GraalVM implementation of R, also known as FastR, is compatible with GNU R, can run R code at unparalleled performance, integrates with the GraalVM ecosystem and provides additional R level features.

Warning: The support for R is experimental. Experimental features might never be included in a production version, or might change significantly before being considered production-ready.

Installing R

The R language can be installed to a GraalVM build with the gu command. See graalvm/bin/gu --help for more information.


GraalVM R engine requires the OpenMP runtime library be installed on the target system. Moreover, to install R packages that contain C/C++ or Fortran code, compilers for those languages must be present on the target system. Note that all these requirements can be satisfied by, e.g., installing the GNU Compiler Collection (GCC).

The installation of R packages uses the configuration from jre/languages/R/etc/Makeconf. This includes, for example, the configuration of C compiler options. If you intend to install R packages, it is recommended to update this configuration to match your target system by running the jre/languages/R/bin/configure_fastr script. Experienced users can also edit the Makeconf file manually.

Search Paths for Packages

The default R library location is within the GraalVM installation directory. In order to allow installation of additional packages for users that do not have write access to the GraalVM installation directory, edit the R_LIBS_USER variable in the jre/languages/R/etc/Renviron file.

Alternatively, the configuration script jre/languages/R/bin/configure_fastr automatically creates a local R library directory in the current user’s home and changes the R_LIBS_USER variable to point to that directory.

Running R Code

Run R code with the R and Rscript commands:

$ R [polyglot options] [R options] [filename]
$ Rscript [polyglot options] [R options] [filename]

GraalVM R engine uses the same polyglot options as other GraalVM languages and the same R options as GNU R, e.g., bin/R --vanilla. Use --help to print the list of supported options. The most important options include:

  • --jvm to enable Java interoperability
  • --polyglot to enable interoperability with other GraalVM languages
  • to pass any options to the JVM, this will be translated to

Note: unlike other GraalVM languages, R does not yet ship with a Native Image of its runtime. Therefore the --native option, which is the default, will still start Rscript on top of JVM, but for the sake of future compatibility the Java interoperability will not be available in such case.

Users can optionally build the native image using:


GraalVM R Engine Compatibility

GraalVM implementation of R, known as FastR, is based on GNU R and reuses the base packages. It is currently based on R 3.5.1, and moves to new major versions of R as they become available and stable. The FastR project, maintains an extensive set of unit tests for all aspects of the R language and the builtin functionality, and these tests are available as part of the R source code. GraalVM R engine aims to be fully compatible with GNU R, including its native interface as used by R packages. It can install and run unmodified complex R packages like ggplot2, Shiny, or Rcpp. As some packages rely on unspecified behavior or implementation details of GNU R, support for packages is work in progress, and some packages might not install successfully or work as expected.

Packages can be installed using the install.packages function or the R CMD INSTALL shell command. By default, R uses fixed snapshot of the CRAN repository1. This behavior can be overridden by explicitly setting the repos argument of the install.packages function. This functionality does not interfere with the checkpoint package. If you are behind a proxy server, make sure to configure the proxy either with environment variables or using the JVM options, e.g.,

Versions of some packages specifically patched for GraalVM implementation of R can be installed using the install.fastr.packages function that downloads them from the GitHub repository. Currently, those are rJava and data.table.

Known limitations of GraalVM implementation of R compared to GNU R:

  • Only small parts of the low-level graphics package are functional. However, the grid package is supported and R can install and run packages based on it like ggplot2. Support for the graphics package in R is planned for future releases.
  • Encoding of character vectors. Related builtins (e.g., Encoding) are available, but do not execute any useful code. Character vectors are represented as Java Strings and therefore encoded in UTF-16 format. GraalVM implementation of R will add support for encoding in future releases.
  • Some parts of the native API (e.g., DATAPTR) expose implementation details that are hard to emulate for alternative implementations of R. These are implemented as needed while testing the GraalVM implementation of R with various CRAN packages.

You can use the compatibility checker to find whether the CRAN packages you are interested in are tested on GraalVM and whether the tests pass successfully.

High Performance

GraalVM runtime optimizes R code that runs for extended periods of time. The speculative optimizations based on the runtime behavior of the R code and dynamic compilation employed by GraalVM runtime are capable of removing most of the abstraction penalty incurred by the dynamism and complexity of the R language.

Let us look at an algorithm in R code. The following example calculates the mutual information of a large matrix:

x <- matrix(runif(1000000), 1000, 1000)
mutual_R <- function(joint_dist) {
 joint_dist <- joint_dist/sum(joint_dist)
 mutual_information <- 0
 num_rows <- nrow(joint_dist)
 num_cols <- ncol(joint_dist)
 colsums <- colSums(joint_dist)
 rowsums <- rowSums(joint_dist)
 for(i in seq_along(1:num_rows)){
  for(j in seq_along(1:num_cols)){
   temp <- log((joint_dist[i,j]/(colsums[j]*rowsums[i])))
    temp = 0
   mutual_information <-
    mutual_information + joint_dist[i,j] * temp
#   user  system elapsed
#  1.321   0.010   1.279

Algorithms such as this one usually require C/C++ code to run efficiently:2

if (!require('RcppArmadillo')) {
x <- matrix(runif(1000000), 1000, 1000)
#   user  system elapsed
#  0.037   0.003   0.040

(Uses r_mutual.cpp.) However, after a few iterations, GraalVM runs the R code efficiently enough to make the performance advantage of C/C++ negligible:

#   user  system elapsed
#  0.063   0.001   0.077

GraalVM implementation of R is primarily aimed at long-running applications. Therefore, the peak performance is usually only achieved after a warmup period. While startup time is currently slower than GNUR’s, due to the overhead from Java class loading and compilation, future releases will contain a native image of R with improved startup.

