4. Manipulating Simple Features

This vignette describes how simple features, i.e. records that come with a geometry, can be manipulated, for the case where these manipulations involve geometries. Manipulations include:

Features are represented by records in an sf object, and have feature attributes (all non-geometry fields) and feature geometry. Since sf objects are a subclass of data.frame or tbl_df, operations on feature attributes work identically to how they work on data.frames, e.g.

library(sf)
nc <- st_read(system.file("shape/nc.shp", package="sf"))
## Reading layer `nc' from data source 
##   `/tmp/RtmpPCyaNK/Rinstd89f89a4dae2/sf/shape/nc.shp' using driver `ESRI Shapefile'
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS:  NAD27
nc <- st_transform(nc, 2264)
nc[1,]
## Simple feature collection with 1 feature and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1193283 ymin: 913341.9 xmax: 1340555 ymax: 1044158
## Projected CRS: NAD83 / North Carolina (ftUS)
##    AREA PERIMETER CNTY_ CNTY_ID NAME  FIPS FIPSNO CRESS_ID BIR74 SID74 NWBIR74
## 1 0.114     1.442  1825    1825 Ashe 37009  37009        5  1091     1      10
##   BIR79 SID79 NWBIR79                       geometry
## 1  1364     0      19 MULTIPOLYGON (((1270814 913...

Many of the tidyverse/dplyr verbs have methods for sf objects. This means that if both sf and dplyr are loaded, manipulations such as selecting a single attribute will return an sf object:

which implies that the geometry is sticky, and gets added automatically. If we want to drop geometry, we can coerce to data.frame first, this drops geometry list-columns:

Subsetting feature sets

We can subset feature sets by using the square bracket notation

nc[1, "NWBIR74"]
## Simple feature collection with 1 feature and 1 field
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1193283 ymin: 913341.9 xmax: 1340555 ymax: 1044158
## Projected CRS: NAD83 / North Carolina (ftUS)
##   NWBIR74                       geometry
## 1      10 MULTIPOLYGON (((1270814 913...

and use the drop argument to drop geometries:

nc[1, "NWBIR74", drop = TRUE]
## [1] 10
## attr(,"class")
## [1] "numeric"

but we can also use a spatial object as the row selector, to select features that intersect with another spatial feature:

Ashe = nc[nc$NAME == "Ashe",]
class(Ashe)
## [1] "sf"         "data.frame"
nc[Ashe,]
## Simple feature collection with 4 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1142157 ymin: 823092 xmax: 1448920 ymax: 1044158
## Projected CRS: NAD83 / North Carolina (ftUS)
##     AREA PERIMETER CNTY_ CNTY_ID      NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
## 1  0.114     1.442  1825    1825      Ashe 37009  37009        5  1091     1
## 2  0.061     1.231  1827    1827 Alleghany 37005  37005        3   487     0
## 18 0.199     1.984  1874    1874    Wilkes 37193  37193       97  3146     4
## 19 0.081     1.288  1880    1880   Watauga 37189  37189       95  1323     1
##    NWBIR74 BIR79 SID79 NWBIR79                       geometry
## 1       10  1364     0      19 MULTIPOLYGON (((1270814 913...
## 2       10   542     3      12 MULTIPOLYGON (((1340555 959...
## 18     200  3725     7     222 MULTIPOLYGON (((1402677 837...
## 19      17  1775     1      33 MULTIPOLYGON (((1171158 868...

We see that in the result set Ashe is included, as the default value for argument op in [.sf is st_intersects, and Ashe intersects with itself. We could exclude self-intersection by using predicate st_touches (overlapping features don’t touch):

Ashe = nc[nc$NAME == "Ashe",]
nc[Ashe, op = st_touches]
## Simple feature collection with 3 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1142157 ymin: 823092 xmax: 1448920 ymax: 1035641
## Projected CRS: NAD83 / North Carolina (ftUS)
##     AREA PERIMETER CNTY_ CNTY_ID      NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
## 2  0.061     1.231  1827    1827 Alleghany 37005  37005        3   487     0
## 18 0.199     1.984  1874    1874    Wilkes 37193  37193       97  3146     4
## 19 0.081     1.288  1880    1880   Watauga 37189  37189       95  1323     1
##    NWBIR74 BIR79 SID79 NWBIR79                       geometry
## 2       10   542     3      12 MULTIPOLYGON (((1340555 959...
## 18     200  3725     7     222 MULTIPOLYGON (((1402677 837...
## 19      17  1775     1      33 MULTIPOLYGON (((1171158 868...

Using dplyr, we can do the same by calling the predicate directly:

nc %>% filter(lengths(st_touches(., Ashe)) > 0)
## Simple feature collection with 3 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1142157 ymin: 823092 xmax: 1448920 ymax: 1035641
## Projected CRS: NAD83 / North Carolina (ftUS)
##    AREA PERIMETER CNTY_ CNTY_ID      NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
## 1 0.061     1.231  1827    1827 Alleghany 37005  37005        3   487     0
## 2 0.199     1.984  1874    1874    Wilkes 37193  37193       97  3146     4
## 3 0.081     1.288  1880    1880   Watauga 37189  37189       95  1323     1
##   NWBIR74 BIR79 SID79 NWBIR79                       geometry
## 1      10   542     3      12 MULTIPOLYGON (((1340555 959...
## 2     200  3725     7     222 MULTIPOLYGON (((1402677 837...
## 3      17  1775     1      33 MULTIPOLYGON (((1171158 868...

