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Do you ever see strange lights in the sky? Do you wonder what really goes on in Area 51? Would you like to use your R hacking skills to get to the bottom of the whole UFO conspiracy? Of course, you would! UFO data from infochimps is the focus of a dat...
"Is there a package for obfuscating code in #rstats?", someone asked. "The S4 object system?!" came the snarky reply. If you're smiling right now, you know that it wouldn't be funny if it weren't at least a little bit true. Options: S3, S4 or R5? There can be little doubt that object oriented...
In Andrew Ng's Machine Learning class, the first section demonstrates gradient descent by using it on a familiar problem, that of fitting a linear function to data. Let's start off, by generating some bogus data with known characteristics. Let's make y just a noisy version of x. Let's also add 3 to give the intercept term something to...
Just recently, I found out that R is limited to 32-bit integers, even on 64-bit hardware. Bummer, huh? As a consequence, the maximum size of a vector is 2^31-1. To be fair, dealing with numeric types across machine architectures is hard. A fixed repr...
Michael Kellen, Director of Technology at Sage Bionetworks, is trying to build a GitHub for science. It's called Synapse and Kellen described it in a talk at the Sage Bionetworks Commons Congress 2012, this past weekend: 'Synapse' Pilot for Building an...
This past Saturday, I hung out at the Seattle branch of the International Open Data Hackathon. The event was hosted at the Pioneer Square office of Socrata, a small company that helps governments provide public open data. A pair of data analysts from Tableau were showing off a visualization for the Washington...
If you look at the programming languages that are popular these days, a few patterns emerge. I'm not talking about languages that have the most hits on the job sites. I'm talking about what the cool kids are coding in - the folks that hang out on hacke...
Here's a quick cheat-sheet on string manipulation functions in R, mostly cribbed from Quick-R's list of String Functions with a few additional links.
substr(x, start=n1, stop=n2)
grep(pattern,x, value=FALSE, ignore.case=FALSE, fixed=FALSE)
gsub(pattern, replacement, x, ignore.case=FALSE, fixed=FALSE)
gregexpr(pattern, text, ignore.case=FALSE, perl=FALSE,
fixed=FALSE)
strsplit(x, split)
paste(..., sep="", collapse=NULL)
sprintf(fmt, ...)
Using MySQL with R is pretty easy, with RMySQL. Here are a few notes to keep me straight on a few things I always get snagged on. Typically, most folks are going to want to analyze data that's already in a MySQL database. Being a little bass-ackwards, I often want to go the other way. One reason to do...
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