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Create interactive Chart.js visualizations for DST data analysis. Use when generating charts, creating visual reports, building dashboards, or displaying trends from database tables.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.
Check data freshness and age for DST tables in DuckDB. Use when determining if data needs refreshing or validating data currency before analysis.
Fetch actual data from Danmarks Statistik API and store in DuckDB. Use when user wants to download and store specific DST table data for analysis.
Perform SQL joins and multi-table analysis on DST data in DuckDB. Use when research requires combining multiple tables on common dimensions (time, region). Provides patterns for common DST dimension joins and multi-table comparisons.
List all Danmarks Statistik tables currently stored in DuckDB with metadata. Use when user wants to know what data is available locally or explore stored tables.
Execute SQL queries on Danmarks Statistik data stored in DuckDB. Use when user needs specific data analysis, filtering, aggregation, or joins. Also includes table summary functionality.
| name | dst-visualization |
| description | Create interactive Chart.js visualizations for DST data analysis. Use when generating charts, creating visual reports, building dashboards, or displaying trends from database tables. |
Create professional, interactive Chart.js visualizations for Danmarks Statistik data. This skill provides templates, color palettes, and implementation patterns for transforming DST data into compelling visual insights.
Best for: Population trends, economic indicators, time-based metrics
Template:
<!DOCTYPE html>
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<style>
body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }
.chart-container { position: relative; width: 90%; margin: 20px auto; }
canvas { max-height: 400px; }
</style>
</head>
<body>
<h2>Population Trend Analysis</h2>
<div class="chart-container">
<canvas id="lineChart"></canvas>
</div>
<script>
const ctx = document.getElementById('lineChart').getContext('2d');
const chart = new Chart(ctx, {
type: 'line',
data: {
labels: ['2019', '2020', '2021', '2022', '2023', '2024'],
datasets: [
{
label: 'Denmark Total',
data: [5841503, 5856002, 5867410, 5882261, 5914284, 5945119],
borderColor: '#1A4D2E',
backgroundColor: 'rgba(26, 77, 46, 0.1)',
borderWidth: 3,
fill: true,
tension: 0.4,
pointRadius: 5,
pointHoverRadius: 7,
pointBackgroundColor: '#1A4D2E',
pointBorderColor: '#fff',
pointBorderWidth: 2
},
{
label: 'Capital Region',
data: [1856062, 1860403, 1863532, 1871312, 1895143, 1916284],
borderColor: '#D4A574',
backgroundColor: 'rgba(212, 165, 116, 0.1)',
borderWidth: 3,
fill: true,
tension: 0.4,
pointRadius: 5,
pointHoverRadius: 7,
pointBackgroundColor: '#D4A574',
pointBorderColor: '#fff',
pointBorderWidth: 2
}
]
},
options: {
responsive: true,
maintainAspectRatio: true,
plugins: {
legend: {
display: true,
position: 'top',
labels: {
font: { size: 14, weight: 'bold' },
padding: 15,
usePointStyle: true
}
},
title: {
display: false
},
tooltip: {
backgroundColor: 'rgba(0, 0, 0, 0.8)',
padding: 12,
titleFont: { size: 14, weight: 'bold' },
bodyFont: { size: 13 },
borderColor: '#1A4D2E',
borderWidth: 2,
callbacks: {
label: function(context) {
return context.dataset.label + ': ' +
context.parsed.y.toLocaleString('da-DK');
}
}
}
},
scales: {
y: {
beginAtZero: false,
title: { display: true, text: 'Population' },
ticks: {
callback: function(value) {
return value.toLocaleString('da-DK');
},
font: { size: 12 }
},
grid: { color: 'rgba(0, 0, 0, 0.05)' }
},
x: {
grid: { display: false },
ticks: { font: { size: 12 } }
}
}
}
});
</script>
</body>
</html>
Best for: Regional comparisons, category analysis, distribution data
Template:
<!DOCTYPE html>
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<style>
body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }
.chart-container { position: relative; width: 90%; margin: 20px auto; }
canvas { max-height: 400px; }
</style>
</head>
<body>
<h2>Regional Population Comparison (2024)</h2>
<div class="chart-container">
<canvas id="barChart"></canvas>
</div>
<script>
const ctx = document.getElementById('barChart').getContext('2d');
const chart = new Chart(ctx, {
type: 'bar',
data: {
labels: [
'Capital Region',
'Zealand',
'Funen',
'South Jutland',
'Central Jutland',
'North Jutland'
],
datasets: [
{
label: 'Population 2024',
data: [1916284, 1310459, 598374, 419682, 1312119, 590782],
backgroundColor: [
'#1A4D2E',
'#2E7D54',
'#4A9B7F',
'#6EB9A0',
'#D4A574',
'#E8C9A0'
],
borderColor: '#1A4D2E',
borderWidth: 2,
borderRadius: 4,
hoverBackgroundColor: '#1A4D2E',
