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Heatmap of nodes (rows) by centrality measures (columns), z-standardized within measure so the diverging palette is meaningful. Optional row clustering groups nodes with similar centrality profiles.
plot_centrality_heatmap( x, measures = NULL, cluster_rows = TRUE, order_by = NULL, show_values = FALSE, value_digits = 1L, low = "#2171B5", mid = "white", high = "#CB181D", limits = c(-2.5, 2.5), title = NULL, subtitle = "z-scored within measure", ... )
A ggplot object.
Centrality data frame (from centrality) or a network input.
centrality
Character vector of measure names.
Logical. Hierarchically cluster rows so nodes with similar profiles are adjacent. Default TRUE.
If cluster_rows = FALSE, optionally the name of a measure to sort rows by (descending). Default: first measure.
cluster_rows = FALSE
Logical. Print z-scores in cells. Default FALSE.
Decimal places for cell values. Default 1.
Color stops for the diverging scale. Defaults to blue -> white -> red.
Numeric c(min, max) z-score range. Values outside are squished to the endpoints. Default c(-2.5, 2.5).
Plot title and subtitle.
Passed to centrality when x is a network.
x
adj <- matrix(c(0,1,1,0,0, 1,0,1,1,0, 1,1,0,1,1, 0,1,1,0,1, 0,0,1,1,0), 5, 5) rownames(adj) <- colnames(adj) <- LETTERS[1:5] plot_centrality_heatmap(adj)
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