About this Tutorial:
⚠️ The writing is a work in progress. The functions work but text retouching⚠️
Please read everything found on the mainpage before continuing; disclaimer and all.
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes (Holland and Leinhardt, 1971; Watts and Strogatz, 1998).
Two versions of this measure exist: the global and the local. The global version was designed to give an overall indication of the clustering in the network, whereas the local gives an indication of the embeddedness of single nodes. - Geek for Geeks
Data Prep
Get only the columns we want to work with
What we want is 1 record for every year and every CSA as a column. To do this, transpose the dataset. Set the CSA labels (first row) as our columns, relabel the index (for clarity) and cast our datatypes.