How to generate a map for property cluster

Question

Could you help me make a graph in R similar to the one I inserted in the image below, which shows the properties on a map, differentiating by cluster. See in my database that I have 4 properties, properties 1 and 3 are of cluster 1 and properties 2 and 4 are of cluster 2. In addition, the database has the coordinates of the properties, so I believe that with this information I can generate a graph similar to what I inserted. Surely, there must be some package in R that does something similar. Any help is welcome!

This link can help: https://rstudio-pubs-static.s3.amazonaws.com/176768_ec7fb4801e3a4772886d61e65885fbdd.html

#database

df<-structure(list(Properties = c(1,2,3,4), 
                   Latitude = c(-24.930473, -24.95575,-24.924161,-24.95579), 
                   Longitude = c(-49.994889, -49.990162,-50.004343, -50.007371), 
                   cluster = c(1,2,1,2)), class = "data.frame", row.names = c(NA, -4L))

  Properties  Latitude Longitude cluster
1          1 -24.93047 -49.99489       1
2          2 -24.95575 -49.99016       2
3          3 -24.92416 -50.00434       1
4          4 -24.95579 -50.00737       2

Example of figure:

Your code

#database

df<-structure(list(Propertie = c(1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
30, 31, 32, 33, 34, 35, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 
49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 
83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 
99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 
112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 
125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 
138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 
151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 
164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 
177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 
190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 
203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 
216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 
229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 
242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 
255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 
268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 
281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 
294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 
307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 
320, 321, 322, 323, 324, 325), Latitude = c(-24.927417, -24.927417, 
-24.927417, -24.927417, -24.930195, -24.930473, -24.946306, -24.949361, 
-24.949361, -24.950195, -24.950195, -24.951584, -24.95575, -24.954084, 
-24.96075, -24.957139, -24.95825, -24.96825, -24.961334, -24.968806, 
-24.976861, -24.982139, -24.986584, -24.985487, -24.994362, -24.994362, 
-24.999084, -24.771583, -24.77186, -24.772138, -24.772138, -24.78686, 
-24.78436, -24.872139, -24.822222, -24.83549, -24.874916, -24.874916, 
-24.874639, -24.865472, -24.873838, -24.87325, -24.858611, -24.874361, 
-24.874361, -24.86, -24.860472, -24.874916, -24.814638, -24.814666, 
-24.818527, -24.818527, -24.822694, -24.822694, -24.845472, -24.844638, 
-24.878528, -24.879639, -24.879639, -24.906028, -24.897972, -24.900278, 
-24.900278, -24.90075, -24.902972, -24.899361, -24.898611, -24.899083, 
-24.913889, -24.908333, -24.914361, -24.914361, -24.924361, -24.915472, 
-24.91075, -24.913805, -24.913528, -24.912139, -24.919917, -24.914083, 
-24.914361, -24.914361, -24.925194, -24.92575, -24.928528, -24.929361, 
-24.934361, -24.935278, -24.922694, -24.927139, -24.927972, -24.931861, 
-24.936861, -24.878537, -24.887972, -24.882972, -24.901583, -24.901667, 
-24.902139, -24.902139, -24.90325, -24.902972, -24.90299, -24.90575, 
-24.905791, -24.899639, -24.899083, -24.875472, -24.878805, -24.883805, 
-24.884916, -24.8905, -24.884083, -24.884087, -24.905194, -24.904125, 
-24.894722, -24.895222, -24.895194, -24.911028, -24.907972, -24.908805, 
-24.919916, -24.919361, -24.919639, -24.919639, -24.920194, -24.920472, 
-24.917972, -24.908805, -24.911305, -24.91325, -24.917416, -24.928528, 
-24.929083, -24.92325, -24.923805, -24.93188, -24.932139, -24.936028, 
-24.935472, -24.937139, -24.923805, -24.922139, -24.922139, -24.926861, 
-24.908805, -24.908333, -24.908805, -24.913805, -24.913805, -24.929638, 
-24.939917, -24.943806, -24.942695, -24.94325, -24.944639, -24.946028, 
-24.94825, -24.954084, -24.956111, -24.958611, -24.958806, -24.959084, 
-24.958528, -24.958528, -24.956584, -24.955833, -24.95825, -24.960833, 
-24.967417, -24.962695, -24.958611, -24.959083, -24.96075, -24.96075, 
-24.964361, -24.961306, -24.961028, -24.962417, -24.965833, -24.964639, 
-24.963806, -24.964917, -24.965472, -24.966861, -24.968528, -24.942972, 
-24.948611, -24.950556, -24.951028, -24.951028, -24.93825, -24.941889, 
-24.943528, -24.944639, -24.945194, -24.945472, -24.949083, -24.946861, 
-24.94825, -24.949361, -24.951306, -24.948805, -24.948, -24.95075, 
-24.952694, -24.959722, -24.961583, -24.96325, -24.96325, -24.96325, 
-24.964639, -24.96575, -24.959361, -24.954639, -24.960472, -24.960472, 
-24.966583, -24.970195, -24.972417, -24.976306, -24.974084, -24.974167, 
-24.974639, -24.979362, -24.979639, -24.980278, -24.982973, -24.982973, 
-24.977417, -24.979639, -24.981028, -24.981028, -24.98325, -24.969361, 
