A dashboard layout displaying multiple metric tiles in a responsive grid, where each tile shows a KPI value with its label, an embedded sparkline showing recent trend, and a change indicator (up/down arrow with percentage). This visualization is essential for operations monitoring and business dashboards where multiple metrics need to be tracked simultaneously at a glance. The combination of current value, trend visualization, and change direction provides comprehensive metric context in a compact format.
#' anyplot.ai
#' dashboard-metrics-tiles: Real-Time Dashboard Tiles
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 92/100 | Created: 2026-05-21
library(ggplot2)
library(dplyr)
library(scales)
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"
COL_GOOD <- "#009E73" # Okabe-Ito 1 — good / brand
COL_WARNING <- "#DDCC77" # imprint amber — warning
COL_CRITICAL <- "#AE3030" # imprint red — critical / bad
# --- Data -------------------------------------------------------------------
# Server health metrics snapshot (6 tiles in 3x2 grid)
metric_names <- c("CPU Usage", "Memory", "Response Time", "Disk I/O", "Throughput", "Error Rate")
value_nums <- c(45.2, 72.1, 118, 38.6, 1247, 0.82)
value_labels <- c("45.2%", "72.1%", "118 ms", "38.6%", "1,247 req/s", "0.82%")
changes <- c(-5.2, 8.1, -14.7, 3.4, 12.3, -22.5)
statuses <- c("good", "warning", "good", "good", "good", "good")
up_is_good <- c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)
n_metrics <- length(metric_names)
n_pts <- 24
status_colors <- ifelse(
statuses == "critical", COL_CRITICAL,
ifelse(statuses == "warning", COL_WARNING, COL_GOOD)
)
change_colors <- ifelse(
(changes > 0 & !up_is_good) | (changes < 0 & up_is_good),
COL_CRITICAL, COL_GOOD
)
arrows <- ifelse(changes > 0, "▲", "▼")
change_labels <- paste0(arrows, " ", sprintf("%.1f", abs(changes)), "%")
metrics_df <- data.frame(
metric = factor(metric_names, levels = metric_names),
value_label = value_labels,
change_label = change_labels,
status_color = status_colors,
change_color = change_colors,
stringsAsFactors = FALSE
)
# Generate sparkline histories (end pinned to current value, with slight trend)
spark_list <- lapply(seq_len(n_metrics), function(i) {
base <- value_nums[i]
chg <- changes[i] / 100 * base
steps <- rnorm(n_pts, mean = chg / n_pts, sd = base * 0.035)
vals <- base - chg + cumsum(steps)
vals[n_pts] <- base
data.frame(
metric = metric_names[i],
t = seq_len(n_pts),
val = vals,
status_color = status_colors[i],
stringsAsFactors = FALSE
)
})
spark_df <- do.call(rbind, spark_list)
spark_df$metric <- factor(spark_df$metric, levels = metric_names)
# Normalise each sparkline to [0.15, 0.65] within the panel's y space
spark_df <- spark_df |>
group_by(metric) |>
mutate(val_norm = rescale(val, to = c(0.15, 0.65))) |>
ungroup()
spark_end <- spark_df[spark_df$t == n_pts, ]
# Annotation positions within the normalised [−0.18, 1.55] y range
label_df <- data.frame(
metric = metrics_df$metric,
x_mid = (n_pts + 1) / 2,
y_value = 1.35,
y_change = 1.08,
y_name = -0.06,
value_label = metrics_df$value_label,
change_label = metrics_df$change_label,
status_color = metrics_df$status_color,
change_color = metrics_df$change_color,
name_color = INK_SOFT,
stringsAsFactors = FALSE
)
# --- Plot -------------------------------------------------------------------
p <- ggplot() +
# Shaded area under sparkline
geom_area(
data = spark_df,
aes(x = t, y = val_norm, fill = status_color, group = metric),
alpha = 0.15,
show.legend = FALSE
) +
# Sparkline
geom_line(
data = spark_df,
aes(x = t, y = val_norm, color = status_color, group = metric),
linewidth = 0.9,
show.legend = FALSE
) +
# Terminal dot
geom_point(
data = spark_end,
aes(x = t, y = val_norm, color = status_color),
size = 2.0,
show.legend = FALSE
) +
# KPI value — large, status-coloured
geom_text(
data = label_df,
aes(x = x_mid, y = y_value, label = value_label, color = status_color),
size = 7,
fontface = "bold",
show.legend = FALSE
) +
# Change indicator with directional arrow
geom_text(
data = label_df,
aes(x = x_mid, y = y_change, label = change_label, color = change_color),
size = 3.2,
show.legend = FALSE
) +
# Metric name label at bottom of tile
geom_text(
data = label_df,
aes(x = x_mid, y = y_name, label = metric, color = name_color),
size = 3.5,
fontface = "bold",
show.legend = FALSE
) +
scale_color_identity() +
scale_fill_identity() +
facet_wrap(~metric, nrow = 2, ncol = 3) +
scale_y_continuous(limits = c(-0.18, 1.55), expand = c(0, 0)) +
scale_x_continuous(expand = expansion(mult = 0.05)) +
labs(
title = "Server Health · dashboard-metrics-tiles · r · ggplot2 · anyplot.ai"
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.4),
panel.grid = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
strip.text = element_blank(),
plot.title = element_text(color = INK, size = 11, hjust = 0.5),
plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
panel.spacing.x = unit(1.5, "lines"),
panel.spacing.y = unit(1.5, "lines")
)
# --- Save -------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Part of Real-Time Dashboard Tiles on anyplot.ai.