A Grammar of Graphics for Kotlin.

Lets-Plot Kotlin API is a Kotlin API for Lets-Plot Multiplatform plotting library,
which is built on the principles of layered graphics first described in the
Leland Wilkinson work The Grammar of Graphics.

Lets-Plot Kotlin API is largely based on the API provided by ggplot2 package well-known to data scientists who use R. To learn more about the Grammar of Graphics, we recommend an excellent book called “ggplot2: Elegant Graphics for Data Analysis”. This will be a good prerequisite for further exploration of the Lets-Plot library.

Inside Kotlin Notebook, Datalore or Jupyter with Kotlin Kernel:

%use lets-plot 

val rand = java.util.Random() val data = mapOf( "rating" to List(200) { rand.nextGaussian() } + List(200) { rand.nextGaussian() * 1.5 + 1.5 }, "cond" to List(200) { "A" } + List(200) { "B" } ) var p = letsPlot(data) p += geomDensity(color = "dark_green", alpha = .3) { x = "rating"; fill = "cond" } p + ggsize(700, 350)

See the "Quickstart" notebook in Datalore or Jupyter nbviewer.

• Usage
  • Notebooks
  • Compose Multiplatform
  • JVM and Kotlin/JS
• Documentation
• What is new in 4.13.0
• Recent Updates in the Gallery
• Change Log
• Code of Conduct
• License

With the help of Lets-Plot Kotlin API you can easily create plots in Kotlin Notebook, Datalore, Jupyter with Kotlin Kernel
or any other notebook that supports Kotlin Kernel.

%use lets-plot 

This "line magic" will apply Lets-Plot library descriptor which adds to your notebook all the boilerplate code necessary to create plots.

By default, library descriptor is bundled with the Kotlin Jupyter Kernel installed in your environment.
However, you can override the default settings using:

%useLatestDescriptors 

In this case the latest library descriptor will be pulled from the Kotlin Jupyter Libraries repository.

%use lets-plot(v=4.13.0, isolatedFrame=false, output="js, ktnb, svg") 

• v - version of the Lets-Plot Kotlin API.

• isolatedFrame - If false: load JS just once per notebook (default in Jupyter).
  If true: include Lets-Plot JS in each output (default in Datalore notebooks).

• output - comma-separated list of output types to store in notebook cells (default: "js, ktnb, svg"). \
  Available types:

  • js - Classic Web output: HTML+JS
  • ktnb - Kotlin Notebook Swing-based rendering
  • svg - Static SVG output
  • png - Static PNG output

  Note: Static images (SVG/PNG) are hidden when js or ktnb outputs are present, and only displayed in environments where JavaScript is not executed (e.g., GitHub).

  This option can be helpful when file size becomes a problem. For example, storing only static output (SVG or PNG) can significantly reduce file size when working with large datasets where plot interactivity is not a priority.

To learn how to embed Lets-Plot charts in Compose Multiplatform applications, please check out the Lets-Plot Compose Frontend project at GitHub.

To learn more about creating plots in JVM or Kotlin/JS environment, please read USAGE_JVM_JS.md.

Examples of using the Lets-Plot Kotlin API in JVM and Kotlin/JS applications are available in the Lets-Plot Kotlin Mini Apps (Demos) GitHub repository.

• Lets-Plot Kotlin API documentation and API reference: Lets-Plot for Kotlin

• A quick introduction to the Grammar of Graphics and Lets-Plot Kotlin API: Lets-Plot Usage Guide

Kotlin: v2.2.20 (was v1.9.25).

• Statistical Summaries Directly on geomSmooth() Plot Layer

  The geomSmooth() layer now includes a labels parameter designed to display statistical summaries of the fitted model directly on the plot.
  This parameter accepts a smoothLabels() object, which provides access to model-specific variables like $R^2$ and the regression equation.

  

  See: example notebook.

• Plot Tags

  Plot tags are short labels attached to a plot.

  

  See: example notebook and updated Plot Layout Diagrams.

• New geomBracket() and geomBracketDodge() Geometries

  New geometries designed primarily for significance bars (p-values) annotations in categorical plots.

  

  See: example notebook.

• Custom Color Palettes in geomImshow()

  The cmap parameter now allows you to specify a list of hex color codes for visualizing grayscale images.
  Also, the new cguide parameter lets you customize the colorbar for grayscale images.

  

  See: example notebook.

• New palette() Method in Color Scales

  Generates a list of hex color codes that can be used with scaleColorManual() to maintain consistent colors across multiple plots.

  See: example notebook.

• New overflow parameter in scaleColorBrewer(), scaleFillBrewer()

  Controls how colors are generated when more colors are needed than the palette provides.
  Options: 'interpolate' ('i'), 'cycle' ('c'), 'generate' ('g').

  See: example notebook.

• New breakWidth Parameter in Positional Scales

  Specifies a fixed distance between axis breaks.

  See examples:

  • datetime scale
  • time (duration) scale
  • log10 scale

• Axis Minor Ticks Customization

  The axisMinorTicks and axisMinorTicksLength parameters in theme().

  See: example notebook.

• Pan/Zoom in gggrid() with Shared Axes

  Pan/Zoom now propagates across subplots with shared axes (sharex/sharey).

  See: example notebook.

• And More

  See CHANGELOG.md for a full list of changes.

See CHANGELOG.md.

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Code and documentation released under the MIT license. Copyright © 2019-2025, JetBrains s.r.o.