Statistical Data Visualization, the 'seaborn' Way

An 'R' port of the 'Python' 'seaborn' library. 'reaborn' mirrors the 'seaborn' public function API (identical function names, argument names, and defaults) and renders visually indistinguishable plots using 'ggplot2'. Because every 'reaborn' plot is a 'ggplot' object, it can be extended with the full 'ggplot2' grammar of graphics.

reaborn

reaborn is an R port of Python's seaborn. It mirrors seaborn's public function API — identical function names, argument names, and defaults — and renders visually indistinguishable plots using ggplot2. You can paste seaborn Python into R and get the same plot.

Because every reaborn plot is a ggplot, you can extend it with the entire ggplot2 grammar of graphics — something seaborn can't do.

install.packages("reaborn")
library(reaborn)

Or install the development version from GitHub:

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

Paste seaborn Python into R

library(reaborn) installs the seaborn default theme and palette globally (like sns.set_theme()), exposes sns.-prefixed aliases for every function, and binds the Python literals True/False/None. So this seaborn snippet runs verbatim:

penguins <- load_dataset("penguins")

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

sns.histplot(data = penguins, x = "flipper_length_mm", hue = "species",
             multiple = "stack", kde = True)

Or write it idiomatically (the sns. prefix is optional):

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

reaborn plots are ggplots

Every function returns a ggplot, so compose freely:

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")

What's implemented

The full classic seaborn function API:

Module	Functions
Relational	`scatterplot` `lineplot` `relplot`
Distributions	`histplot` `kdeplot` `ecdfplot` `rugplot` `displot`
Categorical	`boxplot` `violinplot` `boxenplot` `stripplot` `swarmplot` `barplot` `pointplot` `countplot` `catplot`
Regression	`regplot` `residplot` `lmplot`
Matrix	`heatmap` `clustermap`
Grids	`pairplot` `jointplot` `FacetGrid`
Misc	`palplot` `dogplot`
Palettes	`color_palette` `hls_palette` `husl_palette` `cubehelix_palette` `light_palette` `dark_palette` `diverging_palette` `blend_palette` `mpl_palette` `set_color_codes` …
Theming	`set_theme` `set_style` `set_context` `axes_style` `plotting_context` `despine` `move_legend` …

Fidelity

reaborn matches seaborn by extracting ground-truth constants from a real seaborn install rather than approximating:

Palettes match seaborn's hex codes exactly.
Themes reproduce the five seaborn styles × four contexts.

Python → R conversion notes

Most seaborn calls paste over directly. The remaining edits are language-level:

Python	R
`sns.scatterplot(...)`	works as-is (`sns.scatterplot`)
`data=df, x="col", hue="g"`	works as-is (named args, string columns)
`True` / `False` / `None`	bound for you, or write `TRUE` / `FALSE` / `NULL`
`[1, 2, 3]` (list)	`c(1, 2, 3)`
`{"a": 1}` (dict)	`list(a = 1)`
`(1, 2)` (tuple)	`c(1, 2)`
`df.col`	`df$col`