How to retrieve the interior boundary (points) of a polygon sf object

Dear community,

I am trapped by a seemingly easy task. I would like to write a short R
script to retrieve the interior boundary (points) of a polygon sf object,
like the state boundary of the Contiguous United States, but without the
outline.

I tried several methods, with none fulfilling my objective. Below is the
code, with my observations in the comments:

# Objective: Retrieve the internal boundary of a polygon data set

library(tidyverse)
library(sf)
library(spData)

polygon=st_geometry(us_states)
plot(polygon)

# Method 1: Use st_difference()
st_union(polygon) -> border
plot(border)
st_difference(polygon, border) -> interior
plot(interior) # You can tell that the result is weird.

# Method 2: Retrieve vertices as points, then use st_difference()
st_cast(polygon, 'POINT') -> points_polygon
points_polygon # 3585 features
st_cast(border, 'POINT') -> points_border
points_border # 1275 features
st_difference(points_polygon, points_border) -> points_interior # About 2
minutes to execute
points_interior # 4.57 million features, about 2 GB. why?

# Methods 3: Use st_disjoint instead of st_difference()
polygon[border, op=st_disjoint] # Empty set
points_polygon[border, op=st_disjoint] -> points_interior2
points_interior2 # 132 features; plot() it and doesn't look right
points_polygon[points_border, op=st_disjoint] -> points_interior3
points_interior3 # 3585 features, meaning no points have been taken out

# Methods 4: Retrieve vertices as coordinates, converting them into
tibbles, then anti-join the border coordinates
st_coordinates(polygon) %>% as_tibble -> coords_polygon
coords_polygon# A tibble: 3,585 ? 5
st_coordinates(border) %>% as_tibble -> coords_border
coords_border # A tibble: 1,275 ? 5
anti_join(coords_polygon, coords_border) -> coords_interior
coords_interior # A tibble: 3,585 ? 5, meaning no points have been taken
out, and implying no coordinates in coords_border coincides with the
coordinates in coords_polygon. This is just not reasonable.

Any comments on why I fail and/or how to do it correctly will be helpful.
Thank you in advance!

?? YE, Xiang
THINKING SPATIALLY<http://www.linkedin.com/in/spatialyexiang>.
Ph.D. in Spatial Statistics


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