Ggterm Markdown

Markdown Reports with ggterm

Generate analysis reports with embedded terminal visualizations and reproducible plot specifications.

Report Structure

A well-structured analysis report includes:

1. Title and Overview - What was analyzed and why
2. Data Summary - Shape, columns, key statistics
3. Visualizations - Embedded plots with interpretations
4. Findings - Key insights from the analysis
5. Appendix - Plot specifications for reproducibility

Basic Report Template

import { gg, geom_point, geom_line } from '@ggterm/core'
import { writeFileSync } from 'fs'

// Create plots
const scatterPlot = gg(data)
  .aes({ x: 'x', y: 'y', color: 'category' })
  .geom(geom_point())
  .labs({ title: 'Relationship Analysis' })

const trendPlot = gg(data)
  .aes({ x: 'time', y: 'value' })
  .geom(geom_line())
  .labs({ title: 'Trend Over Time' })

// Render plots to strings
const scatter = scatterPlot.render({ width: 72, height: 18 })
const trend = trendPlot.render({ width: 72, height: 18 })

// Build markdown report
const report = `# Analysis Report: ${datasetName}

## Overview

This report analyzes ${data.length} observations across ${Object.keys(data[0]).length} variables.

## Data Summary

| Metric | Value |
|--------|-------|
| Rows | ${data.length} |
| Columns | ${Object.keys(data[0]).length} |
| Date Range | ${minDate} to ${maxDate} |

## Visualizations

### Scatter Plot

\`\`\`
${scatter}
\`\`\`

**Interpretation**: The scatter plot shows [describe the relationship observed].

### Trend Analysis

\`\`\`
${trend}
\`\`\`

**Interpretation**: The trend indicates [describe the pattern observed].

## Key Findings

1. **Finding 1**: Description of first key insight
2. **Finding 2**: Description of second key insight
3. **Finding 3**: Description of third key insight

## Appendix: Plot Specifications

<details>
<summary>Scatter Plot Spec (JSON)</summary>

\`\`\`json
${JSON.stringify(scatterPlot.spec(), null, 2)}
\`\`\`

</details>

<details>
<summary>Trend Plot Spec (JSON)</summary>

\`\`\`json
${JSON.stringify(trendPlot.spec(), null, 2)}
\`\`\`

</details>

---
*Generated with ggterm*
`

writeFileSync('analysis-report.md', report)
console.log('Report saved to analysis-report.md')

Embedding Multiple Plots

For reports with many visualizations:

interface PlotSection {
  title: string
  plot: GGPlot
  interpretation: string
}

function generateReport(
  title: string,
  overview: string,
  sections: PlotSection[],
  findings: string[]
): string {
  const plotSections = sections.map(({ title, plot, interpretation }) => `
### ${title}

\`\`\`
${plot.render({ width: 72, height: 16 })}
\`\`\`

**Interpretation**: ${interpretation}
`).join('\n')

  const findingsList = findings
    .map((f, i) => `${i + 1}. ${f}`)
    .join('\n')

  const specs = sections.map(({ title, plot }) => `
<details>
<summary>${title} Spec</summary>

\`\`\`json
${JSON.stringify(plot.spec(), null, 2)}
\`\`\`

</details>
`).join('\n')

  return `# ${title}

## Overview

${overview}

## Visualizations

${plotSections}

## Key Findings

${findingsList}

## Appendix: Reproducibility

${specs}

---
*Generated with ggterm*
`
}

Width Guidelines

Choose plot width based on target context:

Including Data Tables

function markdownTable(data: Record<string, unknown>[], columns?: string[]): string {
  const cols = columns || Object.keys(data[0])
  const header = `| ${cols.join(' | ')} |`
  const separator = `| ${cols.map(() => '---').join(' | ')} |`
  const rows = data.map(row =>
    `| ${cols.map(c => String(row[c] ?? '')).join(' | ')} |`
  ).join('\n')

  return `${header}\n${separator}\n${rows}`
}

// Usage
const summaryTable = markdownTable([
  { metric: 'Mean', value: mean.toFixed(2) },
  { metric: 'Median', value: median.toFixed(2) },
  { metric: 'Std Dev', value: std.toFixed(2) },
])

Full Example: EDA Report

import { gg, geom_histogram, geom_boxplot, geom_point, facet_wrap } from '@ggterm/core'
import { writeFileSync } from 'fs'

// Assume data is loaded
const numericCols = ['age', 'income', 'score']
const categoricalCols = ['region', 'segment']

// Generate distribution plots
const distributions = numericCols.map(col => ({
  title: `Distribution of ${col}`,
  plot: gg(data).aes({ x: col }).geom(geom_histogram({ bins: 20 })),
  interpretation: `Shows the distribution of ${col} values.`
}))

// Generate comparison plots
const comparisons = categoricalCols.map(cat => ({
  title: `Score by ${cat}`,
  plot: gg(data).aes({ x: cat, y: 'score' }).geom(geom_boxplot()),
  interpretation: `Compares score across ${cat} groups.`
}))

// Correlation scatter
const correlation = {
  title: 'Age vs Income',
  plot: gg(data)
    .aes({ x: 'age', y: 'income', color: 'region' })
    .geom(geom_point({ alpha: 0.6 })),
  interpretation: 'Shows relationship between age and income by region.'
}

const report = generateReport(
  'Exploratory Data Analysis',
  `Analysis of ${data.length} customer records.`,
  [...distributions, ...comparisons, correlation],
  [
    'Income is right-skewed with median $X',
    'Region A shows significantly higher scores',
    'Age and income show moderate positive correlation'
  ]
)

writeFileSync('eda-report.md', report)

Tips

1. Keep plots compact - Use 16-20 lines height for readability
2. Include interpretations - Don't just show plots, explain them
3. Save specs - Always include PlotSpec JSON for reproducibility
4. Use collapsible sections - <details> tags keep reports clean
5. Match audience - Adjust technical depth to readers

For report templates, see templates/.