openclaw-ggsql

name	openclaw-ggsql
description	Generate charts from tabular data using ggsql SQL syntax extension. Use when: user wants to visualize data as charts without Python/R. Supports: scatter plots, line charts, bar charts, histograms, boxplots, heatmaps. Based on Grammar of Graphics.
homepage	https://ggsql.org
metadata	{"openclaw":{"emoji":"📊","requires":{"bins":[],"skills":["web-access"]}}}

ggsql Visualization Skill

Generate charts from tabular data using ggsql SQL syntax.

Overview

ggsql extends SQL with visualization capabilities based on the Grammar of Graphics. Write familiar SQL queries, add visualization clauses, and get charts - no Python/R needed.

When to Use

✅ USE this skill when:

• "Visualize this data as a scatter plot"
• "Create a histogram of X"
• "Draw a bar chart comparing Y across categories"
• "Plot time series data"
• "Generate heatmap from table"
• User provides tabular data and wants a chart

When NOT to Use

❌ DON'T use this skill when:

• Interactive/dynamic charts → use JavaScript libraries
• Complex statistical visualizations → use Python/R
• Large-scale data dashboards → use dedicated tools
• 3D visualizations → use specialized software

Input Schema

data: <CSV file path | JSON array | SQL table reference>
chart_type: point | line | bar | histogram | boxplot | violin | density | heatmap | pie
mapping:
  x: <column name>        # Required for most charts
  y: <column name>        # Required for point, line, bar, boxplot
  fill: <column name>     # Optional, for color encoding
  color: <column name>    # Optional, for stroke color
  shape: <column name>    # Optional, for point shapes
  size: <column name>     # Optional, for point sizes
options:
  title: <chart title>              # Optional
  subtitle: <chart subtitle>        # Optional
  x_label: <x-axis label>           # Optional
  y_label: <y-axis label>           # Optional
  binwidth: <number>                # For histogram
  facet: <column name>              # For small multiples
  facet_by: <column name>           # For 2D faceting
  scale_x: continuous | discrete | binned | log10
  scale_y: continuous | discrete | binned | log10
  scale_fill: continuous | discrete | binned

Output

• SVG chart file (primary)
• PNG chart file (optional)
• Embedded base64 image for chat display

Chart Types and Templates

Scatter Plot (point)

Required mapping: x, y Optional mapping: fill, color, shape, size

VISUALISE {x} AS x, {y} AS y, {fill} AS fill
FROM {data_source}
DRAW point
LABEL title => '{title}', x => '{x_label}', y => '{y_label}'

Line Chart (line)

Required mapping: x, y Optional mapping: color, linetype

VISUALISE {x} AS x, {y} AS y, {color} AS color
FROM {data_source}
DRAW line
LABEL title => '{title}', x => '{x_label}', y => '{y_label}'

Bar Chart (bar)

Required mapping: x, y (or auto-count with just x) Optional mapping: fill

SELECT {x}, COUNT(*) as count FROM {data_source}
GROUP BY {x}
VISUALISE {x} AS x, count AS y, {fill} AS fill
DRAW bar
LABEL title => '{title}', x => '{x_label}', y => '{y_label}'

Histogram (histogram)

Required mapping: x Optional mapping: fill, binwidth

VISUALISE {x} AS x, {fill} AS fill
FROM {data_source}
DRAW histogram
  SETTING binwidth => {binwidth}
LABEL title => '{title}', x => '{x_label}'

Boxplot (boxplot)

Required mapping: x, y (x categorical, y numeric) Optional mapping: fill

VISUALISE {x} AS x, {y} AS y, {fill} AS fill
FROM {data_source}
DRAW boxplot
LABEL title => '{title}', x => '{x_label}', y => '{y_label}'

Heatmap (tile)

Required mapping: x, y, fill Optional mapping: none

VISUALISE {x} AS x, {y} AS y, {fill} AS fill
FROM {data_source}
DRAW tile
SCALE BINNED fill
LABEL title => '{title}', x => '{x_label}', y => '{y_label}'

Density Plot (density)

Required mapping: x Optional mapping: fill

VISUALISE {x} AS x, {fill} AS fill
FROM {data_source}
DRAW density
LABEL title => '{title}', x => '{x_label}'

Violin Plot (violin)

Required mapping: x, y (x categorical, y numeric) Optional mapping: fill

VISUALISE {x} AS x, {y} AS y, {fill} AS fill
FROM {data_source}
DRAW violin
LABEL title => '{title}', x => '{x_label}', y => '{y_label}'

