Tag Archives: R-bloggers

Visualizing graphs with overlapping node groups

I recently came across some data about multilateral agreements, which needed to be visualized as network plots. This data had some peculiarities that made it more difficult to create a plot that was easy to understand. First, the nodes in the graph were organized in groups but each node could belong to multiple groups or to no group at all. Second, there was one “super node” that was connected to all other nodes (while “normal” nodes were only connected within their group). This made it difficult to find the right layout that showed the connections between the nodes as well as the group memberships. However, digging a little deeper into the R packages igraph and ggraph it is possible to get satisfying results in such a scenario.

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Creating a “balloon plot” as alternative to a heat map with ggplot2

Heat maps are great to compare observations with lots of variables (which must be comparable in terms of unit, domain, etc.). In some cases however, traditional heat maps might not suffice, for example when you want to compare multiple groups of observations. One solution is to use facets. Another solution, which I want to explain here, is to make a “ballon plot” with a fixed grid of rows and columns.

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Parallel Coordinate Plots for Discrete and Categorical Data in R — A Comparison

Parallel Coordinate Plots are useful to visualize multivariate data. R provides several packages/functions to draw Parallel Coordinate Plots (PCPs):

In this post I will compare these approaches using a randomly generated data set with three discrete variables.

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Dynamic column/variable names with dplyr using Standard Evaluation functions

Data manipulation works like a charm in R when using a library like dplyr. An often overlooked feature of this library is called Standard Evaluation (SE) which is also described in the vignette about the related Non-standard Evaluation. It basically allows you to use dynamic arguments in many dplyr functions (“verbs”).

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