tests: update based on zzz-deprecate.R

tests/testthat/test-adjacency.spectral.embedding.R

@@ -266,7 +266,7 @@ test_that("Issue #51 is resolved", {
   pref.matrix <- diag(0.2, 2) + 0.2
   block.sizes <- c(800, 800)
   n <- sum(block.sizes)
-  g <- sbm.game(n, pref.matrix, block.sizes, directed = TRUE)
+  g <- sample_sbm(n, pref.matrix, block.sizes, directed = TRUE)
 
   for (i in 1:25) {
     ase <- embed_adjacency_matrix(g, 2)

tests/testthat/test-attributes.R

@@ -194,7 +194,7 @@ test_that("we can set all attributes some vertices/edges", {
 })
 
 test_that("cannot use vs/es from another graph", {
-  g <- graph.ring(10)
+  g <- make_ring(10)
   g2 <- g + 1
   v <- V(g)[1:4]
   expect_error(g2 - v, "Cannot use a vertex sequence from another graph")

tests/testthat/test-betweenness.R

@@ -91,7 +91,7 @@ test_that("shortest paths are compared with tolerance when calculating betweenne
     5.2605598, 6.6816853, 4.9482123, 1.8989790
   )
 
-  g <- graph.data.frame(edges, directed = FALSE)
+  g <- graph_from_data_frame(edges, directed = FALSE)
   result <- betweenness(g, weights = edges.dists)
 
   expect_that(result[1:5], equals(c("1" = 0, "2" = 44, "3" = 71, "4" = 36.5, "6" = 44)))

tests/testthat/test-bug-154.R

@@ -1,6 +1,6 @@
 test_that("graph.get.subisomorphisms.vf2() works even if the graph has a vertex attribute named x", {
-  g <- graph.full(4)
+  g <- make_full_graph(4)
   V(g)$x <- 1:4
-  subs <- graph.get.subisomorphisms.vf2(g, graph.ring(4))
+  subs <- graph.get.subisomorphisms.vf2(g, make_ring(4))
   expect_equal(length(subs), 24)
 })

tests/testthat/test-coloring.R

@@ -24,7 +24,7 @@ test_that("simplify_and_colorize works", {
 
   expect_true(is_simple(result))
   expect_that(vcount(result), equals(vcount(g)))
-  expect_that(get.edgelist(result), equals(matrix(c(1:4, 2:5), ncol = 2)))
+  expect_that(as_edgelist(result), equals(matrix(c(1:4, 2:5), ncol = 2)))
   expect_that(V(result)$color, equals(c(0, 0, 0, 0, 1)))
   expect_that(E(result)$color, equals(c(1, 4, 1, 2)))
 })

tests/testthat/test-correlated.R

@@ -28,7 +28,7 @@ test_that("sample_correlated_gnp works when p is not given", {
 test_that("sample_correlated_gnp works even for non-ER graphs", {
   set.seed(42)
 
-  g <- grg.game(100, 0.2)
+  g <- sample_grg(100, 0.2)
   g2 <- sample_correlated_gnp(g, corr = 1)
   expect_that(g[], equals(g2[]))
 
@@ -50,7 +50,7 @@ test_that("sample_correlated_gnp corner cases work", {
   set.seed(42)
 
   is.full <- function(g) {
-    g2 <- graph.full(vcount(g), directed = is.directed(g))
+    g2 <- make_full_graph(vcount(g), directed = is_directed(g))
     graph.isomorphic(g, g2)
   }
 
