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I’ve had a lot of different jobs over the past 4 years, and I’ve had some incredible experiences along the way. Lately, I’ve been struggling with what to do next. Or perhaps more accurately, I’ve been struggling with how to de... [Read more...]

My heart sinks a little when I check on my laptop in the morning and the computation I started the night before still hasn’t finished. Even when the data I’m playing with isn’t particularly.... large... (I’m not going to say it), I have a knack for ... [Read more...]

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One of the most common ways people write for() loops is to create an empty results vector and then concatenate each result with the previous (and growing) results vector, like the following.  (Note: wrapping an expression in the function... [Read more...]

Emacs is a great front-end for most of the command line tools. Although R-Studio is pretty good, I think Emacs/ESS is better. I’ve always used Emacs/ESS to run R, since 2007 on Ubuntu, on Windows, and on my MacBook Pro. It gives me the same experiences across all ... [Read more...]

[This article was first published on Rmetrics blogs, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your [Read more...]

I am glad to have found the R package GGally. GGally is a convenient package built upon ggplot2 that contains templates for different plots to be combined into a plot matrix through the function ggpairs. It is a nice alternative to the more limited pairs function. The package has also ... [Read more...]

R is designed as an in-memory application: all of the data you work with must be hosted in the RAM of the machine you're running R on. This optimizes performance and flexibility, but does place contraints on the size of data you're working with (since it must all work in ... [Read more...]

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The beadsplot function is designed for a data frame with a factor column and many observation columns.  This function summarize the data visually.  The builtin iris data is suitable to … Continue reading → [Read more...]

I am about to head home from my fifth time attending the North East Association for Institutional Research (NEAIR), this year in Newport, RI, which was just fantastic. Really great people, interesting talks, and good food. I again taught an Introduction to R and LaTeX for Institutional Research pre-conference workshop ... [Read more...]

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Ordinary least squares continues to be the staple estimator for causal inference for good reason.  In order to help new and veteran OLS users get a better sense of how it is working I have created a shiny app that allows for instant interactivity ... [Read more...]

In case you missed them, here are some articles from October of particular interest to R users: Joe Rickert recounts the R presence at the Strata + Hadoop World conference, including slides from the R and Hadoop tutorial. Hadley Wickham's favorite tools, gadgets and software (including of course R). Revolution R ... [Read more...]

In this blogpost I presented a visualization made with R that shows how almost the whole world expresses its attention to political crises abroad. Here’s another visualization with Tweets in October 2013 that referred to the Lampedusa tragedy in the Mediterranean. But this transnational public space isn’t quite as ... [Read more...]

Once you start experimenting with many different asset allocation algorithms, the computation time of running the back-tests can be substantial. One simple way to solve the computation time problem is to run the back-tests in parallel. I.e. if the asset allocation algorithm does not use the prior period holdings ... [Read more...]

A Question Recently, a student, working on her Senior Thesis at Northland College, asked me the following question: Attached is an Excel file with three “important to R” worksheets. The only thing that connects all 3 worksheets is the Lift.ID … Continue reading → [Read more...]

Despite the map here, I'm not going to talk about yet another fraction of the former Soviet Empire which is taken the form of a people's republic, possibly with witty British Ambassadors.In fact, I'm going to talk about the Stan workshop that I have be... [Read more...]

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some ... [Read more...]