Monthly Archives: September 2013

Doughnut chart in R with googleVis

September 3, 2013
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The guys at Google continue to update and enhance the Chart Tools API. One new recent feature is a pie chart with a hole, or as some call them: donut charts.Thankfully the new functionality is being achieved through new options for the existing pie cha...

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ggplot + powerpoint = wall + head … solution?

September 2, 2013
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ggplot + powerpoint = wall + head … solution?

Confession, by ‘solution?’ I literally mean I’m asking for your thoughts on a solution. Like it or lump it I do a lot of graphs for presentations, largely in powerpoint. That’s the way my colleagues/industry work(s) and it’s not about … Continue reading →

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An example of MapReduce with rmr2

September 2, 2013
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R can be connected with Hadoop through the rmr2 package. The core of this package is mapreduce() function that allows to write some custom MapReduce algorithms. The aim of this article is to show how it works and to provide … Continue reading →

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Fun With Just-In-Time Compiling: Julia, Python, R and pqR

September 2, 2013
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Fun With Just-In-Time Compiling: Julia, Python, R and pqR

Recently I’ve been spending a lot of time trying to learn Julia by doing the problems at Project Euler. What’s great about these problems is that it gets me out of my normal design patterns, since I don’t generally think about prime numbers, factorials and other number theory problems during my normal workday. These problems Fun With Just-In-Time...

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Sentiment Analysis on Twitter with Viralheat API

September 2, 2013
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Sentiment Analysis on Twitter with Viralheat API

Hi there! Some time ago I published a post about doing a sentiment analysis on Twitter. I used two wordlists to do so; one with positive and one with negative words. For the first try of a sentiment analysis it is surely a good way to start but if you want to receive more accurate …

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Poll: R top language for data science three years running

September 2, 2013
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Poll: R top language for data science three years running

KDDNuggets has completed its annual poll of top languages for analytics, data mining and data science, and just as in the prior two years the R language is ranked the most popular. R is used by almost 61% of respondents: R's usage grew year over year as well, up 16% compared to the 2012 poll. By contrast, the rate...

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Showing results from Cox Proportional Hazard Models in R with simPH

September 2, 2013
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Showing results from Cox Proportional Hazard Models in R with simPH

Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time...

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Passing-Bablok Regression: R code for SAS users

September 2, 2013
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Passing-Bablok Regression: R code for SAS users

While at the Joint Statistical Meeting a few weeks ago I was talking to a friend about various aspects to clinical trials. He indicated that no current R package was able to perfectly reproduce Passing-Bablok (PB) regression so that it exactly matched SAS. He ultimately wrote a couple of functions and kindly shared them with

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Easy 3-Minute Guide to Making apply() Parallel over Distributed Grids and Clusters in R

September 1, 2013
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Easy 3-Minute Guide to Making apply() Parallel over Distributed Grids and Clusters in R

Last week I attended a workshop on how to run highly parallel distributed jobs on the Open Science Grid (osg). There I met Derek Weitzel who has made an excellent contribution to advancing R as a high performance computing language by developing BoscoR. BoscoR greatly facilitates the use of the already existing package “GridR” by The post Easy...

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Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
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Latent Variable Analysis with R: Getting Setup with lavaan

Getting Started with Structural Equation Modeling Part 1Getting Started with Structural Equation Modeling: Part 1 Introduction For the analyst familiar with linear regression fitting structural equation models can at first feel strange. In the R environment, fitting structural equation models involves learning new modeling syntax, new plotting...

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