Annotation is important for storytelling, and ggforce provides a family of geoms that makes it easy to draw attention to, and describe, features of the plot. They all work in the same way, but differ in the way they enclose the area you want to draw attention to.
An extensible framework for automatically placing direct labels onto multicolor ‘lattice’ or ‘ggplot2’ plots. Label positions are described using Positioning Methods which can be re-used across several different plots. There are heuristics for examining “trellis” and “ggplot” objects and inferring an appropriate Positioning Method.
ggwordcloud provides a word cloud text geom for ggplot2. The placement algorithm implemented in C++ is an hybrid between the one of wordcloud and the one of wordcloud2.js. The cloud can grow according to a shape and stay within a mask. The size aesthetic is used either to control the font size or the printed area of the words. ggwordcloud also supports arbitrary text rotation. The faceting scheme of ggplot2 can also be used.
ggwordcloud package implements a spiraling algorithm to prevent text labels from overlapping each other.
Pretty word clouds.
wordcloud package implements a spiraling algorithm to prevent text labels from overlapping each other.
Force field simulation of interaction of set of points. Very useful for placing text labels on graphs, such as scatterplots.
I found that functions in the
FField package were not ideal for repelling overlapping rectangles, so I wrote my own.
See this gist for examples of how to use the
FField packages with
A D3 plug-in for automatic label placement using simulated annealing
A D3 layout that places labels avoiding overlaps, with strategies including simulated annealing, greedy and a strategy that removes overlapping labels.
A Voronoi tessellation can assist in labeling scatterplots. The area of the Voronoi cell associated with each point determines whether the point is labeled: points with larger cells tend to have room to accommodate labels.
Sebastian Theophil, Arno Schödl
This paper presents an efficient algorithm for a new variation of the point feature labeling problem. The goal is to position the largest number of point labels such that they do not intersect each other or their points. First we present an algorithm using a greedy algorithm with limited lookahead. We then present an algorithm that iteratively regroups labels, calling the first algorithm on each group, thereby identifying a close to optimal labeling order. The presented algorithm is being used in a commercial product to label charts, and our evaluation shows that it produces results far superior to those of other labeling algorithms.
This might be a good start for a revision of ggrepel.