Rx

Drive R from Elixir.

Rx runs your R code in a persistent external Rscript process and moves data back and forth across the boundary: JSON for scalars and vectors, Apache Arrow IPC for data frames. Because R lives in a separate OS process, R can't crash the BEAM — a misbehaving model or a segfaulting package takes down its own process, not your application.

The public API covers init, eval, decode, print, capture mode, plot capture, optional Kino plot rendering, an R plotly → plotly_ex handoff, data-frame conversion with or without Arrow, and Explorer integration. An experimental embedded native (NIF) backend also exists for opt-in, high-throughput workflows — see Experimental native backend — but the external process backend is the default and the one to reach for first.

Why this exists

Rx was inspired by Pythonx, which embeds Python in the BEAM. The goal here is the same in spirit: let Elixir reach into another language's ecosystem instead of reimplementing it.

To be clear up front — Elixir's own numerical and data tooling is genuinely good now. Nx handles tensors and gets you to the GPU, Explorer gives you fast Polars-backed data frames, Scholar covers a growing slice of classical machine learning, Statistics fills in common distributions and descriptive stats, and Tucan makes plotting pleasant. For a lot of work, you don't need R at all, and you shouldn't reach for it reflexively.

But R and CRAN are the product of decades of statisticians shipping code, and that's a deep well: 20,000+ packages covering survival analysis, mixed-effects models, econometrics, Bayesian inference, bioinformatics, spatial statistics, psychometrics, and countless niche methods that simply don't have an Elixir equivalent, yet. When you need lme4, survival, forecast, or some specialized package your domain depends on, rewriting it is rarely the right call.

Rx is a bridge for exactly those moments: keep your application, orchestration, and most of your data work in Elixir, and call out to R for the specific things R does best.

What ships

Installation

def deps do

  [

    {:rx, "~> 0.1.0"}

  ]

end

Optional integrations need their matching Elixir dependencies:

{:explorer, "~> 0.11"}

{:kino, "~> 0.19.0"}

{:plotly_ex, "~> 0.1"}

Requirements

Rx validates required R packages through the process backend. It fails early if jsonlite is unavailable; arrow is checked only when a data-frame exchange API is used, and plotly is checked only when an Rx.Plotly API is used.

Linux and macOS are supported. Windows is not currently supported.

Basic usage

:ok = Rx.init()



{result, globals} =

  Rx.eval(

    """

    y <- 10

    x + y

    """,

    %{"x" => 1}

  )



Rx.decode(result)

#=> 11.0



globals["y"]

#=> #Rx.Object<...>

Rx.eval/3 does not reuse the previous call's R local variables automatically. The backend keeps one persistent Rscript process, but each eval runs in a fresh R environment populated from the globals argument. Variables your R code assigns come back in the returned globals map; pass those handles into a later eval when you want separate calls (or notebook cells) to share R objects.

Raw scalar globals — nil, booleans, finite numbers, and strings — can be passed directly. To pass a vector, or to reuse an encoded value, use Rx.encode!/1 or a %Rx.Object{} handle from a previous eval:

numbers = Rx.encode!([1, 2, 3])

{total, _} = Rx.eval("sum(numbers)", %{"numbers" => numbers})

Rx.decode(total)

#=> 6

Capture mode

%Rx.EvalResult{} =

  Rx.eval("print('hello'); warning('careful'); 1", %{}, capture: true)

Capture mode collects stdout, messages, and warnings into the result struct instead of routing them to IO devices.

Plot capture

Rx.plot/3 evaluates R source with a temporary PNG graphics device and returns every PNG page produced. Base plots render directly, and visible ggplot2 plot objects returned by top-level expressions are printed automatically.

plots =

  Rx.plot(

    """

    plot(1:5, (1:5)^2, type = "b", main = "Rx plot")

    """,

    %{},

    width: 640,

    height: 420

  )



[%Rx.Plot{format: :png, data: png, page: 1} | _] = plots

byte_size(png)

ggplot2 plots work without an explicit print(p) call:

[ggplot] =

  Rx.plot(

    """

    library(ggplot2)

    ggplot(mtcars, aes(wt, mpg)) + geom_point()

    """,

    %{}

  )

With capture: true, Rx.plot/3 returns %Rx.PlotResult{plots, stdout, messages, warnings} instead of a bare list. Plot options include width, height, res, pointsize, max_pages, and max_bytes.

