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https://github.com/schochastics/edgebundle
R package implementing edge bundling algorithms
https://github.com/schochastics/edgebundle
graph-algorithms network-analysis rstats visualization
Last synced: 4 days ago
JSON representation
R package implementing edge bundling algorithms
- Host: GitHub
- URL: https://github.com/schochastics/edgebundle
- Owner: schochastics
- License: other
- Created: 2020-12-15T09:33:47.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-23T18:57:30.000Z (about 2 months ago)
- Last Synced: 2024-10-26T20:37:39.691Z (18 days ago)
- Topics: graph-algorithms, network-analysis, rstats, visualization
- Language: R
- Homepage: https://schochastics.github.io/edgebundle/
- Size: 15.9 MB
- Stars: 125
- Watchers: 6
- Forks: 10
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Support: .github/SUPPORT.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(edgebundle)
```
# edgebundle 
[](https://github.com/schochastics/edgebundle/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=edgebundle)
[](https://CRAN.R-project.org/package=edgebundle)
An R package that implements several edge bundling/flow and metro map algorithms. So far it includes
- Force directed edge bundling
- Stub bundling ([paper](https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/NoBr13.pdf))
- Hammer bundling ([python code](https://datashader.org/_modules/datashader/bundling.html))
- Edge-path bundling ([paper](https://arxiv.org/abs/2108.05467))
- TNSS flow map ([paper](https://doi.org/10.1080/15230406.2018.1437359))
- Multicriteria Metro map layout ([paper](https://doi.org/10.1109/TVCG.2010.24))
**[ggraph 2.2.0](https://www.data-imaginist.com/posts/2024-02-15-ggraph-2-2-0/) supports edge bundling natively via `geom_edge_bundle_*()` functions. This means that parts of this package are now deprecated.**
## Installation
The package is available on CRAN.
```{r cran, eval=FALSE}
install.packages("edgebundle")
```
The developer version can be installed with
``` {r dev, eval=FALSE}
# install.packages("remotes")
remotes::install_github("schochastics/edgebundle")
```
Note that `edgebundle` imports `reticulate` and uses a pretty big python library (datashader).
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup,warning=FALSE,message=FALSE}
library(edgebundle)
library(igraph)
```
## Edge bundling
The expected input of each edge bundling function is a graph (igraph/network or tbl_graph object) and a node layout.
All functions return a data frame of points along the edges of the network that can be plotted with {{ggplot2}} using `geom_path()` or
`geom_bezier()` for `edge_bundle_stub()`.
```{r example,message=FALSE}
library(igraph)
g <- graph_from_edgelist(
matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = T), F
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
fbundle <- edge_bundle_force(g, xy, compatibility_threshold = 0.1)
head(fbundle)
```
The result can be visualized as follows.
```{r plot,message=FALSE,fig.align='center'}
library(ggplot2)
ggplot(fbundle) +
geom_path(aes(x, y, group = group, col = as.factor(group)),
size = 2, show.legend = FALSE
) +
geom_point(data = as.data.frame(xy), aes(V1, V2), size = 5) +
theme_void()
# simple edge-path bundling example
g <- graph_from_edgelist(matrix(c(1, 2, 1, 6, 1, 4, 2, 3, 3, 4, 4, 5, 5, 6),
ncol = 2, byrow = TRUE
), FALSE)
xy <- cbind(c(0, 10, 25, 40, 50, 50), c(0, 15, 25, 15, 0, -10))
res <- edge_bundle_path(g, xy, max_distortion = 2, weight_fac = 2, segments = 50)
ggplot() +
geom_path(
data = res, aes(x, y, group = group, col = as.factor(group)),
size = 2, show.legend = FALSE
) +
geom_point(data = as.data.frame(xy), aes(V1, V2), size = 5) +
scale_color_manual(values = c("grey66", "firebrick3", "firebrick3", rep("grey66", 4))) +
theme_void()
```
For `edge_bundle_stub()`, you need `geom_bezier()` from the package {{ggforce}}.
```{r bezier,message=FALSE,fig.align='center'}
library(ggforce)
g <- graph.star(10, "undirected")
xy <- matrix(c(
0, 0,
cos(90 * pi / 180), sin(90 * pi / 180),
cos(80 * pi / 180), sin(80 * pi / 180),
cos(70 * pi / 180), sin(70 * pi / 180),
cos(330 * pi / 180), sin(330 * pi / 180),
cos(320 * pi / 180), sin(320 * pi / 180),
cos(310 * pi / 180), sin(310 * pi / 180),
cos(210 * pi / 180), sin(210 * pi / 180),
cos(200 * pi / 180), sin(200 * pi / 180),
cos(190 * pi / 180), sin(190 * pi / 180)
), ncol = 2, byrow = TRUE)
sbundle <- edge_bundle_stub(g, xy, beta = 90)
ggplot(sbundle) +
geom_bezier(aes(x, y, group = group), size = 1.5, col = "grey66") +
geom_point(data = as.data.frame(xy), aes(V1, V2), size = 5) +
theme_void()
```
The typical edge bundling benchmark uses a dataset on us flights, which is included in the package.
