R_Extracting coordinates from SpatialPolygonsDataFrame

Question

Is it only me who have the problem with extracting coordinates of a polygon from SpatialPolygonsDataFrame object? I am able to extract other slots of the object

Answer 1

This question was also addressed on gis.stackexchange, here. I made an example below testing all the options mentioned here by @mdsumner. Also have a look here

library(sp)
library(sf)
#> Warning: package 'sf' was built under R version 3.5.3
#> Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
library(raster)
library(spbabel)
#> Warning: package 'spbabel' was built under R version 3.5.3
library(tmap)

library(microbenchmark)
library(ggplot2)

# Prepare data
data(World)
# Convert from sf to sp objects
atf_sf <- World[World$iso_a3 == "ATF", ]
atf_sp <- as(atf_sf, "Spatial")
atf_sp
#> class       : SpatialPolygonsDataFrame 
#> features    : 1 
#> extent      : 5490427, 5660887, -6048972, -5932855  (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
#> variables   : 15
#> # A tibble: 1 x 15
#>   iso_a3 name  sovereignt continent area  pop_est pop_est_dens economy
#>   <fct>  <fct> <fct>      <fct>     <S3:>   <dbl>        <dbl> <fct>  
#> 1 ATF    Fr. ~ France     Seven se~ 7257~     140       0.0193 6. Dev~
#> # ... with 7 more variables: income_grp <fct>, gdp_cap_est <dbl>,
#> #   life_exp <dbl>, well_being <dbl>, footprint <dbl>, inequality <dbl>,
#> #   HPI <dbl>

# Try various functions:

raster::geom(atf_sp)
#>       object part cump hole       x        y
#>  [1,]      1    1    1    0 5550200 -5932855
#>  [2,]      1    1    1    0 5589907 -5964836
#>  [3,]      1    1    1    0 5660887 -5977490
#>  [4,]      1    1    1    0 5656160 -5996685
#>  [5,]      1    1    1    0 5615621 -6042456
#>  [6,]      1    1    1    0 5490427 -6048972
#>  [7,]      1    1    1    0 5509148 -5995424
#>  [8,]      1    1    1    0 5536900 -5953683
#>  [9,]      1    1    1    0 5550200 -5932855

ggplot2::fortify(atf_sp)
#> Regions defined for each Polygons
#>      long      lat order  hole piece id group
#> 1 5550200 -5932855     1 FALSE     1  8   8.1
#> 2 5589907 -5964836     2 FALSE     1  8   8.1
#> 3 5660887 -5977490     3 FALSE     1  8   8.1
#> 4 5656160 -5996685     4 FALSE     1  8   8.1
#> 5 5615621 -6042456     5 FALSE     1  8   8.1
#> 6 5490427 -6048972     6 FALSE     1  8   8.1
#> 7 5509148 -5995424     7 FALSE     1  8   8.1
#> 8 5536900 -5953683     8 FALSE     1  8   8.1
#> 9 5550200 -5932855     9 FALSE     1  8   8.1

spbabel::sptable(atf_sp)
#> # A tibble: 9 x 6
#>   object_ branch_ island_ order_       x_        y_
#>     <int>   <int> <lgl>    <int>    <dbl>     <dbl>
#> 1       1       1 TRUE         1 5550200. -5932855.
#> 2       1       1 TRUE         2 5589907. -5964836.
#> 3       1       1 TRUE         3 5660887. -5977490.
#> 4       1       1 TRUE         4 5656160. -5996685.
#> 5       1       1 TRUE         5 5615621. -6042456.
#> 6       1       1 TRUE         6 5490427. -6048972.
#> 7       1       1 TRUE         7 5509148. -5995424.
#> 8       1       1 TRUE         8 5536900. -5953683.
#> 9       1       1 TRUE         9 5550200. -5932855.

as.data.frame(as(as(atf_sp, "SpatialLinesDataFrame"),"SpatialPointsDataFrame"))
#>     iso_a3                   name sovereignt               continent
#> 8      ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.1    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.2    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.3    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.4    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.5    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.6    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.7    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#> 8.8    ATF Fr. S. Antarctic Lands     France Seven seas (open ocean)
#>                area pop_est pop_est_dens              economy
#> 8   7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.1 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.2 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.3 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.4 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.5 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.6 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.7 7257.455 [km^2]     140   0.01929051 6. Developing region
#> 8.8 7257.455 [km^2]     140   0.01929051 6. Developing region
#>                  income_grp gdp_cap_est life_exp well_being footprint
#> 8   2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.1 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.2 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.3 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.4 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.5 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.6 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.7 2. High income: nonOECD    114285.7       NA         NA        NA
#> 8.8 2. High income: nonOECD    114285.7       NA         NA        NA
#>     inequality HPI Lines.NR Lines.ID Line.NR coords.x1 coords.x2
#> 8           NA  NA        1        8       1   5550200  -5932855
#> 8.1         NA  NA        1        8       1   5589907  -5964836
#> 8.2         NA  NA        1        8       1   5660887  -5977490
#> 8.3         NA  NA        1        8       1   5656160  -5996685
#> 8.4         NA  NA        1        8       1   5615621  -6042456
#> 8.5         NA  NA        1        8       1   5490427  -6048972
#> 8.6         NA  NA        1        8       1   5509148  -5995424
#> 8.7         NA  NA        1        8       1   5536900  -5953683
#> 8.8         NA  NA        1        8       1   5550200  -5932855

# What about speed? raster::geom is the fastest
res <- microbenchmark(raster::geom(atf_sp),
                      ggplot2::fortify(atf_sp),
                      spbabel::sptable(atf_sp),
                      as.data.frame(as(as(atf_sp, "SpatialLinesDataFrame"),
                                       "SpatialPointsDataFrame")))
ggplot2::autoplot(res)
#> Coordinate system already present. Adding new coordinate system, which will replace the existing one.

^{Created on 2019-03-23 by the reprex package (v0.2.1)}

Answer 2

ggplot2's fortify() function may be deprecated at some point so the broom package is now suggested

library(broom)
broom::tidy(atf_sp)

Answer 3

This took me a while to figure out too. The following function I wrote worked for me. sp.df should be SpatialPolygonsDataFrame.

extractCoords <- function(sp.df)
{
    results <- list()
    for(i in 1:length(sp.df@polygons[[1]]@Polygons))
    {
        results[[i]] <- sp.df@polygons[[1]]@Polygons[[i]]@coords
    }
    results <- Reduce(rbind, results)
    results
}

Answer 4

Use the coordinates() function from the sp package. It should give you the values in a list format.

You can also get the Polygon attribute from the shapefile.

mfile = readOGR(dsn=dsn,layer=layername)
polys = attr(mfile,'polygons')
npolys = length(polys)
for (i in 1:npolys){
  poly = polys[[i]]
  polys2 = attr(poly,'Polygons')
  npolys2 = length(polys2)
  for (j in 1:npolys2){
     #do stuff with these values
     coords = coordinates(polys2[[j]])
  }
}

Answer 5

Finally, I figured out that I didn't parse the output correctly. The correct way to do is bdryData@polygons[[2]]@Polygons[[1]]@coords. Mind the difference in command polygons(Polygons and polygons) and it took me ages to find out.