-24.988056, -24.987139, -24.987139, -24.986584, -24.984639, -24.984639, 
-24.984917, -24.984917, -24.994917, -24.987139, -24.989917, -24.992139, 
-24.991861, -24.991861, -24.989639, -24.989917, -24.989917, -24.991861, 
-24.989639, -24.992417, -24.975195, -24.97325, -24.979361, -24.972694, 
-24.972972, -24.942417, -24.941861, -24.93825, -24.938273, -24.949639, 
-24.948333, -24.948805, -24.949639, -24.949639, -24.951615, -24.951583, 
-24.951615, -24.953611, -24.954639, -24.954639, -24.954639, -24.956861, 
-24.956861, -24.966028, -24.956861, -24.955556, -24.957176, -24.96075, 
-24.960194, -24.960231, -24.980194, -24.969106, -24.986306, -24.986306, 
-24.993806, -24.877972, -24.878889, -24.87686, -24.886305, -24.875749, 
-24.876305, -24.876319, -24.878805, -24.891027, -24.898527, -24.898527, 
-24.904083, -24.904083, -24.905, -24.901328, -24.902138, -24.898268, 
-24.900782, -24.901305, -24.88493, -24.887138, -24.929638, -25.001862, 
-25.004084, -25.011028, -25.000194, -25.000472), Longitude = c(-49.98793, 
-49.98793, -49.98793, -49.988778, -49.98962, -49.994889, -49.999912, 
-49.991273, -49.991273, -49.996551, -49.996551, -49.995704, -49.990162, 
-49.992945, -49.990718, -49.999056, -49.998222, -49.981259, -49.997389, 
-49.979357, -49.999908, -49.995713, -49.980449, -49.995736, -49.980444, 
-49.980444, -49.986852, -50.200149, -50.200172, -50.199602, -50.199603, 
-50.199339, -50.209899, -50.038787, -50.243338, -50.235446, -50.139343, 
-50.139348, -50.154871, -50.164607, -50.179621, -50.179895, -50.226412, 
-50.196297, -50.196297, -50.233639, -50.234066, -50.242649, -50.251816, 
-50.252098, -50.258233, -50.258233, -50.288502, -50.288525, -50.251001, 
-50.261575, -50.039037, -50.044333, -50.044333, -50.015148, -50.115163, 
-50.094472, -50.094472, -50.094899, -50.108204, -50.111829, -50.113653, 
-50.114079, -50.010278, -50.017523, -50.010704, -50.010704, -50.004343, 
-50.087667, -50.106547, -50.103487, -50.116283, -50.117968, -50.101301, 
-50.119913, -50.120191, -50.120191, -50.079593, -50.080167, -50.082112, 
-50.093519, -50.070172, -50.074194, -50.095459, -50.117959, -50.121024, 
-50.094079, -50.102677, -50.129635, -50.140468, -50.143492, -50.166288, 
-50.166426, -50.166816, -50.166844, -50.166024, -50.169635, -50.169635, 
-50.165154, -50.165154, -50.175427, -50.182686, -50.188496, -50.203515, 
-50.208765, -50.208487, -50.220728, -50.24933, -50.24933, -50.190159, 
-50.204603, -50.241421, -50.241576, -50.241849, -50.135746, -50.144894, 
-50.142117, -50.14408, -50.146839, -50.148223, -50.148223, -50.143802, 
-50.144066, -50.151269, -50.163802, -50.159357, -50.160168, -50.159066, 
-50.138232, -50.137107, -50.151288, -50.151001, -50.137376, -50.139061, 
-50.132691, -50.132968, -50.152399, -50.170709, -50.176566, -50.176566, 
-50.173237, -50.195182, -50.196949, -50.197376, -50.209608, -50.209608, 
-50.239872, -50.007371, -50.006579, -50.007931, -50.008523, -50.01044, 
-50.013787, -50.014607, -50.014037, -50.013056, -50.004181, -50.006569, 
-50.004607, -50.008482, -50.008482, -50.026278, -50.030861, -50.018523, 
-50.019444, -50.014903, -50.020181, -50.045875, -50.046301, -50.057121, 
-50.057121, -50.036278, -50.040176, -50.043227, -50.044894, -50.036125, 
-50.050158, -50.055186, -50.04876, -50.053213, -50.062385, -50.061561, 
-50.085727, -50.093361, -50.083352, -50.083227, -50.083228, -50.10488, 
-50.10351, -50.108783, -50.121816, -50.121279, -50.098487, -50.093788, 
-50.104315, -50.10238, -50.107121, -50.108482, -50.111024, -50.124043, 
-50.115723, -50.124343, -50.083375, -50.074315, -50.073515, -50.073514, 
-50.073769, -50.070459, -50.072959, -50.106561, -50.116857, -50.113797, 
-50.113797, -50.103802, -50.007107, -50.001815, -50.005185, -50.022371, 
-50.021685, -50.022111, -50.004597, -50.006269, -50.007778, -50.001843, 
-50.001843, -50.01906, -50.020185, -50.020185, -50.020426, -50.021843, 
-50.06044, -50.00362, -50.00519, -50.00519, -50.007102, -50.024079, 
-50.024079, -50.023778, -50.023778, -50.010732, -50.037686, -50.032936, 
-50.03657, -50.038204, -50.038223, -50.041283, -50.042375, -50.044885, 
-50.043227, -50.05851, -50.03988, -50.062653, -50.087385, -50.077112, 
-50.110996, -50.119061, -50.126279, -50.132691, -50.149052, -50.149052, 
-50.137371, -50.141431, -50.141858, -50.170992, -50.170992, -50.176288, 
-50.176844, -50.176844, -50.14225, -50.142404, -50.142404, -50.142408, 
-50.155432, -50.155432, -50.14852, -50.159344, -50.160579, -50.157409, 
-50.158209, -50.170436, -50.170436, -50.132121, -50.165154, -50.144052, 
-50.144052, -50.13408, -50.263247, -50.264755, -50.26821, -50.257386, 
-50.28265, -50.2924, -50.2924, -50.303516, -50.264891, -50.251543, 
-50.251543, -50.261302, -50.261539, -50.264755, -50.270455, -50.270747, 
-50.294067, -50.290159, -50.290432, -50.315715, -50.320456, -50.251849, 
-49.989338, -49.986551, -49.976296, -50.127404, -50.127654), 
    cluster = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 
    4, 4, 5, 5, 5, 5, 5, 5, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 
    4, 4, 4, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 
    1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 4, 4, 5, 5, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5, 2, 5, 5, 5, 5, 5, 5, 
    5, 5, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 2, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 5, 5)), row.names = c(NA, 
-318L), class = c("tbl_df", "tbl", "data.frame"))


