example.R

###  Usage example


library(sf)
library(dplyr)


## read the shapefile (mine from: http://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/files-fichiers/2016/lda_000b16a_e.zip)
DA <- st_read("C:/Users/DXD9163/Desktop/DA_regrouping/DA_CAN_2016_reduced.shp", stringsAsFactors = F, options = "ENCODING=windows-1252") %>% 
  filter(!is.na(DAUID))

## select only a part of it
DA_qc <- DA[grepl("Quebec",DA$PRNAME),]

## change from singlepart to multipart (polygon to multipolygon)
DA_qc <- DA_qc %>% 
  mutate(area = st_area(DA_qc)) %>% 
  group_by(DAUID) %>% 
  summarise(area=sum(area))

## create a neighbor list
neighb <- st_touches(DA_qc, sparse = T) %>% 
  setNames(1:nrow(.)) 

centroid <- st_centroid(DA_qc)

## make the first grouping with the required target size (it will be a minimum but for some exceptions see below))
gr1 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, centro_pol=centroid)

## evaluate the size of the groups
sapply(gr1, length)
## multiple groups are very small, these can be island or leftover polygons, we need to regroup them to larger group

## Fuse the tiny groups to bigger groups, you can specify a new minimum smaller than the first one, 
##  higher this minimum is, bigger can be your final groups (potentially ~ gr_size + hard_min_size)
final_group <- fuse_tiny_group(initial_gr = gr1, init_sh = DA_qc, hard_min_size = 400)

## the new sizes
sapply(final_group, length)


## Assign the proper group to every polygon and save the shape 
bind_cols(DA_qc,
          do.call("rbind", mapply(function(gr, index) cbind(index, gr),1:length(final_group), final_group)) %>% 
            as.data.frame() %>% 
            arrange(index)
) %>% 
  select(gr) %>% 
  mutate(gr = as.character(gr)) %>% 
  st_write("C:/Users/DXD9163/Desktop/DA_regrouping/sh_separe/result2.shp")




#### test seed:

## should work

# default
gr1 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000)

# seed=1
gr2 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed=1)
identical(gr1,gr2)   # TRUE

# seed=1000
gr3 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed=1000)
identical(gr1,gr3)  # FALSE

# seed="random"
gr4 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed="random")
gr5 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed="random")
identical(gr4,gr5)  # FALSE

set.seed(123)
gr6 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed="random")
set.seed(123)
gr7 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed="random")
identical(gr6,gr7)  # TRUE

## should't work
gr8 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed=0.1)
gr9 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed="patate")
gr10 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed=1:4)
gr10 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed=NULL)
gr11 <- cluster_neighbours(neighb_list = neighb, gr_size = 1000, seed=NA)