Intro Thoughts
Status Quo
library(tidyverse)
Experiment
library(tidyverse)
tibble(x = runif(50),
y = runif(50)) |>
ggplot() +
aes(x = x, y = y) +
ggforce::geom_voronoi_tile() +
geom_point()
layer_data(i = 1) |> head()
## group x y theta orig_x orig_y PANEL colour fill
## 1 1:-1 0.112614 0.786635 1.018779 0.05260938 0.6892061 1 NA #333333FF
## 2 1:-1 0.053710 0.796997 1.560582 0.05260938 0.6892061 1 NA #333333FF
## 3 1:-1 0.044423 0.632866 4.568102 0.05260938 0.6892061 1 NA #333333FF
## 4 1:-1 0.085623 0.641826 5.320959 0.05260938 0.6892061 1 NA #333333FF
## 5 1:-1 0.133893 0.674931 6.109339 0.05260938 0.6892061 1 NA #333333FF
## 6 2:-1 0.773546 1.087819 2.095932 0.84756837 0.9600634 1 NA #333333FF
## linewidth linetype alpha
## 1 0.5 1 NA
## 2 0.5 1 NA
## 3 0.5 1 NA
## 4 0.5 1 NA
## 5 0.5 1 NA
## 6 0.5 1 NA
five-ish from TVDB
library(igraph)
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:lubridate':
##
## %--%, union
## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union
## The following objects are masked from 'package:purrr':
##
## compose, simplify
## The following object is masked from 'package:tidyr':
##
## crossing
## The following object is masked from 'package:tibble':
##
## as_data_frame
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
# Init data
df <- tibble(x = runif(50), y = runif(50))
# Make a graph of delaunay triangulation of the points
delaunay <- deldir::deldir(df$x, df$y)$delsgs
delaunay <- cbind(delaunay$ind1, delaunay$ind2)
g <- igraph::graph_from_edgelist(delaunay, directed = FALSE)
# For every vertex, search an unused colour
for (v in seq_along(V(g))) {
v <- V(g)[v] # Index to current vertex
nn <- ego(g, nodes = v)[[1]] # Current vertex to neighbours
# Determine which colours are already in use
nn_colours <- vertex_attr(g, "colour", index = nn) %||% 0
nn_colours <- unique(nn_colours[!is.na(nn_colours)])
if (length(nn_colours) == 0) {
nn_colours <- 0
}
mx <- max(nn_colours)
if (mx == 0) { # When no colours have been assigned to neighbours
i <- 1L
} else {
# Try to find lowest colour that isn't in use by neighbours
i <- setdiff(seq_len(mx), nn_colours)
if (length(i) > 0) {
i <- i[1]
} else {
i <- mx + 1 # If not found, increase local maximum
}
}
g <- set_vertex_attr(g, "colour", index = v, value = i)
}
df$colour <- vertex_attr(g, "colour")
ggplot(df) +
aes(x = x, y = y) +
ggforce::geom_voronoi_tile(aes(fill = factor(colour), group = 1)) +
geom_point()
ggplot(df) +
aes(x = x, y = y, color = factor(colour)) +
geom_point() +
scale_color_viridis_d()
