R/trav_both.R

In DiagrammeR: Graph/Network Visualization

#' Traverse from one or more selected nodes onto neighboring nodes
#'
#' @description
#'
#' From a graph object of class `dgr_graph` move from one or more nodes present
#' in a selection to other nodes that are connected by edges, replacing the
#' current nodes in the selection with those nodes traversed to. An optional
#' filter by node attribute can limit the set of nodes traversed to.
#'
#' This traversal function makes use of an active selection of nodes. After the
#' traversal, depending on the traversal conditions, there will either be a
#' selection of nodes or no selection at all.
#'
#' Selections of nodes can be performed using the following node selection
#' (`select_*()`) functions: [select_nodes()], [select_last_nodes_created()],
#' [select_nodes_by_degree()], [select_nodes_by_id()], or
#' [select_nodes_in_neighborhood()].
#'
#' Selections of nodes can also be performed using the following traversal
#' (`trav_*()`) functions: [trav_out()], [trav_in()], [trav_both()],
#' [trav_out_node()], [trav_in_node()], [trav_out_until()], or
#' [trav_in_until()].
#'
#' @inheritParams render_graph
#' @param conditions An option to use filtering conditions for the traversal.
#' @param copy_attrs_from Providing a node attribute name will copy those node
#'   attribute values to the traversed nodes. Any values extant on the nodes
#'   traversed to will be replaced.
#' @param copy_attrs_as If a node attribute name is provided in
#'   `copy_attrs_from`, this option will allow the copied attribute values to be
#'   written under a different attribute name. If the attribute name provided in
#'   `copy_attrs_as` does not exist in the graph's ndf, the new node attribute
#'   will be created with the chosen name.
#' @param agg If a node attribute is provided to `copy_attrs_from`, then an
#'   aggregation function is required since there may be cases where multiple
#'   edge attribute values will be passed onto the traversed node(s). To pass
#'   only a single value, the following aggregation functions can be used:
#'   `sum`, `min`, `max`, `mean`, or `median`.
#' @param add_to_selection An option to either add the traversed to nodes to the
#'   active selection of nodes (`TRUE`) or switch the active selection entirely
#'   to those traversed to nodes (`FALSE`, the default case).
#'
#' @return A graph object of class `dgr_graph`.
#'
#' @examples
#' # Set a seed
#' suppressWarnings(RNGversion("3.5.0"))
#' set.seed(23)
#'
#' # Create a simple graph
#' graph <-
#'   create_graph() %>%
#'   add_n_nodes(
#'     n = 2,
#'     type = "a",
#'     label = c("asd", "iekd")) %>%
#'   add_n_nodes(
#'     n = 3,
#'     type = "b",
#'     label = c("idj", "edl", "ohd")) %>%
#'   add_edges_w_string(
#'     edges = "1->2 1->3 2->4 2->5 3->5",
#'     rel = c(NA, "A", "B", "C", "D"))
#'
#' # Create a data frame with node ID values
#' # representing the graph edges (with `from`
#' # and `to` columns), and, a set of numeric values
#' df_edges <-
#'   data.frame(
#'     from = c(1, 1, 2, 2, 3),
#'     to = c(2, 3, 4, 5, 5),
#'     values = round(rnorm(5, 5), 2))
#'
#' # Create a data frame with node ID values
#' # representing the graph nodes (with the `id`
#' # columns), and, a set of numeric values
#' df_nodes <-
#'   data.frame(
#'     id = 1:5,
#'     values = round(rnorm(5, 7), 2))
#'
#' # Join the data frame to the graph's internal
#' # edge data frame (edf)
#' graph <-
#'   graph %>%
#'   join_edge_attrs(df = df_edges) %>%
#'   join_node_attrs(df = df_nodes)
#'
#' # Show the graph's internal node data frame
#' graph %>% get_node_df()
#'
#' # Show the graph's internal edge data frame
#' graph %>% get_edge_df()
#'
#' # Perform a simple traversal from node `3`
#' # to adjacent nodes with no conditions on
#' # the nodes traversed to
#' graph %>%
#'   select_nodes_by_id(nodes = 3) %>%
#'   trav_both() %>%
#'   get_selection()
#'
#' # Traverse from node `2` to any adjacent
#' # nodes, filtering to those nodes that have
#' # numeric values less than `8.0` for
#' # the `values` node attribute
#' graph %>%
#'   select_nodes_by_id(nodes = 2) %>%
#'   trav_both(
#'     conditions = values < 8.0) %>%
#'   get_selection()
#'
#' # Traverse from node `5` to any adjacent
#' # nodes, filtering to those nodes that
#' # have a `type` attribute of `b`
#' graph %>%
#'   select_nodes_by_id(nodes = 5) %>%
#'   trav_both(
#'     conditions = type == "b") %>%
#'   get_selection()
#'
#' # Traverse from node `2` to any adjacent
#' # nodes, and use multiple conditions for the
#' # traversal
#' graph %>%
#'   select_nodes_by_id(nodes = 2) %>%
#'   trav_both(
#'     conditions =
#'       type == "a" &
#'       values > 8.0) %>%
#'   get_selection()
#'
#' # Traverse from node `2` to any adjacent
#' # nodes, and use multiple conditions with
#' # a single-length vector
#' graph %>%
#'   select_nodes_by_id(nodes = 2) %>%
#'   trav_both(
#'     conditions =
#'       type == "a" | values > 8.0) %>%
#'   get_selection()
#'
#' # Traverse from node `2` to any adjacent
#' # nodes, and use a regular expression as
#' # a filtering condition
#' graph %>%
#'   select_nodes_by_id(nodes = 2) %>%
#'   trav_both(
#'     conditions = grepl("..d", label)) %>%
#'   get_selection()
#'
#' # Create another simple graph to demonstrate
#' # copying of node attribute values to traversed
#' # nodes
#' graph <-
#'   create_graph() %>%
#'   add_path(n = 5) %>%
#'   select_nodes_by_id(nodes = c(2, 4)) %>%
#'   set_node_attrs_ws(
#'     node_attr = value,
#'     value = 5)
#'
#' # Show the graph's internal node data frame
#' graph %>% get_node_df()
#'
#' # Show the graph's internal edge data frame
#' graph %>% get_edge_df()
#'
#' # Perform a traversal from the inner nodes
#' # (`2` and `4`) to their adjacent nodes (`1`,
#' # `3`, and `5`) while also applying the node
#' # attribute `value` to target nodes; node `3`
#' # will obtain a `value` of 10 since a traversal
#' # to `3` will occur from `2` and `4` (and
#' # multiple values passed will be summed)
#' graph <-
#'   graph %>%
#'   trav_both(
#'     copy_attrs_from = value,
#'     agg = "sum")
#'
#' # Show the graph's internal node data frame
#' # after this change
#' graph %>% get_node_df()
#'
#' @export
trav_both <- function(
    graph,
    conditions = NULL,
    copy_attrs_from = NULL,
    copy_attrs_as = NULL,
    agg = "sum",
    add_to_selection = FALSE
) {