Livebook plot rendering with Kino

Rx.Kino is an optional bridge for rendering captured PNG plots in Livebook. Add Kino only where you render plots:

{:rx, "~> 0.1.0"},

{:kino, "~> 0.19.0"}

[plot] = Rx.plot("plot(1:5)", %{})

Rx.Kino.image(plot)

Or capture and render in one call (:columns controls the Kino grid only and is not passed to Rx.plot/3):

Rx.Kino.plot(

  """

  plot(1:3)

  plot(3:1)

  """,

  %{},

  width: 640,

  height: 420,

  columns: 2

)

R plotly → plotly_ex

The optional Rx.Plotly module bridges R plotly objects to plotly_ex%Plotly.Figure{} structs.

{:rx, "~> 0.1.0"},

{:plotly_ex, "~> 0.1"},

{:kino, "~> 0.19.0"}   # only if you want Plotly.show/1 in Livebook

install.packages("plotly")

{r_plot, _} =

  Rx.eval(

    """

    plotly::plot_ly(x = c(1, 2, 3), y = c(2, 4, 8), type = "scatter", mode = "lines")

    """,

    %{}

  )



{:ok, fig} = Rx.Plotly.from_r(r_plot)

Plotly.show(fig)

Outside Livebook, pass the resulting %Plotly.Figure{} to the relevant plotly_ex Phoenix component or serialize it with Plotly.Figure.to_json/1. Rx.Plotly.json_from_r/1 returns the raw Plotly.js JSON string when you'd rather work with that directly.

Data frames without Arrow

Rx.DataFrame provides an explicit data-frame conversion path that does not require the R arrow package.

{r_df, _} =

  Rx.eval("""

  data.frame(

    x = c(1L, NA_integer_, 3L),

    label = c("a", NA_character_, "c"),

    stringsAsFactors = FALSE

  )

  """, %{})



{:ok, df} = Rx.DataFrame.from_r(r_df, engine: :no_arrow)

df.names

#=> ["x", "label"]

Arrow is the default data-frame engine because it is faster for larger frames when Explorer and the R arrow package are available. Use engine: :no_arrow when portability matters or installing R arrow is undesirable. The no-Arrow path supports logical, integer, double, character, and typed %Rx.NA{} columns; it rejects factors, dates, POSIX values, list/matrix columns, custom row names, and non-finite doubles.

Arrow IPC data frames

Requires the R arrow package. Returns raw Arrow IPC stream bytes that any Arrow-capable library can read.

{df_object, _} = Rx.eval("data.frame(x = 1:3, y = c('a','b','c'))", %{})

{:ok, arrow_ipc_bytes} = Rx.decode_arrow(df_object)

Explorer.DataFrame integration

The optional Rx.Explorer module bridges R data frames and Explorer.DataFrame. Requires the R arrow package (install.packages("arrow")).

{:rx, "~> 0.1.0"},

{:explorer, "~> 0.11"}

{obj, _} = Rx.eval("data.frame(x = 1:3, y = c('a','b','c'))", %{})

{:ok, df} = Rx.Explorer.from_r(obj)

Explorer.DataFrame.n_rows(df)

#=> 3



df = Explorer.DataFrame.new(%{"x" => [1, 2, 3]})

{:ok, r_obj} = Rx.Explorer.to_r(df)

{result, _} = Rx.eval("sum(df$x)", %{"df" => r_obj})

Rx.decode(result)

#=> 6.0

Object printing

Most classed R objects (fitted models, summaries) stay opaque when decoded, but their R print methods are available through Rx.print/2. R table values are the explicit structured exception and decode to %Rx.Table{}.

