Creates an edges weights matrix from the output of distance_euclidean().
edges_weights_matrix(distances, lower = TRUE, upper = TRUE, diag = TRUE)a data.frame with the following three columns: from
(the first node of the edge), to (the second node of the edge), and
weight (the weight of the edge between the two nodes, e.g. a distance).
a logical value. If TRUE (default), keep values in the lower
triangle of the matrix. Otherwise they will be replaced by NA.
a logical value. If TRUE (default), keep values in the upper
triangle of the matrix. Otherwise they will be replaced by NA.
a logical value. If TRUE (default), keep values in the
diagonal of the matrix. Otherwise they will be replaced by NA.
An edges weights matrix of dimensions n x n, where n is the
number of nodes (sites).
# Import Adour sites ----
path_to_file <- system.file("extdata", "adour_survey_sampling.csv",
package = "chessboard")
adour_sites <- read.csv(path_to_file)
# Select the 15 first sites ----
adour_sites <- adour_sites[1:15, ]
# Create node labels ----
adour_sites <- create_node_labels(adour_sites,
location = "location",
transect = "transect",
quadrat = "quadrat")
# Convert sites to sf object (POINTS) ----
adour_sites_sf <- sf::st_as_sf(adour_sites,
coords = c("longitude", "latitude"),
crs = "epsg:2154")
# Compute distances between pairs of sites along the Adour river ----
adour_dists <- distance_euclidean(adour_sites_sf)
# Create Edges weights matrix ----
edges_weights_matrix(adour_dists)
#> 1-1 1-2 1-3 1-4 1-5 2-1 2-2 2-3
#> 1-1 0.000 2500.469 5000.938 7501.407 10001.876 2500.469 3536.197 5591.219
#> 1-2 2500.469 0.000 2500.469 5000.938 7501.407 3536.197 2500.469 3536.197
#> 1-3 5000.938 2500.469 0.000 2500.469 5000.938 5591.219 3536.197 2500.469
#> 1-4 7501.407 5000.938 2500.469 0.000 2500.469 7907.178 5591.219 3536.197
#> 1-5 10001.876 7501.407 5000.938 2500.469 0.000 10309.698 7907.178 5591.219
#> 2-1 2500.469 3536.197 5591.219 7907.178 10309.698 0.000 2500.469 5000.938
#> 2-2 3536.197 2500.469 3536.197 5591.219 7907.178 2500.469 0.000 2500.469
#> 2-3 5591.219 3536.197 2500.469 3536.197 5591.219 5000.938 2500.469 0.000
#> 2-4 7907.178 5591.219 3536.197 2500.469 3536.197 7501.407 5000.938 2500.469
#> 2-5 10309.698 7907.178 5591.219 3536.197 2500.469 10001.876 7501.407 5000.938
#> 3-1 5000.938 5591.219 7072.395 9015.569 11182.438 2500.469 3536.197 5591.219
#> 3-2 5591.219 5000.938 5591.219 7072.395 9015.569 3536.197 2500.469 3536.197
#> 3-3 7072.395 5591.219 5000.938 5591.219 7072.395 5591.219 3536.197 2500.469
#> 3-4 9015.569 7072.395 5591.219 5000.938 5591.219 7907.178 5591.219 3536.197
#> 3-5 11182.438 9015.569 7072.395 5591.219 5000.938 10309.698 7907.178 5591.219
#> 2-4 2-5 3-1 3-2 3-3 3-4 3-5
#> 1-1 7907.178 10309.698 5000.938 5591.219 7072.395 9015.569 11182.438
#> 1-2 5591.219 7907.178 5591.219 5000.938 5591.219 7072.395 9015.569
#> 1-3 3536.197 5591.219 7072.395 5591.219 5000.938 5591.219 7072.395
#> 1-4 2500.469 3536.197 9015.569 7072.395 5591.219 5000.938 5591.219
#> 1-5 3536.197 2500.469 11182.438 9015.569 7072.395 5591.219 5000.938
#> 2-1 7501.407 10001.876 2500.469 3536.197 5591.219 7907.178 10309.698
