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I have two spatial objects, one is points nc_point (n=50) and the other is polygons sample_polygons (n=20). Some of the points are fallen in polygons, which can be identified using the nc_point[sample_polygons , ] function.

My question is how to obtain a subset of all the points that are NOT fallen in any polygons, i.e. the rest of the points? 

# points layer contain 50 points
nc_point <- st_read(system.file("shape/nc.shp", package="sf")) %>% 
  st_centroid()

# the number of polygons is 20
set.seed(1)
sample_polygons <- st_read(system.file("shape/nc.shp", package="sf")) %>% 
  sample_n(20) %>% 
  select(geometry) # to mimic a situation where points are only identified using spatial correlation. 



# points that fall in polygons can be identified using: 
points_in <- nc_point[sample_polygons , ]

# how to find out points that are not fallen in any polygons?

Thanks, Phil

Phil Wu
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    It's hard to give specifics without a [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example), but the documentation for `sf::st_join` shows you that the default function used for joining `sf` objects is `st_intersects`, but you can supply other functions to `join`. The docs give a list of suggestions for other functions you might use – camille Jan 08 '19 at 17:36
  • thanks @camille, I've included a reproducible example. – Phil Wu Jan 08 '19 at 18:34

2 Answers2

3

You could return an output with TRUE/FALSE to select your points. For example, look for length-0 elements in the list:

outside <- sapply(st_intersects(nc_point, sample_polygons),function(x){length(x)==0})

That gives you a logical vector you can subset with:

points_out <- nc_point[outside, ]
points_out

Simple feature collection with 80 features and 14 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -84.05976 ymin: 34.07663 xmax: -75.80982 ymax: 36.49101
epsg (SRID):    4267
proj4string:    +proj=longlat +datum=NAD27 +no_defs
First 10 features:
    AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79                   geometry
1  0.114     1.442  1825    1825        Ashe 37009  37009        5  1091     1      10  1364     0      19  POINT (-81.49826 36.4314)
2  0.061     1.231  1827    1827   Alleghany 37005  37005        3   487     0      10   542     3      12 POINT (-81.12515 36.49101)
3  0.143     1.630  1828    1828       Surry 37171  37171       86  3188     5     208  3616     6     260 POINT (-80.68575 36.41252)
4  0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1     123   830     2     145  POINT (-76.0275 36.40728)
5  0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9    1066  1606     3    1197 POINT (-77.41056 36.42228)
7  0.062     1.547  1834    1834      Camden 37029  37029       15   286     0     115   350     2     139  POINT (-76.23435 36.4012)
8  0.091     1.284  1835    1835       Gates 37073  37073       37   420     0     254   594     2     371 POINT (-76.70448 36.44423)
9  0.118     1.421  1836    1836      Warren 37185  37185       93   968     4     748  1190     2     844 POINT (-78.11043 36.39697)
10 0.124     1.428  1837    1837      Stokes 37169  37169       85  1612     1     160  2038     5     176 POINT (-80.23428 36.40034)
11 0.114     1.352  1838    1838     Caswell 37033  37033       17  1035     2     550  1253     2     597 POINT (-79.33477 36.39347)
Carlos Xavier Bonilla
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2

Similar to Carlos answer but a tad simpler, leveraging function lengths to get the length of each element of the intersects list: 

outside <- nc_point[!lengths(st_intersects(nc_point, sample_polygons)), ]
# same as `outside <- nc_point[lengths(st_intersects(nc_point, sample_polygons)) == 0, ]`
outside

Simple feature collection with 80 features and 14 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -84.05976 ymin: 34.07663 xmax: -75.80982 ymax: 36.49101
epsg (SRID):    4267
proj4string:    +proj=longlat +datum=NAD27 +no_defs
First 10 features:
    AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79
1  0.114     1.442  1825    1825        Ashe 37009  37009        5  1091     1      10  1364     0      19
2  0.061     1.231  1827    1827   Alleghany 37005  37005        3   487     0      10   542     3      12
3  0.143     1.630  1828    1828       Surry 37171  37171       86  3188     5     208  3616     6     260
4  0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1     123   830     2     145
5  0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9    1066  1606     3    1197
7  0.062     1.547  1834    1834      Camden 37029  37029       15   286     0     115   350     2     139
8  0.091     1.284  1835    1835       Gates 37073  37073       37   420     0     254   594     2     371
9  0.118     1.421  1836    1836      Warren 37185  37185       93   968     4     748  1190     2     844
10 0.124     1.428  1837    1837      Stokes 37169  37169       85  1612     1     160  2038     5     176
11 0.114     1.352  1838    1838     Caswell 37033  37033       17  1035     2     550  1253     2     597
                     geometry
1   POINT (-81.49826 36.4314)
2  POINT (-81.12515 36.49101)
3  POINT (-80.68575 36.41252)
4   POINT (-76.0275 36.40728)
5  POINT (-77.41056 36.42228)
7   POINT (-76.23435 36.4012)
8  POINT (-76.70448 36.44423)
9  POINT (-78.11043 36.39697)
10 POINT (-80.23428 36.40034)
11 POINT (-79.33477 36.39347)
lbusett
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