Blog Posts by Andrew B. Collier / @datawookie

I read Data Mining with Rattle and R by Graham Williams over a year ago. It’s not a new book and I’ve just been tardy in writing up a review. That’s not to say that I have not used the book in the interim: it’s been on my desk at work ever since and I’ve dipped into it from time to time. As a reference for ongoing analyses it’s an extremely helpful resource.

Today we’ll be looking at the Distances package, which implements a range of distance metrics. This might seem a rather obscure topic, but distance calculation is at the core of all clustering techniques (which are next on the agenda), so it’s prudent to know a little about how they work.

It’s all very well generating myriad statistics characterising your data. How do you know whether or not those statistics are telling you something interesting? Hypothesis Tests. To that end, we’ll be looking at the HypothesisTests package today.

Today I’m looking at the Distributions package.

Julia has native support for calling C and Fortran functions. There are also add on packages which provide interfaces to C++, R and Python. We’ll have a brief look at the support for C and R here. Further details on these and the other supported languages can be found on GitHub.

Sudoku-as-a-Service is a great illustration of Julia’s integer programming facilities. Julia has several packages which implement various flavours of optimisation: JuMP, JuMPeR, Gurobi, CPLEX, DReal, CoinOptServices and OptimPack. We’re not going to look at anything quite as elaborate as Sudoku today, but focus instead on finding the extrema in some simple (or perhaps not so simple) mathematical functions. At this point you might find it interesting to browse through this catalog of test functions for optimisation.

There’s a variety of options for plotting in Julia. We’ll focus on those provided by Gadfly and Plotly.

PhysicalConstants is a Julia package which has the values of a range of physical constants. Currently MKS and CGS units are supported.

R has an extensive range of builtin datasets, which are useful for experimenting with the language. The RDatasets package makes many of these available within Julia. We’ll see another way of accessing R’s datasets in a couple of days’ time too. In the meantime though, check out the documentation for RDatasets and then read on below.

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