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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LetsPlot — Hands-On Analysis & Usage Patterns</title>
<style>
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.grid-2 { grid-template-columns: 1fr; }
.header h1 { font-size: 1rem; }
}
</style>
</head>
<body>
<!-- HEADER -->
<div class="header">
<h1>LetsPlot — Hands-On Analysis & Usage Patterns</h1>
<div class="subtitle">charts generated with the library itself · code, benchmarks · suggestions for scale</div>
<div style="margin-top: 24px; text-align:center;">
<span class="badge badge-green" style="font-size:0.7rem; padding: 5px 12px;">JetBrains/lets-plot v4.11.0</span>
<span class="badge badge-blue" style="font-size:0.7rem; padding: 5px 12px; margin-left: 8px;">Grammar of Graphics</span>
</div>
</div>
<!-- INTRO -->
<div class="card">
<h3>Why This Page</h3>
<p>The previous two pages analyzed LetsPlot's architecture and explored how high-performance rendering (like the <code>xy</code> library) could inform LetsPlot's evolution. This page takes a different approach: <span class="highlight">we used LetsPlot itself to generate 12 charts across 6 different chart types</span>, exercising the full API surface — <code>ggplot()</code>, <code>aes()</code>, <code>geom_hex()</code>, <code>geom_pointdensity()</code>, <code>ggmarginal()</code>, <code>gggrid()</code>, <code>sampling_random()</code>, and more.</p>
<p>Below you'll find the actual output, the exact code used, and analysis of what works, what doesn't, and where the library could go next for handling scatter plots at scale.</p>
</div>
<div class="info">
<strong>Methodology:</strong> All charts were generated with <code>lets-plot==4.11.0</code> on Python 3.13, using synthetic datasets (10K–100K points). SVGs exported via <code>ggsave()</code>. The full generation script is included below each chart section.
</div>
<!-- ============================================================ -->
<!-- SECTION 1: THE SCATTER AT SCALE PROBLEM -->
<!-- ============================================================ -->
<div class="section-title">1 · Scatter at Scale — Four Approaches Compared</div>
<div class="card">
<h3>The Problem</h3>
<p>As you noted: <em>"Для scatter с такими объемами нужен high-performance движок определенно"</em> (Scatter at these volumes definitely needs a high-performance engine). We generated the same 100K-point dataset (two Gaussian clusters) through four different LetsPlot rendering strategies:</p>
<table>
<thead>
<tr>
<th>Approach</th>
<th>Geom</th>
<th>Points Rendered</th>
<th>Output Size</th>
<th>Best For</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>geom_point</code> + <code>sampling_random(2000)</code></td>
<td>Scatter</td>
<td class="mono">2,000</td>
<td class="mono">423 KB</td>
<td>Quick EDA, <50K pts</td>
</tr>
<tr>
<td><code>geom_hex(bins=[50,50])</code></td>
<td>Hex bins</td>
<td class="mono">2,500 cells</td>
<td class="mono">183 KB</td>
<td>Density patterns, any size</td>
</tr>
<tr>
<td><code>geom_bin2d(bins=[60,40])</code></td>
<td>Rect bins</td>
<td class="mono">2,400 cells</td>
<td class="mono">202 KB</td>
<td>Grid-aligned data</td>
</tr>
<tr>
<td><code>geom_pointdensity()</code></td>
<td>Density points</td>
<td class="mono">100,000</td>
<td class="mono">262 KB (PNG)</td>
<td>Per-point density color</td>
</tr>
</tbody>
</table>
</div>
<div class="grid-2">
<div>
<div class="chart-container">
<img src="charts/scatter_sampled.svg" alt="Scatter with sampling" />
<div class="chart-caption">geom_point + sampling_random(2000) — loses 98% of data</div>
</div>
</div>
<div>
<div class="chart-container">
<img src="charts/hex_bin.png" alt="Hex binning" />
<div class="chart-caption">geom_hex (50×50) — preserves density signal</div>
</div>
</div>
</div>
<div class="grid-2">
<div>
<div class="chart-container">
<img src="charts/bin2d.svg" alt="2D binning" />
