Andrew B. Collier / @datawookie

First install the SQLiteODBC and unixODBC packages. Have a quick look at the documentation for unixODBC and SQLiteODBC.

This is a small package I put together quickly to satisfy an immediate need: generating abbreviated URLs in R. As it happens I require this functionality in a couple of projects, so it made sense to have a package to handle the details. It’s not perfect but it does the job. The code is available from GitHub along with vague usage information.

In essence the functionality is simple: first authenticate to shortening service (goo.gl and Bitly are supported at present) then shorten or expand URLs as required. The {longurl} package will perform the latter function too, possibly with greater efficiency.

The previous post looked at metaprogramming in Julia, considering how to write code that will generate or modify other code. Today’s post considers a somewhat less esoteric, yet powerful topic: Parallel processing.

As opposed to many other languages, where parallel computing is bolted on as an afterthought, Julia was designed from the start with parallel computing in mind. It has a number of native features which lend themselves to efficient implementation of parallel algorithms. It also has packages which facilitate cluster computing (using MPI, for example). We won’t be looking at those, but focusing instead on coroutines, generic parallel processing and parallel loops.

Your code won’t be terribly interesting without ways of getting data in and out. Ways to do that with Julia will be the subject of today’s post.

Console IO

Direct output to the Julia terminal is done via print() and println(), where the latter appends a newline to the output.

julia> print(3, " blind "); print("mice!\n")
3 blind mice!
julia> println("Hello World!")
Hello World!

Terminal input is something that I never do, but it’s certainly possible. readline() will read keyboard input until the first newline.

Yesterday I had a look at Julia’s support for Functional Programming. Naturally it also has structures for conventional program flow like conditionals, iteration and exception handling.

An earlier post looked at how to work with functions in Julia. This time we’ll dig into Functional Programming, an approach to coding which, not surprisingly, depends on writing functions.

Julia performs Just-in-Time (JIT) compilation using a Low Level Virtual Machine (LLVM) to create machine-specific assembly code. The first time a function is called, Julia compiles the function’s source code and the results are cached and used for any subsequent calls to the same function. However, there are some additional wrinkles to this story.

The previous post considered a selection of development environments for working with Julia. Now we’re going to look at a topic which is central to almost every programming task: variables.

Most coding involves the assignment and manipulation of variables. Julia is dynamically typed, which means that you don’t need to declare explicitly a variable’s data type. It also means that a single variable name can be associated with different data types at various times. Julia has a sophisticated, yet extremely flexible, system for dealing with data types. covered in great detail by the official documentation. My notes below simply highlight some salient points I uncovered while digging around.

As a long-term R user I’ve found that there are few tasks (analytical or otherwise) that R cannot immediately handle. Or be made to handle after a bit of hacking! However, I’m always interested in learning new tools. A month or so ago I attended a talk entitled Julia’s Approach to Open Source Machine Learning by John Myles White at ICML in Lille, France. What John told us about Julia was impressive and intriguing. I felt compelled to take a closer look. Like most research tasks, my first stop was the Wikipedia entry, which was suitably informative.

Reading Bayesian Computation with R by Jim Albert (Springer, 2009) inspired a fit of enthusiasm. Admittedly, I was on a plane coming back from Amsterdam and looking for distractions. I decided to put together a Shiny app to illustrate successive Bayesian updates. I had not yet seen anything that did this to my satisfaction. I like to think that my results come pretty close.