Large financial taxonomies, such as the U.S. GAAP, are too large to visualize with traditional network graphs techniques. The result -- even with thoughtful grouping, coloring, node sizing, layout algorithms, and interactive zoom -- is typically a hairball. In addition, the final product is highly dependent on the layout algorithm, a choice which often depends more on developer/data analyst asthetic preference than the underlying data. This abstraction serves to complicate analysis and leaves us no closer to the end goal: insight.
An alternative to network graphs are adjacency matrices. Although less pretty, these matrices can contain just as much information as a network tree. They too are an abstraction of the data, but in a much simpler structure and are scalable to large sizes.
This app used heatmap matrices to visualize relationships between elements. The impetus for this project is a problem in visualizing elements that were used together in the same filings.
Requires ggplot2 and plotly to generate the plots.
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