我有一个基于运动的跟踪数据集（每秒 10 个数据点），我正在考虑构建基于 voronoi 的图来可视化它。可以通过ggplot和为单个数据点构建它ggforce：
df<- as_tibble(list(Latitude = c(52.2153439, 52.2150796, 52.2154053, 52.2154117,
52.215447, 52.2150971, 52.2150732, 52.2150843,
52.2155136, 52.2151642),
Longitude = c(-2.1630693, -2.1628415, -2.16287, -2.1628658,
-2.1628006, -2.1629321, -2.1630911, -2.1630671,
-2.1625132, -2.1628316),
Name = c("Player One", "Player Two",
"Player Three", "Player Four",
"Player Five", "Player Six",
"Player Seven", "Player Eight",
"Player Nine", "Player Ten")))
但是，当我扩展数据集以包含每个玩家的多个数据点并用于gganimate将其转换为镶嵌时，我遇到了问题。由于它经常涉及重复点，其中玩家在不同时间点覆盖了相同的经纬度，底层的 voronoi 函数（来自deldir？）不喜欢重复点。
我已经从头开始研究构建 voronoi，但由于数据集的分组性质，到目前为止还没有做到。
df2 <- structure(list(Latitude = c(52.2153439, 52.2153458, 52.2153475,
52.2153496, 52.2153517, 52.2153532, 52.2153554, 52.2153578, 52.2153601,
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52.215173, 52.2151726, 52.2151723), Longitude = c(-2.1630693,
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5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L), .Label = c("Player One", "Player Two", "Player Three",
"Player Four", "Player Five", "Player Six", "Player Seven",
"Player Eight", "Player Nine", "Player Ten"), class = "factor")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -510L))
虽然我想采取进一步的步骤来改善最终结果（基于速度/行进方向的权重），但此链接是我希望能够做的一个示例
有人有链接/指南来构建这种性质的 voronois 吗？
更新*
我今天花了更多时间自己整理情节，下面我制作了下面的 gif。使用deldir我为数据集中的每个时间点创建单独的 voronoi。然后通过嵌套的 for 循环将这些提取到一个包含所有 voronoi 数据的数据框中
library(tidy verse)
library(deldir)
library(gganimate)
df_list2 <- split(df_2, df_2$time)
del_list <- lapply(df_list2, function(x){deldir(x$Longitude, x$Latitude, z=x$Name)})
a <- list()
b <- list()
for(i in 1:length(del_list)){
del_df <- del_list[[i]]
tile_list <- tile.list(del_df)
for(z in 1:length(tile_list)){
x <- tile_list[[z]]$x
y <- tile_list[[z]]$y
df1 <- data.frame(x,y)
a[[z]] <- df1
df <- bind_rows(a, .id='id')
}
b[[i]] <- df
plot_df <- bind_rows(b, .id='id2')
}
p <- plot_df %>%
ggplot(aes(x,y, fill=id)) +
geom_polygon(alpha=0.3) +
transition_states(id2) +
ease_aes('linear')
anim_save("test.gif", p, width = 400, height = 400, fps = 10)
它确实在数据质量方面存在一些问题，因此转换不是很平滑，但我可以清理它们
