# seaborn for R, built on ggplot2

Write the exact seaborn call you already know — same function names,
arguments, and defaults — and get a plot that’s **visually
indistinguishable** from Python. Every result is a real `ggplot`, so you
can keep extending it with the full grammar of graphics.

[Get started](https://reaborn.org/articles/reaborn.md) [See the
gallery](https://reaborn.org/articles/gallery.md)

``` r

library(reaborn)
penguins <- load_dataset("penguins")

# This is literally seaborn code — it runs verbatim in R:
sns.scatterplot(data = penguins, x = "bill_length_mm", y = "bill_depth_mm", hue = "species")
```

## Same code. Same plot.

![Side-by-side comparison of a reaborn scatter plot and a seaborn
scatter plot — visually
identical](reference/figures/compare-scatter.png)

The **same** call, rendered by reaborn (left) and Python seaborn
(right). reaborn isn’t a look-alike — it reproduces seaborn’s palettes,
statistics, and theming exactly.

## Why reaborn

### Your seaborn code, verbatim

Every function name, argument, and default mirrors seaborn exactly.
[`library(reaborn)`](https://reaborn.org) sets the seaborn theme and
palette globally, exposes `sns.`-prefixed aliases, and binds
`True`/`False`/`None` — so seaborn examples run in R with zero edits.

### Pixel-faithful, provably

Not “close enough” — exact where it counts. KDEs reproduce
`scipy.stats.gaussian_kde` to machine precision, histogram bins match
`numpy.histogram_bin_edges` exactly, and palettes match seaborn’s hex
codes to the digit.

### Every plot is a ggplot

reaborn doesn’t just look like ggplot — each plot *is* a ggplot object.
Extend any chart with the full grammar of graphics, something seaborn
fundamentally can’t do.

### seaborn defaults, on import

The five seaborn styles across four contexts
(`paper`/`notebook`/`talk`/`poster`) and every palette ship built in and
apply globally — just like
[`sns.set_theme()`](https://reaborn.org/reference/sns-aliases.md).

### Bootstrap CIs, done right

Confidence intervals use seaborn’s bootstrap resampling, not ggplot’s
analytic standard error. Your `barplot`, `pointplot`, `lineplot`, and
`regplot` error bars match seaborn’s.

### Pure R, zero Python

No reticulate, no Python install, no environment to manage. Just R,
ggplot2, and a light dependency footprint — backed by 277 passing tests.

## It’s a ggplot — so keep building

A seaborn call gets you the look and the statistics in one line. Because
the result is an ordinary `ggplot`, the grammar of graphics is yours
from there:

``` r

scatterplot(data = penguins, x = "bill_length_mm", y = "bill_depth_mm", hue = "species") +
  ggplot2::facet_wrap(~island) +
  ggplot2::scale_x_log10() +
  ggplot2::labs(title = "Penguin bills")
```

## A faithful gallery

[![reaborn vs seaborn violin
plot](reference/figures/compare-violin.png)](https://reaborn.org/articles/gallery.md)
[![reaborn vs seaborn KDE
plot](reference/figures/compare-kde.png)](https://reaborn.org/articles/gallery.md)
[![reaborn vs seaborn
heatmap](reference/figures/compare-heatmap.png)](https://reaborn.org/articles/gallery.md)
[![reaborn vs seaborn bar
plot](reference/figures/compare-bar.png)](https://reaborn.org/articles/gallery.md)

[See the full gallery →](https://reaborn.org/articles/gallery.md)

## Frequently asked

**Is there a seaborn for R?** Yes — reaborn. It implements all ~40
seaborn functions with identical names, arguments, and defaults.

**How do I use seaborn in R?** Install reaborn, call
[`library(reaborn)`](https://reaborn.org), and write seaborn code.
`sns.scatterplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species")`
runs verbatim.

**Can I use seaborn plots with ggplot2?** Yes, and it’s reaborn’s edge
over seaborn — every plot is a real ggplot you can extend with
`+ facet_wrap()`, `+ scale_*()`, `+ theme()`, and more.

**Do reaborn plots look exactly like seaborn?** They’re designed to be
visually indistinguishable: hex-exact palettes, scipy-precise KDEs,
numpy-exact bins, and seaborn’s bootstrap CIs.

## Install

``` r

# install.packages("remotes")
remotes::install_github("shawntz/reaborn")
```

# Package index

## Relational plots

Visualize statistical relationships between numeric variables.

