Blog Archives

The First NY R Conference

April 30, 2015
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The First NY R Conference

by Joseph Rickert Last Friday and Saturday the NY R Conference briefly lit up Manhattan's Union Square neighborhood as the center of the R world. You may have caught some of the glow on twitter. Jared Lander, volunteers from the New York Open Statistical Programming Meetup along with the staff at Workbench (the conference venue) set the bar pretty...

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Situational Baseball: Analyzing Runs Potential Statistics

April 28, 2015
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Situational Baseball: Analyzing Runs Potential Statistics

by Mark Malter After reading the book, Analyzing Baseball with R, by Max Marchi and Jim Albert, I decided to expand on some of their ideas relating to runs created and put them into an R shiny app . The Server and UI code are linked at the bottom of the Introduction tab. I downloaded the Retrosheet play-by-play data...

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The new science journalism and open science

April 23, 2015
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by Joseph Rickert The New York Times is quietly changing the practice of science journalism. The Tuesday April 21, 2015 article: Ebola Lying in Wait, reports on "A growing body of scientific clues - some ambiguous, other substantive" that the Ebola virus may have lain dormant in West African rain forest for years before igniting last year's outbreak. In...

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R for more powerful clustering

April 21, 2015
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R for more powerful clustering

by Vidisha Vachharajani Freelance Statistical Consultant R showcases several useful clustering tools, but the one that seems particularly powerful is the marriage of hierarchical clustering with a visual display of its results in a heatmap. The term “heatmap” is often confusing, making most wonder – which is it? A "colorful visual representation of data in a matrix" or "a...

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R User Group Meetings this week in the Bay Area and around the world

April 16, 2015
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R User Group Meetings this week in the Bay Area and around the world

by Joseph Rickert Tracking R user group meetings is a good way to stay informed about what's happening in the R world. On Tuesday the Bay Area useR Group (BARUG) met at AdRoll in San Francisco. It was a mini-conference with 6 talks: Bryan Galvin our host at AdRoll (many thanks for the pizza and beer) kicked off the...

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RPowerLabs: Electric power system virtual laboratories online

April 14, 2015
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RPowerLabs: Electric power system virtual laboratories online

by Ben Ubah Founder, RPowerLabs No disregard to R's colleagues, R is pioneering the creation of online virtual electric power system laboratories via RPowerLABS. RPowerLABS is a project, with the vision of deploying online, a vast array of highly demanded power system simulations for teaching and research using R. It started as an attempt to apply R to electric...

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Where are the R users?

April 9, 2015
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Where are the R users?

by Joseph Rickert A recent post by David Smith included a map that shows the locations of R user groups around the world. While is exhilarating to see how R user groups span the globe, the map does not give any idea about the size of the community at each location. The following plot, constructed from information on the...

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Exploring San Francisco with choroplethrZip

April 7, 2015
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Exploring San Francisco with choroplethrZip

by Ari Lamstein Introduction Today I will walk through an analysis of San Francisco Zip Code Demographics using my new R package choroplethrZip. This package creates choropleth maps of US Zip Codes and connects to the US Census Bureau. A choropleth is a map that shows boundaries of regions (such as zip codes) and colors those regions according to...

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Coarse Grain Parallelism with foreach and rxExec

April 2, 2015
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by Joseph Rickert I have written a several posts about the Parallel External Memory Algorithms (PEMAs) in Revolution Analytics’ RevoScaleR package, most recently about rxBTrees(), but I haven’t said much about rxExec(). rxExec() is not itself a PEMA, but it can be used to write parallel algorithms. Pre-built PEMAs such as rxBTrees(), rxLinMod(), etc are inherently parallel algorithms designed...

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Targeted Learning R Packages for Causal Inference and Machine Learning

March 31, 2015
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Targeted Learning R Packages for Causal Inference and Machine Learning

by Sherri Rose Assistant Professor of Health Care Policy Harvard Medical School Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals. The theory offers a general template for creating targeted maximum likelihood estimators for a data structure, nonparametric or semiparametric statistical model,...

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