A matrix-based representation of a network or graph where rows and columns represent nodes and cell color indicates the presence or weight of edges between them. This visualization complements node-link diagrams by excelling at revealing clusters, structural patterns, and density in large or dense networks where node-link layouts become cluttered. Reordering nodes by cluster, degree, or community membership exposes block-diagonal structure and makes group boundaries immediately visible.
#' anyplot.ai
#' heatmap-adjacency: Network Adjacency Matrix Heatmap
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 92/100 | Created: 2026-09-05
library(ggplot2)
library(dplyr)
library(tidyr)
library(ragg)
set.seed(42)
# --- Theme tokens ------------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
# --- Data: coworker collaboration network ------------------------------------
# 30 employees across 3 departments; node labels carry the department prefix
# so group boundaries are visible directly in the axis ticks (per spec note
# on large networks). Within-department pairs collaborate more often and more
# intensely than cross-department pairs, producing block-diagonal structure.
departments <- c("Engineering", "Marketing", "Sales")
dept_short <- c(Engineering = "ENG", Marketing = "MKT", Sales = "SLS")
n_per_dept <- 10
nodes <- tibble::tibble(department = rep(departments, each = n_per_dept)) %>%
group_by(department) %>%
mutate(idx = row_number()) %>%
ungroup() %>%
mutate(node = sprintf("%s-%02d", dept_short[department], idx))
n_nodes <- nrow(nodes)
weight_mat <- matrix(NA_real_, n_nodes, n_nodes)
for (i in seq_len(n_nodes)) {
for (j in seq_len(n_nodes)) {
if (j <= i) next
same_dept <- nodes$department[i] == nodes$department[j]
edge_prob <- if (same_dept) 0.75 else 0.12
if (runif(1) < edge_prob) {
weight <- if (same_dept) runif(1, 4, 10) else runif(1, 1, 4)
weight_mat[i, j] <- weight
weight_mat[j, i] <- weight # undirected graph: fill both triangles
}
}
}
rownames(weight_mat) <- nodes$node
colnames(weight_mat) <- nodes$node
adjacency_df <- as.data.frame(weight_mat) %>%
tibble::rownames_to_column("source") %>%
pivot_longer(-source, names_to = "target", values_to = "weight") %>%
mutate(
source = factor(source, levels = nodes$node),
target = factor(target, levels = nodes$node)
)
# --- Department-boundary markers ---------------------------------------
# Positions along each discrete axis where one department's block ends and
# the next begins, drawn as subtle divider lines to reinforce the
# block-diagonal structure beyond what tick labels alone convey.
boundaries <- head(cumsum(table(nodes$department)[departments]), -1) + 0.5
# --- Plot ---------------------------------------------------------------
p <- ggplot(adjacency_df, aes(x = target, y = source, fill = weight)) +
geom_tile(color = PAGE_BG, linewidth = 0.15) +
geom_vline(xintercept = boundaries, color = INK_SOFT, linewidth = 0.3, alpha = 0.5) +
geom_hline(yintercept = boundaries, color = INK_SOFT, linewidth = 0.3, alpha = 0.5) +
scale_fill_gradient(
low = "#009E73",
high = "#4467A3",
na.value = ELEVATED_BG, # absent edges (incl. diagonal): near-bg, distinct from data
name = "Meetings\nper month",
guide = guide_colorbar(barheight = unit(9, "cm"), barwidth = unit(0.4, "cm"))
) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0), limits = rev(nodes$node)) +
coord_fixed(ratio = 1) +
labs(
title = "heatmap-adjacency · r · ggplot2 · anyplot.ai",
subtitle = "Color intensity = meetings per month; near-background tiles indicate no regular contact",
x = "Employee", y = "Employee"
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text.x = element_text(color = INK_SOFT, size = 8, angle = 90, hjust = 1, vjust = 0.5),
axis.text.y = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = 12),
plot.subtitle = element_text(color = INK_SOFT, size = 8),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 10),
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
plot.margin = margin(10, 10, 10, 10)
)
# --- Save ---------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 6,
height = 6,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/heatmap-adjacency/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "heatmap-adjacency",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/heatmap-adjacency/r/ggplot2",
"hub": "https://anyplot.ai/heatmap-adjacency",
"code_json": "https://api.anyplot.ai/specs/heatmap-adjacency/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/heatmap-adjacency",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-adjacency/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-adjacency/r/ggplot2/plot-dark.png",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Network Adjacency Matrix Heatmap on anyplot.ai.