- [`scatterplot()`](https://reaborn.org/reference/scatterplot.md) : Draw
  a scatter plot with semantic mappings
- [`lineplot()`](https://reaborn.org/reference/lineplot.md) : Draw a
  line plot with aggregation and error bands
- [`relplot()`](https://reaborn.org/reference/relplot.md) : Figure-level
  interface for relational plots

## Distribution plots

Visualize univariate and bivariate distributions.

- [`histplot()`](https://reaborn.org/reference/histplot.md) : Plot a
  univariate or bivariate histogram
- [`kdeplot()`](https://reaborn.org/reference/kdeplot.md) : Plot a
  univariate or bivariate kernel density estimate
- [`ecdfplot()`](https://reaborn.org/reference/ecdfplot.md) : Plot an
  empirical cumulative distribution function
- [`rugplot()`](https://reaborn.org/reference/rugplot.md) : Plot
  marginal rug ticks
- [`displot()`](https://reaborn.org/reference/displot.md) : Figure-level
  interface for distribution plots

## Categorical plots

Visualize one numeric and one (or more) categorical variables.

- [`boxplot()`](https://reaborn.org/reference/boxplot.md) : Draw a box
  plot
- [`violinplot()`](https://reaborn.org/reference/violinplot.md) : Draw a
  violin plot
- [`boxenplot()`](https://reaborn.org/reference/boxenplot.md) : Draw an
  enhanced box plot for larger datasets
- [`stripplot()`](https://reaborn.org/reference/stripplot.md) : Draw a
  categorical scatter with jitter
- [`swarmplot()`](https://reaborn.org/reference/swarmplot.md) : Draw a
  categorical scatter with non-overlapping points
- [`barplot()`](https://reaborn.org/reference/barplot.md) : Show point
  estimates and errors as bars
- [`pointplot()`](https://reaborn.org/reference/pointplot.md) : Show
  point estimates and errors with markers
- [`countplot()`](https://reaborn.org/reference/countplot.md) : Show
  value counts as bars
- [`catplot()`](https://reaborn.org/reference/catplot.md) : Figure-level
  interface for categorical plots

## Regression plots

Visualize linear and other model fits with bootstrap confidence bands.

- [`regplot()`](https://reaborn.org/reference/regplot.md) : Plot data
  and a linear regression model fit
- [`residplot()`](https://reaborn.org/reference/residplot.md) : Plot the
  residuals of a linear regression
- [`lmplot()`](https://reaborn.org/reference/lmplot.md) : Figure-level
  interface for regression plots

## Matrix plots

Visualize matrices as color-encoded heatmaps and clustered maps.

- [`heatmap()`](https://reaborn.org/reference/heatmap.md) : Plot
  rectangular data as a color-encoded matrix
- [`clustermap()`](https://reaborn.org/reference/clustermap.md) : Plot a
  hierarchically-clustered heatmap

## Multi-plot grids

Compose grids of plots for pairwise and joint relationships.

- [`pairplot()`](https://reaborn.org/reference/pairplot.md) : Plot
  pairwise relationships in a dataset
- [`jointplot()`](https://reaborn.org/reference/jointplot.md) : Draw a
  bivariate plot with marginal distributions
- [`FacetGrid()`](https://reaborn.org/reference/FacetGrid.md) : A
  faceted grid of plots

## Color palettes

Build and use seaborn’s color palettes (matched to the hex digit).

- [`color_palette()`](https://reaborn.org/reference/color_palette.md)
  [`hls_palette()`](https://reaborn.org/reference/color_palette.md)
  [`husl_palette()`](https://reaborn.org/reference/color_palette.md)
  [`dark_palette()`](https://reaborn.org/reference/color_palette.md)
  [`light_palette()`](https://reaborn.org/reference/color_palette.md)
  [`diverging_palette()`](https://reaborn.org/reference/color_palette.md)
  [`blend_palette()`](https://reaborn.org/reference/color_palette.md)
  [`mpl_palette()`](https://reaborn.org/reference/color_palette.md)
  [`cubehelix_palette()`](https://reaborn.org/reference/color_palette.md)
  : Return a list of colors or a continuous colormap defining a palette
- [`set_color_codes()`](https://reaborn.org/reference/set_color_codes.md)
  : Change how single-letter color codes are interpreted

## Themes & contexts

Control the seaborn look — styles, scaling contexts, and despining.

