The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Do, share, teach and learn data science
An easy way to access R packages
Let us host your Shiny applications
A single home for R & Python Data Science Teams
Scale, develop, and collaborate across R & Python
Easily share your insights
Control and distribute packages
RStudio
RStudio Server
Shiny Server
R Packages
RStudio Cloud
RStudio Public Package Manager
shinyapps.io
RStudio Team
RStudio Workbench
RStudio Connect
RStudio Package Manager
rstudio::conf 2018
Tidying up your network analysis with tidygraph and ggraph
February 26, 2018
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.
I’m a software engineer at RStudio, focusing most of my time on developing tools for data visualisation. I’m the author of gganimate, ggraph, ggforce among others and co-author of ggplot2