R_Extracting coordinates from SpatialPolygonsDataFrame
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
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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 #>#> 1 ATF Fr. ~ France Seven se~ 7257~ 140 0.0193 6. Dev~ #> # ... with 7 more variables: income_grp , gdp_cap_est , #> # life_exp , well_being , footprint , inequality , #> # HPI # 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_ #> #> 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)
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