Monthly Archives: April 2014

brainR: Put your brain on the Cloud!

April 29, 2014
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brainR: Put your brain on the Cloud!

In my work, we have come across problems where we wanted to visualize data in 4 dimensions (4D). The data came as brains, which are represented as 3D volumes and we wanted to visualize them at different time points (hence 4D). We were using images that are acquired one image per visit (CT structural scans,

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“[” with the apply() functions, revisited

April 29, 2014
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“[” with the apply() functions, revisited

I’d mentioned in the fall that one could use "[" in the apply-type functions, like this: I just realized that you can use this with matrices, too. If you have a list of matrices, you can pull out rows and columns with this technique. As you can see, my data isn’t “tidy.”

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Scraping SSL Labs Server Test Results With R

April 29, 2014
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NOTE: Qualys allows automated access to their SSL Server Test site in their T&C’s, and the R fucntion/script provided here does its best to adhere to their guidelines. However, if you launch multiple scripts at one time and catch their attention you will, no doubt, be banned. This post will show you how to do some basic web page data...

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Writing an R package from scratch

April 29, 2014
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Writing an R package from scratch

As I have worked on various projects at Etsy, I have accumulated a suite of functions that help me quickly produce tables and charts that I find useful. Because of the nature of iterative development, it often happens that I … Continue reading →

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Slidify Old Book Images from Google Books

April 29, 2014
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I really enjoy reading old books, especially finance books, from Google Books.  With all the changes in the world, their continued relevance amazes me.  For instance, as I prepared for my talk about Wealth and Skill, I rediscovered Developing Financi...

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Predict which shoppers will become repeat buyers

April 29, 2014
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Predict which shoppers will become repeat buyers

by James P. Peruvankal Kaggle just announced a competition to predict which shoppers will become repeat buyers. To aid with algorithmic development, they have provided complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. Files containing the incentives offered to each shopper as well as their post-incentive behavior are also...

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On Functional Diversity metrics

April 29, 2014
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On Functional Diversity metrics

Summary: FD is getting very popular, so I figured that would be good to post not only the code (mostly borrowed) I am using: https://github.com/ibartomeus/fundiv, but also what I learnt of it. This posts assume you have read about FD calculation … Continue reading →

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Dave Giles on "MCMC for Econometrics Students"

April 29, 2014
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Dave Giles on "MCMC for Econometrics Students"

In an excellent four part series of posts in March, Dave Giles introduces Markov Chain Monte Carlo (MCMC) and Gibbs samplers.  In these posts he gives a cogent explanation for the reasoning and mechanics involved in this branch of econometrics/sta...

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On the trade history and dynamics of NBA teams

April 28, 2014
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On the trade history and dynamics of NBA teams

While good draft picks and deft management can help you win championships, there is no doubt that NBA teams can massively gain, or lose, by trading players with one another. Here, I played around with some publicly available data given at basketball-reference.com, and had a look at the numbers behind all trades undertaken in the NBA from 1948 to

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Data Until I Die: My blog title and statement of values

April 28, 2014
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Data Until I Die: My blog title and statement of values

When I started keeping this Blog, my intent was to write about and keep helpful snippets of R code that I used in the line of work.  It was the start of my second job after grad school and I … Continue reading →

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