Open and merge multiple shapefiles

or more precisely union many spatial tables in R in a tidy way
R
spatial
Author

Michaël

Published

2019-03-27

Modified

2024-02-25

This post is a mess. See the updated version.

→ So we’ll use purrr::map and tidyr::unnest.

NB : now in 2021, I advise to simply use:

 fs::dir_ls("my_folder", regexp = ".*\\.shp$") %>% 
    purrr::map_dfr(sf::read_sf) 

or now in 2023, because map_dfr() is being superseded:

fs::dir_ls("my_folder", regexp = ".*\\.shp$") %>% 
  purrr::map(sf::read_sf) %>% 
  dplyr::bind_rows()
dir_ls("my_folder", regexp = ".*\\.shp$") %>% 
  purrr::map(sf:::read_sf) %>%
  purrr::list_rbind() %>%
  sf::st_sf()

As of October 2019 this method doesn’t work any longer, due to an update in the vctrs package.
https://github.com/r-spatial/sf/issues/1172.
So your best bet is to have the same structure in your shapefile and use:

dir_ls("shp", glob = "*.shp") %>%
  map(read_sf) %>%
  do.call(rbind, .)

It works again with vctrs 0.2.2 (February 2020)


First get some data, the communes of three french départements:

library(tidyverse)
library(sf)
library(fs)
library(httr)
library(leaflet)

# https://fr.actualitix.com/blog/shapefiles-des-departements-de-france.html

url <-  c("https://fr.actualitix.com/blog/actgeoshap/01-Ain.zip",
          "https://fr.actualitix.com/blog/actgeoshap/73-savoie.zip",
          "https://fr.actualitix.com/blog/actgeoshap/74-haute-savoie.zip")

dep <- str_extract(url, "\\d{2}.*$")

list(url, dep) %>% 
  pwalk(~ GET(.x, write_disk(.y)))

walk(dep, unzip, junkpaths = TRUE, exdir = "shp")

We can then create a 3 rows data frame containing a list-column in which we store the sf object. Then we just unnest it. This operation erases the sf-class, we have to add it back.

res <- dir_ls("shp", glob = "*.shp") %>% 
  tibble(fname = .) %>%
  mutate(data = map(fname, read_sf)) %>%
  unnest(data) %>%
  st_sf() %>%
  st_set_crs("EPSG:2154")

write_sf(res, "shp/3dep.shp")

res %>% 
  st_transform(4326) %>% 
  leaflet() %>%
    addPolygons() %>% 
    addTiles()

Bonus: we have the source filename stored in the resulting shapefile.

Footnotes

  1. We could have used:

    dir_ls("shp", glob = "*.shp") %>%
      map(read_sf) %\>%
      do.call(rbind, .)

    but the column structure doesn’t match here…↩︎