictive Modeling of Big Data with R. Michael E. Driscoll, Ph.D. July 22, 2009 OSCON. “Hard-working Middle Class” Hypothesis. (from Jessica Hagy’s thisisindexed.com). Munge & Model OECD Data.

pello
Download Presentation

Open Source Analytics Visualization and Predictive Modeling of Big Data with R

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Open Source AnalyticsVisualization and Predictive Modeling of Big Data with R Michael E. Driscoll, Ph.D. July 22, 2009 OSCON

  2. “Hard-working Middle Class” Hypothesis (from Jessica Hagy’s thisisindexed.com)

  3. Munge & Model OECD Data gdp <- read.csv('gdp.csv')hours <- read.csv('hours.csv')gdp.hours <- merge(hours,gdp)gdp.hours$freetime <- 4380 - gdp.hours$hours attach(gdp.hours)plot(freetime ~ gdp)m <- lm(freetime ~ gdp,data=gdp.hours)abline(m,col=3,lw=2)pm <- loess(freetime ~ gdp)lines(spline(gdp,fitted(pm)))

  4. Visualize the Analysis: is it True?

  5. modeling Big Data

  6. 100 thousand gene measures

  7. 1 million transactions during this presentation

  8. If You Liked ____, You’ll Love ___ !

  9. 1 billion clicks during this presentation

  10. 1 million pitches thrown since 2007

  11. A Tale of Two Pitchers Hamels Webb

  12. xyplot(x ~ y, data=pitch)

  13. xyplot(x ~ y, groups=type, data=pitch)

  14. xyplot(x ~ y | type, data=pitch)

  15. xyplot(x ~ y | type, data=pitch, fill.color = pitch$color, panel = function(x,y, fill.color, …, subscripts) { fill <- fill.color[subscripts] panel.xyplot(x,y, fill= fill, …) })

  16. xyplot(x ~ y | type, data=pitch, fill.color = pitch$color, panel = function(x,y, fill.color, …, subscripts) { fill <- fill.color[subscripts] panel.xyplot(x, y, fill= fill, …) })

  17. visualizing Big Data

  18. ggplot2 =grammar ofgraphics

  19. qplot(carat, price, data = diamonds)

  20. qplot(log(carat), log(price), data = diamonds) qplot(carat, price, log=“xy”, data = diamonds) OR

  21. qplot(log(carat), log(price), data = diamonds, alpha = I(1/20))

  22. qplot(log(carat), log(price), data = diamonds, alpha=I(1/20)) + facet_grid(. ~ color)

  23. R on the cloud

  24. Data Desktop

  25. vs Coding Clicking

  26. LinuxApacheMySQLR http://labs.dataspora.com/gameday

  27. Final thoughts

More Related