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:
- aggregating feature sets
- summarising feature sets
- joining two feature sets based on feature geometry
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/RtmpCeSUES/Rinst1c3a7f097bcb/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: 913326.4 xmax: 1340553 ymax: 1044143
## 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 (((1270813 913...prints the first record.
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:
library(dplyr)
nc %>% select(NWBIR74) %>% head(2)
## Simple feature collection with 2 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 1193283 ymin: 913326.4 xmax: 1441000 ymax: 1044143
## Projected CRS: NAD83 / North Carolina (ftUS)
## NWBIR74 geometry
## 1 10 MULTIPOLYGON (((1270813 913...
## 2 10 MULTIPOLYGON (((1340553 959...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: 913326.4 xmax: 1340553 ymax: 1044143
## Projected CRS: NAD83 / North Carolina (ftUS)
## NWBIR74 geometry
## 1 10 MULTIPOLYGON (((1270813 913...and use the drop argument to drop geometries:
but we can also use a spatial object as the row selector, to select features that intersect with another spatial feature:
Ashe = nc[nc$NAME == "Ashe",]
class(Ashe)
## [1] "sf" "data.frame"
nc[Ashe,]
## Simple feature collection with 4 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 1142157 ymin: 823077.4 xmax: 1448917 ymax: 1044143
## Projected CRS: NAD83 / North Carolina (ftUS)
## AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74
## 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091 1
## 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487 0
## 18 0.199 1.984 1874 1874 Wilkes 37193 37193 97 3146 4
## 19 0.081 1.288 1880 1880 Watauga 37189 37189 95 1323 1
## NWBIR74 BIR79 SID79 NWBIR79 geometry
## 1 10 1364 0 19 MULTIPOLYGON (((1270813 913...
## 2 10 542 3 12 MULTIPOLYGON (((1340553 959...
## 18 200 3725 7 222 MULTIPOLYGON (((1402673 837...
## 19 17 1775 1 33 MULTIPOLYGON (((1171157 868...We see that in the result set Ashe is included, as the
default value for argument op in [.sf is
st_intersects(), and Ashe intersects with
itself. We could exclude self-intersection by using predicate
st_touches() (overlapping features don’t touch):
Ashe = nc[nc$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: 823077.4 xmax: 1448917 ymax: 1035625
## 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 (((1340553 959...
## 18 200 3725 7 222 MULTIPOLYGON (((1402673 837...
## 19 17 1775 1 33 MULTIPOLYGON (((1171157 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: 823077.4 xmax: 1448917 ymax: 1035625
## 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 (((1340553 959...
## 2 200 3725 7 222 MULTIPOLYGON (((1402673 837...
## 3 17 1775 1 33 MULTIPOLYGON (((1171157 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: 406265 ymin: 48359.7 xmax: 3052877 ymax: 1044143
## Projected CRS: NAD83 / North Carolina (ftUS)
## frac74 geometry
## 1 0.0020406588 MULTIPOLYGON (((454155.5 58...
## 2 0.0009922276 POLYGON ((1372051 837036.9,...
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 geom
## 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 geom
## 1 1 POINT (0 0)
## 2 2 POINT (1 1)
## 3 3 GEOMETRYCOLLECTION EMPTY
right_join(x, y)
## Joining with `by = join_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 EMPTYJoining 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...