context("ggnet")
if ("package:igraph" %in% search()) {
detach("package:igraph")
}
require(network , quietly = TRUE) # network objects
require(sna , quietly = TRUE) # placement and centrality
require(ggplot2 , quietly = TRUE) # grammar of graphics
require(grid , quietly = TRUE) # arrows
require(scales , quietly = TRUE) # sizing
require(intergraph , quietly = TRUE) # test igraph conversion
test_that("examples", {
### --- start: documented examples
# random adjacency matrix
x <- 10
ndyads <- x * (x - 1)
density <- x / ndyads
m <- matrix(0, nrow = x, ncol = x)
dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ])
m[ row(m) != col(m) ] <- runif(ndyads) < density
m
# random undirected network
n <- network::network(m, directed = FALSE)
n
ggnet(n, label = TRUE, alpha = 1, color = "white", segment.color = "black")
# random groups
g <- sample(letters[ 1:3 ], 10, replace = TRUE)
# color palette
p <- c("a" = "steelblue", "b" = "forestgreen", "c" = "tomato")
p = ggnet(n, node.group = g, node.color = p, label = TRUE, color = "white")
expect_equal(length(p$layers), 3)
expect_true(!is.null(p$mapping$colour))
### --- end: documented examples
### --- test deprecations
# test mode = "geo"
xy = gplot.layout.circle(n)
n %v% "lon" = xy[, 1]
n %v% "lat" = xy[, 2]
expect_warning(ggnet(n, mode = "geo"), "deprecated")
# test names = c(x, y)
expect_warning(ggnet(n, names = c("a", "b")), "deprecated")
# test quantize.weights
expect_warning(ggnet(n, quantize.weights = TRUE))
# test subset.threshold
expect_warning(ggnet(n, subset.threshold = 2))
# test top8.nodes
expect_warning(ggnet(n, top8.nodes = TRUE))
# test trim.labels
expect_warning(ggnet(n, trim.labels = TRUE))
# # test subset.threshold by removing all nodes
# expect_warning(
# expect_error(
# ggnet(n, subset.threshold = 11),
# "NA/NaN/Inf"
# ),
# "NaNs produced"
# )
#
# p <- ggnet(n, mode = "geo")
# expect_equal(p$data$X1, xy[, 1])
# expect_equal(p$data$X2, xy[, 2])
# test user-submitted weights
ggnet(n, weight = sample(1:2, 10, replace = TRUE))
# test segment.label
x = sample(letters, network.edgecount(n))
p <- ggnet(n, segment.label = x)
expect_true(p$layers[[2]]$mapping$x == "midX")
expect_true(p$layers[[2]]$mapping$y == "midY")
# test weight.cut
n %v% "weights" = 1:10
ggnet(n, weight.method = "weights", weight.cut = TRUE)
### --- test errors in set_node
expect_error(ggnet(n, group = NA), "incorrect")
expect_error(ggnet(n, group = 1:3), "incorrect")
expect_error(ggnet(n, label = TRUE, label.size = -10:-1), "incorrect")
expect_error(ggnet(n, size = "phono"), "incorrect")
ggnet(n, group = "weights")
### --- test errors in set_edges
expect_error(ggnet(n, segment.label = NA), "incorrect")
expect_error(ggnet(n, segment.label = 1:3), "incorrect")
expect_error(ggnet(n, segment.label = -11:-1), "incorrect") # unnecessary
# expect_error(ggnet(n, size = "phono"), "incorrect")
n %e% "weights" = sample(1:2, network.edgecount(n), replace = TRUE)
ggnet(n, segment.label = "weights")
ggnet(n, segment.label = "a")
### --- test mode = c(x, y)
ggnet(n, mode = matrix(1, ncol = 2, nrow = 10))
ggnet(n, mode = c("lon", "lat"))
expect_error(ggnet(n, mode = c("xx", "yy")), "not found")
n %v% "abc" = "abc"
expect_error(ggnet(n, mode = c("abc", "abc")), "not numeric")