w1<-convexhull.xy(df$Longitude[df$cluster==1], df$Latitude[df$cluster==1])
w2<-convexhull.xy(df$Longitude[df$cluster==2], df$Latitude[df$cluster==2])
w3<-convexhull.xy(df$Longitude[df$cluster==3], df$Latitude[df$cluster==3])
w4<-convexhull.xy(df$Longitude[df$cluster==4], df$Latitude[df$cluster==4])
w5<-convexhull.xy(df$Longitude[df$cluster==5], df$Latitude[df$cluster==5])

p1<-st_as_sf(w1, crs=4269)
p2<-st_as_sf(w2, crs=4269)
p3<-st_as_sf(w3, crs=4269)
p4<-st_as_sf(w4, crs=4269)
p5<-st_as_sf(w5, crs=4269)

poly<-rbind(p1,p2,p3,p4,p5)
poly[,"cluster"]<-c(1,2,3,4,5)

pts<-st_as_sf(df, coords=c("Longitude", "Latitude"), crs=4269)

tmap_mode("plot")
tm_shape(poly)+
  tm_polygons(col="cluster", palette=c("darkolivegreen","skyblue","skyblue","yellow","pink"), style="cat", title="cluster")+
  tm_shape(pts)+
  tm_dots(size=2)+
  tm_layout(legend.outside = TRUE)

