use native pipe

NAMESPACE

@@ -272,7 +272,6 @@ import(tidygraph)
 import(vctrs)
 importFrom(MASS,bandwidth.nrd)
 importFrom(MASS,kde2d)
-importFrom(dplyr,"%>%")
 importFrom(dplyr,arrange)
 importFrom(dplyr,do)
 importFrom(dplyr,group_by)
@@ -358,7 +357,6 @@ importFrom(igraph,delete_vertex_attr)
 importFrom(igraph,distances)
 importFrom(igraph,edge_attr)
 importFrom(igraph,ends)
-importFrom(igraph,get.edge.ids)
 importFrom(igraph,gorder)
 importFrom(igraph,graph_attr)
 importFrom(igraph,gsize)

R/autograph.R

@@ -18,7 +18,7 @@
 #'
 #' @examples
 #' library(tidygraph)
-#' gr <- create_notable('herschel') %>%
+#' gr <- create_notable('herschel') |>
 #'   mutate(class = sample(letters[1:3], n(), TRUE)) %E>%
 #'   mutate(weight = runif(n()))
 #'

R/edges.R

@@ -198,28 +198,28 @@ expand_edge_aes <- function(x) {
   }
   x
 }
-#' @importFrom dplyr %>% group_by top_n ungroup
+#' @importFrom dplyr group_by top_n ungroup
 collapse_all_edges <- function(edges) {
   from <- pmin(edges$from, edges$to)
   to <- pmax(edges$to, edges$from)
   id <- paste(from, to, sep = '-')
   if (anyDuplicated(id)) {
     edges$.id <- id
-    edges <- edges %>%
-      group_by(.data$.id) %>%
-      top_n(1) %>%
+    edges <- edges |>
+      group_by(.data$.id) |>
+      top_n(1) |>
       ungroup()
   }
   data_frame0(edges)
 }
-#' @importFrom dplyr %>% group_by top_n ungroup
+#' @importFrom dplyr group_by top_n ungroup
 collapse_dir_edges <- function(edges) {
   id <- paste(edges$from, edges$to, sep = '-')
   if (anyDuplicated(id)) {
     edges$.id <- id
-    edges <- edges %>%
-      group_by(.data$.id) %>%
-      top_n(1) %>%
+    edges <- edges |>
+      group_by(.data$.id) |>
+      top_n(1) |>
       ungroup()
   }
   data_frame0(edges)

R/facet_graph.R

@@ -21,7 +21,7 @@
 #'
 #' @examples
 #' library(tidygraph)
-#' gr <- as_tbl_graph(highschool) %>%
+#' gr <- as_tbl_graph(highschool) |>
 #'   mutate(popularity = as.character(cut(centrality_degree(mode = 'in'),
 #'     breaks = 3,
 #'     labels = c('low', 'medium', 'high')

R/facet_nodes.R

@@ -10,7 +10,7 @@
 #'
 #' @examples
 #' library(tidygraph)
-#' gr <- as_tbl_graph(highschool) %>%
+#' gr <- as_tbl_graph(highschool) |>
 #'   mutate(popularity = as.character(cut(centrality_degree(mode = 'in'),
 #'     breaks = 3,
 #'     labels = c('low', 'medium', 'high')

