Bipartite network analysis in r2/29/2024 Ok, so what else can we do with these graph representations? Well, wouldn’t it be nice if could easily summarise and visualise mapping objects? especially more complex ones?… and thus began my journey down the rabbit hole of graph data structures and ggplot2 extensions. I call this graph-based representation a Crossmap. Bipartite provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) networks, a.k.a. When the graph represents recoding or redistributing (i.e. collapsing or splitting) values, weights will be between 0 and 1. The weighted part refers to the addition of a numeric attribute to each link. In the case of recoding or redistributing data, it turns out that thinking of mapping objects as directed bipartite weighted graphs is quite informative for designing assertions.Īs a quick reminder, bipartite graphs are graphs where the nodes or vertices can be split into two disjoint sets, and edges or links are only allowed between the two sets, not within. However, it’s not always obvious what assertions you should be checking. Now, where do graphs come in? Well, assertive programming is a good preventive measure against funny business in your data wrangling pipelines. Add a combination of recoding, aggregating and disaggregating numeric counts (e.g. occupation level statistics, or population by administrative area) to your data wrangling pipeline and you’re only one coding mistake away from accidentally (and often silently) dropping or corrupting some of your data (trust me, I’ve done it before). Of course this is a somewhat trivial example that you could quickly check by looking at the code, but as mappings get more complex and involve more categories, it becomes less obvious how to ensure you’re actually performing the intended transformations. No 2 old columns should get the same new name, and a single old column being renamed into 2 new columns is just duplicating data. Bipartite projections are of interest in social network analysis because they allow us to construct a network from artifact affiliations, which are often. The package bipartite in R is extremely useful for those of us who study plant-animal interactions. For example, when renaming columns with dplyr::rename(.), where the … takes new = old pairs, you probably only want 1-to-1 relations. Now given a particular mapping object, you might want to verify that it has certain properties before using it.
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