Creating this Map
Data Wrangling Notes
- hc-maps required country codes to map countries’ ranks (data values) to their location on the map. (two steps) The data I used was a table that labeled countries using a three-letter code and listed countries’ ranks by year. (insert screenshot) Since there was no guarantee that these three-letter codes were the same as ISA-3 (a standard set of country codes that could be used by the map), I mapped them to country names using the table ‘List of Countries’ on the IMO website and a LOOKUP function. I then mapped the country names to the map’s country codes to the codes hc-key. (In retrospect, it might’ve been better to map them to a more generalisable set such as ISA-2 or ISA-3.)
- Unfortunately the GeoJSON (this maps data values for each country to a visual map) I chose did not have Hong Kong or Macau down as countries, so I had to leave those data out. ? I thought it was better to leave them out than to average their rank with China’s and represent that as the rank for China as a whole because Hong Kong and Macau have small populations and geographical area relative to mainland China. I will check for a map that does have these regions included.
- Some countries did not participate in this year’s IMO, so in my first chart (which included only countries’ 2016 IMO rankings) their rank was an empty string. This was translated into a value of 0, which put them as a top-ranked country. I had to delete these countries’ data from that graph.
Data Visualisation Notes
While using two colours in the colour axis doesn’t make the graph the most visually appealing, it presents the rankings more clearly than one colour would. E.g.when I only used shades of blue, it was harder to tell the top-10-ranking countries from those who consistently ranked 15-30.