R/geom_axis_hive.R

@@ -1,21 +1,21 @@
 #' @rdname ggraph-extensions
 #' @format NULL
 #' @usage NULL
-#' @importFrom dplyr %>% group_by mutate slice ungroup
+#' @importFrom dplyr group_by mutate slice ungroup
 #' @export
 StatAxisHive <- ggproto('StatAxisHive', StatFilter,
   setup_data = function(data, params) {
-    data <- data %>%
-      group_by(.data$angle, .data$section, .data$PANEL) %>%
+    data <- data |>
+      group_by(.data$angle, .data$section, .data$PANEL) |>
       mutate(
         x = min(.data$r) * cos(.data$angle[1]) * 1.1,
         y = min(.data$r) * sin(.data$angle[1]) * 1.1,
         xend = max(.data$r) * cos(.data$angle[1]) * 1.1,
         yend = max(.data$r) * sin(.data$angle[1]) * 1.1,
         max_r = max(.data$r),
         min_r = min(.data$r)
-      ) %>%
-      slice(1) %>%
+      ) |>
+      slice(1) |>
       ungroup()
     data_frame0(data)
   },
@@ -26,7 +26,7 @@ StatAxisHive <- ggproto('StatAxisHive', StatFilter,
 #' @format NULL
 #' @usage NULL
 #' @importFrom grid textGrob nullGrob
-#' @importFrom dplyr %>% group_by summarise
+#' @importFrom dplyr group_by summarise
 #' @export
 GeomAxisHive <- ggproto('GeomAxisHive', GeomSegment,
   draw_panel = function(data, panel_scales, coord, label = TRUE, axis = TRUE, label_colour = 'black') {
@@ -35,8 +35,8 @@ GeomAxisHive <- ggproto('GeomAxisHive', GeomSegment,
     data$xend <- data$xend / 1.1
     data$yend <- data$yend / 1.1
     data <- coord$transform(data, panel_scales)
-    label_data <- data %>%
-      group_by(.data$axis) %>%
+    label_data <- data |>
+      group_by(.data$axis) |>
       summarise(
         x = max(.data$max_r) * cos(mean(.data$angle)),
         y = max(.data$max_r) * sin(mean(.data$angle)),
@@ -132,15 +132,15 @@ GeomAxisHive <- ggproto('GeomAxisHive', GeomSegment,
 #' @examples
 #' # Plot the flare import graph as a hive plot
 #' library(tidygraph)
-#' flareGr <- as_tbl_graph(flare$imports) %>%
+#' flareGr <- as_tbl_graph(flare$imports) |>
 #'   mutate(
 #'     type = dplyr::case_when(
 #'       centrality_degree(mode = 'in') == 0 ~ 'Source',
 #'       centrality_degree(mode = 'out') == 0 ~ 'Sink',
 #'       TRUE ~ 'Both'
 #'     )
-#'   ) %>%
-#'   activate(edges) %>%
+#'   ) |>
+#'   activate(edges) |>
 #'   mutate(
 #'     type = dplyr::case_when(
 #'       grepl('flare.analytics', paste(.N()$name[from], .N()$name[to])) ~ 'Analytics',

R/geom_conn_bundle.R

@@ -58,7 +58,7 @@
 #' @examples
 #' # Create a graph of the flare class system
 #' library(tidygraph)
-#' flareGraph <- tbl_graph(flare$vertices, flare$edges) %>%
+#' flareGraph <- tbl_graph(flare$vertices, flare$edges) |>
 #'   mutate(
 #'     class = map_bfs_chr(node_is_root(), .f = function(node, dist, path, ...) {
 #'       if (dist <= 1) {

R/geom_edge.R

@@ -2,7 +2,7 @@
 #' @format NULL
 #' @usage NULL
 #' @importFrom grid segmentsGrob polylineGrob gpar
-#' @importFrom dplyr %>% group_by_ do ungroup
+#' @importFrom dplyr group_by_ do ungroup
 #' @importFrom ggforce interpolateDataFrame
 #' @export
 GeomEdgePath <- ggproto('GeomEdgePath', GeomPath,

R/geom_edge_arc.R

@@ -81,14 +81,14 @@
 #' @examples
 #' require(tidygraph)
 #' # Make a graph with different directions of edges
-#' gr <- create_notable('Meredith') %>%
-#'   convert(to_directed) %>%
-#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) %>%
-#'   activate(edges) %>%
+#' gr <- create_notable('Meredith') |>
+#'   convert(to_directed) |>
+#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) |>
+#'   activate(edges) |>
 #'   mutate(
 #'     class = sample(letters[1:3], n(), replace = TRUE),
 #'     switch = sample(c(TRUE, FALSE), n(), replace = TRUE)
-#'   ) %>%
+#'   ) |>
 #'   reroute(from = to, to = from, subset = switch)
 #'
 #' ggraph(gr, 'linear') +

R/geom_edge_bend.R

@@ -68,9 +68,9 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_tree(20, 4) %>%
-#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) %>%
-#'   activate(edges) %>%
+#' gr <- create_tree(20, 4) |>
+#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) |>
+#'   activate(edges) |>
 #'   mutate(class = sample(letters[1:3], n(), replace = TRUE))
 #'
 #' ggraph(gr, 'tree') +

