This is one of my favorite ggplot2 plots I’ve ever made, but it makes me sad. Can you deduce what this plot conveys? Explain the sporadically dashed colored horizontal bands. Explain the red vertical bars. Explain the black vertical bars. If you answer all three correctly, and you can ... [Read more...]

So, I got some great solutions to my dplyr mutation problem to share. Just wait until you see these things! Remember, I was having trouble reconciling two date columns into a minimum value in the presence of NA values. Here’s the fake data agai... [Read more...]

Yesterday I ran into a fairly complex issue regarding dplyr mutation and I wanted to get your take on my solution. I have two data frames with the same identifiers and two different date columns which I need to merge into one date column, with the val... [Read more...]

Huh… I didn’t realize just how similar rvest was to XML until I did a bit of digging. After my wonderful experience using dplyr and tidyr recently, I decided to revisit some of my old RUNNING code and see if it could use an upgrade by swapping out the ... [Read more...]

Arun Srinivasan is the man! Once he saw that his data.table solution to the TidyR Challenge had an issue, he fixed it! His solution is below along with a quick equivalence test to my original solution, and check out this stackOverflow question for a... [Read more...]

In my last post I described a data set that was a bit cumbersome to parse and I wanted to tidy it up before I could begin visually exploring. You know, the fun part. I wasn’t 100% happy with my solution so I candidly asked the internet to Help Me ... [Read more...]

Last week I was handed a drug prescription data set and asked to create some interesting graphics. But before I can even get to the fun part, I was faced with actually transforming the set into something that ggplot2 could read. Obviously I can’t share the data, but Tyler ... [Read more...]

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Hash Table Performance in R: Part III In Part I of this series, I explained how R hashed environments are superior to vectors or lists when you’re in need of an associative array. In Part II I explained the three main operations you want to perform on hash tables ... [Read more...]

Hash Table Performance in R: Part II In Part I of this series, I explained how R hashed environments are superior to vectors or lists when you need a hash table for your work. I also teased that in this post I would explain the caveats associated with that choice, ... [Read more...]

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I presented rApache to the public for the first time at the Directions in Statistical Computing workshop in August 2005 (paper), almost seven years ago. It might have been novel, maybe even crazy at the time, but I think rApache showed people a new way to bring R to the web. ... [Read more...]

Please contact me if you’d like to take over maintainership of this popular R package. [Read more...]

In A Capitalist’s Dilemma, Whoever Wins on Tuesday, Clayton Christensen lays out three kinds of innovations through which an industry cycles: Empowering Innovations - those that offer products and services to a new customer base. The classic empowering (or disruptive) innovation is Ford Motor Company’s introduction of the ... [Read more...]

In Part I, I described how you can deploy your Rook applications with rApache. This post describes how you can do it with R itself. But before we get into that, I’d like to explain the off-again on-again relationship Rook has had with CRAN, R’s package archive network. ... [Read more...]

With this release comes a minor change in behavior: for requests that have been configured with RFileEval, RFileHandler, or using the r-script handler, rApache will set the working directory to the file’s directory. For instance with a Rook deployment like this:

 <Location /hmisc> 
        SetHandler r-handler 
        RFileEval "/home/hornerj/Hmisc/config.R:Rook::Server$call(app)" 
</Location>

It makes sense to change the working ... [Read more...]

Since rApache 1.1.15 you’ve been able to deploy you Rook applications like so:

# Run the Rook application named 'app'. On each request, the expression 
# 'Rook::Server$call(app)' is evaluated in an environment populated by
# rookapp.R. 'app' is expected to be found in that environment.
<Location /test/RookApp>
        SetHandler r-handler
        RFileEval /path/to/Rook/App/rookapp.R:Rook::Server$call(app)
</Location>

Let’s go through the above example step by step, starting with the Location directive from apache. The Location Directive Works on URLs In apache, the Location directive works on the URL space of the server. ... [Read more...]