4. Manipulating Simple Features

knitr::opts_chunk$set(collapse = TRUE)

This vignetted 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 identical to how they work on data.frames, e.g.

library(sf)
nc <- st_read(system.file("shape/nc.shp", package="sf"))
nc <- st_transform(nc, 2264)
nc[1,]

prints the first record.

Many of the tidyverse/dplyr verbs have methods for sf objects. This means that given that 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)

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)

Subsetting feature sets

We can subset feature sets by using the square bracket notation

nc[1, "NWBIR74"]

and use the drop argument to drop geometries:

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

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)
nc[Ashe,]

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]

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

nc %>% filter(lengths(st_touches(., Ashe)) > 0)

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 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)
merge(x, y, all = TRUE)
right_join(x, y)

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)
st_join(y, x)

and the geometry retained is that of the first argument.

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

st_join(x, y, join = st_covers) # no matching y records: points don't cover circles
st_join(y, x, join = st_covers) # matches for those circles covering a point


Try the sf package in your browser

Any scripts or data that you put into this service are public.

sf documentation built on July 24, 2019, 5:05 p.m.