```{r example-code,eval = FALSE}
g <- us_flights
xy <- cbind(V(g)$longitude, V(g)$latitude)
verts <- data.frame(x = V(g)$longitude, y = V(g)$latitude)
fbundle <- edge_bundle_force(g, xy, compatibility_threshold = 0.6)
sbundle <- edge_bundle_stub(g, xy)
hbundle <- edge_bundle_hammer(g, xy, bw = 0.7, decay = 0.5)
pbundle <- edge_bundle_path(g, xy, max_distortion = 12, weight_fac = 2, segments = 50)
states <- map_data("state")
p1 <- ggplot() +
geom_polygon(
data = states, aes(long, lat, group = group),
col = "white", size = 0.1, fill = NA
) +
geom_path(
data = fbundle, aes(x, y, group = group),
col = "#9d0191", size = 0.05
) +
geom_path(
data = fbundle, aes(x, y, group = group),
col = "white", size = 0.005
) +
geom_point(
data = verts, aes(x, y),
col = "#9d0191", size = 0.25
) +
geom_point(
data = verts, aes(x, y),
col = "white", size = 0.25, alpha = 0.5
) +
geom_point(
data = verts[verts$name != "", ],
aes(x, y), col = "white", size = 3, alpha = 1
) +
labs(title = "Force Directed Edge Bundling") +
ggraph::theme_graph(background = "black") +
theme(plot.title = element_text(color = "white"))
p2 <- ggplot() +
geom_polygon(
data = states, aes(long, lat, group = group),
col = "white", size = 0.1, fill = NA
) +
geom_path(
data = hbundle, aes(x, y, group = group),
col = "#9d0191", size = 0.05
) +
geom_path(
data = hbundle, aes(x, y, group = group),
col = "white", size = 0.005
) +
geom_point(
data = verts, aes(x, y),
col = "#9d0191", size = 0.25
) +
geom_point(
data = verts, aes(x, y),
col = "white", size = 0.25, alpha = 0.5
) +
geom_point(
data = verts[verts$name != "", ], aes(x, y),
col = "white", size = 3, alpha = 1
) +
labs(title = "Hammer Edge Bundling") +
ggraph::theme_graph(background = "black") +
theme(plot.title = element_text(color = "white"))
alpha_fct <- function(x, b = 0.01, p = 5, n = 20) {
(1 - b) * (2 / (n - 1))^p * abs(x - (n - 1) / 2)^p + b
}
p3 <- ggplot() +
geom_polygon(
data = states, aes(long, lat, group = group),
col = "white", size = 0.1, fill = NA
) +
ggforce::geom_bezier(
data = sbundle, aes(x, y,
group = group,
alpha = alpha_fct(..index.. * 20)
), n = 20,
col = "#9d0191", size = 0.1, show.legend = FALSE
) +
ggforce::geom_bezier(
data = sbundle, aes(x, y,
group = group,
alpha = alpha_fct(..index.. * 20)
), n = 20,
col = "white", size = 0.01, show.legend = FALSE
) +
geom_point(
data = verts, aes(x, y),
col = "#9d0191", size = 0.25
) +
geom_point(
data = verts, aes(x, y),
col = "white", size = 0.25, alpha = 0.5
) +
geom_point(
data = verts[verts$name != "", ], aes(x, y),
col = "white", size = 3, alpha = 1
) +
labs(title = "Stub Edge Bundling") +
ggraph::theme_graph(background = "black") +
theme(plot.title = element_text(color = "white"))
p4 <- ggplot() +
geom_polygon(
data = states, aes(long, lat, group = group),
col = "white", size = 0.1, fill = NA
) +
geom_path(
data = pbundle, aes(x, y, group = group),
col = "#9d0191", size = 0.05
) +
geom_path(
data = pbundle, aes(x, y, group = group),
col = "white", size = 0.005
) +
geom_point(
data = verts, aes(x, y),
col = "#9d0191", size = 0.25
) +
geom_point(
data = verts, aes(x, y),
col = "white", size = 0.25, alpha = 0.5
) +
geom_point(
data = verts[verts$name != "", ], aes(x, y),
col = "white", size = 3, alpha = 1
) +
labs(title = "Edge-Path Bundling") +
ggraph::theme_graph(background = "black") +
theme(plot.title = element_text(color = "white"))
p1
p2
p3
p4
```
```{r,fig.width=8,fig.height=6,fig.align='center',out.width="95%",echo=FALSE}
knitr::include_graphics("man/figures/flights_fdeb.png")
knitr::include_graphics("man/figures/flights_heb.png")
knitr::include_graphics("man/figures/flights_seb.png")
knitr::include_graphics("man/figures/flights_peb.png")
```
## Flow maps
A flow map is a type of thematic map that represent movements. It may thus be considered a hybrid of a map and a flow diagram.
