This is the second part about my project that deals with the Twitter network of members of the Bundestag. After getting the necessary data, which was explained in part 1, we will now focus on creating a network graph with links between the representatives’ Twitter accounts for exploratory network analysis.
Read More →A Twitter network of members of the 19th German Bundestag – part I
For the R tutorial that I gave at the WZB in the previous semester, I gave an introduction on how to query web APIs – specifically the Twitter API – and automated data extraction from websites (i.e. web scraping). I showed an example that combined both of these techniques for the goal of getting data about the Twitter activities of members of the current (19th) German Bundestag, which is the federal German parliament. The focus was especially on the question “who follows who” on Twitter. I thought it’s a nice little project showing how to use the Twitter API, do web scraping, combine the collected data and do some exploratory network analysis – all within the R environment. So I decided to polish the code a little bit, put in on GitHub and wrote two blog posts. The first part, i.e. this part, is all about getting the data.
Three ways of visualizing a graph on a map
When visualizing a network with nodes that refer to a geographic place, it is often useful to put these nodes on a map and draw the connections (edges) between them. By this, we can directly see the geographic distribution of nodes and their connections in our network. This is different to a traditional network plot, where the placement of the nodes depends on the layout algorithm that is used (which may for example form clusters of strongly interconnected nodes).
In this blog post, I’ll present three ways of visualizing network graphs on a map using R with the packages igraph, ggplot2 and optionally ggraph. Several properties of our graph should be visualized along with the positions on the map and the connections between them. Specifically, the size of a node on the map should reflect its degree, the width of an edge between two nodes should represent the weight (strength) of this connection (since we can’t use proximity to illustrate the strength of a connection when we place the nodes on a map), and the color of an edge should illustrate the type of connection (some categorical variable, e.g. a type of treaty between two international partners).
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