Is there a seaborn for R?

Yes. reaborn is a faithful R port of Python's seaborn, built on ggplot2. It implements all ~40 seaborn functions (scatterplot, lineplot, histplot, kdeplot, boxplot, violinplot, barplot, heatmap, pairplot, jointplot, and more) with identical names, arguments, and defaults.

How do I use seaborn in R?

Install reaborn and call library(reaborn). That sets seaborn's theme and palette globally (like sns.set_theme()) and exposes sns.-prefixed aliases, so sns.scatterplot(data=penguins, x='bill_length_mm', y='bill_depth_mm', hue='species') runs verbatim in R.

Can I use seaborn plots with ggplot2?

Yes. Every reaborn plot is a real ggplot object, so you can extend it with the full ggplot2 grammar of graphics, e.g. scatterplot(...) + facet_wrap(~g) + scale_x_log10() + theme(...). This is something seaborn itself cannot do.

Do reaborn plots look exactly like seaborn?

They are designed to be visually indistinguishable. Palettes match seaborn's hex codes to the digit, KDEs reproduce scipy.stats.gaussian_kde to machine precision, histogram bins match numpy.histogram_bin_edges exactly, and confidence intervals use seaborn's bootstrap rather than ggplot's analytic standard error.

Package index • reaborn

Relational plots

Visualize statistical relationships between numeric variables.

scatterplot(): Draw a scatter plot with semantic mappings
lineplot(): Draw a line plot with aggregation and error bands
relplot(): Figure-level interface for relational plots

Distribution plots

Visualize univariate and bivariate distributions.

histplot(): Plot a univariate or bivariate histogram
kdeplot(): Plot a univariate or bivariate kernel density estimate
ecdfplot(): Plot an empirical cumulative distribution function
rugplot(): Plot marginal rug ticks
displot(): Figure-level interface for distribution plots

Categorical plots

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

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

Regression plots

Visualize linear and other model fits with bootstrap confidence bands.

regplot(): Plot data and a linear regression model fit
residplot(): Plot the residuals of a linear regression
lmplot(): Figure-level interface for regression plots

Matrix plots

Visualize matrices as color-encoded heatmaps and clustered maps.

heatmap(): Plot rectangular data as a color-encoded matrix
clustermap(): Plot a hierarchically-clustered heatmap

Multi-plot grids

Compose grids of plots for pairwise and joint relationships.

pairplot(): Plot pairwise relationships in a dataset
jointplot(): Draw a bivariate plot with marginal distributions
FacetGrid(): A faceted grid of plots

Color palettes

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

color_palette() hls_palette() husl_palette() dark_palette() light_palette() diverging_palette() blend_palette() mpl_palette() cubehelix_palette(): Return a list of colors or a continuous colormap defining a palette
set_color_codes(): Change how single-letter color codes are interpreted

Themes & contexts

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

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

Datasets & utilities

Load example datasets and manipulate colors.

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

Python compatibility

Helpers that let pasted seaborn Python run unchanged.

True False None: Python literal compatibility values
sns-aliases sns.scatterplot sns.lineplot sns.relplot sns.histplot sns.kdeplot sns.ecdfplot sns.rugplot sns.displot sns.boxplot sns.violinplot sns.boxenplot sns.stripplot sns.swarmplot sns.barplot sns.pointplot sns.countplot sns.catplot sns.regplot sns.residplot sns.lmplot sns.heatmap sns.clustermap sns.pairplot sns.jointplot sns.FacetGrid sns.palplot sns.dogplot sns.color_palette sns.hls_palette sns.husl_palette sns.mpl_palette sns.dark_palette sns.light_palette sns.diverging_palette sns.blend_palette sns.cubehelix_palette sns.set_color_codes sns.set_theme sns.set sns.reset_defaults sns.reset_orig sns.axes_style sns.set_style sns.plotting_context sns.set_context sns.set_palette sns.desaturate sns.saturate sns.set_hls_values sns.move_legend sns.despine sns.load_dataset sns.get_dataset_names: seaborn-style sns. function aliases