R/geom_edge_bundle_path.R

@@ -278,7 +278,7 @@ geom_edge_bundle_path0 <- function(mapping = NULL, data = get_edges(),
     )
   )
 }
-#' @importFrom igraph gsize delete_edges shortest_paths get.edge.ids is_directed
+#' @importFrom igraph gsize delete_edges shortest_paths is_directed as_edgelist
 path_bundle <- function(graph, nodes, from, to, directed = directed, max_distortion = 2, weight_fac = 2) {
   m <- gsize(graph)
   lock <- rep(FALSE, m)
@@ -294,6 +294,13 @@ path_bundle <- function(graph, nodes, from, to, directed = directed, max_distort
     cli::cli_warn("Ignoring {.arg directed} for undirected graphs")
   }
   mode <- if (is.null(directed) || !directed) "all" else "out"
+
+  all_edges <- as_edgelist(graph)
+  if (directed) {
+    all_edges <- paste(all_edges[,1], "-", all_edges[,2])
+  } else {
+    all_edges <- paste(pmin(all_edges[,1], all_edges[,2]), "-", pmax(all_edges[,1], all_edges[,2]))
+  }
   # iterate
   for (e in edges_order) {
     s <- from[e]
@@ -315,8 +322,15 @@ path_bundle <- function(graph, nodes, from, to, directed = directed, max_distort
       next()
     }
     all_edges_on_path <- rep(as.integer(path), each = 2)
-    all_edges_on_path <- all_edges_on_path[-c(1, length(all_edges_on_path))]
-    all_edges_on_path <- get.edge.ids(graph, all_edges_on_path, directed = mode != "all", error = FALSE, multi = TRUE)
+    all_edges_on_path <- matrix(all_edges_on_path[-c(1, length(all_edges_on_path))], ncol = 2, byrow = TRUE)
+    if (directed) {
+      all_edges_on_path <- paste(all_edges_on_path[,1], "-", all_edges_on_path[,2])
+    } else {
+      all_edges_on_path <- paste(pmin(all_edges_on_path[,1], all_edges_on_path[,2]),
+                                 "-",
+                                 pmax(all_edges_on_path[,1], all_edges_on_path[,2]))
+    }
+    all_edges_on_path <- all_edges %in% all_edges_on_path
     lock[all_edges_on_path] <- TRUE
     paths[[e]] <- path
   }

R/geom_edge_density.R

@@ -39,8 +39,8 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_notable('bull') %>%
-#'   activate(edges) %>%
+#' gr <- create_notable('bull') |>
+#'   activate(edges) |>
 #'   mutate(class = sample(letters[1:3], n(), replace = TRUE))
 #'
 #' ggraph(gr, 'stress') +

R/geom_edge_diagonal.R

@@ -76,9 +76,9 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_tree(20, 4) %>%
-#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) %>%
-#'   activate(edges) %>%
+#' gr <- create_tree(20, 4) |>
+#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) |>
+#'   activate(edges) |>
 #'   mutate(class = sample(letters[1:3], n(), replace = TRUE))
 #'
 #' ggraph(gr, 'tree') +

R/geom_edge_elbow.R

@@ -74,10 +74,10 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' irisDen <- hclust(dist(iris[1:4], method = 'euclidean'), method = 'ward.D2') %>%
-#'   as_tbl_graph() %>%
-#'   mutate(class = sample(letters[1:3], n(), TRUE)) %>%
-#'   activate(edges) %>%
+#' irisDen <- hclust(dist(iris[1:4], method = 'euclidean'), method = 'ward.D2') |>
+#'   as_tbl_graph() |>
+#'   mutate(class = sample(letters[1:3], n(), TRUE)) |>
+#'   activate(edges) |>
 #'   mutate(class = sample(letters[1:3], n(), TRUE))
 #'
 #' ggraph(irisDen, 'dendrogram', circular = TRUE) +

R/geom_edge_fan.R

@@ -77,8 +77,8 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_notable('bull') %>%
-#'   convert(to_directed) %>%
+#' gr <- create_notable('bull') |>
+#'   convert(to_directed) |>
 #'   bind_edges(data.frame(from = c(1, 2, 2, 3), to = c(2, 1, 3, 2))) %E>%
 #'   mutate(class = sample(letters[1:3], 9, TRUE)) %N>%
 #'   mutate(class = sample(c('x', 'y'), 5, TRUE))
@@ -250,17 +250,17 @@ geom_edge_fan0 <- function(mapping = NULL, data = get_edges(),
     )
   )
 }
-#' @importFrom dplyr %>% group_by arrange mutate n ungroup transmute
+#' @importFrom dplyr group_by arrange mutate n ungroup transmute
 create_fans <- function(from, to, params) {
   from$.id <- paste(pmin(from$from, to$to), pmax(from$from, to$to), sep = '-')
   from$.orig_ind <- seq_len(nrow(from))
-  position <- from %>%
-    group_by(.data$PANEL, .data$.id) %>%
-    arrange(.data$from) %>%
-    mutate(position = seq_len(n()) - 0.5 - n() / 2) %>%
-    mutate(position = .data$position * ifelse(.data$from < .data$to, 1, -1)) %>%
-    ungroup() %>%
-    arrange(.data$.orig_ind) %>%
+  position <- from |>
+    group_by(.data$PANEL, .data$.id) |>
+    arrange(.data$from) |>
+    mutate(position = seq_len(n()) - 0.5 - n() / 2) |>
+    mutate(position = .data$position * ifelse(.data$from < .data$to, 1, -1)) |>
+    ungroup() |>
+    arrange(.data$.orig_ind) |>
     transmute(position = .data$position)
   position <- position$position
   max_fans <- max(table(from$.id))

