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ab454a1
feat(ggplot2): implement horizon-basic
github-actions[bot] Aug 18, 2026
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chore(ggplot2): add metadata for horizon-basic
github-actions[bot] Aug 18, 2026
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chore(ggplot2): update quality score 89 and review feedback for horiz…
github-actions[bot] Aug 18, 2026
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131 changes: 131 additions & 0 deletions plots/horizon-basic/implementations/r/ggplot2.R
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#' anyplot.ai
#' horizon-basic: Horizon Chart
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 89/100 | Created: 2026-08-18

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"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"

# Imprint diverging endpoints (imprint_div) — folded bands climb toward these
NEG_HUE <- "#AE3030" # matte red — below baseline
POS_HUE <- "#4467A3" # blue — above baseline

# --- Data ---------------------------------------------------------------------
# 8 microservices reporting CPU-utilization deviation from their rolling
# baseline, sampled hourly over ~8 days. Each series is z-scored so all lanes
# share the same magnitude scale, which is what makes the folded bands
# comparable across the panel.
services <- c(
"api-gateway", "auth-service", "payment-svc", "search-index",
"user-profile", "notification", "cache-layer", "recommender"
)
n_series <- length(services)
n_time <- 200

start_time <- as.POSIXct("2024-06-01 00:00:00", tz = "UTC")
dates <- start_time + (0:(n_time - 1)) * 3600

t_idx <- 0:(n_time - 1)
freqs <- 0.02 + seq_len(n_series) * 0.005
phases <- seq_len(n_series) * 0.7
amps <- 1 + seq_len(n_series) * 0.1

trend_matrix <- outer(t_idx, seq_len(n_series), function(t, i) {
sin(t * freqs[i] + phases[i]) * amps[i]
})
noise_matrix <- matrix(rnorm(n_time * n_series, 0, 0.6), nrow = n_time)
spike_mask <- matrix(runif(n_time * n_series) > 0.97, nrow = n_time)
spike_matrix <- matrix(rnorm(n_time * n_series, 0, 3), nrow = n_time) * spike_mask
z_matrix <- scale(trend_matrix + noise_matrix + spike_matrix) # z-score per column

df <- tibble::tibble(
date = rep(dates, times = n_series),
series = factor(rep(services, each = n_time), levels = services),
value = as.numeric(z_matrix)
)

# --- Fold into horizon bands ---------------------------------------------------
# 3 bands per polarity: band k covers the k-th slice of |value|, clipped and
# rescaled into the fixed lane height, so a deeper band = bigger deviation.
n_bands <- 3
row_height <- 1
lane_gap <- 0.88 # leaves a visible gap between stacked lanes
band_height <- max(abs(df$value)) / n_bands

df <- df %>%
mutate(
series_index = as.integer(series),
lane_base = (n_series - series_index) * row_height,
pos_1 = pmin(pmax(value - 0 * band_height, 0), band_height) / band_height * row_height * lane_gap,
pos_2 = pmin(pmax(value - 1 * band_height, 0), band_height) / band_height * row_height * lane_gap,
pos_3 = pmin(pmax(value - 2 * band_height, 0), band_height) / band_height * row_height * lane_gap,
neg_1 = pmin(pmax(-value - 0 * band_height, 0), band_height) / band_height * row_height * lane_gap,
neg_2 = pmin(pmax(-value - 1 * band_height, 0), band_height) / band_height * row_height * lane_gap,
neg_3 = pmin(pmax(-value - 2 * band_height, 0), band_height) / band_height * row_height * lane_gap
)

lane_lookup <- df %>% distinct(series, lane_base)

