This is a companion discussion topic for the original entry at:
Graphs and networks are a prevalent data structure within many domains of data science. Efficient classes and algorithms for network analysis has been available in R for a long time with e.g. the igraph and network packages that also provides the means to plot standard node-edge diagrams. Unfortunately, due to the nature of network data, the advances in data analysis and visualisation workflows that ggplot2 and dplyr (among others) has brought to R, has not been directly applicable to graph and network data. In this talk I’ll present the tidygraph and ggraph packages that has been developed with the aim of bringing graph and network data into the tidyverse. The talk will cover the design philosophy of the two packages and include lots of examples showing how classic network analysis tasks can be solved with the help of tidygraph and ggraph.
Slides: Tidying up your network analysis with tidygraph and ggraph
GitHub: Thomas Lin Pedersen
Thomas Lin Pedersen - Analytics Programmer
After graduating from Technical University of Denmark with a PhD in Bioinformatics, Thomas is now data scientist at the Danish Tax Authorities where he focuses on developing tools and visualisations. In the spare time Thomas is a serial R package developer with well over a dozen released packages. Much of his development work is focused on the ggplot2 ecosystem, either as a contributor to the ggplot2 package itself, or through one of his own packages extending the framework, such as ggforce and and ggraph.