Convert sets of spatial coordinates to polygons in R using sf

Question

Each element of my list contains a set of spatial coordinates that I would like to convert to polygons using sf. Each set of coordinates is sorted in the order I would like to "connect the dots" and the first and last rows are identical, to close the polygons. Each list element is named with a unique identifier which I would like to retain as an attribute in the sf output.

I have adapted code from the sf-related answer here:

Convert sequence of longitude and latitude to polygon via sf in R

but my case differs in that I have multiple sets of coordinates (each of which should produce a separate polygon) whereas that question had a single set of coordinates (resulting in one polygon).

My specific question is how can I use sf to produce a sf polygon object containing multiple polygons in separate rows, each created with the coordinates in one of my list elements.

Thank you in advance for any suggestions or help.

Mark

---

My sample data, resulting from dput(), are at the end of this question and my code is:

points_df<-arrange(dat,SitePondGpsRep,DateTime_local) #sort on DateTime_local for proper sequence

points_df<-dplyr::select(points_df,SitePondGpsRep,Longitude,Latitude) #drop columns, for upcoming st_polygon call (requires numerics only)

points_ls<-split(points_df,points_df$SitePondGpsRep) #dataframe to list

points_ls<-lapply(points_ls, function(x) { x["SitePondGpsRep"] <- NULL; x }) #delete SitePondGpsRep column, it’s retained in list names

points_ls<-lapply(points_ls,function(x) {as.matrix(x)}) #convert to matrix for upcoming st_sf call

points_ls<-lapply(points_ls,function(x) {rbind(x,x[1,])}) #close poly, first and last point must be same

polys <- st_sf(st_sfc(st_polygon(points_ls)), crs = 4326) #create polys, but only one polygon is created when I expected three polygons

str(polys); glimpse(polys); plot(polys) #check output

Sample data:

dat <- structure(list(SitePondGpsRep = c("BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1"
), DateTime_local = c("2018-05-30 10:49:04", "2018-05-30 10:49:05", 
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Answer 1

The easier way to approach this rather than using split and lapply is to use the ability of sf to work well with dplyr tools, especially grouped operations. We can:

1. Create points for each vertex of the polygons with the coords argument of st_as_sf,
2. group_by your id and summarise to combine the points into a MULTIPOINT for each polygon,
3. Use st_cast to convert to POLYGON, drawing the lines between each vertex.

dat <- structure(list(SitePondGpsRep = c("BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1"), DateTime_local = c("2018-05-30 10:49:04", "2018-05-30 10:49:05", "2018-05-30 10:49:06", "2018-05-30 10:49:07", "2018-05-30 10:49:08", "2018-05-30 10:49:09", "2018-05-30 10:49:10", "2018-05-30 10:49:27", "2018-05-30 10:49:28", "2018-05-30 10:49:29", "2018-05-30 10:49:30", "2018-05-30 10:49:31", "2018-05-30 10:49:32", "2018-05-30 10:49:33", "2018-05-30 10:49:34", "2018-05-30 10:49:35", "2018-05-30 10:49:36", "2018-05-30 10:49:37", "2018-05-30 10:49:38", "2018-05-30 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10:50:03", "2018-05-30 10:50:04", "2018-05-30 10:50:05", "2018-05-30 10:50:06", "2018-05-30 10:50:07", "2018-05-30 10:50:10", "2018-05-30 10:50:11", "2018-05-30 10:50:12", "2018-05-30 10:50:13", "2018-05-30 10:50:14", "2018-05-30 10:50:15", "2018-05-30 10:50:16", "2018-05-30 10:50:37", "2018-05-30 10:50:44", "2018-05-30 10:49:05", "2018-05-30 10:49:06", "2018-05-30 10:49:07", "2018-05-30 10:49:08", "2018-05-30 10:49:09", "2018-05-30 10:49:10", "2018-05-30 10:49:11", "2018-05-30 10:49:12", "2018-05-30 10:49:19", "2018-05-30 10:49:21", "2018-05-30 10:49:22", "2018-05-30 10:49:26", "2018-05-30 10:49:27", "2018-05-30 10:49:30", "2018-05-30 10:49:31", "2018-05-30 10:49:32", "2018-05-30 10:49:33", "2018-05-30 10:49:34", "2018-05-30 10:49:35", "2018-05-30 10:49:36", "2018-05-30 10:49:37", "2018-05-30 10:49:38", "2018-05-30 10:49:39", "2018-05-30 10:49:40", "2018-05-30 10:49:41", "2018-05-30 10:49:42", "2018-05-30 10:49:43", "2018-05-30 10:49:44", "2018-05-30 10:49:45", "2018-05-30 10:49:46", "2018-05-30 10:49:47", "2018-05-30 10:49:48", "2018-05-30 10:49:49", "2018-05-30 10:49:50", "2018-05-30 10:49:51", "2018-05-30 10:49:52", "2018-05-30 10:49:54", "2018-05-30 10:49:57", "2018-05-30 10:49:58", "2018-05-30 10:49:59", "2018-05-30 10:50:00", "2018-05-30 10:50:01", "2018-05-30 10:50:02", "2018-05-30 10:50:03", "2018-05-30 10:50:04", "2018-05-30 10:50:05", "2018-05-30 10:50:06", "2018-05-30 10:50:07", "2018-05-30 10:50:08", "2018-05-30 10:50:09", "2018-05-30 10:50:10", "2018-05-30 10:50:11", "2018-05-30 10:50:12", "2018-05-30 10:50:13", "2018-05-30 10:50:14", "2018-05-30 10:50:15", "2018-05-30 10:50:16"), Latitude = c(51.9851623569, 51.9851641171, 51.9851674698, 51.9851741754, 51.9851825573, 51.9851923641, 51.9852027576, 51.9853603374, 51.985360086, 51.9853615109, 51.9853631873, 51.9853626005, 51.9853596669, 51.9853546377, 51.9853501953, 51.9853491057, 51.9853499439, 51.9853510335, 51.9853526261, 51.9853537157, 51.9853544701, 51.9853550568, 51.9853562303, 51.985358661, 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-105.0761289988, -105.076121036, -105.0761198625, -105.0761174317, -105.0761130732, -105.076108044, -105.0761036016, -105.0761017576, -105.0761008356, -105.0760996621, -105.0760973152, -105.0760949682, -105.0760931242, -105.0760921184, -105.0760929566, -105.0760953873, -105.0760967284, -105.0760972314)), class = "data.frame", row.names = c(NA, -177L))
library(tidyverse)
library(sf)
dat %>%
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) %>%
  group_by(SitePondGpsRep) %>%
  summarise(geometry = st_combine(geometry)) %>%
  st_cast("POLYGON") %>%
  plot()

Created on 2018-10-05 by the reprex package (v0.2.0).

Answer 2

library(sfheaders) on CRAN from 20191004 can convert data.frames to sf objects.

library(sf)
library(sfheaders)

sf <- sfheaders::sf_polygon(
  obj = dat
  , x = "Longitude"
  , y = "Latitude"
  , polygon_id = "SitePondGpsRep"
)

sf
# Simple feature collection with 3 features and 1 field
# geometry type:  POLYGON
# dimension:      XY
# bbox:           xmin: -105.077 ymin: 51.98512 xmax: -105.0761 ymax: 51.98542
# epsg (SRID):    NA
# proj4string:    
#   id                       geometry
# 1 BURR-1-1-1 POLYGON ((-105.0768 51.9851...
# 2 BURR-1-3-1 POLYGON ((-105.0768 51.9851...
# 3 BURR-1-4-1 POLYGON ((-105.0768 51.9851...