sf

I have millions of GPS coordinates and want to quickly add a column of the country of the coordinates. My current method works but is extremely slow: library(data.table) #REPRODUCE DATA data <- data.table(latitude=sample(seq(47,52,by=0.001), 1000000, replace = TRUE), longitude=sample(seq(8,23,by=0.001), 1000000, replace = TRUE)) #REQUIRED PACKAGES if (!require("sp")) install.packages("sp") if (!require("rworldmap")) install.packages("rworldmap") if (!require("sf")) install.packages("sf") library(sp) library(rworldmap) library(sf) #CURRENT SLOW FUNCTION coords2country = function(points,latcol

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

I'm wondering if I have maximized the speed at which a mean of an area buffered around a point in a raster can be extracted. Can performance be improved any further on these LOCALLY? I use parallel mclapply already, and I know I could get further gains by setting up and running this on a cluster (use a cluster or get more cpu's is not the answer I'm looking for). Replicate some data: library(raster) library(parallel) library(truncnorm) library(gdalUtils) library(velox) library(sf) ras <- raster(ncol=1000, nrow=1000, xmn=2001476, xmx=11519096, ymn=9087279, ymx=17080719) ras[]=rtruncnorm(n=ncell

I've got a data.frame with lat s and lng s that define the boundaries of rectangular boxes, like so geohash north_lat south_lat east_lng west_lng 1 gbsuv 48.69141 48.64746 -4.306641 -4.350586 2 gbsuy 48.69141 48.64746 -4.262695 -4.306641 What's the easiest way to convert this into an sf object that holds a column of POLYGON s? The key to creating polygons is that the coordinates have to be in sequence to form a closed area (i.e., the last point is the same as the first point). So your data will need a bit of manipulation to create the coordinates, and put them in order. In my example I've done

I work with satellite data organized on an irregular two-dimensional grid whose dimensions are scanline (along track dimension) and ground pixel (across track dimension). Latitude and longitude information for each centre pixel are stored in auxiliary coordinate variables, as well as the four corners coordinate pairs (latitude and longitude coordinates are given on the WGS84 reference ellipsoid). The data is stored in netCDF4 files. What I am trying to do is efficiently plotting these files (and possibly a combination of files—next step!) on a projected map. My approach so far, inspired by

Starting from a shapefile containing a fairly large number (about 20000) of potentially partially-overlapping polygons, I'd need to extract all the sub-polygons originated by intersecting their different "boundaries". In practice, starting from some mock-up data: library(tibble) library(dplyr) library(sf) ncircles <- 9 rmax <- 120 x_limits <- c(-70,70) y_limits <- c(-30,30) set.seed(100) xy <- data.frame( id = paste0("id_", 1:ncircles), x = runif(ncircles, min(x_limits), max(x_limits)), y = runif(ncircles, min(y_limits), max(y_limits))) %>% as_tibble() polys <- st_as_sf(xy, coords = c(2,3)) %>