  # Get the time of function start
  time_function_start <- Sys.time()

  # Get the name of the function
  fcn_name <- get_calling_fcn()

  # Validation: Graph object is valid
  check_graph_valid(graph)

  # Validation: Graph contains nodes
  check_graph_contains_nodes(graph)

  # Validation: Graph contains edges
  check_graph_contains_edges(graph)

  # Validation: Graph object has valid node selection
  check_graph_contains_node_selection(
    graph,
    c("Any traversal requires an active selection.",
      "This type of traversal requires a selection of nodes."))

  # Get the requested `copy_attrs_from`
  copy_attrs_from <-
    rlang::enquo(copy_attrs_from) %>% rlang::get_expr() %>% as.character()

  # Get the requested `copy_attrs_as`
  copy_attrs_as <-
    rlang::enquo(copy_attrs_as) %>% rlang::get_expr() %>% as.character()

  if (length(copy_attrs_from) == 0) {
    copy_attrs_from <- NULL
  }

  if (length(copy_attrs_as) == 0) {
    copy_attrs_as <- NULL
  }

  if (!is.null(copy_attrs_as) && !is.null(copy_attrs_from) &&
      copy_attrs_as == copy_attrs_from) {

    copy_attrs_as <- NULL
  }

  # Get the selection of nodes as the starting
  # nodes for the traversal
  starting_nodes <- graph$node_selection$node

  # Get the graph's edge data frame
  edf <- graph$edges_df

  # Get the graph's node data frame
  ndf <- graph$nodes_df

  # Find all nodes that are connected to the
  # starting nodes via outgoing edges
  valid_nodes <-
    graph %>%
    get_nbrs(starting_nodes) %>%
    as.integer()

  valid_nodes <-
    dplyr::tibble(id = valid_nodes) %>%
    dplyr::inner_join(ndf, by = "id") %>%
    dplyr::distinct()