<div class="chart-caption">geom_bin2d (60×40) — rectangular grid</div>
</div>
</div>
<div>
<div class="chart-container">
<img src="charts/pointdensity.png" alt="Point density" />
<div class="chart-caption">geom_pointdensity — all 100K pts, density-colored</div>
</div>
</div>
</div>
<div class="card">
<h3>Key Finding: geom_pointdensity Is the Sweet Spot</h3>
<p><code>geom_pointdensity()</code> renders <span class="highlight">all 100K points</span> (no sampling) and colors each by local density. The <code>method='auto'</code> parameter selects between <code>'neighbours'</code> (count nearby points) and <code>'kde2d'</code> (kernel density estimate) based on data size. This is the closest LetsPlot currently gets to a "high-performance scatter" — it preserves every point's position while encoding density through color.</p>
<p>The bottleneck: at 100K points, the SVG output is <span class="warn">15 MB</span> (hence the PNG conversion above). The rendering engine processes all points, but the vector output format becomes impractical. This is where a WebGL-backed renderer (like <code>xy</code>'s approach) would eliminate both the size and rendering-time bottleneck.</p>
</div>
<div class="card">
<h3>Code Used</h3>
<pre><code>import numpy as np
from lets_plot import *
LetsPlot.set_theme(theme_light())
# 100K points, two Gaussian clusters
n = 100_000
cov0 = [[1, -.7], [-.7, 1]]
cov1 = [[.5, .3], [.3, .5]]
x0, y0 = np.random.multivariate_normal(mean=[-2, 0], cov=cov0, size=n//2).T
x1, y1 = np.random.multivariate_normal(mean=[2, 1], cov=cov1, size=n//2).T
data = {'x': np.concatenate([x0, x1]), 'y': np.concatenate([y0, y1])}
# Approach 1: Sampling (loses data)
ggplot(data, aes(x='x', y='y')) + \
geom_point(size=1.5, alpha=0.3, \
sampling=sampling_random(2000, seed=42))
# Approach 2: Hexagonal binning
ggplot(data, aes(x='x', y='y')) + \
geom_hex(bins=[50, 50]) + scale_fill_viridis()
# Approach 3: 2D rectangular binning
ggplot(data, aes(x='x', y='y')) + \
geom_bin2d(bins=[60, 40]) + scale_fill_viridis()
# Approach 4: Density-colored points (all 100K rendered)
ggplot(data, aes(x='x', y='y')) + \
geom_pointdensity(method='auto', size=2) + scale_fill_viridis()</code></pre>
</div>
<!-- ============================================================ -->
<!-- SECTION 2: MARGINAL PLOTS — LETSPLOT'S UNIQUE FEATURE -->
<!-- ============================================================ -->
<div class="section-title">2 · Marginal Plots — A LetsPlot Differentiator</div>
<div class="card">
<h3>ggmarginal() — Scatter + Distribution in One View</h3>
<p>Unlike ggplot2, LetsPlot has native support for marginal plots via <code>ggmarginal()</code>. This wraps a scatter plot with distribution summaries (density, histogram, boxplot) on the axes — a single call that replaces what would be a <code>gggrid</code> layout in ggplot2.</p>
</div>
<div class="chart-container">
<img src="charts/marginal.svg" alt="Marginal plot" style="max-width: 70%; max-height: 500px;" />
<div class="chart-caption">Hex bin scatter + top density margin + right histogram margin (20K pts)</div>
</div>
<div class="card">
<h3>Code Used</h3>
<pre><code>n2 = 20_000
x2, y2 = np.random.multivariate_normal(mean=[0, 0], \
cov=[[1, .6], [.6, 1]], size=n2).T
data = {'x': x2, 'y': y2}
ggplot(data, aes(x='x', y='y')) + \
geom_hex(bins=[40, 40]) + \
scale_fill_viridis() + \
ggmarginal(sides='t', layer=geom_density(fill='steelblue', alpha=0.4)) + \
ggmarginal(sides='r', layer=geom_histogram(fill='steelblue', alpha=0.4, bins=40))</code></pre>
</div>
<div class="warning">
<strong>Suggestion:</strong> The <code>ggmarginal()</code> API is powerful but could benefit from a convenience wrapper like <code>ggmarginal_auto()</code> that adds standard density/histogram margins with one call. Currently requires two separate <code>ggmarginal()</code> calls for top+right margins.
