Weekend Project - Cinnamon DE Applet

I’ve recently switched from using Xfce as my main desktop environment to Cinnamon. There are pluses and minuses to each but, so far, I am enjoying Cinnamon quite a lot. I wanted to personalize my desktop a little bit, beyond just changing the theme, and thought that making an applet would be a fun way to do this.

My favorite website to view Halo statistics stopped working recently. So my idea was to create a tiny applet where I could view current arena rankings for any player. And so 'minihalostats' was born over this past weekend.

The Cinnamon applet tutorial is pretty bad, to say the least. Not only is it outdated, but the code given in the tutorial won’t even run! There are also no links to further documentation or any additional reading. I ended up learning mostly from looking at the code of other people’s applets. The Cinnamon Spices Github repo was an excellent source for this.

Even given the frustrating lack of documentation I enjoyed the process and even learned a little bit of Javascript.

Code for 'minihalostats' is up on my Github.

Webbased Digital Picture Frame

My wife and I moved to Germany from Canada some time ago and every once-in-a-while we get a little homesick. So I wanted to do a project that can help a little bit in this area.

I wrote a webpage where my family can upload pictures and a short message, if they want. The pictures are sent directly to my home server via an http post request. The request is parsed and the metadata is stored in an SQLite database, while the pictures themselves are stored in a directory. In the interest of security, I made sure that this directory is not accessible outside of my personal network. My sister does not want pictures of her children on the internet (a good decision in my opinion) so this seemed like a good option to accommodate that.

To display the pictures, a CGI script queries the SQLite database for a random image and uses it as the background in webpage. This is convenient because this allows me easily super-impose text using a bit of CSS. After all images have been displayed once, it resets and starts the cycle over again. Chromium is set up on the Raspberry Pi to auto-refresh the page every minute with a new picture.

For the display, I used the official Raspberry Pi touchscreen. The quality of the picture is, honestly, pretty bad and I would recommend shopping around for a nicer quality display if this project interests you.

All-in-all I’m quite happy with how it turned out and my wife definitely enjoys seeing new pictures of our family back home.

The code has also been uploaded to my Github.

Canadian 2016 Census - Population and Dwellings

View everything here

About a month ago, Statistics Canada finally started releasing summary statistics for the 2016 census. The long-form census was re-introduced last year so over the course of this year there should be lots of interesting data to look through. As of right now, only information on population and dwelling counts has been released with age and sex demographics scheduled for the beginning of May 2017.

I wanted to play around with a couple new tools like leaflet and highcharts and the census population data was the perfect test dataset. Leaflet is an awesome mapping library that feels really snappy in a browser and the R wrapper is incredibly simple to use. I definitely recommend it for any type of geographic visualizations. Flexdashboard was used to create a single-page html file that I then hosted on my webserver.

I don't have much to say about the data since I'm not really in a position to make any kind of conclusions. It's mostly just interesting to see how things have changed in Canada over the past 5 years.

haloR - A New R Halo API Wrapper

Earlier this week Microsoft released Halo Wars 2, a followup to the original that has somewhat of a cult following. In contrast to the mainline Halo titles, Halo Wars is a real-time strategy game. I've played through the Halo Wars 2 campaign and dipped my feet a little bit into the multiplayer. It isn't really for me (I prefer FPS) but it was still an enjoyable experience.

Similar to Halo 5, Microsoft and 343i have decided to open up much of the game details to the public through their Halo API. I really enjoyed digging through Halo 5 data and it was a big engagement point for my interest in the game. Kudos to MS/343i for the work they do on this stuff.

Even though I don't plan to continue playing the game, I decided to update my Halo R API wrapper to now include functions to easily get data from the Halo Wars 2 endpoints. Installation instructions and a tiny example can be found on the haloR Github.

Before using it, I suggest reading through the documentation provided my 343i since the documentation for my package is kind of sparse and the returned objects can be a little bit cryptic without a reference.

And as an additional small example, I pulled some Halo Wars 2 data for the game mode 'Rumble'. This is a new mode where players have infinite resources and don't have to worry about their economy. I wanted to see which leader had the highest winrates so I pulled a bunch of matches and graphed their percentages (at the top of this post). It's interesting that the two main story characters, Cutter and Atriox, have the highest winrates.

Simple Regression Trees in Julia

Being a data analyst, it’s a bit embarrassing how little experience I have with the new hotness of machine learning. I recently had a conversation with an individual who mentioned that they often employ decision and regression trees as a data exploration method and this prompted me to start looking into them.

Decision and regression trees are an awesome tool because of how transparent the end result is. It’s easy to understand and to explain to others who might be weary of implementing something opaque. In the simplest trees, they ask a series of yes-no questions such as: is a certain variable greater than some number. With each question you progress through different paths until you reach a terminal node. This terminal node will give you a prediction, either a classification or a value, depending on the type of tree. This process is extremely easy to follow and is the biggest selling point of decision trees.

Another advantage of decision trees is the simplicity of the algorithm used to create the tree. There are 3 basic steps that go into creating a tree. The first is a calculation on some cost function that we want to minimize. In the case of regression trees the cost function is usually just the mean squared error of all observations at that particular node. Secondly, each variable is iterated over to find the optimal way to divide the observations into two groups. Optimal, in this case, refers to the smallest mean squared error. And finally, once the optimal division is found the process is repeated on the two subgroups. This continues until certain predefined conditions are reached like minimum number of observations at a node.

In fact, the algorithm is so simple I decided to implement a basic regression tree in Julia as a learning exercise. Julia is an awesome statistical computing language thats main advantage is speed. Code written in Julia is often several times faster than the equivalent R or Python code for non-trivial calculations. My implementation is rather limited compared to the ‘rpart’ package in R or even the ‘DecisionTrees.jl’ package available in Julia. The idea was to gain a better understanding of how decision trees actually work and not to replace any of the already great implementations available.

I tested my implementation on the 'cu.summary' dataset from 'rpart'. This dataset contains information on a small number of cars and regressing on mileage gives the following tree:
Price < 9415.84 : 1
  Price < 6696.9 : 2
    4 : 34.0 : 3
    7 : 30.714285714285715 : 3
  Type IN String["Small","Sporty","Compact"] : 2
    Price < 11475.8 : 3
      Reliability IN String["average","Much worse"] : 4
        4 : 27.25 : 5
        6 : 24.166666666666668 : 5
      Reliability IN String["Much worse","better"] : 4
        4 : 21.0 : 5
        7 : 24.428571428571427 : 5
    Type IN String["Medium"] : 3
      Reliability IN String["Much better","worse"] : 4
        6 : 21.333333333333332 : 5
        5 : 22.2 : 5
      6 : 19.333333333333332 : 4

The labels show the decision that is made at each node. The lines that begin with a number show the number of observations that were placed in that bin along with the average mileage of those observations. The output isn’t pretty but it isn’t that difficult to follow since the tree is pretty shallow.

And, as always, I’ve uploaded my code to Github.