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- dplyr::bind_rows doesn’t work on sf objects ;
- base::rbind only work on two tables and so that’s not straightforward to use*.
So we’ll use purrr::map and tidyr::unnest.
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_as_sf() %>% st_set_crs(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.
* We could have used
dir_ls("shp", glob = "*.shp") %>% map(read_sf) %>% do.call(rbind, .)
but the column structure doesn’t match here…
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