- Canadian Political Party Financials - Comparative Transparency Report (CPPF-CTR)
- First Nations Financial Transparency Act (FNFTA)
My introduction to R was actually a one month long mooc. I had enough experience in general coding, plus I had recently familiarized myself with functional programming through the classic text Structure and Interpretation of Computer Programs that I was able to fill in the blanks when it came to writing effective code—though certainly I had a lot of dictionary style referencing to do.
Following this, I have taken on a few data science projects close to my heart. I offer some of them here.
Canadian Political Party Financials - Comparative Transparency Report (CPPF-CTR)
August 5, 2015
In anticipation of the 2015 Canadian Federal Election, A local politician asked me to perform a comparative analysis of Canadian political party financial successes. In particular, I was asked to look at how well the various parties over the years have balanced their budgets:
I took on this project with dedication and enthusiasm as it was a nice way to gain further experience in the area of data science. It also gave me real world experience in using R:
which I feel is always better than the artificial practice you'd get in a course. It was completely open ended with no guidance whatsoever. I had to not only find the appropriate data sets, I had to use them to build my own so I could analyze and interpret the results. It was a lot of fun. I could get into this kind of work I think.
In any case, as I've already written up the findings elsewhere, I'll refer you to the full report here.
First Nations Financial Transparency Act (FNFTA)
July 25, 2015
As an independent project, I decided to build a comparative data set with the information provided by the First Nations Financial Transparency Act. Doing so would allow First Nations (Native Americans) in Canada to counter—with hard evidence—unfortunate mainstream narratives of First Nations' governmental corruption.
I am Inuit myself, so this Federal Act does not apply to my people, but as Indigenous folk in Canada, we still share many of the same social problems: It's good to help each other out where we can. This project also gave me the opportunity to grow my skills in statistics and in using the R programming language:
Not only did I convert and clean many smaller independent data sets, I accumulated them into a large one in which I could perform the appropriate analysis. This comparative data set also allowed me to analyze auxiliary considerations like the distribution of accounting firms chosen to audit the original reports:
I'm quite proud of my report. I put in a lot of effort to thoroughly document the whole process. Under normal circumstances this might be considered overkill, but I wanted to develop as many best practices as I could, so as to learn as much as possible to make it easier in the future when I take on other similar data science projects. You can see the full report here.