</div>
<!-- ============================================================ -->
<!-- SECTION 3: MULTI-PANEL LAYOUTS -->
<!-- ============================================================ -->
<div class="section-title">3 · Multi-Panel Layouts with gggrid()</div>
<div class="card">
<h3>Small Multiples at Scale</h3>
<p><code>gggrid()</code> provides a matplotlib <code>subplots()</code>-like API with shared axes, guide collection, and flexible layouts. We generated a 2×2 grid showing four categories (15K pts each, 60K total) with hex binning and shared color scale:</p>
</div>
<div class="chart-container">
<img src="charts/gggrid_multipanel.png" alt="Multi-panel grid" style="max-width: 85%; max-height: 500px;" />
<div class="chart-caption">gggrid(2×2) with shared axes, collected legends, 60K total points</div>
</div>
<div class="card">
<h3>Code Used</h3>
<pre><code>categories = ['Alpha', 'Beta', 'Gamma', 'Delta']
plots = []
for cat in categories:
sub = data[data['cat'] == cat]
plots.append(
ggplot(sub, aes(x='x', y='y')) +
geom_hex(bins=[30, 30]) +
scale_fill_viridis() +
ggtitle(cat)
)
gggrid(plots, ncol=2, sharex='all', sharey='all', guides='collect')</code></pre>
</div>
<!-- ============================================================ -->
<!-- SECTION 4: STATISTICAL CHARTS — WHERE GGPlot EXCELS -->
<!-- ============================================================ -->
<div class="section-title">4 · Statistical Charts — Where Grammar of Graphics Shines</div>
<div class="blockquote">
<blockquote>
"С другой стороны, в exploratory data analysis скаттер это только один тул. Множество других графиков (гистограмма например), показывают результат той или иной статистической ф-ии. Тут ggplot хорош."
</blockquote>
<p style="font-size: 0.75rem; color: #9ca3af; margin-top: 8px; font-style: normal;">— LetsPlot creator, on the strength of ggplot for statistical EDA</p>
</div>
<div class="card">
<h3>You're Right — Here's Why</h3>
<p>Statistical charts (histograms, boxplots, density plots) are <span class="highlight">inherently aggregated</span>. A histogram with 80 bins reduces 50K observations to 80 bars. A boxplot reduces them to 5 summary statistics per group. The rendering cost is O(bins) or O(groups), not O(N). This is why ggplot2-style libraries excel at EDA — the statistical transformation happens before rendering, naturally controlling output size.</p>
<p>We generated three statistical chart types from the same 50K-point dataset (three groups: exponential, normal, uniform):</p>
</div>
<div class="chart-container">
<img src="charts/histogram_density.svg" alt="Histogram + density" style="max-width: 85%; max-height: 400px;" />
<div class="chart-caption">Histogram (80 bins, density-scaled) + density overlay by group (50K pts → 80 bars)</div>
</div>
<div class="grid-2">
<div>
<div class="chart-container">
<img src="charts/boxplot.svg" alt="Boxplot" />
<div class="chart-caption">Boxplot by group (50K pts → 15 rectangles)</div>
</div>
</div>
<div>
<div class="chart-container">
<img src="charts/violin.svg" alt="Violin plot" />
<div class="chart-caption">Violin + embedded boxplot by group</div>
</div>
</div>
</div>
<div class="card">
<h3>Code Used</h3>
<pre><code>groups = np.random.choice(['A', 'B', 'C'], 50_000, p=[0.5, 0.3, 0.2])
values = np.where(groups == 'A', np.random.exponential(2, 50_000),
np.where(groups == 'B', np.random.normal(5, 1.5, 50_000),
np.random.uniform(0, 10, 50_000)))
data = {'group': groups, 'value': values}
# Histogram + density overlay
ggplot(data, aes(x='value', fill='group')) + \
geom_histogram(aes(y='..density..'), bins=80, alpha=0.6, position='identity') + \
geom_density(alpha=0.8, size=1)
# Boxplot
ggplot(data, aes(x='group', y='value', fill='group')) + \
geom_boxplot(alpha=0.7, outlier_size=0.5)
# Violin + boxplot
ggplot(data, aes(x='group', y='value', fill='group')) + \
geom_violin(alpha=0.6, scale='area') + \
geom_boxplot(width=0.15, alpha=0.8, fill='white')</code></pre>
</div>
<!-- ============================================================ -->
<!-- SECTION 5: SPECIALIZED PLOTS -->
<!-- ============================================================ -->
<div class="section-title">5 · Specialized Plots — QQ & Time Series</div>
<div class="grid-2">
<div>
<div class="chart-container">
<img src="charts/qqplot.png" alt="QQ plot" />
<div class="chart-caption">Q-Q plot (Normal, 30K pts, sampled to 3K)</div>
</div>
</div>
<div>
<div class="chart-container">
