An R package that implements several edge bundling/flow and metro map algorithms. So far it includes
All bundlers are also reachable through a single
edge_bundle(object, xy, type = ).
ggraph
2.2.0 added native force-directed edge bundling via
geom_edge_bundle_*(). edgebundle remains
useful as a ggplot-agnostic toolkit that returns plain data frames and
additionally provides methods ggraph does not: divided/directed
bundling, stub, edge-path, MINGLE, a Python-free hammer, and flow
maps.
The package is available on CRAN.
install.packages("edgebundle")The developer version can be installed with
# install.packages("remotes")
remotes::install_github("schochastics/edgebundle")library(edgebundle)
library(igraph)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().
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)
#> x y index group
#> 1 0.00000000 1.000000 0.00000000 1
#> 2 0.00611816 1.199768 0.03030303 1
#> 3 0.00987237 1.297670 0.06060606 1
#> 4 0.01929293 1.524269 0.09090909 1
#> 5 0.02790686 1.686429 0.12121212 1
#> 6 0.03440142 1.812852 0.15151515 1The result can be visualized as follows.
library(ggplot2)
ggplot(fbundle) +
geom_path(aes(x, y, group = group, col = as.factor(group)),
linewidth = 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)),
linewidth = 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}}.
library(ggforce)
g <- make_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), linewidth = 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.
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", linewidth = 0.1, fill = NA
) +
geom_path(
data = fbundle, aes(x, y, group = group),
col = "#9d0191", linewidth = 0.05
) +
geom_path(
data = fbundle, aes(x, y, group = group),
col = "white", linewidth = 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$name != "", ],
aes(x, y), col = "white", size = 3, alpha = 1
) +
labs(title = "Force Directed Edge Bundling") +
ggraph::theme_graph(background = "black") +
theme(plot.title = element_text(color = "white"))
p2 <- ggplot() +
geom_polygon(
data = states, aes(long, lat, group = group),
col = "white", linewidth = 0.1, fill = NA
) +
geom_path(
data = hbundle, aes(x, y, group = group),
col = "#9d0191", linewidth = 0.05
) +
geom_path(
data = hbundle, aes(x, y, group = group),
col = "white", linewidth = 0.005
) +
geom_point(
data = verts, aes(x, y),
col = "#9d0191", size = 0.25
) +
geom_point(
data = verts, aes(x, y),
col = "white", size = 0.25, alpha = 0.5
) +
geom_point(
data = verts[verts$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", linewidth = 0.1, fill = NA
) +
ggforce::geom_bezier(
data = sbundle, aes(x, y,
group = group,
alpha = alpha_fct(..index.. * 20)
), n = 20,
col = "#9d0191", linewidth = 0.1, show.legend = FALSE
) +
ggforce::geom_bezier(
data = sbundle, aes(x, y,
group = group,
alpha = alpha_fct(..index.. * 20)
), n = 20,
col = "white", linewidth = 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$name != "", ], aes(x, y),
col = "white", size = 3, alpha = 1
) +
labs(title = "Stub Edge Bundling") +
ggraph::theme_graph(background = "black") +
theme(plot.title = element_text(color = "white"))
p4 <- ggplot() +
geom_polygon(
data = states, aes(long, lat, group = group),
col = "white", linewidth = 0.1, fill = NA
) +
geom_path(
data = pbundle, aes(x, y, group = group),
col = "#9d0191", linewidth = 0.05
) +
geom_path(
data = pbundle, aes(x, y, group = group),
col = "white", linewidth = 0.005
) +
geom_point(
data = verts, aes(x, y),
col = "#9d0191", size = 0.25
) +
geom_point(
data = verts, aes(x, y),
col = "white", size = 0.25, alpha = 0.5
) +
geom_point(
data = verts[verts$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
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 recommended layout is flow_tree(), an
angle-restricted spiral tree: it is planar, keeps node positions fixed,
needs no dummy nodes, and is controlled by a single angle parameter.
xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
flow <- flow_tree(cali2010, xy, root = 4, alpha = 40)The package also implements the older TNSS layout based on
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().
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")
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}}.
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, linewidth = 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")
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.
Note: metro_multicriteria() is deprecated and
will be removed in a future release. Use
graphlayouts::layout_as_metromap() instead.
# 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")
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.