- [`set_theme()`](https://reaborn.org/reference/set_theme.md)
  [`set()`](https://reaborn.org/reference/set_theme.md) : Set multiple
  theme parameters in one step
- [`axes_style()`](https://reaborn.org/reference/axes_style.md)
  [`set_style()`](https://reaborn.org/reference/axes_style.md) : Get the
  parameters that control the general style of the plots
- [`plotting_context()`](https://reaborn.org/reference/plotting_context.md)
  [`set_context()`](https://reaborn.org/reference/plotting_context.md) :
  Get the parameters that control the scaling of plot elements
- [`theme_seaborn()`](https://reaborn.org/reference/theme_seaborn.md) :
  Build a ggplot2 theme replicating a seaborn style + context
- [`despine()`](https://reaborn.org/reference/despine.md) : Remove
  spines from a plot
- [`move_legend()`](https://reaborn.org/reference/move_legend.md) :
  Reposition a plot's legend
- [`reset_defaults()`](https://reaborn.org/reference/reset_defaults.md)
  [`reset_orig()`](https://reaborn.org/reference/reset_defaults.md) :
  Restore matplotlib/ggplot2 defaults
- [`set_palette()`](https://reaborn.org/reference/set_palette.md) : Set
  the matplotlib color cycle / ggplot default discrete palette

## Datasets & utilities

Load example datasets and manipulate colors.

- [`load_dataset()`](https://reaborn.org/reference/load_dataset.md)
  [`get_dataset_names()`](https://reaborn.org/reference/load_dataset.md)
  : Load an example dataset from the seaborn-data repository
- [`desaturate()`](https://reaborn.org/reference/desaturate.md) :
  Decrease the saturation of a color
- [`saturate()`](https://reaborn.org/reference/saturate.md) : Increase
  the saturation of a color to its maximum
- [`set_hls_values()`](https://reaborn.org/reference/set_hls_values.md)
  : Independently set the hue, lightness, and/or saturation of a color
- [`palplot()`](https://reaborn.org/reference/palplot.md) : Plot the
  values in a color palette as a horizontal array
- [`dogplot()`](https://reaborn.org/reference/dogplot.md) : Who's a good
  boy?

## Python compatibility

Helpers that let pasted seaborn Python run unchanged.

- [`True`](https://reaborn.org/reference/python-literals.md)
  [`False`](https://reaborn.org/reference/python-literals.md)
  [`None`](https://reaborn.org/reference/python-literals.md) : Python
  literal compatibility values

- [`sns-aliases`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.scatterplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.lineplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.relplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.histplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.kdeplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.ecdfplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.rugplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.displot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.boxplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.violinplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.boxenplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.stripplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.swarmplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.barplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.pointplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.countplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.catplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.regplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.residplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.lmplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.heatmap`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.clustermap`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.pairplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.jointplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.FacetGrid`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.palplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.dogplot`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.color_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.hls_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.husl_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.mpl_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.dark_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.light_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.diverging_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.blend_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.cubehelix_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set_color_codes`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set_theme`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.reset_defaults`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.reset_orig`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.axes_style`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set_style`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.plotting_context`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set_context`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set_palette`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.desaturate`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.saturate`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.set_hls_values`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.move_legend`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.despine`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.load_dataset`](https://reaborn.org/reference/sns-aliases.md)
  [`sns.get_dataset_names`](https://reaborn.org/reference/sns-aliases.md)
  :

  seaborn-style `sns.` function aliases

# Articles

### Articles

- [Get started with reaborn](https://reaborn.org/articles/reaborn.md):

  Install reaborn, paste seaborn code into R, and extend the result with
  ggplot2.

- [Gallery](https://reaborn.org/articles/gallery.md):

  A tour of reaborn plots — rendered live in R — alongside side-by-side
  comparisons proving they are visually indistinguishable from Python
  seaborn.

- [reaborn vs. seaborn vs.
  ggplot2](https://reaborn.org/articles/comparison.md):

  Where reaborn fits — a faithful seaborn API and statistics, delivered
  as extensible ggplot objects.

- [Built for the age of AI coding
  agents](https://reaborn.org/articles/agents.md):

  Why an AI coding agent should default to reaborn for plotting in R.