@@ -77,12 +77,12 @@ test_that("permutation works for sample_correlated_gnp", {
   g <- erdos.renyi.game(10, .3)
   perm <- sample(vcount(g))
   g2 <- sample_correlated_gnp(g, corr = .99999, p = .3, permutation = perm)
-  g <- permute.vertices(g, perm)
+  g <- permute(g, perm)
   expect_that(g[], equals(g2[]))
 
   g <- erdos.renyi.game(10, .3)
   perm <- sample(vcount(g))
   g2 <- sample_correlated_gnp(g, corr = 1, p = .3, permutation = perm)
-  g <- permute.vertices(g, perm)
+  g <- permute(g, perm)
   expect_that(g[], equals(g2[]))
 })

tests/testthat/test-count.multiple.R

@@ -1,5 +1,5 @@
 test_that("any_multiple(), count_multiple(), which_multiple() works", {
-  # g <- barabasi.game(10, m = 3, algorithm = "bag")
+  # g <- sample_pa(10, m = 3, algorithm = "bag")
   g <- graph_from_edgelist(cbind(
     c(2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10),
     c(1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 3, 4, 3, 1, 1, 1, 3, 1, 2, 4, 1, 1, 2, 4, 1, 4, 1)
@@ -25,15 +25,15 @@ test_that("any_multiple(), count_multiple(), which_multiple() works", {
 
 
   ## Direction of the edge is important
-  expect_false(any_multiple(graph(c(1, 2, 2, 1))))
-  expect_that(which_multiple(graph(c(1, 2, 2, 1))), equals(c(FALSE, FALSE)))
+  expect_false(any_multiple(make_graph(c(1, 2, 2, 1))))
+  expect_that(which_multiple(make_graph(c(1, 2, 2, 1))), equals(c(FALSE, FALSE)))
   expect_that(
-    which_multiple(graph(c(1, 2, 2, 1), dir = FALSE)),
+    which_multiple(make_graph(c(1, 2, 2, 1), dir = FALSE)),
     equals(c(FALSE, TRUE))
   )
 
   ## Remove multiple edges but keep multiplicity
-  # g <- barabasi.game(10, m = 3, algorithm = "bag")
+  # g <- sample_pa(10, m = 3, algorithm = "bag")
   g <- graph_from_edgelist(cbind(
     c(2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10),
     c(1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 4, 1, 4, 1, 1, 6, 4, 1, 5, 8)

tests/testthat/test-dimSelect.R

@@ -1,8 +1,8 @@
 test_that("dimensionality selection works", {
   set.seed(42)
 
-  k <- graph.famous("zachary")
-  ev <- eigen(get.adjacency(k), only.values = TRUE)$values
+  k <- make_graph("zachary")
+  ev <- eigen(as_adjacency_matrix(k), only.values = TRUE)$values
   kdim <- dim_select(ev)
   expect_that(kdim, equals(4))
 

tests/testthat/test-dot.product.game.R

@@ -15,10 +15,10 @@ test_that("Dot product rng works", {
 
   vecs <- replicate(5, rep(1 / 2, 4))
   g <- sample_dot_product(vecs)
-  expect_that(g[], is_equivalent_to(graph.full(5)[]))
+  expect_that(g[], is_equivalent_to(make_full_graph(5)[]))
 
   g2 <- sample_dot_product(vecs, directed = TRUE)
-  expect_that(g2[], is_equivalent_to(graph.full(5, directed = TRUE)[]))
+  expect_that(g2[], is_equivalent_to(make_full_graph(5, directed = TRUE)[]))
 
   vecs <- replicate(100, rep(sqrt(1 / 8), 4))
   g <- sample_dot_product(vecs)

tests/testthat/test-get.shortest.paths.R

@@ -21,13 +21,10 @@ test_that("shortest_paths works", {
   g <- graph_from_data_frame(as.data.frame(edges))
 
   all1 <- all_shortest_paths(g, "s", "t", weights = NA)$vpaths
-  all2 <- all_shortest_paths(g, "s", "t")$vpaths
 
   s1 <- shortest_paths(g, "s", "t", weights = NA)
-  s2 <- get.shortest.paths(g, "s", "t")
 
   expect_true(s1$vpath %in% all1)
-  expect_true(s2$vpath %in% all2)
 })
 