#> 2-2 5000.938 7501.407 3536.197 2500.469 3536.197 5591.219 7907.178
#> 2-3 2500.469 5000.938 5591.219 3536.197 2500.469 3536.197 5591.219
#> 2-4 0.000 2500.469 7907.178 5591.219 3536.197 2500.469 3536.197
#> 2-5 2500.469 0.000 10309.698 7907.178 5591.219 3536.197 2500.469
#> 3-1 7907.178 10309.698 0.000 2500.469 5000.938 7501.407 10001.876
#> 3-2 5591.219 7907.178 2500.469 0.000 2500.469 5000.938 7501.407
#> 3-3 3536.197 5591.219 5000.938 2500.469 0.000 2500.469 5000.938
#> 3-4 2500.469 3536.197 7501.407 5000.938 2500.469 0.000 2500.469
#> 3-5 3536.197 2500.469 10001.876 7501.407 5000.938 2500.469 0.000
# Create Edges weights matrix (with options) ----
edges_weights_matrix(adour_dists, lower = FALSE)
#> 1-1 1-2 1-3 1-4 1-5 2-1 2-2 2-3
#> 1-1 0 2500.469 5000.938 7501.407 10001.876 2500.469 3536.197 5591.219
#> 1-2 NA 0.000 2500.469 5000.938 7501.407 3536.197 2500.469 3536.197
#> 1-3 NA NA 0.000 2500.469 5000.938 5591.219 3536.197 2500.469
#> 1-4 NA NA NA 0.000 2500.469 7907.178 5591.219 3536.197
#> 1-5 NA NA NA NA 0.000 10309.698 7907.178 5591.219
#> 2-1 NA NA NA NA NA 0.000 2500.469 5000.938
#> 2-2 NA NA NA NA NA NA 0.000 2500.469
#> 2-3 NA NA NA NA NA NA NA 0.000
#> 2-4 NA NA NA NA NA NA NA NA
#> 2-5 NA NA NA NA NA NA NA NA
#> 3-1 NA NA NA NA NA NA NA NA
#> 3-2 NA NA NA NA NA NA NA NA
#> 3-3 NA NA NA NA NA NA NA NA
#> 3-4 NA NA NA NA NA NA NA NA
#> 3-5 NA NA NA NA NA NA NA NA
#> 2-4 2-5 3-1 3-2 3-3 3-4 3-5
#> 1-1 7907.178 10309.698 5000.938 5591.219 7072.395 9015.569 11182.438
#> 1-2 5591.219 7907.178 5591.219 5000.938 5591.219 7072.395 9015.569
#> 1-3 3536.197 5591.219 7072.395 5591.219 5000.938 5591.219 7072.395
#> 1-4 2500.469 3536.197 9015.569 7072.395 5591.219 5000.938 5591.219
#> 1-5 3536.197 2500.469 11182.438 9015.569 7072.395 5591.219 5000.938
#> 2-1 7501.407 10001.876 2500.469 3536.197 5591.219 7907.178 10309.698
#> 2-2 5000.938 7501.407 3536.197 2500.469 3536.197 5591.219 7907.178
#> 2-3 2500.469 5000.938 5591.219 3536.197 2500.469 3536.197 5591.219
#> 2-4 0.000 2500.469 7907.178 5591.219 3536.197 2500.469 3536.197
#> 2-5 NA 0.000 10309.698 7907.178 5591.219 3536.197 2500.469
#> 3-1 NA NA 0.000 2500.469 5000.938 7501.407 10001.876
#> 3-2 NA NA NA 0.000 2500.469 5000.938 7501.407
#> 3-3 NA NA NA NA 0.000 2500.469 5000.938
#> 3-4 NA NA NA NA NA 0.000 2500.469
#> 3-5 NA NA NA NA NA NA 0.000
edges_weights_matrix(adour_dists, upper = FALSE)
#> 1-1 1-2 1-3 1-4 1-5 2-1 2-2 2-3
#> 1-1 0.000 NA NA NA NA NA NA NA
#> 1-2 2500.469 0.000 NA NA NA NA NA NA
#> 1-3 5000.938 2500.469 0.000 NA NA NA NA NA
#> 1-4 7501.407 5000.938 2500.469 0.000 NA NA NA NA
#> 1-5 10001.876 7501.407 5000.938 2500.469 0.000 NA NA NA
#> 2-1 2500.469 3536.197 5591.219 7907.178 10309.698 0.000 NA NA
#> 2-2 3536.197 2500.469 3536.197 5591.219 7907.178 2500.469 0.000 NA
#> 2-3 5591.219 3536.197 2500.469 3536.197 5591.219 5000.938 2500.469 0.000
#> 2-4 7907.178 5591.219 3536.197 2500.469 3536.197 7501.407 5000.938 2500.469
#> 2-5 10309.698 7907.178 5591.219 3536.197 2500.469 10001.876 7501.407 5000.938
#> 3-1 5000.938 5591.219 7072.395 9015.569 11182.438 2500.469 3536.197 5591.219
#> 3-2 5591.219 5000.938 5591.219 7072.395 9015.569 3536.197 2500.469 3536.197
