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#' @title Plot rank intervals
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#' @description Compute rank intervals (minimal and maximal possible rank) and visualize them with ggplot.
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#' @param P A partial ranking as matrix object calculated with [neighborhood_inclusion]
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#' or [positional_dominance].
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#' @param cent.df A data frame containing centrality scores of indices (optional). See Details.
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#' @param ties.method String specifying how ties are treated in the base \code{\link[base]{rank}} function.
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#' @details
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#' If a data frame of centrality scores is added, the respective ranks of nodes are
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#' shown in the intervals. Note that some points might fall outside of the
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#' intervals depending how ties are treated.
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#'
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#' @return a ggplot object.
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#' @author David Schoch
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#' @seealso [rank_intervals]
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#' @examples
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#' library(igraph)
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#' library(ggplot2)
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#' g <- graph.empty(n=11,directed = FALSE)
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#' g <- add_edges(g,c(1,11,2,4,3,5,3,11,4,8,5,9,5,11,6,7,6,8,
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#' 6,10,6,11,7,9,7,10,7,11,8,9,8,10,9,10))
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#' P <- neighborhood_inclusion(g)
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#' \dontrun{plot_rank_intervals(P)}
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#'
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#' #adding index based rankings
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#' cent_scores <- data.frame(
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#' degree = degree(g),
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#' betweenness = round(betweenness(g),4),
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#' closeness = round(closeness(g),4),
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#' eigenvector = round(eigen_centrality(g)$vector,4))
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#' \dontrun{plot_rank_intervals(P,cent.df=cent_scores)}
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#' @export
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plot_rank_intervals <- function(P, cent.df,ties.method="min") {
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if (!requireNamespace("ggplot2", quietly = TRUE)) {
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stop("ggplot2 needed for this function to work. Please install it.", call. = FALSE)
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}
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n <- nrow(P)
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if (is.null(rownames(P))) {
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names <- paste0("V",1:nrow(P))
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} else {
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names <- rownames(P)
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}
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intervals <- netrankr::rank_intervals(P)
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intervals$node <- as.character(names)
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df <- data.frame(interval = rep(c("max_rank", "min_rank", "mid_point"), each = n),
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node = rep(names, 3),
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rank = c(intervals$max_rank, intervals$min_rank, intervals$mid_point),
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order = rep(rank(intervals$mid_point, ties.method = "first"), 3),
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mid_point = rep(intervals$mid_point, 3))
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if (missing(cent.df)) {
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ggplot2::ggplot(df, ggplot2::aes_(x = ~stats::reorder(node, order), y = ~rank, group = ~node)) +
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ggplot2::geom_line(col = "#8F8F8F") +
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ggplot2::geom_point(col = "#8F8F8F",
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shape = ifelse(df$interval == "min_rank", 24,
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ifelse(df$interval == "max_rank", 25, 3)), fill = "#8F8F8F") +
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ggplot2::scale_y_continuous(breaks = pretty(1:nrow(P))) +
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ggplot2::theme_bw() +
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ggplot2::theme(text = ggplot2::element_text(family = "Times", size = 14),
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axis.text.x = ggplot2::element_text(angle = ifelse(any(nchar(names) > 2), 45, 0), hjust = 1),
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legend.position = "bottom",
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panel.border = ggplot2::element_blank(),
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panel.grid = ggplot2::element_blank(),
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axis.ticks = ggplot2::element_blank()) +
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ggplot2::labs(x = "", y = "Rank", shape = "", colour = "")
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} else {
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no.indices <- ncol(cent.df)
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cent.df.long <- data.frame(node = rep(names, no.indices),
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mid_point = rep(intervals$mid_point, no.indices),
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index = rep(names(cent.df), each = n),
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rank = c(apply(cent.df, 2, function(x) rank(x, ties.method = ties.method))))
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ggplot2::ggplot(df, ggplot2::aes_(x = ~stats::reorder(node, mid_point),
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y = ~rank, group = ~node)) +
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ggplot2::geom_line(col = "#8F8F8F") +
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ggplot2::geom_point(col = "#8F8F8F",
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shape = ifelse(df$interval == "min_rank", 24,
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ifelse(df$interval == "max_rank", 25, 3)), fill = "#8F8F8F") +
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ggplot2::geom_jitter(data = cent.df.long,
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ggplot2::aes_(x = ~stats::reorder(node, mid_point),
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y = ~rank, shape = ~index), size = 2,
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width = 0.1, height = 0) +
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ggplot2::scale_shape_manual(values = 3:(3 + no.indices)) +
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ggplot2::scale_y_continuous(breaks = pretty(1:nrow(P))) +
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ggplot2::theme_bw() +
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ggplot2::theme(text = ggplot2::element_text(family = "Times", size = 14),
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axis.text.x = ggplot2::element_text(angle = ifelse(any(nchar(names) > 2), 45, 0), hjust = 1),
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legend.position = "bottom",
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panel.border = ggplot2::element_blank(),
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panel.grid = ggplot2::element_blank(),
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axis.ticks = ggplot2::element_blank()) +
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ggplot2::labs(x = "", y = "Rank", shape = "", colour = "")
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}
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}
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schochastics