 test_that("shortest_paths can handle negative weights", {

tests/testthat/test-graph.atlas.R

@@ -1,9 +1,9 @@
 test_that("graph.atlas works", {
-  g124 <- graph.atlas(124)
+  g124 <- graph_from_atlas(124)
   expect_true(graph.isomorphic(g124, make_graph(c(1, 2, 2, 3, 3, 4, 4, 5, 1, 5, 1, 3, 2, 6),
     directed = FALSE
   )))
-  g234 <- graph.atlas(234)
+  g234 <- graph_from_atlas(234)
   expect_true(graph.isomorphic(g234, make_graph(c(1, 6, 2, 6, 3, 6, 4, 6, 5, 6),
     n = 7,
     directed = FALSE

tests/testthat/test-hrg.R

@@ -11,7 +11,7 @@ test_that("as.hclust.igraphHRG() works", {
   set.seed(42)
 
   g <- make_graph("zachary")
-  hrg <- hrg.fit(g)
+  hrg <- fit_hrg(g)
   expect_snapshot({
     summary(as.hclust(hrg))
   })

tests/testthat/test-hsbm.R

@@ -10,28 +10,28 @@ test_that("HSBM works", {
   g <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 0)
   expect_that(ecount(g), equals(172))
   expect_that(vcount(g), equals(100))
-  expect_false(is.directed(g))
+  expect_false(is_directed(g))
 
   set.seed(42)
 
   g2 <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 1)
   expect_that(ecount(g2), equals(ecount(g) + 10 * 9 * (90 + 10) / 2))
   expect_that(vcount(g2), equals(100))
-  expect_true(is.simple(g2))
+  expect_true(is_simple(g2))
 
   set.seed(42)
 
   g3 <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 1e-15)
   expect_that(ecount(g3), equals(ecount(g)))
   expect_that(vcount(g3), equals(100))
-  expect_true(is.simple(g3))
+  expect_true(is_simple(g3))
 
   set.seed(42)
 
   g4 <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 1 - 1e-15)
   expect_that(ecount(g4), equals(ecount(g2)))
   expect_that(vcount(g4), equals(100))
-  expect_true(is.simple(g4))
+  expect_true(is_simple(g4))
 })
 
 test_that("HSBM with 1 cluster per block works", {
@@ -88,7 +88,7 @@ test_that("HSBM with list arguments works", {
     m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),
     C = list(C1, C2, C3, C4), p = 1
   )
-  expect_true(is.simple(gg1))
+  expect_true(is_simple(gg1))
 
   set.seed(42)
   gg11 <- sample_hierarchical_sbm(21,
@@ -109,7 +109,7 @@ test_that("HSBM with list arguments works", {
     m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),
     C = list(C1, C2, C3, C4), p = 0
   )
-  expect_true(is.simple(gg2))
+  expect_true(is_simple(gg2))
 
   gg22 <- sample_hierarchical_sbm(21,
     m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),

tests/testthat/test-identical_graphs.R

@@ -10,7 +10,7 @@ test_that("identical_graphs works", {
 })
 
 test_that("identical_graphs considers attributes", {
-  g <- barabasi.game(10)
+  g <- sample_pa(10)
   g2 <- g
 
   expect_true(identical_graphs(g, g2))

tests/testthat/test-laplacian.spectral.embedding.R

@@ -211,7 +211,7 @@ test_that("Undirected, weighted, D-A case works", {
   E(g)$weight <- sample(1:5, ecount(g), replace = TRUE)
 
   no <- 3
-  A <- as(Matrix::Matrix(diag(graph.strength(g)), doDiag = FALSE), "generalMatrix") - g[]
+  A <- as(Matrix::Matrix(diag(strength(g)), doDiag = FALSE), "generalMatrix") - g[]
   ss <- eigen(A)
 