# --- Plot -----------------------------------------------------------------
p <- ggplot(df, aes(x = date)) +
geom_hline(
data = lane_lookup, aes(yintercept = lane_base),
color = scales::alpha(INK_MUTED, 0.35), linewidth = 0.3
) +
geom_ribbon(aes(ymin = lane_base, ymax = lane_base + pos_1, group = series), fill = POS_HUE, alpha = 0.35) +
geom_ribbon(aes(ymin = lane_base, ymax = lane_base + pos_2, group = series), fill = POS_HUE, alpha = 0.65) +
geom_ribbon(aes(ymin = lane_base, ymax = lane_base + pos_3, group = series), fill = POS_HUE, alpha = 1.0) +
geom_ribbon(aes(ymin = lane_base, ymax = lane_base + neg_1, group = series), fill = NEG_HUE, alpha = 0.35) +
geom_ribbon(aes(ymin = lane_base, ymax = lane_base + neg_2, group = series), fill = NEG_HUE, alpha = 0.65) +
geom_ribbon(aes(ymin = lane_base, ymax = lane_base + neg_3, group = series), fill = NEG_HUE, alpha = 1.0) +
scale_x_datetime(date_labels = "%b %d", expand = expansion(mult = c(0.01, 0.01))) +
scale_y_continuous(
breaks = lane_lookup$lane_base + row_height / 2,
labels = as.character(lane_lookup$series),
expand = expansion(mult = c(0.03, 0.08))
) +
labs(
title = "horizon-basic · r · ggplot2 · anyplot.ai",
subtitle = "CPU-utilization deviation (z-score) per microservice, folded into 3 bands",
x = "Date",
caption = "Blue = above baseline, red = below — deeper shade means a larger deviation",
y = NULL
) +
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.x = element_text(color = INK, size = 10),
axis.text.x = element_text(color = INK_SOFT, size = 8),
axis.ticks.x = element_line(color = INK_SOFT, linewidth = 0.3),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.3),
axis.text.y = element_text(color = INK_SOFT, size = 8, hjust = 1),
axis.ticks.y = element_blank(),
plot.title = element_text(color = INK, size = 12, face = "bold"),
plot.subtitle = element_text(color = INK_SOFT, size = 8),
plot.caption = element_text(color = INK_MUTED, size = 7, hjust = 0),
plot.margin = margin(10, 16, 10, 10)
)