The package so far implements a spatial one-to-many flow layout algorithm using
triangulation and approximate Steiner trees.
The function `tnss_tree()` expects a one-to-many flow network (i.e. a weighted star graph), a layout for the nodes and
a set of dummy nodes created with `tnss_dummies()`.
```{r example_flow,eval=FALSE}
library(ggraph)
xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
xy_dummy <- tnss_dummies(xy, 4)
gtree <- tnss_tree(cali2010, xy, xy_dummy, 4, gamma = 0.9)
ggraph(gtree, "manual", x = V(gtree)$x, y = V(gtree)$y) +
geom_polygon(data = us, aes(long, lat, group = group), fill = "#FDF8C7", col = "black") +
geom_edge_link(aes(width = flow, col = sqrt((xy[root, 1] - ..x..)^2 + (xy[root, 2] - ..y..)^2)),
lineend = "round", show.legend = FALSE
) +
scale_edge_width(range = c(0.5, 4), trans = "sqrt") +
scale_edge_color_gradient(low = "#cc0000", high = "#0000cc") +
geom_node_point(aes(filter = tnss == "leaf"), size = 1) +
geom_node_point(aes(filter = (name == "California")), size = 5, shape = 22, fill = "#cc0000") +
theme_graph() +
labs(title = "Migration from California (2010) - Flow map")
```
```{r,fig.width=8,fig.height=6,fig.align='center',out.width="95%",echo=FALSE}
knitr::include_graphics("man/figures/cali2010_flow.png")
```
To smooth the tree, use `tnss_smooth()`. Note that this changes the object type and
you need to visualize it with {{ggplot2}} rather than {{ggraph}}.
```{r example_flow_smooth,eval=FALSE}
smooth_df <- tnss_smooth(gtree, bw = 5, n = 20)
ggplot() +
geom_polygon(data = us, aes(long, lat, group = group), fill = "#FDF8C7", col = "black") +
geom_path(
data = smooth_df, aes(x, y, group = destination, size = flow),
lineend = "round", col = "firebrick3", alpha = 1
) +
theme_void() +
scale_size(range = c(0.5, 3), guide = "none") +
labs(title = "Migration from California (2010) - Flow map smoothed")
```
```{r,fig.width=8,fig.height=6,fig.align='center',out.width="95%",echo=FALSE}
knitr::include_graphics("man/figures/cali2010_flow_smoothed.png")
```
See [this gallery](http://minard.schochastics.net/) for more examples and code.
## Metro Maps
Metro map(-like) graph drawing follow certain rules, such as octilinear edges. The algorithm
implemented in the packages uses hill-climbing to optimize several features desired in a metro map.
The package includes the metro map of Berlin as an example.
```{r metro_example, eval = FALSE}
# the algorithm has problems with parallel edges
g <- simplify(metro_berlin)
xy <- cbind(V(g)$lon, V(g)$lat) * 100
# the algorithm is not very stable. try playing with the parameters
xy_new <- metro_multicriteria(g, xy, l = 2, gr = 0.5, w = c(100, 100, 1, 1, 100), bsize = 35)
# geographic layout
ggraph(metro_berlin, "manual", x = xy[, 1], y = xy[, 2]) +
geom_edge_link0(aes(col = route_I_counts), edge_width = 2, show.legend = FALSE) +
geom_node_point(shape = 21, col = "white", fill = "black", size = 3, stroke = 0.5)
# schematic layout
ggraph(metro_berlin, "manual", x = xy_new[, 1], y = xy_new[, 2]) +
geom_edge_link0(aes(col = route_I_counts), edge_width = 2, show.legend = FALSE) +
geom_node_point(shape = 21, col = "white", fill = "black", size = 3, stroke = 0.5) +
theme_graph() +
labs(title = "Subway Network Berlin")
```
```{r,fig.width=8,fig.height=6,fig.align='center',out.width="95%",echo=FALSE}
knitr::include_graphics("man/figures/metro_berlin.png")
```
## Disclaimer
Edge bundling is able to produce neat looking network visualizations. However, they do not
necessarily enhance readability. After experimenting with several methods, it became quite
evident that the algorithms are very sensitive to the parameter settings (and often really only work in the showcase examples...). Consult the original literature (if they even provide any guidelines) or experiment yourself and **do not expect any miracles**.