   D <- ss$values
@@ -473,8 +473,8 @@ test_that("Directed, weighted case works", {
   E(g)$weight <- sample(1:5, ecount(g), replace = TRUE)
 
   no <- 3
-  O12 <- diag(1 / sqrt(graph.strength(g, mode = "out")))
-  P12 <- diag(1 / sqrt(graph.strength(g, mode = "in")))
+  O12 <- diag(1 / sqrt(strength(g, mode = "out")))
+  P12 <- diag(1 / sqrt(strength(g, mode = "in")))
   A <- O12 %*% g[] %*% P12
   ss <- svd(A)
 

tests/testthat/test-layout.sugiyama.R

@@ -1,5 +1,5 @@
 test_that("layout_with_sugiyama does not demote matrices to vectors in res$layout.dummy", {
   ex <- graph_from_literal(A -+ B:C, B -+ C:D)
-  layex <- layout.sugiyama(ex, layers = NULL)
+  layex <- layout_with_sugiyama(ex, layers = NULL)
   expect_that(nrow(layex$layout.dummy), equals(1))
 })

tests/testthat/test-notable.R

@@ -1,9 +1,3 @@
-test_that("notable graphs work with make_graph", {
-  g <- make_graph("Levi")
-  g2 <- graph.famous("Levi")
-  expect_true(identical_graphs(g, g2))
-})
-
 test_that("make_graph for notable graphs is case insensitive", {
   g <- make_graph("Levi")
   g2 <- make_graph("levi")

tests/testthat/test-operators.R

@@ -55,7 +55,7 @@ test_that("Union of directed named graphs", {
     make_graph(~ 1:2:3:4:5, 1 -+ 2, 1 -+ 3, 2 -+ 4, 3 -+ 4, 1 -+ 5, 4 -+ 5)
   )
 
-  gg <- graph.union(graphs)
+  gg <- union.igraph(graphs)
 
   expect_equal(vcount(gg), 5)
   expect_equal(ecount(gg), 10)
@@ -77,17 +77,17 @@ test_that("edge reversal works", {
   g <- make_graph(~ 1 -+ 2, 1 -+ 3, 1 -+ 4, 2 -+ 3, 3 -+ 4)
   g2 <- reverse_edges(g)
   expect_that(vcount(g2), equals(vcount(g)))
-  expect_that(get.edgelist(g2), equals(get.edgelist(g)[, c(2, 1)]))
+  expect_that(as_edgelist(g2), equals(as_edgelist(g)[, c(2, 1)]))
 
   # graph with isolated vertices
   g <- make_graph(~ 1:2:3:4:5, 1 -+ 2, 1 -+ 4)
   g2 <- reverse_edges(g)
   expect_that(vcount(g2), equals(vcount(g)))
-  expect_that(get.edgelist(g2), equals(get.edgelist(g)[, c(2, 1)]))
+  expect_that(as_edgelist(g2), equals(as_edgelist(g)[, c(2, 1)]))
 })
 
 test_that("t() is aliased to edge reversal for graphs", {
   g <- make_graph(~ 1 -+ 2, 1 -+ 3, 1 -+ 4, 2 -+ 3, 3 -+ 4)
   expect_that(vcount(t(g)), equals(vcount(g)))
-  expect_that(get.edgelist(t(g)), equals(get.edgelist(g)[, c(2, 1)]))
+  expect_that(as_edgelist(t(g)), equals(as_edgelist(g)[, c(2, 1)]))
 })

tests/testthat/test-operators4.R

@@ -65,7 +65,7 @@ test_that("union of unnamed graphs works", {
   E(g1)$b1 <- letters[1:10]
   E(g2)$b2 <- letters[11:23]
 
-  g <- graph.union(g1, g2)
+  g <- union.igraph(g1, g2)
 
   expect_that(
     sort(graph_attr_names(g)),
@@ -109,7 +109,7 @@ test_that("union of named graphs works", {
   E(g1)$b1 <- letters[1:10]
   E(g2)$b2 <- letters[11:23]
 