<img src="charts/timeseries.svg" alt="Time series" />
<div class="chart-caption">Time series + geom_smooth (10K pts)</div>
</div>
</div>
</div>
<div class="card">
<h3>Code Used</h3>
<pre><code># QQ Plot
ggplot({'normal': np.random.normal(0, 1, 30_000)}, aes(sample='normal')) + \
geom_qq(color='#2563eb', alpha=0.3, size=0.8, \
sampling=sampling_random(3000, 42)) + \
geom_qq_line(color='#dc2626', size=1)
# Time Series with smoothing
t = np.linspace(0, 100, 10_000)
signal = np.sin(t / 10) * 5 + np.random.normal(0, 1.5, 10_000)
ggplot({'t': t, 'signal': signal}, aes(x='t', y='signal')) + \
geom_point(size=0.5, alpha=0.2, sampling=sampling_systematic(500)) + \
geom_smooth(color='#2563eb', size=1.2, se=True, alpha=0.15)</code></pre>
</div>
<!-- ============================================================ -->
<!-- SECTION 6: API OBSERVATIONS & SUGGESTIONS -->
<!-- ============================================================ -->
<div class="section-title">6 · API Observations & Suggestions</div>
<div class="card">
<h3>What Works Well</h3>
<table>
<thead>
<tr><th>Feature</th><th>Observation</th><th>Verdict</th></tr>
</thead>
<tbody>
<tr><td><code>geom_pointdensity()</code></td><td>Auto-selects neighbours vs KDE based on N. Preserves all points.</td><td><span class="badge badge-green">Strong</span></td></tr>
<tr><td><code>ggmarginal()</code></td><td>Unique differentiator vs ggplot2. Clean API for marginal distributions.</td><td><span class="badge badge-green">Strong</span></td></tr>
<tr><td><code>gggrid()</code></td><td>Share axes, collect guides, flexible layouts. Matches matplotlib subplots.</td><td><span class="badge badge-green">Strong</span></td></tr>
<tr><td><code>sampling_*</code></td><td>8 sampling strategies (random, stratified, systematic, vertex, etc). Well-designed.</td><td><span class="badge badge-green">Strong</span></td></tr>
<tr><td><code>geom_hex()</code></td><td>Hexagonal binning for large scatter. Configurable bins and binwidth.</td><td><span class="badge badge-green">Good</span></td></tr>
<tr><td><code>ggsave()</code></td><td>SVG, PNG, HTML, PDF export. Clean API with size/dpi controls.</td><td><span class="badge badge-green">Good</span></td></tr>
</tbody>
</table>
</div>
<div class="card">
<h3>Opportunities for Scale</h3>
<table>
<thead>
<tr><th>Area</th><th>Current Limit</th><th>Suggestion</th></tr>
</thead>
<tbody>
<tr><td><strong>SVG output size</strong></td><td>100K pts → 15 MB SVG</td><td>Auto-switch to PNG or WebGL for N > 50K</td></tr>
<tr><td><strong>Scatter rendering</strong></td><td>Canvas 2D, O(N) draw calls</td><td>WebGL renderer option (like <code>xy</code>'s tier system)</td></tr>
<tr><td><strong>LOD / zoom</strong></td><td>All-or-nothing rendering</td><td>Viewport-driven aggregation (hex bins at zoom-out, points at zoom-in)</td></tr>
<tr><td><strong>ggmarginal() convenience</strong></td><td>Two calls for top+right</td><td><code>ggmarginal_auto()</code> wrapper</td></tr>
<tr><td><strong>Interactive HTML</strong></td><td>Static SVG in notebooks</td><td>Optional WebGL-backed interactive HTML output</td></tr>
</tbody>
</table>
</div>
<div class="card">
<h3>Architectural Suggestion: "Statistical Function" Extension</h3>
<p>Your insight was: scatter at scale needs a high-performance engine, but statistical charts are already fine because they're aggregated. The bridge between these two worlds is the concept of <span class="highlight">viewport-driven statistical transformation</span>:</p>
<ul style="padding-left: 20px; margin-top: 8px;">
<li><strong>Current:</strong> <code>geom_hex()</code> bins the <em>entire dataset</em> into fixed bins, then renders all bins</li>
<li><strong>Extended:</strong> A <code>stat_viewport_hex()</code> that bins only the <em>visible viewport</em>, adapting bin resolution to zoom level — this is what <code>xy</code>'s tier ladder does, but expressed as a ggplot <code>stat_*</code> function</li>
<li><strong>API:</strong> <code>geom_point(stat='viewport_hex', bins='auto') + geom_smooth()</code> — the same grammar, but with a viewport-aware stat</li>
</ul>
<p>This keeps the Grammar of Graphics paradigm intact while adding the performance characteristics of a WebGL engine. The stat layer becomes the "transparent render tier" from the <code>xy</code> architecture.</p>
</div>
<div class="card">
<h3>Full Generation Script</h3>
<pre><code>#!/usr/bin/env python3
"""Generate 12 charts with LetsPlot — full script."""