Answer 1

@Antonio, I think this might be the solution you are after, but it requires at least three points per cluster to work, which from your figure I am assuming you have in your full dataset. The trick is to create convex hulls and convert them into polygons. This can be accomplished using the convexhull.xy() function in the spatstat:: package. Then these can be converted into simple features in the sf:: package, and then drawn with your mapping package of choice. I personally am a fan of the tmap:: package. Here is a reproducible example. Note, I had to add two more points to your example data to make this work (you cannot compute a polygon from only two points).

##Loading Necessary Packages##
library(spatstat)#For convexhull.xy() function
library(tmap)# For drawing the map
library(sf) #To create simple features for mapping

##Loading Example Data##
df<-structure(list(Properties = c(1,2,3,4,5,6), 
                   Latitude = c(-24.930473, -24.95575,-24.924161,-24.95579, -24.94557, -24.93267), 
                   Longitude = c(-49.994889, -49.990162,-50.004343, -50.007371, -50.01542, -50.00702), 
                   cluster = c(1,2,1,2,1,2)), class = "data.frame", row.names = c(NA, -6L))

##Calculating convexhulls for each cluster##
w1<-convexhull.xy(df$Longitude[df$cluster==1], df$Latitude[df$cluster==1])
w2<-convexhull.xy(df$Longitude[df$cluster==2], df$Latitude[df$cluster==2])

##Converting hulls to simple features.  Note, I assumed that you are using the EPSG 4269 projection (WGS84)
p1<-st_as_sf(w1, crs=4269)
p2<-st_as_sf(w2, crs=4269)

#Combining the two simple features together
poly<-rbind(p1,p2)

#Labelling the clusters
poly[,"cluster"]<-c(1,2)

#Creating a point simple feature from your property data in the dataframe
pts<-st_as_sf(df, coords=c("Longitude", "Latitude"), crs=4269)

#Setting the mapping mode to plot.  Change this to "view" if you want an interactive map
tmap_mode("plot")

#Drawing the map
tm_shape(poly)+
  tm_polygons(col="cluster", palette=c("darkolivegreen", "skyblue"), style="cat", title="cluster")+
tm_shape(pts)+
  tm_dots(size=2)+
tm_layout(legend.outside = TRUE)

Answer 2

One approach would be use a voronoi partition. ggvoronoi will do this for you with ggplot2, and you could easily overlay it on a ggmap map.

There is also a st_voronoi function in the sf package which will create a voronoi partition shapefile from a MULTIPOINT shape (see update below).

Here is a simple example using your data. I have removed duplicated points (i.e. the same point in different clusters) which break the voronoi algorithm!

library(tidyverse) #specifically ggplot2 and dplyr for the pipe
library(ggvoronoi)

df %>% distinct(Longitude, Latitude, .keep_all = TRUE) %>% 
  ggplot(aes(x = Longitude, y = Latitude, fill = factor(cluster), label = cluster)) +
  geom_voronoi() +
  geom_text() 

Update:

To do this with sf_voronoi you can do the following (unlike ggvoronoi, sf_voronoi works without having to weed out the duplicates)...

pts_vor <- pts %>% st_union() %>%    #merge points into a MULTIPOINT
       st_voronoi() %>%              #calculate voronoi polygons
       st_cast() %>%                 #next three lines return it to a useable list of polygons
       data.frame(geometry = .) %>%  
       st_sf(.) %>%
       st_join(., pts)               #merge with clusters and other variables
  
pts_vor %>% ggplot(aes(fill = factor(cluster))) + 
       geom_sf(colour = NA) +        #draw voronoi tiles (no borders)
       geom_sf_text(data = pts, aes(label = cluster)) + #plot points
       coord_sf(xlim = c(-50.35, -49.95), ylim = c(-25.05, -24.75))