Use a geospatial polygon to identify or remove points located outside the polygon. This function also includes an option buffer argument which can be used to add a specific distance buffer around the polygon prior to filtering.
Usage
nw.filterspatial(
points,
polygon,
mode,
buffer = F,
buffer_units = NULL,
buffer_output = NULL,
proj = NULL,
output = NULL
)Arguments
- points
sf; A spatial feature containing point data.
- polygon
sf; A spatial polygon representing the area for which you want to include datafrom.
- mode
"flag"or"remove"; A character string defining if the user wants the identified nesting attempts to be flagged with "FLAGGED" in a new column. Or removed from the dataset.- buffer
numeric; Optional distance to buffer around the polygon
- buffer_units
"km"or"mi"; A character string defining the buffer distance units as either kilometers or miles.- buffer_output
logical; Optionally export the generated buffered polygon to the global enviornment.
- proj
a PROJ.4 string; Optional definition for map projection. Defaults to Lambert Conformal Conic.
- output
character; An optional character string to custom name the output spatial dataframe.
Examples
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
set.seed(123)
# Define simple polygon
square_coords <- matrix(c(0, 0, # bottom-left
1, 0, # bottom-right
1, 1, # top-right
0, 1, # top-left
0, 0), # close the square back to bottom-left
ncol = 2, byrow = TRUE)
# Create an `sf` polygon
square_polygon <- sf::st_polygon(list(square_coords)) %>%
sf::st_sfc() %>% sf::st_sf() %>%
sf::st_set_crs(4326) # Set a coordinate reference system (WGS84)
# Generate random points around the square, convert to sf object
random_points <- data.frame(Attempt.ID = seq(1:30),
x = runif(30, -0.5, 1.5),
y = runif(30, -0.5, 1.5))
points_sf <- sf::st_as_sf(random_points, coords = c("x", "y"), crs = 4326)
# Run the spatial filter
nw.filterspatial(points = points_sf,
polygon = square_polygon,
mode = "flag",
proj = "+proj=longlat +datum=WGS84 +no_defs",
output = "geofiltered.data")
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> ... Identified nesting attempts have been noted with 'FLAGGED' in the new dataset in column 'Flagged.Location'.