Monthly Archives: February 2013

My R-Package Development Cheat Sheet

February 7, 2013
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In case you have no experience in writing an R-package yourself  but would like to start developing one right away, this post might be helpful.I'm about to finish my first own (serious) R-package these days (more on the package itself later). Whil...

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Analyze web traffic data with Google Analytics and R

February 7, 2013
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Analyze web traffic data with Google Analytics and R

If you run an e-commerce site, blog or other web property there's a good chance you use Google Analytics to monitor traffic, look at visitor sources, and measure conversions. And while Google Analytics is quite powerful at looking at historic activity on your site, it lacks much in the way of predictive analytics. That's where R shines of course,...

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Slideshows in R

February 7, 2013
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A while ago I was asked to give a presentation at my job about using R to create statistical graphics. I had also just read some reviews of the Slidify package in R and I thought it would be extremely appropriate to create my presentation about visuali...

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Getting staRted with R.

February 7, 2013
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Getting staRted with R.

As a PhD student and researcher, I often hear friends and colleagues say that they want to learn R, but that the learning curve is so steep that they can't seem to get started.  It's true that learning any tool as powerful as R can be confusing at...

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Slightly silly D3 example: shift one datapoint to get a significant result

February 7, 2013
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Slightly silly D3 example: shift one datapoint to get a significant result

Have you ever seen these scatterplots that report a significant correlation between X and Y, and look like it’s just the one point to the upper-right driving the correlation? Thanks to this interactive tool, you too can do this at home.

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Why cost and fuel efficiency are unrelated: Uncorrelated manifest variables can share the same latent causes

February 7, 2013
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Why cost and fuel efficiency are unrelated: Uncorrelated manifest variables can share the same latent causes

In structural equation modelling, we are typically proposing theoretical causes of observed phenomena. These are termed "latent" (the unobserved causes) and manifest (the observed variables we measure, otherwise known as data).Importantly, the theoretical causes of behavior need not have a structure remotely resembling the correlations observed in the data. You might have hundreds of columns of...

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Using ARPACK to compute the largest eigenvalue of a matrix

February 7, 2013
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Thanks to Gábor Csárdi, author of the R interface to ARPACK, for this example of using (the R/Igraph interface to) arpack for finding the largest eigenvalue of a matrix. The key insight is that arpack solves the function passed to … Continue reading →

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Creating ‘Tags’ For PPC Keywords

February 7, 2013
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Creating ‘Tags’ For PPC Keywords

When performing search engine marketing, it is usually beneficial to construct a system for making sense of keywords and their performance. While one could construct Bayesian Belief Networks to model the process of consumers clicking on ads, I have found that using ’tags’ to categorize keywords is just as useful for conducting post-hoc analysis on the effectiveness of marketing

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Data analysis class

February 7, 2013
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Data analysis class

I've been writing software to help others do data analysis for a number of years and at the same time trying to work up my nerve to try my own analysis. Why let other people have all the fun? So, when I saw that Jeffrey Leek, biostatistician at Johns Hopkins and coauthor of Simply Statistics, was teaching...

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R Bootcamp @ Sector67 in Madison

February 6, 2013
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I am pleased to announce that together with Justin Meyer (also from the Wisconsin Department of Public Instruction) I will be presenting a two hour version of the R Bootcamp. Sector67 is a collaborative maker/hacker space in Madison, and is a great ven...

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