hoverBorderColor: '#fff',
hoverBorderWidth: 3
}
]
},
options: {
indexAxis: 'y',
responsive: true,
maintainAspectRatio: true,
plugins: {
legend: { display: false },
tooltip: {
backgroundColor: 'rgba(0, 0, 0, 0.8)',
padding: 12,
titleFont: { size: 14, weight: 'bold' },
bodyFont: { size: 13 },
borderColor: '#1A4D2E',
borderWidth: 2,
callbacks: {
label: function(context) {
return 'Population: ' +
context.parsed.x.toLocaleString('da-DK');
}
}
}
},
scales: {
x: {
beginAtZero: true,
ticks: {
callback: function(value) {
return (value / 1000).toFixed(0) + 'K';
},
font: { size: 12 }
},
grid: { color: 'rgba(0, 0, 0, 0.05)' }
},
y: {
grid: { display: false },
ticks: { font: { size: 12 } }
}
}
}
});
</script>
</body>
</html>
// DST Brand Greens
const dstGreens = {
darkGreen: '#1A4D2E', // Primary - strong, corporate
mediumGreen: '#2E7D54', // Secondary
lightGreen: '#4A9B7F', // Tertiary
paleGreen: '#6EB9A0' // Light accent
};
// DST Brand Tans/Golds
const dstGolds = {
gold: '#D4A574', // Warm accent
lightGold: '#E8C9A0', // Light accent
darkGold: '#C18A3E' // Deep accent
};
// Neutral Support Colors
const neutrals = {
darkGray: '#333333',
mediumGray: '#666666',
lightGray: '#CCCCCC',
white: '#FFFFFF'
};
// Data Series Palette (6-color recommended)
const dataPalette = [
'#1A4D2E', // Dark Green (Primary)
'#D4A574', // Gold (Accent)
'#2E7D54', // Medium Green
'#E8C9A0', // Light Gold
'#4A9B7F', // Light Green
'#C18A3E' // Dark Gold
];
| Color | Use Case | Best For |
|---|---|---|
| #1A4D2E | Primary data series, main metrics | Top-level trends, primary indicators |
| #2E7D54 | Secondary data series | Secondary metrics, comparisons |
| #4A9B7F | Tertiary data series | Supporting data |
| #6EB9A0 | Highlights, annotations | Important changes, annotations |
| #D4A574 | Accent, contrast | Alternative categories, variations |
| #E8C9A0 | Light accents, backgrounds | Supporting information, secondary data |
Extract data from DuckDB query results:
import duckdb
import json
# Connect to DuckDB
conn = duckdb.connect('data/dst.db')
# Query data
result = conn.execute("""
SELECT tid, område, indhold
FROM dst_folk1a
WHERE område IN ('000', '101', '147')
ORDER BY tid, område
""").fetchall()
# Transform to chart.js format
labels = sorted(set(row[0] for row in result))
datasets = {}
for tid, område, value in result:
if område not in datasets:
datasets[område] = []
datasets[område].append(value)
chart_data = {
"labels": labels,
"datasets": [
{
"label": region_name,
"data": values,
"borderColor": color
}
for (region_name, values, color) in prepare_datasets(datasets)
]
}
print(json.dumps(chart_data))
Use the appropriate chart template (line or bar) and customize:
// Modify chart options:
options: {
responsive: true, // Mobile-friendly
maintainAspectRatio: true, // Prevent distortion
plugins: {
legend: {
position: 'top', // Move legend
labels: { font: { size: 14 } }
},
tooltip: {
// Custom formatting for tooltips
callbacks: {
label: function(context) {
// Format numbers with Danish locale
return context.dataset.label + ': ' +
context.parsed.y.toLocaleString('da-DK');
}
}
}
}
}
# Save as HTML file
# Open in browser for interactive viewing
# Use browser dev tools to save as image/PDF
// Query multiple regions over time
labels: years,
datasets: [
{ label: 'Capital Region', data: capital_values, borderColor: '#1A4D2E' },
{ label: 'Jutland', data: jutland_values, borderColor: '#D4A574' },
{ label: 'Islands', data: islands_values, borderColor: '#2E7D54' }
]
// Show trends with comparison to baseline/average
datasets: [
{ label: 'Actual Values', data: values, borderColor: '#1A4D2E' },
{ label: 'Average', data: averages, borderColor: '#CCCCCC',
borderDash: [5, 5], tension: 0.1 }
]
options: {
scales: {
x: { stacked: true },
y: { stacked: true }
}
}
// Useful for composition analysis
// For comparing different metrics with different scales
datasets: [
{ yAxisID: 'y', label: 'Population', ... },
{ yAxisID: 'y1', label: 'Growth Rate %', ... }
],
scales: {
y: { type: 'linear', position: 'left' },
y1: { type: 'linear', position: 'right' }
}
<!-- Using bar chart template above -->
<!-- Replace regions and 2024 data with your query -->
<!-- Multiple line charts, one per region -->
<!-- Use responsive grid for layout -->
<div style="display: grid; grid-template-columns: 1fr 1fr;">
<div class="chart-container"><canvas id="capital"></canvas></div>
<div class="chart-container"><canvas id="jutland"></canvas></div>
</div>
// Save chart as image
const image = document.getElementById('lineChart').toDataURL('image/png');
const link = document.createElement('a');
link.href = image;
link.download = 'population-chart.png';
link.click();
https://cdn.jsdelivr.net/npm/chart.js (CDN)