expect_error(ggnet(n, mode = matrix(1, ncol = 2, nrow = 9)), "coordinates length")
### --- test arrow.size
expect_error(ggnet(n, arrow.size = -1), "incorrect arrow.size")
expect_warning(ggnet(n, arrow.size = 1), "arrow.size ignored")
### --- test arrow.gap
expect_error(expect_warning(ggnet(n, arrow.size = 12, arrow.gap = -1)), "incorrect arrow.gap")
expect_warning(ggnet(n, arrow.size = 12, arrow.gap = 0.1), "arrow.gap ignored")
m <- network::network(m, directed = TRUE)
ggnet(m, arrow.size = 12, arrow.gap = 0.05)
### --- test degree centrality
ggnet(n, weight = "degree")
### --- test weight.min, weight.max and weight.cut
# test weight.min
expect_error(ggnet(n, weight = "degree", weight.min = -1), "incorrect weight.min")
expect_message(ggnet(n, weight = "degree", weight.min = 1), "weight.min removed")
expect_warning(ggnet(n, weight = "degree", weight.min = 99), "removed all nodes")
# test weight.max
expect_error(ggnet(n, weight = "degree", weight.max = -1), "incorrect weight.max")
expect_message(ggnet(n, weight = "degree", weight.max = 99), "weight.max removed")
expect_warning(ggnet(n, weight = 1:10, weight.max = 0.5), "removed all nodes")
expect_error(ggnet(n, weight = "abc"), "incorrect weight.method")
# test weight.cut
expect_error(ggnet(n, weight.cut = NA), "incorrect weight.cut")
expect_error(ggnet(n, weight.cut = "a"), "incorrect weight.cut")
expect_warning(ggnet(n, weight.cut = 3), "weight.cut ignored")
ggnet(n, weight = "degree", weight.cut = 3)
### --- test node.group and node.color
expect_warning(ggnet(n, group = 1:10, node.color = "blue"), "unequal length")
### --- test node labels and label sizes
ggnet(n, label = letters[ 1:10 ], color = "white")
ggnet(n, label = "abc", color = "white", label.size = 4, size = 12)
expect_error(ggnet(n, label = letters[ 1:10 ], label.size = "abc"), "incorrect label.size")
### --- test node placement
expect_error(ggnet(n, mode = "xyz"), "unsupported")
expect_error(ggnet(n, mode = letters[1:3]), "incorrect mode")
### --- test label.trim
expect_error(ggnet(n, label = TRUE, label.trim = "xyz"), "incorrect label.trim")
ggnet(n, label = TRUE, color = "white", label.trim = 1)
ggnet(n, label = TRUE, color = "white", label.trim = toupper)
### --- test layout.exp
expect_error(ggnet(n, layout.exp = "xyz"))
ggnet(n, layout.exp = 0.1)
### --- test bipartite functionality
# weighted adjacency matrix
bip = data.frame(event1 = c(1, 2, 1, 0),
event2 = c(0, 0, 3, 0),
event3 = c(1, 1, 0, 4),
row.names = letters[1:4])
# weighted bipartite network
bip = network(bip,
matrix.type = "bipartite",
ignore.eval = FALSE,
names.eval = "weights")
# test bipartite mode
ggnet(bip, group = "mode")
### --- test network coercion
expect_warning(ggnet(network(matrix(1, nrow = 2, ncol = 2), loops = TRUE)), "self-loops")
expect_error(ggnet(1:2), "network object")
expect_error(ggnet(network(data.frame(1:2, 3:4), hyper = TRUE)), "hyper graphs")
expect_error(ggnet(network(data.frame(1:2, 3:4), multiple = TRUE)), "multiplex graphs")
### --- test igraph functionality
# test igraph conversion
p = ggnet(asIgraph(n))
expect_null(p$guides$colour)
expect_equal(length(p$layers), 2)
# test igraph degree
library(igraph)
ggnet(n, weight = "degree")
expect_true(TRUE)
})
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