  # If no rows returned, then there are no
  # valid traversals, so return the same graph
  # object without modifying the selection
  if (nrow(valid_nodes) == 0) {
    return(graph)
  }

  # If traversal conditions are provided then
  # pass in those conditions and filter the
  # data frame of `valid_nodes`
  # Maybe quo_is_null ?
  if (!rlang::quo_is_null(rlang::enquo(conditions))) {

    valid_nodes <- valid_nodes %>% dplyr::filter({{ conditions }})
  }

  # If the option is taken to copy node attribute
  # values to the traversed nodes, perform the join
  # operations
  if (!is.null(copy_attrs_from)) {

    from_join <-
      valid_nodes %>%
      dplyr::select("id") %>%
      dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "from")) %>%
      dplyr::inner_join(ndf %>% dplyr::select("id", !!enquo(copy_attrs_from)), by = c("to" = "id")) %>%
      dplyr::select("id", !!enquo(copy_attrs_from))

    to_join <-
      valid_nodes %>%
      dplyr::select("id") %>%
      dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "to")) %>%
      dplyr::inner_join(ndf %>% dplyr::select("id", !!enquo(copy_attrs_from)), by = c("from" = "id")) %>%
      dplyr::select("id", !!enquo(copy_attrs_from))

    nodes <-
      from_join %>%
      dplyr::union_all(to_join) %>%
      dplyr::group_by(id) %>%
      dplyr::summarize(!!copy_attrs_from :=
                         match.fun(!!agg)(!!as.name(copy_attrs_from),
                                           na.rm = TRUE)) %>%
      dplyr::right_join(ndf, by = "id") %>%
      dplyr::relocate(id, type, label) %>%
      as.data.frame(stringsAsFactors = FALSE)

    # Get column numbers that end with ".x" or ".y"
    split_var_x_col <-
      grep("\\.x$", colnames(nodes))

    split_var_y_col <-
      grep("\\.y$", colnames(nodes))

    if (is.null(copy_attrs_as)) {

      # Selectively merge in values to the existing
      # edge attribute column
      for (i in seq_len(nrow(nodes))) {
        if (!is.na(nodes[i, split_var_x_col])) {
          nodes[i, split_var_y_col] <- nodes[i, split_var_x_col]
        }
      }
    }

    if (!is.null(copy_attrs_as)) {

      # Reorder the columns generated
      nodes <-
        nodes[, c(c(1:(ncol(nodes) - 2)), split_var_y_col, split_var_x_col)]
    }

    # Rename the ".y" column
    colnames(nodes)[split_var_y_col] <- copy_attrs_from

    if (is.null(copy_attrs_as)) {

      # Drop the ".x" column
      nodes <- nodes[-split_var_x_col]

    } else {

      # Rename the two columns
      colnames(nodes)[split_var_x_col] <- copy_attrs_from
      colnames(nodes)[split_var_y_col] <- copy_attrs_as
    }

    # Update the graph's internal node data frame
    graph$nodes_df <- nodes
  }

  # If no rows returned, then there are no
  # valid traversals, so return the same graph
  # object without modifying the selection
  if (nrow(valid_nodes) == 0) {
    return(graph)
  }

  # Obtain vector with node ID selection of nodes
  # already present
  nodes_prev_selection <- graph$node_selection$node

  if (add_to_selection) {

    # If TRUE supplied to `add_to_selection` add
    # the nodes to which we traversed to the
    # previous selection
    nodes_combined <- union(nodes_prev_selection, valid_nodes$id)

    graph$node_selection <-
      replace_graph_node_selection(
        graph = graph,
        replacement = nodes_combined)

  } else {

    # Add the node ID values to the active selection
    # of nodes in `graph$node_selection`
    graph$node_selection <-
      replace_graph_node_selection(
        graph = graph,
        replacement = valid_nodes$id)
  }

  # Replace `graph$edge_selection` with an empty df
  graph$edge_selection <- create_empty_esdf()

  # Update the `graph_log` df with an action
  graph$graph_log <-
    add_action_to_log(
      graph_log = graph$graph_log,
      version_id = nrow(graph$graph_log) + 1L,
      function_used = fcn_name,
      time_modified = time_function_start,
      duration = graph_function_duration(time_function_start),
      nodes = nrow(graph$nodes_df),
      edges = nrow(graph$edges_df))

  # Write graph backup if the option is set
  if (graph$graph_info$write_backups) {
    save_graph_as_rds(graph = graph)
  }

  graph
}