Blog Archives

Data Visualization doesn’t need to be biased

September 23, 2011
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Data Visualization doesn’t need to be biased

At the FlowingData blog, data visualization commentator and Visualize This author Nathan Yau lists 5 misconceptions about visualization: Software does everything (Nathan notes "Personally, I use a lot of R and have a lot of fun in Illustrator", but uses a lot of other tools as well.) Visualization is for making data flashy The more information in a single...

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Are new SEC rules enough to prevent another Flash Crash?

September 22, 2011
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Are new SEC rules enough to prevent another Flash Crash?

At 2:42PM on March 10 2010, without warning, the Dow Jones Industrial Index plunged more than 1000 points in just 5 minutes. It remains the biggest one-day decline in this stock market index in history. On an intra-day basis, anyway: by the end of the day, the market had regained 600 points of the drop. At the time, the...

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Slides and replay from "R and Hadoop" webinar

September 21, 2011
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Slides and replay from "R and Hadoop" webinar

So ... there's clearly a lot of interest in integrating R and Hadoop. Today's webinar was a record-setter for Revolution Analytics, with more than 1000 people signing up to learn how to access Hadoop data from R with the packages from the open-source RHadoop project. If you didn't catch the live webinar, don't fret: the slides and replay are...

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R 2.14 to be released on October 31; R 2.13 patch on September 13

September 19, 2011
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The next major release of R has been announced: R 2.14.0 is scheduled for October 31. Details are still coming in about the new features planned for this release, but R core member Luke Tierney has revealed some of the performance improvements expected, and R core member Brian Ripley has spoken of forthcoming low-level support for multi-threaded computing and...

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How to extract time series from large timestamped logs with R

September 16, 2011
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Revolution Analytics' Joe Rickert has a new post on inside-R.org, demonstrating how you can use R and the RevoScaleR package to extract time series data from time-stamped logs (in this case, the "US Domestic Flights From 1990 to 2009" dataset on Infochimps): Analyzing time series data of all sorts is a fundamental business analytics task to which the R...

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How Lloyd’s of London uses R for Insurance

September 15, 2011
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How Lloyd’s of London uses R for Insurance

Lloyd's is the world's leading specialist insurance market, and is often the first to insure new, unusual or complex risks. So it's no surprise that Lloyd's is one of the many companies that use R and its advanced capabilities for data analysis to help manage its insurance risks. At the useR! conference last month, Lloyd's analysts Markus Gesmann, Viren...

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Using Google Spreadsheets with R: an update

September 15, 2011
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Prompted by a rush of visitors from Andrew Gelman's blog, I went back and updated the details of my post from 2009 on reading data from Google Spreadsheets into R. Since then, Google had switched to using a secure (https) connection for Google Docs, which required some tweaks to the code. If you haven't seen it before, it's a...

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Revolution Analytics Fall Webinar Series

September 14, 2011
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We've lined up what we think is an amazing series of R-related webinars over the next couple of months. These free 30-60 minute webinars will cover a wide range of topics: big-data analysis in R with the RevoScaleR package, Hadoop and Netezza; introductions to R for SAS users and for R users new to Revolution R; and applications of...

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How to program MapReduce jobs in Hadoop with R

September 13, 2011
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MapReduce is a powerful programming framework for efficiently processing very large amounts of data stored in the Hadoop distributed filesystem. But while several programming frameworks for Hadoop exist, few are tuned to the needs of data analysts who typically work in the R environment as opposed to general-purpose languages like Java. That's why the dev team at Revolution Analytics...

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Speed up recursion in R 600-fold with Rcpp

September 12, 2011
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Rcpp package co-author Dirk Eddelbuettel provides another case study in speeding up R code by rewriting repeatedly-called R code as inline C++ functions, using the classic Fibonacci recursion algorithm as an example. The speed gains here are impressive -- over 600x compared to native recursive R code -- but you could also improve performance by using a more efficient,...

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