Get started with reaborn

reaborn is an R port of Python’s seaborn, built on ggplot2. It mirrors seaborn’s function API exactly and renders visually indistinguishable plots — and because every result is a real ggplot, you can keep extending it with the grammar of graphics.

Install

install.packages("reaborn")

Or install the development version from GitHub:

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

One import sets the scene

Attaching reaborn does three things, mirroring import seaborn as sns followed by sns.set_theme():

library(reaborn)

It sets the seaborn theme and palette globally, so every plot inherits the familiar seaborn look.
It exposes sns.-prefixed aliases for every function, so pasted Python runs verbatim.
It binds the Python literals True, False, and None to R’s TRUE, FALSE, and NULL.

Your first plot

Load one of seaborn’s example datasets and make a plot. This is literally seaborn syntax — string column names, named arguments, the sns. prefix:

penguins <- load_dataset("penguins")

sns.scatterplot(data = penguins, x = "bill_length_mm", y = "bill_depth_mm",
                hue = "species")

Prefer idiomatic R? Drop the sns. prefix — the bare names work too:

histplot(data = penguins, x = "flipper_length_mm", hue = "species",
         multiple = "stack", kde = TRUE)

Every plot is a ggplot

This is reaborn’s superpower over seaborn. A plotting call returns a ggplot object, so you can layer on facets, scales, themes, and extra geoms:

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, by island")

The function families

reaborn implements all ~40 seaborn functions. A few entry points:

Goal	Function(s)
Relationships between numeric variables	`scatterplot()`, `lineplot()`, `relplot()`
Distributions	`histplot()`, `kdeplot()`, `ecdfplot()`, `displot()`
Categorical comparisons	`boxplot()`, `violinplot()`, `barplot()`, `stripplot()`, `catplot()`
Model fits	`regplot()`, `lmplot()`
Matrices	`heatmap()`, `clustermap()`
Multi-plot grids	`pairplot()`, `jointplot()`

See the Gallery for live examples of each, and the function reference for full argument lists.

Coming from seaborn?

In most cases you change nothing but the language host. After library(reaborn), the sns. aliases, the global theme, and the True/False/None literals are all in scope.

Python (seaborn)	R (reaborn)
`import seaborn as sns`	`library(reaborn)`
`sns.set_theme()`	automatic on load
`sns.scatterplot(data=df, x="a", y="b", hue="g")`	same line, verbatim
`True` / `False` / `None`	`True` / `False` / `None` (bound to `TRUE`/`FALSE`/`NULL`)
`[1, 2, 3]` · `{"a": 1}` · `(1, 2)`	`c(1, 2, 3)` · `list(a = 1)` · `c(1, 2)`

The one thing that’s truly different — and better — is what you do after the call: instead of mutating a matplotlib Axes, you add ggplot2 layers with +.