-  g <- graph.union(g1, g2)
+  g <- union.igraph(g1, g2)
 
   expect_that(
     sort(graph_attr_names(g)),
@@ -390,7 +390,7 @@ test_that("union of non-named graphs keeps attributes properly", {
   E(g)$weight <- sample(ecount(g))
   E(g2)$weight <- sample(ecount(g2))
 
-  gu <- graph.union(g, g2)
+  gu <- union.igraph(g, g2)
 
   rn <- function(D) {
     rownames(D) <- paste(D[, 1], D[, 2], sep = "-")

tests/testthat/test-rewire.R

@@ -1,21 +1,21 @@
 test_that("rewire(each_edge(mode='in')) keeps the in-degree distribution", {
-  g <- barabasi.game(1000)
+  g <- sample_pa(1000)
 
   g2 <- g %>% rewire(each_edge(mode = "in", multiple = T, prob = 0.2))
   expect_that(degree(g, mode = "in"), equals(degree(g2, mode = "in")))
   expect_false(all(degree(g, mode = "out") == degree(g2, mode = "out")))
 })
 
 test_that("rewire(each_edge(mode='out')) keeps the out-degree distribution", {
-  g <- barabasi.game(1000)
+  g <- sample_pa(1000)
 
   g2 <- g %>% rewire(each_edge(mode = "out", multiple = T, prob = 0.2))
   expect_that(degree(g, mode = "out"), equals(degree(g2, mode = "out")))
   expect_false(all(degree(g, mode = "in") == degree(g2, mode = "in")))
 })
 
 test_that("rewire() with zero probability does not do anything", {
-  g <- barabasi.game(100)
+  g <- sample_pa(100)
   g2 <- g %>% rewire(each_edge(prob = 0))
   expect_true(identical_graphs(g, g2))
 })

tests/testthat/test-scan.R

@@ -144,12 +144,12 @@ test_that("Issue 18 is really resolved", {
 
   g <- make_graph(el)
 
-  sc1 <- sapply(graph.neighborhood(g, order = 1, mode = "all"), ecount)
+  sc1 <- sapply(make_ego_graph(g, order = 1, mode = "all"), ecount)
   sc2 <- local_scan(graph.us = g, mode = "all", k = 1)
   expect_that(sc1, equals(sc2))
 
-  g2 <- induced.subgraph(g, 5:8)
-  sc21 <- sapply(graph.neighborhood(g2, order = 1, mode = "all"), ecount)
+  g2 <- induced_subgraph(g, 5:8)
+  sc21 <- sapply(make_ego_graph(g2, order = 1, mode = "all"), ecount)
   sc22 <- local_scan(graph.us = g2, mode = "all", k = 1)
   expect_that(sc21, equals(sc22))
 })
@@ -164,8 +164,8 @@ test_that("Issue 20 is resolved", {
 })
 
 test_that("FUN argument works, #32", {
-  r1 <- local_scan(graph.ring(10), k = 1, FUN = "ecount")
-  r2 <- local_scan(graph.ring(10), k = 1, FUN = ecount)
+  r1 <- local_scan(make_ring(10), k = 1, FUN = "ecount")
+  r2 <- local_scan(make_ring(10), k = 1, FUN = ecount)
   expect_that(r1, equals(rep(2, 10)))
   expect_that(r2, equals(rep(2, 10)))
 })

tests/testthat/test-transitivity.R

@@ -23,7 +23,7 @@ test_that("transitivity works", {
 
 test_that("no integer overflow", {
   set.seed(42)
-  g <- graph.star(80000, mode = "undirected") + edges(sample(2:1000), 100)
+  g <- make_star(80000, mode = "undirected") + edges(sample(2:1000), 100)
   mtr <- min(transitivity(g, type = "local"), na.rm = TRUE)
   expect_true(mtr > 0)
 })