R/geom_edge_hive.R

@@ -74,15 +74,15 @@
 #' @examples
 #' # Plot the flare import graph as a hive plot
 #' library(tidygraph)
-#' flareGr <- as_tbl_graph(flare$imports) %>%
+#' flareGr <- as_tbl_graph(flare$imports) |>
 #'   mutate(
 #'     type = dplyr::case_when(
 #'       centrality_degree(mode = 'in') == 0 ~ 'Source',
 #'       centrality_degree(mode = 'out') == 0 ~ 'Sink',
 #'       TRUE ~ 'Both'
 #'     )
-#'   ) %>%
-#'   activate(edges) %>%
+#'   ) |>
+#'   activate(edges) |>
 #'   mutate(
 #'     type = dplyr::case_when(
 #'       grepl('flare.analytics', paste(.N()$name[from], .N()$name[to])) ~ 'Analytics',

R/geom_edge_link.R

@@ -134,9 +134,9 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_notable('bull') %>%
-#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) %>%
-#'   activate(edges) %>%
+#' gr <- create_notable('bull') |>
+#'   mutate(class = sample(letters[1:3], n(), replace = TRUE)) |>
+#'   activate(edges) |>
 #'   mutate(class = sample(letters[1:3], n(), replace = TRUE))
 #'
 #' ggraph(gr, 'stress') +

R/geom_edge_parallel.R

@@ -68,8 +68,8 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_notable('bull') %>%
-#'   convert(to_directed) %>%
+#' gr <- create_notable('bull') |>
+#'   convert(to_directed) |>
 #'   bind_edges(data.frame(from = c(1, 2, 2, 3), to = c(2, 1, 3, 2))) %E>%
 #'   mutate(class = sample(letters[1:3], 9, TRUE)) %N>%
 #'   mutate(class = sample(c('x', 'y'), 5, TRUE))
@@ -230,16 +230,16 @@ geom_edge_parallel0 <- function(mapping = NULL, data = get_edges(),
     )
   )
 }
-#' @importFrom dplyr %>% group_by arrange summarise n ungroup pull
+#' @importFrom dplyr group_by arrange summarise n ungroup pull
 edge_positions <- function(from, to, params) {
   from$.id <- paste(pmin(from$from, to$to), pmax(from$from, to$to), sep = '-')
   from$.orig_ind <- seq_len(nrow(from))
-  from %>%
-    group_by(.data$PANEL, .data$.id) %>%
-    arrange(.data$from) %>%
-    mutate(position = seq_len(n()) - 0.5 - n() / 2) %>%
-    mutate(position = .data$position * ifelse(.data$from < .data$to, 1, -1)) %>%
-    ungroup() %>%
-    arrange(.data$.orig_ind) %>%
+  from |>
+    group_by(.data$PANEL, .data$.id) |>
+    arrange(.data$from) |>
+    mutate(position = seq_len(n()) - 0.5 - n() / 2) |>
+    mutate(position = .data$position * ifelse(.data$from < .data$to, 1, -1)) |>
+    ungroup() |>
+    arrange(.data$.orig_ind) |>
     pull(.data$position)
 }

R/geom_edge_point.R

@@ -42,11 +42,11 @@
 #'
 #' @examples
 #' require(tidygraph)
-#' gr <- create_notable('zachary') %>%
-#'   mutate(group = group_infomap()) %>%
-#'   morph(to_split, group) %>%
-#'   activate(edges) %>%
-#'   mutate(edge_group = as.character(.N()$group[1])) %>%
+#' gr <- create_notable('zachary') |>
+#'   mutate(group = group_infomap()) |>
+#'   morph(to_split, group) |>
+#'   activate(edges) |>
+#'   mutate(edge_group = as.character(.N()$group[1])) |>
 #'   unmorph()
 #'
 #' ggraph(gr, 'matrix', sort.by = node_rank_hclust()) +