docs: replace graph() with make_graph() in examples

R/adjacency.R

@@ -239,7 +239,7 @@ graph.adjacency.sparse <- function(adjmatrix, mode = c(
     res <- add_edges(res, edges = t(as.matrix(el[, 1:2])), attr = weight)
   } else {
     edges <- unlist(apply(el, 1, function(x) rep(unname(x[1:2]), x[3])))
-    res <- graph(n = vc, edges, directed = (mode == "directed"))
+    res <- make_graph(n = vc, edges, directed = (mode == "directed"))
   }
   res
 }

R/attributes.R

@@ -931,7 +931,7 @@ igraph.i.attribute.combination <- function(comb) {
 #' @keywords graphs
 #' @examples
 #'
-#' g <- graph(c(1, 2, 1, 2, 1, 2, 2, 3, 3, 4))
+#' g <- make_graph(c(1, 2, 1, 2, 1, 2, 2, 3, 3, 4))
 #' E(g)$weight <- 1:5
 #'
 #' ## print attribute values with the graph

R/centrality.R

@@ -1194,10 +1194,10 @@ bonpow.sparse <- function(graph, nodes = V(graph), loops = FALSE,
 #' @examples
 #'
 #' # Generate some test data from Bonacich, 1987:
-#' g.c <- graph(c(1, 2, 1, 3, 2, 4, 3, 5), dir = FALSE)
-#' g.d <- graph(c(1, 2, 1, 3, 1, 4, 2, 5, 3, 6, 4, 7), dir = FALSE)
-#' g.e <- graph(c(1, 2, 1, 3, 1, 4, 2, 5, 2, 6, 3, 7, 3, 8, 4, 9, 4, 10), dir = FALSE)
-#' g.f <- graph(
+#' g.c <- make_graph(c(1, 2, 1, 3, 2, 4, 3, 5), dir = FALSE)
+#' g.d <- make_graph(c(1, 2, 1, 3, 1, 4, 2, 5, 3, 6, 4, 7), dir = FALSE)
+#' g.e <- make_graph(c(1, 2, 1, 3, 1, 4, 2, 5, 2, 6, 3, 7, 3, 8, 4, 9, 4, 10), dir = FALSE)
+#' g.f <- make_graph(
 #'   c(1, 2, 1, 3, 1, 4, 2, 5, 2, 6, 2, 7, 3, 8, 3, 9, 3, 10, 4, 11, 4, 12, 4, 13),
 #'   dir = FALSE
 #' )
@@ -1379,9 +1379,9 @@ alpha.centrality.sparse <- function(graph, nodes = V(graph), alpha = 1,
 #' @examples
 #'
 #' # The examples from Bonacich's paper
-#' g.1 <- graph(c(1, 3, 2, 3, 3, 4, 4, 5))
-#' g.2 <- graph(c(2, 1, 3, 1, 4, 1, 5, 1))
-#' g.3 <- graph(c(1, 2, 2, 3, 3, 4, 4, 1, 5, 1))
+#' g.1 <- make_graph(c(1, 3, 2, 3, 3, 4, 4, 5))
+#' g.2 <- make_graph(c(2, 1, 3, 1, 4, 1, 5, 1))
+#' g.3 <- make_graph(c(1, 2, 2, 3, 3, 4, 4, 1, 5, 1))
 #' alpha_centrality(g.1)
 #' alpha_centrality(g.2)
 #' alpha_centrality(g.3, alpha = 0.5)

R/centralization.R

@@ -82,7 +82,7 @@ NULL
 #' centralize(deg, tmax)
 #'
 #' # The most centralized graph according to eigenvector centrality
-#' g0 <- graph(c(2, 1), n = 10, dir = FALSE)
+#' g0 <- make_graph(c(2, 1), n = 10, dir = FALSE)
 #' g1 <- make_star(10, mode = "undirected")
 #' centr_eigen(g0)$centralization
 #' centr_eigen(g1)$centralization

R/conversion.R

@@ -342,7 +342,7 @@ as_edgelist <- function(graph, names = TRUE) {
 #' plot(ug3, layout = layout_in_circle, edge.label = E(ug3)$weight)
 #' }
 #'
-#' g4 <- graph(c(
+#' g4 <- make_graph(c(
 #'   1, 2, 3, 2, 3, 4, 3, 4, 5, 4, 5, 4,
 #'   6, 7, 7, 6, 7, 8, 7, 8, 8, 7, 8, 9, 8, 9,
 #'   9, 8, 9, 8, 9, 9, 10, 10, 10, 10

R/data_frame.R

@@ -245,12 +245,12 @@ graph_from_edgelist <- function(el, directed = TRUE) {
       names <- unique(as.character(t(el)))
       ids <- seq(names)
       names(ids) <- names
-      res <- graph(unname(ids[t(el)]), directed = directed)
+      res <- make_graph(unname(ids[t(el)]), directed = directed)
       rm(ids)
       V(res)$name <- names
     } else {
       ## normal edge list
-      res <- graph(t(el), directed = directed)
+      res <- make_graph(t(el), directed = directed)
     }
   }
 

