Monthly Archives: June 2013

Opel Corsa Diesel Usage

June 24, 2013
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Opel Corsa Diesel Usage

I wanted to extend my car weight distribution calculation of June 16 from only 2000 to years 2000 to 2013. Unfortunately, come Sunday afternoon the code seemed too slow and not even the beginning of a post. So, I went on to another calculation I w...

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Streamline Your Mechanical Turk Workflow with MTurkR

June 24, 2013
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Streamline Your Mechanical Turk Workflow with MTurkR

I’ve been using Thomas Leeper‘s MTurkR package to administer my most recent Mechanical Turk study—an extension of work on representative-constituent communication claiming credit for pork benefits, with Justin Grimmer and Sean Westwood.  MTurkR is excellent, making it quick and easy to: test … Continue reading →

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Creating a BI Dashboard: Part 1

June 23, 2013
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Creating a BI Dashboard: Part 1

Introduction A few first posts of this blog will demonstrate how to build each report hosted by the business intelligence (BI) application dashboard shown below (see Fig. 1). This application uses the following tools and technologies R – a free software environment for statistical computing and graphics, ASP.NET MVC4 – a free framework for building

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Revisualizing the best cities in the US in 2012- Shiny + googleVis = Incredibly powerful

June 23, 2013
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Revisualizing the best cities in the US in 2012- Shiny + googleVis = Incredibly powerful

This is the last time I will talk about visualizing the best cities of 2012 based on Bloomberg Businessweek's rankings. In an earlier post on this topic, interactive applications to plot bar graphs and histograms for different characteristics...

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Principal Components Analysis Shiny App

June 23, 2013
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Principal Components Analysis Shiny App

I’ve recently started experimenting with making Shiny apps, and today I wanted to make a basic app for calculating and visualizing principal components analysis (PCA). Here is the basic interface I came up with. Test drive the app for yourself using the code below or  check out the the R code HERE. Above is an example of the

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Parallel computation with helper threads in pqR

June 23, 2013
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Parallel computation with helper threads in pqR

One innovative feature of pqR (my new, faster, version of R), is that it can perform some numeric computations in “helper” threads, in parallel with other such numeric computations, and with interpretive operations performed in the “master” thread. This can potentially speed up your computations by a factor as large as the number of processor cores

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Time Is on My Side – A Small Example for Text Analytics on a Stream

June 23, 2013
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Time Is on My Side – A Small Example for Text Analytics on a Stream

Introduction and Background While my last posting was about recommendation in the context of Location Based Social Networks there are also other interesting topics regarding the analysis of unstructured data. The most established one is probably Text Analytics/Mining focusing on all sorts of text data.For me, coming from spatial analysis, these topic is relatively new but I couldn’t help noticing...

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Got Bootstrap?

June 23, 2013
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Got Bootstrap?

This week I read the book by Michael Chernick and Robert LaBudde, An Introduction to Bootstrap Methods with Applications to R. It’s an interesting oeuvre for useRs of all stripes. I strongly recommend check it out. The book brings lots of examples of bootstrapping applications, such as standard errors, confidence intervals, hypothesis testing, and even

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GRNN and PNN

June 23, 2013
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GRNN and PNN

From the technical prospective, people usually would choose GRNN (general regression neural network) to do the function approximation for the continuous response variable and use PNN (probabilistic neural network) for pattern recognition / classification problems with categorical outcomes. However, from the practical standpoint, it is often not necessary to draw a fine line between GRNN

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Generating Alerts From Guardian University Tables Data

June 23, 2013
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Generating Alerts From Guardian University Tables Data

One of the things I’ve been pondering with respect to the whole data journalism process is how journalists without a lot of statistical training can quickly get a feel for whether there may be interesting story leads in a dataset, or how they might be able to fashion “alerts” that bring attention to data elements

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