#> 3-3 7072.395 5591.219 5000.938 5591.219 7072.395 5591.219 3536.197 2500.469
#> 3-4 9015.569 7072.395 5591.219 5000.938 5591.219 7907.178 5591.219 3536.197
#> 3-5 11182.438 9015.569 7072.395 5591.219 5000.938 10309.698 7907.178 5591.219
#> 2-4 2-5 3-1 3-2 3-3 3-4 3-5
#> 1-1 NA NA NA NA NA NA NA
#> 1-2 NA NA NA NA NA NA NA
#> 1-3 NA NA NA NA NA NA NA
#> 1-4 NA NA NA NA NA NA NA
#> 1-5 NA NA NA NA NA NA NA
#> 2-1 NA NA NA NA NA NA NA
#> 2-2 NA NA NA NA NA NA NA
#> 2-3 NA NA NA NA NA NA NA
#> 2-4 0.000 NA NA NA NA NA NA
#> 2-5 2500.469 0.000 NA NA NA NA NA
#> 3-1 7907.178 10309.698 0.000 NA NA NA NA
#> 3-2 5591.219 7907.178 2500.469 0.000 NA NA NA
#> 3-3 3536.197 5591.219 5000.938 2500.469 0.000 NA NA
#> 3-4 2500.469 3536.197 7501.407 5000.938 2500.469 0.000 NA
#> 3-5 3536.197 2500.469 10001.876 7501.407 5000.938 2500.469 0
edges_weights_matrix(adour_dists, diag = FALSE)
#> 1-1 1-2 1-3 1-4 1-5 2-1 2-2 2-3
#> 1-1 NA 2500.469 5000.938 7501.407 10001.876 2500.469 3536.197 5591.219
#> 1-2 2500.469 NA 2500.469 5000.938 7501.407 3536.197 2500.469 3536.197
#> 1-3 5000.938 2500.469 NA 2500.469 5000.938 5591.219 3536.197 2500.469
#> 1-4 7501.407 5000.938 2500.469 NA 2500.469 7907.178 5591.219 3536.197
#> 1-5 10001.876 7501.407 5000.938 2500.469 NA 10309.698 7907.178 5591.219
#> 2-1 2500.469 3536.197 5591.219 7907.178 10309.698 NA 2500.469 5000.938
#> 2-2 3536.197 2500.469 3536.197 5591.219 7907.178 2500.469 NA 2500.469
#> 2-3 5591.219 3536.197 2500.469 3536.197 5591.219 5000.938 2500.469 NA
#> 2-4 7907.178 5591.219 3536.197 2500.469 3536.197 7501.407 5000.938 2500.469
#> 2-5 10309.698 7907.178 5591.219 3536.197 2500.469 10001.876 7501.407 5000.938
#> 3-1 5000.938 5591.219 7072.395 9015.569 11182.438 2500.469 3536.197 5591.219
#> 3-2 5591.219 5000.938 5591.219 7072.395 9015.569 3536.197 2500.469 3536.197
#> 3-3 7072.395 5591.219 5000.938 5591.219 7072.395 5591.219 3536.197 2500.469
#> 3-4 9015.569 7072.395 5591.219 5000.938 5591.219 7907.178 5591.219 3536.197
#> 3-5 11182.438 9015.569 7072.395 5591.219 5000.938 10309.698 7907.178 5591.219
#> 2-4 2-5 3-1 3-2 3-3 3-4 3-5
#> 1-1 7907.178 10309.698 5000.938 5591.219 7072.395 9015.569 11182.438
#> 1-2 5591.219 7907.178 5591.219 5000.938 5591.219 7072.395 9015.569
#> 1-3 3536.197 5591.219 7072.395 5591.219 5000.938 5591.219 7072.395
#> 1-4 2500.469 3536.197 9015.569 7072.395 5591.219 5000.938 5591.219
#> 1-5 3536.197 2500.469 11182.438 9015.569 7072.395 5591.219 5000.938
#> 2-1 7501.407 10001.876 2500.469 3536.197 5591.219 7907.178 10309.698
#> 2-2 5000.938 7501.407 3536.197 2500.469 3536.197 5591.219 7907.178
#> 2-3 2500.469 5000.938 5591.219 3536.197 2500.469 3536.197 5591.219
#> 2-4 NA 2500.469 7907.178 5591.219 3536.197 2500.469 3536.197
#> 2-5 2500.469 NA 10309.698 7907.178 5591.219 3536.197 2500.469
#> 3-1 7907.178 10309.698 NA 2500.469 5000.938 7501.407 10001.876
#> 3-2 5591.219 7907.178 2500.469 NA 2500.469 5000.938 7501.407
#> 3-3 3536.197 5591.219 5000.938 2500.469 NA 2500.469 5000.938
#> 3-4 2500.469 3536.197 7501.407 5000.938 2500.469 NA 2500.469
#> 3-5 3536.197 2500.469 10001.876 7501.407 5000.938 2500.469 NA