R/flow.R

@@ -74,7 +74,7 @@
 #' min_cut(g, capacity = rep(1, vcount(g)))
 #' min_cut(g, value.only = FALSE, capacity = rep(1, vcount(g)))
 #'
-#' g2 <- graph(c(1, 2, 2, 3, 3, 4, 1, 6, 6, 5, 5, 4, 4, 1))
+#' g2 <- make_graph(c(1, 2, 2, 3, 3, 4, 1, 6, 6, 5, 5, 4, 4, 1))
 #' E(g2)$capacity <- c(3, 1, 2, 10, 1, 3, 2)
 #' min_cut(g2, value.only = FALSE)
 #' @family flow

R/hrg.R

@@ -346,7 +346,7 @@ as.igraph.igraphHRG <- function(x, ...) {
   ll <- ifelse(x$left < 0, -x$left + ovc, x$left + 1)
   rr <- ifelse(x$right < 0, -x$right + ovc, x$right + 1)
   edges <- c(rbind(seq_len(ivc) + ovc, ll), rbind(seq_len(ivc) + ovc, rr))
-  res <- graph(edges)
+  res <- make_graph(edges)
 
   V(res)$name <- c(
     if (!is.null(x$names)) x$names else as.character(1:ovc),

R/incidence.R

@@ -68,7 +68,7 @@ graph.incidence.sparse <- function(incidence, directed, mode, multiple,
     }
 
     edges <- unlist(apply(el, 1, function(x) rep(unname(x[1:2]), x[3])))
-    res <- graph(n = n1 + n2, edges, directed = directed)
+    res <- make_graph(n = n1 + n2, edges, directed = directed)
   }
 
   set_vertex_attr(res, "type", value = c(rep(FALSE, n1), rep(TRUE, n2)))

R/iterators.R

@@ -458,21 +458,21 @@ simple_vs_index <- function(x, i, na_ok = FALSE) {
 #'
 #' # -----------------------------------------------------------------
 #' # nei() special function
-#' g <- graph(c(1, 2, 2, 3, 2, 4, 4, 2))
+#' g <- make_graph(c(1, 2, 2, 3, 2, 4, 4, 2))
 #' V(g)[.nei(c(2, 4))]
 #' V(g)[.nei(c(2, 4), "in")]
 #' V(g)[.nei(c(2, 4), "out")]
 #'
 #' # -----------------------------------------------------------------
 #' # The same with vertex names
-#' g <- graph(~ A -+ B, B -+ C:D, D -+ B)
+#' g <- make_graph(~ A -+ B, B -+ C:D, D -+ B)
 #' V(g)[.nei(c("B", "D"))]
 #' V(g)[.nei(c("B", "D"), "in")]
 #' V(g)[.nei(c("B", "D"), "out")]
 #'
 #' # -----------------------------------------------------------------
 #' # Resolving attributes
-#' g <- graph(~ A -+ B, B -+ C:D, D -+ B)
+#' g <- make_graph(~ A -+ B, B -+ C:D, D -+ B)
 #' V(g)$color <- c("red", "red", "green", "green")
 #' V(g)[color == "red"]
 #'

R/make.R

@@ -940,7 +940,7 @@ graph_from_literal_i <- function(mf) {
 
   ids <- seq(along.with = v)
   names(ids) <- v
-  res <- graph(unname(ids[edges]), n = length(v), directed = directed)
+  res <- make_graph(unname(ids[edges]), n = length(v), directed = directed)
   if (simplify) res <- simplify(res)
   res <- set_vertex_attr(res, "name", value = v)
   res

R/plot.common.R

@@ -197,7 +197,7 @@ i.postprocess.layout <- function(maybe_layout) {
 #' @keywords graphs
 #' @examples
 #'
-#' g <- graph(c(
+#' g <- make_graph(c(
 #'   0, 1, 1, 0, 1, 2, 1, 3, 1, 3, 1, 3,
 #'   2, 3, 2, 3, 2, 3, 2, 3, 0, 1
 #' ) + 1)

R/simple.R

@@ -64,7 +64,7 @@
 #' @keywords graphs
 #' @examples
 #'
-#' g <- graph(c(1, 2, 1, 2, 3, 3))
+#' g <- make_graph(c(1, 2, 1, 2, 3, 3))
 #' is_simple(g)
 #' is_simple(simplify(g, remove.loops = FALSE))
 #' is_simple(simplify(g, remove.multiple = FALSE))

