# 4. Manipulating Simple Features

#### Edzer Pebesma

For a better version of the sf vignettes see https://r-spatial.github.io/sf/articles/

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.frame`s, e.g.

    library(sf)
    nc <- st_read(system.file("shape/nc.shp", package="sf"))
    ## Reading layer `nc' from data source
    ##   `/tmp/RtmptFGv0J/Rinst55299643833b3/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:

    nc |> as.data.frame() |> select(NWBIR74) |> head(2)
    ##   NWBIR74
    ## 1      10
    ## 2      10

## 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:

    nc[1, "NWBIR74", drop = TRUE]
    ## [1] 10

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(nc, 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)
    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: POINT
    ## 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 POINT 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 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...