# --- Save --------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
267 changes: 267 additions & 0 deletions plots/horizon-basic/metadata/r/ggplot2.yaml
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library: ggplot2
language: r
specification_id: horizon-basic
created: '2026-08-18T01:02:51Z'
updated: '2026-08-18T01:07:58Z'
generated_by: claude-sonnet
workflow_run: 32086277878
issue: 1877
language_version: 4.4.1
library_version: 3.5.1
preview_url_light: https://storage.googleapis.com/anyplot-images/plots/horizon-basic/r/ggplot2/plot-light.png
preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/horizon-basic/r/ggplot2/plot-dark.png
preview_html_light: null
preview_html_dark: null
quality_score: 89
review:
strengths:
- 'Correct, idiomatic horizon-chart folding technique: geom_ribbon layers with per-band
alpha (0.35/0.65/1.0) faithfully encode magnitude within each of the 3 folded
bands per polarity'
- 'Correct semantic use of the Imprint diverging colormap endpoints (#AE3030 matte
red / #4467A3 blue) for below/above-baseline deviation — matches imprint_div guidance
exactly rather than misapplying the categorical palette to a continuous, diverging
quantity'
- 'Both themes render correctly: page background matches #FAF8F1/#1A1A17, all chrome
(title, subtitle, axis text, caption) uses theme-adaptive INK/INK_SOFT/INK_MUTED
tokens, and the data colors are pixel-identical between light and dark renders'
- 'Clean, reproducible, KISS-structured code: set.seed(42), a single linear dplyr
pipeline, no functions/classes, no fake interactive elements'
- Realistic, neutral dataset (8 microservice CPU-deviation z-scores over ~8 days,
hourly) that exercises all 3 fold bands, including fully-saturated spikes, and
stays within the spec's 5-50 series / 100-1000 point guidance
- No text overlap or edge clipping across either render; mandated title format 'horizon-basic
· r · ggplot2 · anyplot.ai' reproduced exactly
weaknesses:
- 'Design Excellence/storytelling is competent but not yet exceptional: the 8 lanes
are equally weighted with no annotation or callout drawing the eye to the most
notable anomaly (e.g. the fully-saturated auth-service spike) — a small highlight
or callout would raise DE-03 above the current ''visual hierarchy via alpha only''
level'
- 'VQ-03: the lightest band (alpha=0.35) is quite subtle against the #FAF8F1 light
background for near-baseline deviations, making the finest gradation harder to
distinguish than the medium/deep bands — consider nudging the lowest alpha up
slightly (e.g. 0.42-0.48) for better separation from the page background without
disturbing the deep-band contrast'
- plot.margin = margin(10, 16, 10, 10) is quite tight at dpi=400 (~55-90 source
px on a 3200x1800 canvas); the render itself isn't cut off, but a slightly more
generous margin (especially top-right) would give the layout more breathing room
and push DE-02/VQ-05 polish further
- No small legend/swatch maps the 3 discrete alpha steps to an approximate z-score
range — the caption states 'deeper shade means a larger deviation' but a reader
can't tell what z-score each band boundary represents; a compact 3-swatch key
near the caption would strengthen VQ-06/DE-01 without adding real estate cost
image_description: |-
Light render (plot-light.png):
Background: Warm off-white, consistent with #FAF8F1 — not pure white.
Chrome: Bold dark title "horizon-basic · r · ggplot2 · anyplot.ai" top-left; medium-grey subtitle "CPU-utilization deviation (z-score) per microservice, folded into 3 bands" below it; 8 dark-grey service-name labels along the left (api-gateway, auth-service, payment-svc, search-index, user-profile, notification, cache-layer, recommender); "Date" axis title and "Jun 02/04/06/08" tick labels along the bottom in soft grey; a lighter tertiary caption at the very bottom explaining the blue/red + shade-depth encoding. All text is clearly readable against the light background — no light-on-light issues found.
Data: Each of the 8 lanes shows a jagged horizon silhouette built from stacked blue (#4467A3-family) and red (#AE3030-family) ribbons with three visible intensity steps (pale, medium, fully saturated) — deeper shade correctly reads as larger deviation. Thin pale dividers separate the lanes cleanly with no overlap between adjacent lanes.
Legibility verdict: PASS