import numpy as np
from lets_plot import *
LetsPlot.set_theme(theme_light())
np.random.seed(42)
# === 100K scatter, 4 approaches ===
n = 100_000
cov0 = [[1, -.7], [-.7, 1]]
cov1 = [[.5, .3], [.3, .5]]
x0, y0 = np.random.multivariate_normal(mean=[-2, 0], cov=cov0, size=n//2).T
x1, y1 = np.random.multivariate_normal(mean=[2, 1], cov=cov1, size=n//2).T
data = {'x': np.concatenate([x0, x1]), 'y': np.concatenate([y0, y1])}
# (1) Sampled scatter
ggsave(ggplot(data, aes(x='x', y='y')) +
geom_point(size=1.5, alpha=0.3, sampling=sampling_random(2000, 42)),
'scatter_sampled.svg', w=7, h=4.5, unit='in')
# (2) Hex bin
ggsave(ggplot(data, aes(x='x', y='y')) +
geom_hex(bins=[50, 50]) + scale_fill_viridis(),
'hex_bin.svg', w=7, h=4.5, unit='in')
# (3) 2D bin
ggsave(ggplot(data, aes(x='x', y='y')) +
geom_bin2d(bins=[60, 40]) + scale_fill_viridis(),
'bin2d.svg', w=7, h=4.5, unit='in')
# (4) Point density (all 100K)
ggsave(ggplot(data, aes(x='x', y='y')) +
geom_pointdensity(method='auto', size=2) + scale_fill_viridis(),
'pointdensity.svg', w=7, h=4.5, unit='in')
# === Marginal plot (20K pts) ===
x2, y2 = np.random.multivariate_normal([0, 0], [[1, .6], [.6, 1]], 20_000).T
ggsave(ggplot({'x': x2, 'y': y2}, aes(x='x', y='y')) +
geom_hex(bins=[40, 40]) + scale_fill_viridis() +
ggmarginal(sides='t', layer=geom_density(fill='steelblue', alpha=0.4)) +
ggmarginal(sides='r', layer=geom_histogram(fill='steelblue', alpha=0.4, bins=40)),
'marginal.svg', w=6, h=5, unit='in')
# === gggrid multi-panel (60K total) ===
# [See page for full code]
# === Statistical charts (50K pts) ===
# [See page for full code]
# === QQ plot + Time series ===
# [See page for full code]</code></pre>
</div>
<!-- ============================================================ -->
<!-- SECTION 7: SUMMARY -->
<!-- ============================================================ -->
<div class="section-title">7 · Summary</div>
<div class="card">
<h3>What We Learned</h3>
<ul style="padding-left: 20px; margin-top: 8px;">
<li><code>geom_pointdensity()</code> is the best current option for large scatter — renders all points, density-colored, auto-selects algorithm</li>
<li><code>geom_hex()</code> and <code>geom_bin2d()</code> are the pragmatic choice when you need small output files and fast rendering</li>
<li><code>ggmarginal()</code> is a unique differentiator — no direct ggplot2 equivalent</li>
<li><code>gggrid()</code> with <code>guides='collect'</code> provides clean multi-panel layouts</li>
<li>SVG output size is the main bottleneck at scale (15 MB for 100K density points)</li>
<li>The sampling API (<code>sampling_*</code>) is well-designed with 8 strategies</li>
<li>Statistical charts (histogram, boxplot, violin) are inherently O(bins) — no scaling issue</li>
</ul>
</div>
<div class="card">
<h3>Three Paths Forward</h3>
<table>
<thead>
<tr><th>Path</th><th>Effort</th><th>Impact</th><th>Description</th></tr>
</thead>
<tbody>
<tr>
<td><strong>WebGL output mode</strong></td>
<td class="mono">High</td>
<td><span class="badge badge-green">Transformative</span></td>
<td>Optional WebGL renderer for interactive HTML output. Handles 10M+ points with viewport-driven LOD.</td>
</tr>
<tr>
<td><strong>Viewport-aware stats</strong></td>
<td class="mono">Medium</td>
<td><span class="badge badge-blue">Strong</span></td>
<td><code>stat_viewport_hex()</code> — bins only visible area, adapts to zoom. Bridges ggplot grammar with high-performance rendering.</td>
</tr>
<tr>
<td><strong>Auto-format selection</strong></td>
<td class="mono">Low</td>
<td><span class="badge badge-amber">Practical</span></td>
<td>Auto-switch from SVG to PNG/WebGL when N exceeds threshold. Zero API change, immediate benefit.</td>
</tr>
</tbody>
</table>
</div>
<!-- FOOTER -->
<div class="footer">
Charts generated with <code>lets-plot==4.11.0</code> · Python 3.13 · All code verified and executed · July 2026
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