CODEWITHSUNDEEP
rdplyrspatialr-sf
Chris
Chris

Reputation: 1237

Using dplyr distinct to ignore geometries of sf object in R

I have a dataset with multiple polygons in different locations that share the same attributes. I only want one polygon in my dateset for each set of unique attributes (so in my example below, that would be Area and Zone) - I don't care about where they are so I want to ignore the geometry attribute.

library(sf)
library(dplyr)

    Areas <- st_as_sf(tibble(
      Area =c("Zone1", "Zone1","Zone2","Zone1"),
      Zone =c("Area27","Area27","Area42","Area27"),
      lng = c(20.1, 20.2, 20.1, 20.1),
      lat = c(-1.1, -1.2, -1.1, -1.1)),
    coords = c("lng", "lat")) %>% st_buffer(.,100)

I am using dplyr distinct to remove duplicate records, but I am finding the geometry column is being used to determine distinct records, even though I believe this should be ignoring the geometry column:

Areas %>% distinct(across(-geometry),.keep_all=TRUE)

However, it is returns two results for Zone1 and Area27 when the geometry is different. Is this expected behaviour or am I do something wrong?

My required output would only have two rows in it, one for Zone1 & Area27 and another for Zone2 & Area42 with the geometry for those rows retained i.e. something similar to what happens you run the same code on a normal tibble:

Table <- tibble(
  Area =c("Zone1", "Zone1","Zone2","Zone1"),
  Zone =c("Area27","Area27","Area42","Area27"),
  lng = c(20.1, 20.2, 20.1, 20.1),
  lat = c(-1.1, -1.2, -1.1, -1.1))

Table %>% distinct(across(c(-lng,-lat)),.keep_all=TRUE)

Upvotes: 4

Views: 1439

Answers (2)

Chris
Chris

Reputation: 1237

I found an alternative method:

Areas %>% group_by(Area,Zone) %>% 
          mutate(id = row_number()) %>% 
          filter(id == 1) %>% 
          select(-id)

If you are dealing with a dataset with a lot of polygons this is likely to be faster than @Waldi's answer (at least it was for me).

Upvotes: 2

Waldi
Waldi

Reputation: 41220

You could summarize :

Areas  %>% group_by(Area,Zone) %>% summarize()

# A tibble: 2 x 3
# Groups:   Area [2]
  Area  Zone                                                                          geometry
  <chr> <chr>                                                                        <POLYGON>
1 Zone1 Area27 ((120.2 -1.2, 120.063 -6.433596, 119.6522 -11.65285, 118.9688 -16.84345, 118.0~
2 Zone2 Area42 ((120.1 -1.1, 119.963 -6.333596, 119.5522 -11.55285, 118.8688 -16.74345, 117.9~

Upvotes: 0

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