Example of my work: 

​I can use R to adapt existing code in a way that allows me to produce unique information graphics that can visually communicate a deeper story of political change. 

The first map plots each UK parliamentary and colours it according to the party that won that respective seat. It also has an interactive feature which when a user hovers over a specific constituency it produces a box that summarises the constituency's key statistics, such as the winning party and their current majority size. 

Further to this, these trends can also be plotted into scatter diagrams with a line of best fit (which in my thesis uses a probit regression line). This helps to show how these trends affect the probability of a party winning, or gaining, a constituency. Therefore, such work can show reasons  behind why some constituencies turn blue, or stay Red.  

Moreover, I can use R to further adapt existing code in a way that allows me to produce heat maps, which can show the scale of change from one event to another, in this example from one general election to another, 2017-'19.

The first map plots the change of the Conservative Party vote in each UK parliamentary constituency between the 2017 and 2019 general election (excluding Scottish and Northern Ireland seats).   

The maps beneath, instead of showing change between two time-points, displays the level of support for a given party, in this case Labour. It shows the level of support, in terms of the percentage of Labour's vote, within each constituency within England and Wales. 

This example of my work demonstrates my ability to understand available example code and then adapt it to show wider trends, and in this case tell a more in-depth story about geographical political change in Britain. To see an example of a written story using these maps, please visit and read this following blog post https://www.coastalaction.co.uk/single-post/constituencies-2010-19-political-change-in-britain

The two maps beneath shows my ability to adapt such maps based on a given criteria. The map to the left shows every seat that voted to leave the EU and which party won these Leave seats. The Map on the right highlights which constituencies were areas that changed control and which party was the party who gained this seat (shown through the colour of the seat). I have also eliminated Northern Ireland seats, again showing my ability to analyse data based on a given criteria

Another example of how these maps can construct a story is through comparing different variable's geographical trends. For example, the four maps above highlight how Leave and Remain trends have increasingly shaped the outcomes of the two main parties' performance, especially when comparing such trends to the change in the two main parties' vote share across the decade .

Crucially, this indicates that broader long-term trends are remoulding British politics, and from this understanding a wider story can be constructed. To see read this story in more detail, please visit and read this following blog post

https://www.coastalaction.co.uk/single-post/constituencies-2010-19-political-change-in-britain

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