Blog Archives

Speed up R "for" loops 50x with Rcpp

June 23, 2011
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Christian Gunning has a great example of using Rcpp to speed up a for loop in R. For his agent-based simulation, Christian needed to repeatedly call the rbinom function in a loop. (Unfortunately, you can't pass a vector to the size argument, which would have solved the problem.) Using the aaply function (from the plyr package) took about 38...

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Video: Two R talks from Hadley Wickham

June 22, 2011
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On his recent tour to the Bay Area, Hadley Wickham have two interesting R-related talks, for which video has been made available by Google Tech Talks. At the June Bay Area R User Group meeting, Hadley spoke on the future of interactive data visualization in R. Building on his experiences creating the ggplot2 package (which is still under development...

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Five things Biologists should know about Statistics

June 21, 2011
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In a thoughtful blog post, Bioinformatician Ewan Birney (Head of Nucleotide Data at the European Bioinformatics Institute) talks about the importance of Statistics to biologists: Biology is really about stats. Indeed, the foundation of much of frequentist statistics - RA Fisher and colleagues - were totally motivated by biological problems. He also cites the "Five statistical things I wished...

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R in the Bioinformatics Knowledgeblog

June 21, 2011
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The Knowledge Blog progect is a new, light-weight way of publishing scientific, academic and technical knowledge on the web, across several scientific disciplines. One such discipline is bioinformatics, and the Bioinformatics Knowledgeblog contains useful scientific reference material for bioinformatics, including several resources for R users. There you'll find an R Guide for Complete Beginners, a tutorial on analyzing microarray...

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Where Ichiro Hits

June 16, 2011
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Where Ichiro Hits

Google research scientist Peter Hauck used Weka and k-means cluster analysis to describe where Mariners right-fielder Ichiro favours hitting the baseball. He then used R to visualize the 6 clusters the k-means analysis identified: I sometimes find K-means clusting tough to explain as a statistical technique, but this makes for a great example: if you're a fielder facing Ichiro,...

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5000 R questions on stackoverflow.com

June 16, 2011
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The R tag on stackoverflow.com hit a milestone yesterday: 5000 questions about the R language. (The 5000th question was about the fortunes package, incidentally -- thanks to Andrie de Vries for pointing this out on Twitter.) Stackoverflow.com continue...

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The Big Analytics Revolution starts with R

June 15, 2011
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Thanks to everyone who attended our webinar The 'Big Analytics' Revolution Starts with R yesterday. If you missed the live session, you can download the presentation slides (PDF) and the 30-minute replay video (WMV) from the Revolution Analytics website. The presentation focuses on the isse of Big Data, and how businesses can use advanced analytics methods implemented in the...

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Hot Job in IT: Data Science

June 14, 2011
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CIO Magazine today has an article on the "6 Hottest New Jobs in IT" in which features Data Science and R at #2: "There's now an intellectual consensus in business that the only way to run an enterprise is to use analytics with data scientists to find opportunities," says Norman Nie, CEO of Revolution Analytics, which produces the first...

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The R-Files: Jeroen Ooms

June 9, 2011
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The R-Files: Jeroen Ooms

"The R-Files" is an occasional series from Revolution Analytics, where we profile prominent members of the R Community. Name: Jeroen Ooms Background: Ph.D. Candidate, Statistics, UCLA Nationality: Netherlands Years Using R: 3 1/2 Known for: Developing web applications for popular R packages including ggplot2, lme4, stockplot and irttool Jeroen Ooms is a statistical consultant and R enthusiast currently pursuing...

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Real-time Analytics for Capital Markets with Revolution R

June 8, 2011
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In the 2011 edition of the Sybase Capital Markets Guide, Revolution Analytics CTO David Champagne talks about the need for up-to-date analytics in Finance, and how you can integrate Revolution R with quality real-time data sources. Here's an excerpt: R represents a radically different approach to the challenges posed by analyzing increasingly large and complex data sets. Because it...

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