Separating/exploding spatial features with R

R
rspatial
Geospatial
Author

Philippe Massicotte

Published

August 15, 2023

It has been a while since I posted something! But today I wanted to quickly experiment and create a map where I could separate/explode the spatial geometries. Let’s first download the polygon data for Canada.

library(sf)
library(tidyverse)

canada <- rnaturalearth::ne_states(country = "canada", returnclass = "sf") |>
  st_make_valid()

As we can see, there are three different regions.

canada |>
  distinct(region)
#>            region
#> 1  Western Canada
#> 2  Eastern Canada
#> 3 Northern Canada

These regions can be visualized using ggplot2 and sf.

# Get a good CRS for Canada
crsuggest::suggest_crs(canada)
#> # A tibble: 10 × 6
#>    crs_code crs_name                        crs_type crs_gcs crs_units crs_proj4
#>    <chr>    <chr>                           <chr>      <dbl> <chr>     <chr>    
#>  1 5931     WGS 84 / EPSG Arctic Regional … project…    4326 m         +proj=lc…
#>  2 3979     NAD83(CSRS) / Canada Atlas Lam… project…    4617 m         +proj=lc…
#>  3 3978     NAD83 / Canada Atlas Lambert    project…    4269 m         +proj=lc…
#>  4 3348     NAD83(CSRS) / Statistics Canad… project…    4617 m         +proj=lc…
#>  5 3347     NAD83 / Statistics Canada Lamb… project…    4269 m         +proj=lc…
#>  6 5926     WGS 84 / EPSG Arctic Regional … project…    4326 m         +proj=lc…
#>  7 5921     WGS 84 / EPSG Arctic Regional … project…    4326 m         +proj=lc…
#>  8 6350     NAD83(2011) / Conus Albers      project…    6318 m         +proj=ae…
#>  9 5072     NAD83(NSRS2007) / Conus Albers  project…    4759 m         +proj=ae…
#> 10 5071     NAD83(HARN) / Conus Albers      project…    4152 m         +proj=ae…

canada <- canada |>
  st_transform(5931)

canada |>
  ggplot(aes(fill = region)) +
  geom_sf()

With this following code, we can find the centroid of each region and shift the centers of the new polygons.

canada_exploded <- canada |>
  group_by(region) |>
  mutate(region_centroid = st_centroid(st_union(geometry))) |>
  ungroup() |>
  mutate(geometry = (geometry - region_centroid) * 0.9 + region_centroid)

st_crs(canada_exploded)
#> Coordinate Reference System: NA

As seen above, for a reason I do not know, we have lost the CRS after the operation, so I have to set it back before visualizing it.

canada_exploded <- canada_exploded |>
  st_set_crs(5931)

canada_exploded |>
  ggplot(aes(fill = region)) +
  geom_sf()

Bonus: if we want to get a little bit more fancy, we can add shadows with ggfx:

library(ggfx)

canada_exploded |>
  ggplot(aes(fill = region)) +
  with_shadow(
    geom_sf(),
    sigma = 5,
    x_offset = 20,
    y_offset = 20
  ) +
  theme(
    legend.position = "none"
  )

Session info 
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.1 (2023-06-16)
#>  os       Ubuntu 23.04
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#>  ui       X11
#>  language en_CA:en
#>  collate  en_CA.UTF-8
#>  ctype    en_CA.UTF-8
#>  tz       America/Toronto
#>  date     2023-08-16
#>  pandoc   2.17.1.1 @ /usr/bin/ (via rmarkdown)
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Citation

BibTeX citation:
@online{massicotte2023,
  author = {Massicotte, Philippe},
  title = {Separating/Exploding Spatial Features with {R}},
  date = {2023-08-15},
  url = {https://www.pmassicotte.com/posts/2023-08-15-map-explode},
  langid = {en}
}
For attribution, please cite this work as:
Massicotte, Philippe. 2023. “Separating/Exploding Spatial Features with R.” August 15, 2023. https://www.pmassicotte.com/posts/2023-08-15-map-explode.