R/structural.properties.R

@@ -1118,7 +1118,7 @@ reciprocity <- function(graph, ignore.loops = TRUE,
 #' g3 <- sample_gnp(n = 10, 0.4)
 #'
 #' # loop edges
-#' g <- graph(c(1, 2, 2, 2, 2, 3)) # graph with a self-loop
+#' g <- make_graph(c(1, 2, 2, 2, 2, 3)) # graph with a self-loop
 #' edge_density(g, loops = FALSE) # this is wrong!!!
 #' edge_density(g, loops = TRUE) # this is right!!!
 #' edge_density(simplify(g), loops = FALSE) # this is also right, but different
@@ -1522,7 +1522,7 @@ girth <- function(graph, circle = TRUE) {
 #' @examples
 #'
 #' # Loops
-#' g <- graph(c(1, 1, 2, 2, 3, 3, 4, 5))
+#' g <- make_graph(c(1, 1, 2, 2, 3, 3, 4, 5))
 #' any_loop(g)
 #' which_loop(g)
 #'

demo/crashR.R

@@ -169,7 +169,7 @@ pause()
 ### Graphs
 ## Create a small graph, A->B, A->C, B->C, C->E, D
 ## A=1, B=2, C=3, D=4, E=5
-g <- graph(c(1, 2, 1, 3, 2, 3, 3, 5), n = 5)
+g <- make_graph(c(1, 2, 1, 3, 2, 3, 3, 5), n = 5)
 
 pause()
 
@@ -180,7 +180,7 @@ pause()
 
 ### Create an undirected graph as well
 ## A--B, A--C, B--C, C--E, D
-g2 <- graph(c(1, 2, 1, 3, 2, 3, 3, 5), n = 5, dir = FALSE)
+g2 <- make_graph(c(1, 2, 1, 3, 2, 3, 3, 5), n = 5, dir = FALSE)
 g2
 
 pause()
@@ -215,7 +215,7 @@ as.directed(as.undirected(g))
 pause()
 
 ### Multiple edges
-g <- graph(c(1, 2, 1, 2, 1, 3, 2, 3, 4, 5), n = 5)
+g <- make_graph(c(1, 2, 1, 2, 1, 3, 2, 3, 4, 5), n = 5)
 g
 
 is_simple(g)
@@ -230,7 +230,7 @@ is_simple(g)
 pause()
 
 ### Loop edges
-g <- graph(c(1, 1, 1, 2, 1, 3, 2, 3, 4, 5), n = 5)
+g <- make_graph(c(1, 1, 1, 2, 1, 3, 2, 3, 4, 5), n = 5)
 g
 
 is_simple(g)

tests/testthat/test-alpha.centrality.R

@@ -1,27 +1,27 @@
 test_that("dense alpha_centrality works", {
-  g.1 <- graph(c(1, 3, 2, 3, 3, 4, 4, 5))
+  g.1 <- make_graph(c(1, 3, 2, 3, 3, 4, 4, 5))
   ac1 <- alpha_centrality(g.1, sparse = FALSE)
   expect_that(ac1, equals(c(1, 1, 3, 4, 5)))
 
-  g.2 <- graph(c(2, 1, 3, 1, 4, 1, 5, 1))
+  g.2 <- make_graph(c(2, 1, 3, 1, 4, 1, 5, 1))
   ac2 <- alpha_centrality(g.2, sparse = FALSE)
   expect_that(ac2, equals(c(5, 1, 1, 1, 1)))
 
-  g.3 <- graph(c(1, 2, 2, 3, 3, 4, 4, 1, 5, 1))
+  g.3 <- make_graph(c(1, 2, 2, 3, 3, 4, 4, 1, 5, 1))
   ac3 <- alpha_centrality(g.3, alpha = 0.5, sparse = FALSE)
   expect_that(ac3, equals(c(76, 68, 64, 62, 30) / 30))
 })
 
 test_that("sparse alpha_centrality works", {
-  g.1 <- graph(c(1, 3, 2, 3, 3, 4, 4, 5))
+  g.1 <- make_graph(c(1, 3, 2, 3, 3, 4, 4, 5))
   ac1 <- alpha_centrality(g.1, sparse = TRUE)
   expect_that(ac1, equals(c(1, 1, 3, 4, 5)))
 
-  g.2 <- graph(c(2, 1, 3, 1, 4, 1, 5, 1))
+  g.2 <- make_graph(c(2, 1, 3, 1, 4, 1, 5, 1))
   ac2 <- alpha_centrality(g.2, sparse = TRUE)
   expect_that(ac2, equals(c(5, 1, 1, 1, 1)))
 
-  g.3 <- graph(c(1, 2, 2, 3, 3, 4, 4, 1, 5, 1))
+  g.3 <- make_graph(c(1, 2, 2, 3, 3, 4, 4, 1, 5, 1))
   ac3 <- alpha_centrality(g.3, alpha = 0.5, sparse = TRUE)
   expect_that(ac3, equals(c(76, 68, 64, 62, 30) / 30))
 })

tests/testthat/test-are.connected.R

@@ -4,7 +4,7 @@ test_that("are_adjacent works", {
   expect_true(are_adjacent(g, "B", "A"))
   expect_false(are_adjacent(g, "A", "D"))
 
-  g2 <- graph(c(1, 2, 2, 3, 3, 4), dir = FALSE)
+  g2 <- make_graph(c(1, 2, 2, 3, 3, 4), dir = FALSE)
   expect_true(are_adjacent(g2, 1, 2))
   expect_true(are_adjacent(g2, 3, 2))
   expect_false(are_adjacent(g2, 4, 1))