Pie Chart (pie with polar projection)

Required mapping: fill Optional mapping: none

SELECT {fill}, COUNT(*) as count FROM {data_source}
GROUP BY {fill}
VISUALISE {fill} AS fill, count AS y
DRAW bar
PROJECT polar
LABEL title => '{title}'

Scale Types

Scale	Use Case	Example
CONTINUOUS	Numeric values	SCALE CONTINUOUS x FROM [0, null]
DISCRETE	Categories	SCALE DISCRETE fill TO ['red', 'blue']
BINNED	Binning continuous	SCALE BINNED fill
ORDINAL	Ordered categories	SCALE ORDINAL x
IDENTITY	Direct values	SCALE IDENTITY color
log10	Log transform	SCALE CONTINUOUS y VIA log10

Faceting (Small Multiples)

1D Faceting

VISUALISE {x} AS x, {y} AS y
FROM {data_source}
DRAW point
FACET {facet_column}

2D Faceting

VISUALISE {x} AS x, {y} AS y
FROM {data_source}
DRAW point
FACET {facet_column} BY {facet_by_column}

Multi-layer Charts

Combine multiple DRAW clauses:

VISUALISE {x} AS x, {y} AS y
FROM {data_source}
DRAW line
  MAPPING {group} AS color
DRAW point
  MAPPING {group} AS fill
LABEL title => '{title}'

Execution Methods

Method 1: WASM Playground (Recommended for Testing)

1. Visit https://ggsql.org/wasm/
2. Use built-in datasets: ggsql:penguins, ggsql:airquality
3. Upload CSV via "Upload Data" button
4. Run SQL and save as SVG/PNG

Method 2: CLI (Local Execution)

# Install
cargo install ggsql-cli

# Run
ggsql-cli run -f input.sql -o output.svg

Method 3: Jupyter Kernel

uv tool install ggsql-jupyter
ggsql-jupyter --install

Examples

Example 1: Scatter Plot with Color

Input:

data: penguins.csv
chart_type: point
mapping:
  x: bill_length_mm
  y: bill_depth_mm
  fill: species
options:
  title: Penguin Bill Dimensions
  x_label: Bill Length (mm)
  y_label: Bill Depth (mm)

Generated SQL:

SELECT * FROM 'penguins.csv'
VISUALISE bill_length_mm AS x, bill_depth_mm AS y, species AS fill
DRAW point
LABEL
  title => 'Penguin Bill Dimensions',
  x => 'Bill Length (mm)',
  y => 'Bill Depth (mm)'

Example 2: Histogram

Input:

data: sales.csv
chart_type: histogram
mapping:
  x: revenue
options:
  title: Revenue Distribution
  binwidth: 1000

Generated SQL:

SELECT * FROM 'sales.csv'
VISUALISE revenue AS x
DRAW histogram
  SETTING binwidth => 1000
LABEL title => 'Revenue Distribution'

Example 3: Faceted Scatter Plot

Input:

data: penguins.csv
chart_type: point
mapping:
  x: bill_length_mm
  y: bill_depth_mm
  fill: species
options:
  title: Penguins by Island
  facet: island

Generated SQL:

SELECT * FROM 'penguins.csv'
VISUALISE bill_length_mm AS x, bill_depth_mm AS y, species AS fill
DRAW point
FACET island
LABEL title => 'Penguins by Island'

Example 4: Multi-layer Chart

Input:

data: sales.csv
chart_type: multi
mapping:
  x: date
  y: revenue
  group: region
options:
  title: Revenue Trend by Region

Generated SQL:

SELECT * FROM 'sales.csv'
VISUALISE date AS x, revenue AS y
DRAW line
  MAPPING region AS color
DRAW point
  MAPPING region AS fill
LABEL title => 'Revenue Trend by Region'

SQL Generation Logic

When receiving YAML input:

1. Parse chart_type to select template
2. Validate required mapping fields
3. Build VISUALISE clause from mapping
4. Add DRAW clause with layer type
5. Add optional clauses: SCALE, FACET, LABEL
6. If data is CSV, use FROM 'path/to/file.csv'
7. If data is table reference, use FROM table_name

Notes

• ggsql is in alpha stage (v0.2.7), syntax may evolve
• WASM Playground is the easiest way to test
• No Python/R environment needed
• Charts are static (SVG/PNG), not interactive
• Grammar of Graphics philosophy: compose from layers, scales, coordinates

Resources

• Official Website
• Syntax Documentation
• Example Gallery
• GitHub Repository
• WASM Playground