R/get_agg_degree_out.R

In rich-iannone/DiagrammeR: Graph/Network Visualization

#' Get an aggregate value from the outdegree of nodes
#'
#' @description
#'
#' Get a single, aggregate value from the outdegree values for all nodes in a
#' graph, or, a subset of graph nodes.
#'
#' @inheritParams render_graph
#' @param agg The aggregation function to use for summarizing outdegree values
#'   from graph nodes. The following aggregation functions can be used: `sum`,
#'   `min`, `max`, `mean`, or `median`.
#' @param conditions An option to use filtering conditions for the nodes to
#'   consider.
#'
#' @return A vector with an aggregate outdegree value.
#'
#' @examples
#' # Create a random graph using the
#' # `add_gnm_graph()` function
#' graph <-
#'   create_graph() %>%
#'   add_gnm_graph(
#'     n = 20,
#'     m = 35,
#'     set_seed = 23) %>%
#'   set_node_attrs(
#'     node_attr = value,
#'     values = rnorm(
#'       n = count_nodes(.),
#'       mean = 5,
#'       sd = 1) %>% round(1))
#'
#' # Get the mean outdegree value from all
#' # nodes in the graph
#' graph %>%
#'   get_agg_degree_out(
#'     agg = "mean")
#'
#' # Other aggregation functions can be used
#' # (`min`, `max`, `median`, `sum`); let's
#' # get the median in this example
#' graph %>%
#'   get_agg_degree_out(
#'     agg = "median")
#'
#' # The aggregation of outdegree can occur
#' # for a subset of the graph nodes and this
#' # is made possible by specifying `conditions`
#' # for the nodes
#' graph %>%
#'   get_agg_degree_out(
#'     agg = "mean",
#'     conditions = value < 5.0)
#'
#' @export
get_agg_degree_out <- function(
    graph,
    agg,
    conditions = NULL
) {

  # Validation: Graph object is valid
  check_graph_valid(graph)

  # If filtering conditions are provided then
  # pass in those conditions and filter the ndf
  if (!rlang::quo_is_null(rlang::enquo(conditions))) {

    # Extract the node data frame from the graph
    ndf <- get_node_df(graph)

    # Apply filtering conditions to the ndf
    ndf <- dplyr::filter(.data = ndf, {{ conditions }})

    # Get a vector of node ID values
    node_ids <-
      ndf %>%
      dplyr::pull("id")
  }

  # Get a data frame with outdegree values for
  # all nodes in the graph
  outdegree_df <- get_degree_out(graph)

  if (exists("node_ids")) {
    outdegree_df <-
      outdegree_df %>%
      dplyr::filter(id %in% node_ids)
  }

  # Verify that the value provided for `agg`
  # is one of the accepted aggregation types
  arg_match0(agg, c("sum", "min", "max", "mean", "median"))

  # Get the aggregate value of total degree based
  # on the aggregate function provided
  fun <- match.fun(agg)

  outdegree_agg <-
    outdegree_df %>%
    dplyr::group_by() %>%
    dplyr::summarize(fun(outdegree, na.rm = TRUE), .groups = "drop") %>%
    purrr::flatten_dbl()

  outdegree_agg
}