I would like to filter sfc_multipoint data by the size of the sfc_multipoints, i.e. the number of points/coordinate pairs contained in each row. Suppose I want to keep only rows with no more than 3 points from the following data set, based on mapview::breweries:
library(mapview)
library(sf)
library(tidyverse)
pts <- breweries %>%
group_by(zipcode) %>%
summarise()
In order to do this, I need to know the number of points within each sfc_multipoint. In theory I could export the coordinates via as("spatial") and count rows, but that is not only ugly, but also not really feasible for data sets of any realistic size. Any suggestions?
You could try dplyr::count() or summarize(n = n()) to read the number of rows within a given zipcode, but the breweries dataset seems to have some duplicates so this might be misleading.
breweries %>%
count(zipcode)
#----------
zipcode n geometry
1 90562 1 POINT (11.15795 49.53495)
2 90614 1 POINT (10.85194 49.4208)
3 90763 1 POINT (10.99625 49.44171)
4 91054 3 MULTIPOINT ((11.00901 49.59...
5 91097 2 MULTIPOINT ((10.76099 49.59...
Or only unique points (note the change in 91054)
breweries %>%
distinct(zipcode, geometry) %>%
count(zipcode)
#-----
zipcode n geometry
1 90562 1 POINT (11.15795 49.53495)
2 90614 1 POINT (10.85194 49.4208)
3 90763 1 POINT (10.99625 49.44171)
4 91054 2 MULTIPOINT ((11.00901 49.59...
5 91097 2 MULTIPOINT ((10.76099 49.59...
You could also try mapview::npts()
breweries %>%
group_by(zipcode) %>%
summarize() %>%
rowwise() %>%
mutate(n = npts(geometry))
#----
zipcode geometry n
* <chr> <GEOMETRY [°]> <dbl>
1 90562 POINT (11.15795 49.53495) 1
2 90614 POINT (10.85194 49.4208) 1
3 90763 POINT (10.99625 49.44171) 1
4 91054 MULTIPOINT ((11.00901 49.59511), (11.00505 49.60255)) 2
5 91097 MULTIPOINT ((10.76099 49.59044), (10.76954 49.59015)) 2