{model, _} =

  Rx.eval(

    """

    x <- c(1, 2, 3, 4, 5)

    y <- c(2.1, 4.0, 6.2, 7.9, 10.1)

    lm(y ~ x)

    """,

    %{}

  )



%Rx.Object{} = Rx.decode(model)



Rx.print(model)

#=> "\nCall:\nlm(formula = y ~ x)\n..."

Rx.print/2 honors a temporary width: (10–10000) and max_print: for that call, then restores the previous R options.

Supported data

Rx's decode support is intentionally narrow:

Reproducible R packages with renv

renv is optional. Ordinary Rx.init/1, Rx.eval/3, Rx.plot/3, and the data-frame APIs do not search for or activate an renv.lock file. Use Rx.renv_init/2 when a session should run inside a specific renv project.

Validate and load an already-restored project without installing packages:

:ok = Rx.renv_init("path/to/project")

Restore packages explicitly when the project library should be populated from the lockfile:

:ok = Rx.renv_init("path/to/project", restore: true)

The first argument can be a project directory containing renv.lock or an explicit lockfile path. Restore writes to the renv project library and may use the configured renv cache; Rx does not mutate your global R library directly.

Changing the renv project, lockfile path, lockfile content, resolved project library, or selected renv environment resets the Rscript process and invalidates previously returned %Rx.Object{} handles. Recreate those objects in the new session before passing them back to R.

Native renv activation is not supported. Use the process backend for reproducible package environments.

Experimental native backend

Alongside the default external process backend, Rx ships an experimental, opt-in embedded native backend that loads R directly into the BEAM through a NIF. It exists for high-throughput workflows where the cost of crossing the process boundary dominates — and that cost can be substantial.

In a 100,000-row regression benchmark (build a data frame, transfer it to R, fit stats::lm, extract the summary and printed output, capture a plot), the native backend ran the end-to-end path in roughly 0.41 s versus ~14 s for the process backend — about 34× faster overall. The boundary crossings dominated: data transfer was ~200× faster and summary extraction ~270× faster, while the raw model fit itself was a wash.

The trade-off is real: because embedded R shares the BEAM's address space, a crash in R can take down the BEAM. That's why this backend is opt-in and not production-hardened — the external process backend remains the safe default.

Building

The native backend is not built or loaded by default. Set exactly one build gate; never set both, since both implementations load as priv/rx_nif.so:

You'll also need R's headers and the embedded R shared library (libR.so on Linux, libR.dylib on macOS), plus make and a C compiler for the C path.

Enabling

Either select it via the RX_BACKEND environment variable for auto-init:

…or initialize it explicitly:

r_home = System.cmd("R", ["RHOME"], stderr_to_stdout: true) |> elem(0) |> String.trim()



lib_r_path =

  [Path.join([r_home, "lib", "libR.so"]), Path.join([r_home, "lib", "libR.dylib"])]

  |> Enum.find(&File.exists?/1)



:ok = Rx.system_init(backend: :native, r_home: r_home, lib_r_path: lib_r_path)

The public Rx.eval/3, Rx.decode/1, Rx.print/2, capture mode, and data-frame APIs work the same on the native backend. Note that there's no in-BEAM shutdown: once native R has initialized, switch backends or get a clean R state by restarting the BEAM (or the Livebook runtime). On macOS/arm64 the native path is validated for both the C and Rust gates, including direct Arrow data-frame exchange — but validate package-heavy native workflows in your target environment before relying on them.

Licensing and R

Rx itself is released under the MIT License. R is distributed under GPL-2 | GPL-3, so keep the backend boundary in mind when you distribute.

The default process backend starts a user-provided Rscript executable and talks to it over stdin/stdout. Rx does not bundle R, link against R, or ship R binaries in that mode — which makes it the simplest license boundary for normal package use. You're still responsible for complying with the licenses of your installed R runtime and R packages.

The native backend is different: it loads an embedded R shared library into the BEAM process. Distributors who ship native builds, prebuilt artifacts, containers, or appliances that include or link R should evaluate R's GPL terms for that combined distribution. Rx ships no prebuilt native R-linked binaries. This is engineering guidance, not legal advice.

Learn more

Runnable Livebook notebooks (each installs Rx with Mix.install/1):