Get multiple polygons for scattered data in R - r

Question

I have point cloud data of an area (x,y,z coordinates)
The plot of X and Y looks like:
I am trying to get polygons of different clusters in this data. I tried the following:
points <- df [,1:2] # x and y coordinates
pts <- st_as_sf(points, coords=c('X','Y'))
conc <- concaveman(pts, concavity = 0.5, length_threshold = 0)
Seems like I just get a single polygon binding the whole data. conc$polygons is a list of one variable.
How can I define multiple polygons? What am I missing when I am using concaveman and what all it can provide?

Answer 1

It's hard to tell from your example what variable defines your clusters. Below is an example with some simulated clusters using ggplot2 and data.table (adapted from here).
library(data.table)
library(ggplot2)
# Simulate data:
set.seed(1)
n_cluster = 50
centroids = cbind.data.frame(
x=rnorm(5, mean = 0, sd=5),
y=rnorm(5, mean = 0, sd=5)
)
dt = rbindlist(
lapply(
1:nrow(centroids),
function(i) {
cluster_dt = data.table(
x = rnorm(n_cluster, mean = centroids$x[i]),
y = rnorm(n_cluster, mean = centroids$y[i]),
cluster = i
)
}
)
)
dt[,cluster:=as.factor(cluster)]
# Find convex hull of each point by cluster:
hulls = dt[,.SD[chull(x,y)],by=.(cluster)]
# Plot:
p = ggplot(data = dt, aes(x=x, y=y, colour=cluster)) +
geom_point() +
geom_polygon(data = hulls,aes(fill=cluster,alpha = 0.5)) +
guides(alpha=F)
This produces the following output:
Edit
If you don't have predefined clusters, you can use a clustering algorithm. As a simple example, see below for a solution using kmeans with 5 centroids.
# Estimate clusters (e.g. kmeans):
dt[,km_cluster := as.factor(kmeans(.SD,5)$cluster),.SDcols=c("x","y")]
# Find convex hull of each point:
hulls = dt[,.SD[chull(x,y)],by=.(km_cluster)]
# Plot:
p = ggplot(data = dt, aes(x=x, y=y, colour=km_cluster)) +
geom_point() +
geom_polygon(data = hulls,aes(fill=km_cluster,alpha = 0.5)) +
guides(alpha=F)
In this case the output for the estimated clusters is almost equivalent to the constructed ones.