Aggregating or summarizing feature sets

Suppose we want to compare the 1974 fraction of SID (sudden infant death) of the counties that intersect with Ashe to the remaining ones. We can do this by:

a <- aggregate(nc[, c("SID74", "BIR74")], list(Ashe_nb = lengths(st_intersects(nc, Ashe)) > 0), sum)
(a <- a %>% mutate(frac74 = SID74 / BIR74) %>% select(frac74))
## Simple feature collection with 2 features and 1 field
## Geometry type: GEOMETRY
## Dimension:     XY
## Bounding box:  xmin: 406262.2 ymin: 48374.87 xmax: 3052887 ymax: 1044158
## Projected CRS: NAD83 / North Carolina (ftUS)
##         frac74                       geometry
## 1 0.0020406588 MULTIPOLYGON (((454152.6 58...
## 2 0.0009922276 POLYGON ((1372054 837052.3,...
plot(a[2], col = c(grey(.8), grey(.5)))
plot(st_geometry(Ashe), border = '#ff8888', add = TRUE, lwd = 2)

Joining two feature sets based on attributes

The usual join verbs of base R (merge) and of dplyr (left_join, etc) work for sf objects as well; the joining takes place on attributes (ignoring geometries). In case of no matching geometry, an empty geometry is substituted. The second argument should be a data.frame (or similar), not an sf object:

x = st_sf(a = 1:2, geom = st_sfc(st_point(c(0,0)), st_point(c(1,1))))
y = data.frame(a = 2:3)
merge(x, y)
## Simple feature collection with 1 feature and 1 field
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 1 ymin: 1 xmax: 1 ymax: 1
## CRS:           NA
##   a    geometry
## 1 2 POINT (1 1)
merge(x, y, all = TRUE)
## Simple feature collection with 3 features and 1 field (with 1 geometry empty)
## Geometry type: GEOMETRY
## Dimension:     XY
## Bounding box:  xmin: 0 ymin: 0 xmax: 1 ymax: 1
## CRS:           NA
##   a                 geometry
## 1 1              POINT (0 0)
## 2 2              POINT (1 1)
## 3 3 GEOMETRYCOLLECTION EMPTY
right_join(x, y)
## Joining, by = "a"
## Simple feature collection with 2 features and 1 field (with 1 geometry empty)
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 1 ymin: 1 xmax: 1 ymax: 1
## CRS:           NA
##   a        geom
## 1 2 POINT (1 1)
## 2 3 POINT EMPTY

Joining two feature sets based on geometries

For joining based on spatial intersections (of any kind), st_join is used:

x = st_sf(a = 1:3, geom = st_sfc(st_point(c(1,1)), st_point(c(2,2)), st_point(c(3,3))))
y = st_buffer(x, 0.1)
x = x[1:2,]
y = y[2:3,]
plot(st_geometry(x), xlim = c(.5, 3.5))
plot(st_geometry(y), add = TRUE)

The join method is a left join, retaining all records of the first attribute:

st_join(x, y)
## Simple feature collection with 2 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 1 ymin: 1 xmax: 2 ymax: 2
## CRS:           NA
##   a.x a.y        geom
## 1   1  NA POINT (1 1)
## 2   2   2 POINT (2 2)
st_join(y, x)
## Simple feature collection with 2 features and 2 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 1.9 ymin: 1.9 xmax: 3.1 ymax: 3.1
## CRS:           NA
##   a.x a.y                           geom
## 2   2   2 POLYGON ((2.1 2, 2.099863 1...
## 3   3  NA POLYGON ((3.1 3, 3.099863 2...

and the geometry retained is that of the first argument.

The spatial join predicate can be controlled with any function compatible with st_intersects (the default), e.g.

st_join(x, y, join = st_covers) # no matching y records: points don't cover circles
## Simple feature collection with 2 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 1 ymin: 1 xmax: 2 ymax: 2
## CRS:           NA
##   a.x a.y        geom
## 1   1  NA POINT (1 1)
## 2   2  NA POINT (2 2)
st_join(y, x, join = st_covers) # matches for those circles covering a point
## Simple feature collection with 2 features and 2 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 1.9 ymin: 1.9 xmax: 3.1 ymax: 3.1
## CRS:           NA
##   a.x a.y                           geom
## 2   2   2 POLYGON ((2.1 2, 2.099863 1...
## 3   3  NA POLYGON ((3.1 3, 3.099863 2...

4. Manipulating Simple Features

Edzer Pebesma

Subsetting feature sets

Aggregating or summarizing feature sets

Joining two feature sets based on attributes

Joining two feature sets based on geometries