In R Overlap spatial polygons dataframe (spdf) and summarise number of features of 1st spdf overlapped by 2nd spdf

Question

I have tried several sources but no luck. Please see my codes below and I state the problem at the end of codes. I have created random hexagonal grids over large areas and wanted to summarize how many of them fall under features of 2nd spatial polygon data frame.

library (sf)
library(dplyr)
library(raster)

# load 2nd spdf

Read ibra polygons as sf object. To download paste 'Interim Biogeographic Regionalisation for Australia (IBRA)' in the search item then click on 'Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)'

ibra <- st_read("ibra7_subregions.shp")
ibra <- st_transform(ibra, crs = 4326)

# ibra has >2000 features (i.e., rows) for 89 regions of same name, group them together
ibraGrid <- ibra %>%
  group_by(REG_NAME_7) %>%
  st_sf() %>%
  mutate(cellid = row_number()) %>%
  summarise()

colnames(ibraGrid)[1] <- "id"

# crop ibra to specific boundary
box <- extent(112,155,-45,-10)
ibraGrid <- st_crop(ibraGrid, box)


# make dataframe of spatial grid (1st spdf)
ran.p <- st_sample(au, size = 1040)

Load shp of au from here then click on "nsaasr9nnd_02211a04es_geo___.zip".

au <- st_read("aust_cd66states.shp")
au <- st_transform(au, crs = 4326)

# create grid around multipoints
rand_sampl_Grid <- ran.p %>%
  st_make_grid(cellsize = 0.1, square = F) %>%
  st_intersection(au) %>%
  st_cast("MULTIPOLYGON") %>%
  st_sf() %>%
  mutate(cellid = row_number())


# sampled grid per ibra region
density_per_ib_grid <- ibraGrid %>%
  st_join(rand_sampl_Grid) %>% 
  mutate(overlap = ifelse(!is.na(id), 1, 0)) %>%
  group_by(cellid) %>%
  summarize(num_sGrid = sum(overlap))

Everything worked well. But, I expected that the length of View(density_per_ib_grid$num_sGrid) would be equal to the number of features in ibraGrid (i.e., 89). Currently, View(density_per_ib_grid$num_sGrid) has length of features equal to rand_sample_Grid (i.e., ~1040). In addition, I want to repeat the process for 100 times so that num_sGrid would be the mean of 100 iterations.

The above codes worked desirably using larger spdf (which is ibraGrid in this casae) created from coordinates. Any suggestions/feedback will be highly appreciated.

Answer 1

I have figured out the solution. The last codes section in the above question should be as:

richness_per_ib_grid <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
  group_by(id) %>%
  count()

out <- as.data.frame(int.result)[,-3] # print output as data frame.

Therefore the complete answer for the question above should be:

library (sf)
library(dplyr)
library(raster)

# load 2nd spdf

Read ibra polygons as sf object. To download paste 'Interim Biogeographic Regionalisation for Australia (IBRA)' in the search item then click on 'Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)'

ibra <- st_read("ibra7_subregions.shp")
ibra <- st_transform(ibra, crs = 4326)

# ibra has >2000 features (i.e., rows) for 89 regions of same name, group them together
ibraGrid <- ibra %>%
  group_by(REG_NAME_7) %>%
  st_sf() %>%
  summarise()

colnames(ibraGrid)[1] <- "id"

# crop ibra to specific boundary
box <- extent(112,155,-45,-10)
ibraGrid <- st_crop(ibraGrid, box)


# make dataframe of spatial grid (1st spdf)
ran.p <- st_sample(au, size = 1040)

Load shp of au from here then click on "nsaasr9nnd_02211a04es_geo___.zip".

au <- st_read("aust_cd66states.shp")
au <- st_transform(au, crs = 4326)

# create grid around multipoints
rand_sampl_Grid <- ran.p %>%
  st_make_grid(cellsize = 0.1, square = F) %>%
  st_intersection(au) %>%
  st_cast("MULTIPOLYGON") %>%
  st_sf()


# sampled grid per ibra region
density_per_ib_grid <- <- st_intersection(ibraGrid, rand_sampl_Grid) %>% 
  group_by(id) %>%
  count()

out <- as.data.frame(int.result)[,-3] # print output as data frame.