Dark render (plot-dark.png):
Background: Warm near-black, consistent with #1A1A17 — not pure black.
Chrome: Identical layout to the light render; title/subtitle/axis-text/caption all switch to light off-white/grey tones (no dark-on-dark failures — checked service labels, x-axis ticks, and the bottom caption, all clearly legible against the dark background).
Data: Blue and red band colors are visually identical to the light render (only the page background and text tokens flipped, as required) — confirms correct theme-adaptive implementation with fixed data-color identity.
Legibility verdict: PASS
criteria_checklist:
visual_quality:
score: 28
max: 30
items:
- id: VQ-01
name: Text Legibility
score: 7
max: 8
passed: true
comment: All font sizes explicitly set via theme(); readable in both themes;
title/subtitle/caption sized sensibly with no overflow or clipping.
- id: VQ-02
name: No Overlap
score: 6
max: 6
passed: true
comment: No overlap between lanes, tick labels, or title/subtitle/caption
in either render.
- id: VQ-03
name: Element Visibility
score: 5
max: 6
passed: true
comment: Bands well adapted to 200-point density, but the lowest alpha (0.35)
band is a bit subtle against the light-theme page background.
- id: VQ-04
name: Color Accessibility
score: 2
max: 2
passed: true
comment: Blue/matte-red pairing (not red-green) is CVD-safe and clearly distinguishable.
- id: VQ-05
name: Layout & Canvas
score: 4
max: 4
passed: true
comment: Plot fills the canvas well, nothing cut off, balanced margins.
- id: VQ-06
name: Axis Labels & Title
score: 2
max: 2
passed: true
comment: '''Date'' axis label plus descriptive subtitle/caption stating the
z-score unit and encoding.'
- id: VQ-07
name: Palette Compliance
score: 2
max: 2
passed: true
comment: Correctly uses the imprint_div diverging endpoints (#AE3030/#4467A3)
for the continuous, baseline-centered deviation; theme-correct backgrounds
and chrome in both renders.
design_excellence:
score: 15
max: 20
items:
- id: DE-01
name: Aesthetic Sophistication
score: 6
max: 8
passed: true
comment: Thoughtful diverging-palette semantics and a genuinely well-executed
horizon-fold technique, above a configured-default look but short of full
publication polish.
- id: DE-02
name: Visual Refinement
score: 5
max: 6
passed: true
comment: Grid removed, spines minimal, subtle lane dividers; margins are a
little tight (10-16pt @ dpi=400).
- id: DE-03
name: Data Storytelling
score: 4
max: 6
passed: true
comment: Alpha intensity creates visual hierarchy that draws the eye to spikes,
but no explicit callout/annotation highlights the standout anomaly.
spec_compliance:
score: 15
max: 15
items:
- id: SC-01
name: Plot Type
score: 5
max: 5
passed: true
comment: Genuine horizon chart via folded, layered ribbon bands.
- id: SC-02
name: Required Features
score: 4
max: 4
passed: true
comment: 3 bands, mirrored positive/negative coloring, meaningful zero baseline,
magnitude-driven color intensity, 8 series all present.
- id: SC-03
name: Data Mapping
score: 3
max: 3
passed: true
comment: X=date, Y=lane per series, color=folded magnitude — correct and complete.
- id: SC-04
name: Title & Legend
score: 3
max: 3
passed: true
comment: Title exactly matches the mandated 'horizon-basic · r · ggplot2 ·
anyplot.ai' format; series names serve as the per-lane legend.
data_quality:
score: 14
max: 15
items:
- id: DQ-01
name: Feature Coverage
score: 5
max: 6
passed: true
comment: Shows all 3 bands including full-saturation spikes across multiple
series; not every series exhibits every band depth equally.
- id: DQ-02
name: Realistic Context
score: 5
max: 5
passed: true
comment: Neutral, plausible microservice CPU-monitoring scenario.
- id: DQ-03
name: Appropriate Scale
score: 4
max: 4
passed: true
comment: Z-scored trend+noise+spike data is a sensible, well-scaled representation
of deviation from baseline.
code_quality:
score: 10
max: 10
items:
- id: CQ-01
name: KISS Structure
score: 3
max: 3
passed: true
comment: Linear imports -> data -> fold -> plot -> save, no functions/classes.
- id: CQ-02
name: Reproducibility
score: 2
max: 2
passed: true
comment: set.seed(42).
- id: CQ-03
name: Clean Imports
score: 2
max: 2
passed: true
comment: ggplot2, dplyr, scales, ragg all used; no unused imports.
- id: CQ-04
name: Code Elegance
score: 2
max: 2
passed: true
comment: Clean vectorized fold logic, no fake UI or over-engineering.
- id: CQ-05
name: Output & API
score: 1
max: 1
passed: true
comment: ggsave via ragg::agg_png at plot-{THEME}.png, correct width/height/dpi.
library_mastery:
score: 7
max: 10
items:
- id: LM-01
name: Idiomatic Usage
score: 4
max: 5
passed: true
comment: Idiomatic layered geom_ribbon + scale_x_datetime/scale_y_continuous
+ theme system usage.
- id: LM-02
name: Distinctive Features
score: 3
max: 5
passed: true
comment: Creative layer-composition to build a horizon chart from primitives
ggplot2 doesn't provide natively, though the technique is replicable in
other grammar-of-graphics-style libraries.
verdict: REJECTED
impl_tags:
dependencies: []
techniques:
- layer-composition
- manual-ticks
patterns:
- data-generation
- matrix-construction
dataprep:
- normalization
- time-series
styling:
- alpha-blending
- minimal-chrome
- publication-ready