GraalVM Integration

The R language integration with the GraalVM ecosystem includes:

To start debugging the code start the R script with --inspect option

$ Rscript --inspect myScript.R

Note that GNU R compatible debugging using, for example, debug(myFunction) is also supported.


GraalVM supports several other programming languages, including JavaScript, Ruby, Python, and LLVM. GraalVM implementation of R also provides an API for programming language interoperability that lets you execute code from any other language that GraalVM supports. Note that you must start the R script with --polyglot to have access to other GraalVM languages.

GraalVM execution of R provides the following interoperability primitives:

  • eval.polyglot('languageId', 'code') evaluates code in some other language, the languageId can be, e.g., js.
  • eval.polyglot(path = '/path/to/file.extension') evaluates code loaded from a file. The language is recognized from the extension.
  • export('polyglot-value-name', rObject) exports an R object so that it can be imported by other languages.
  • import('exported-polyglot-value-name') imports a polyglot value exported by some other language.

Please use the ?functionName syntax to learn more. The following example demonstrates the interoperability features:

# get an array from Ruby
x <- eval.polyglot('ruby', '[1,2,3]')
# [1] 1

# get a JavaScript object
x <- eval.polyglot(path='r_example.js')
# [1] "value"

# use R vector in JavaScript
export('robj', c(1,2,3))
eval.polyglot('js', paste0(
    'rvalue = Polyglot.import("robj"); ',
    'console.log("JavaScript: " + rvalue.length);'))
# JavaScript: 3
# NULL -- the return value of eval.polyglot

(Uses r_example.js.)

R vectors are presented as arrays to other languages. This includes single element vectors, e.g. 42L or NA. However, single element vectors that do not contain NA can be typically used in places where the other languages expect a scalar value. Array subscript or similar operation can be used in other languages to access individual elements of an R vector. If the element of the vector is not NA, the actual value is returned as a scalar value. If the element is NA, then a special object that looks like null is returned. The following Ruby code demonstrates this.

vec = Polyglot.eval("R", "c(NA, 42)")
p vec[0].nil?
# true
p vec[1]
# 42

vec = Polyglot.eval("R", "42")
p vec.to_s
# "[42]"
p vec[0]
# 42

The foreign objects passed to R are implicitly treated as specific R types. The following table gives some examples.

Example of foreign object (Java) Viewed ‘as if’ on the R side
int[] {1,2,3} c(1L,2L,3L)
int[][] { {1, 2, 3}, {1, 2, 3} } matrix(c(1:3,1:3),nrow=3)
int[][] { {1, 2, 3}, {1, 3} } not supported: raises error
Object[] {1, ‘a’, ‘1’} list(1L, ‘a’, ‘1’)
42 42L

In the following code example, we can simply just pass the Ruby array to the R built-in function sum, which will work with the Ruby array as if it was integer vector.

sum(eval.polyglot('ruby', '[1,2,3]'))

Foreign objects can be also explicitly wrapped into adapters that make them look like the desired R type. In such a case, no data copying occurs if possible. The code snippet below shows the most common use cases.

# gives list instead of an integer vector
as.list(eval.polyglot('ruby', '[1,2,3]'))

# assume the following Java code:
# public class ClassWithArrays {
#   public boolean[] b = {true, false, true};
#   public int[] i = {1, 2, 3};
# }

x <- new('ClassWithArrays'); # see Java interop below

# gives: list(c(T,F,T), c(1L,2L,3L))

For more details, please refer to the executable specification of the implicit and explicit foreign objects conversions.

Note that R contexts started from other languages or Java (as opposed to via the bin/R script) will default to non-interactive mode, similar to bin/Rscript. This has implications on console output (results are not echoed) and graphics (output defaults to a file instead of a window), and some packages may behave differently in non-interactive mode.

See the Polyglot Reference and the Embedding documentation for more information about interoperability with other programming languages.

Interoperability with Java

GraalVM R engine provides built-in interoperability with Java. Java class objects can be obtained via java.type(...). The standard new function interprets string arguments as a Java class if such class exists. new also accepts Java types returned from java.type. Fields and methods of Java objects can be accessed using the $ operator. Additionally, you can use awt(...) to open an R drawing device directly on a Java Graphics surface, for more details see Java Based Graphics.

The following example creates a new Java BufferedImage object, plots random data to it using R’s grid package, and shows the image in a window using Java’s AWT framework. Note that you must start the R script with --jvm to have access to Java interoperability.

openJavaWindow <- function () {
   # create image and register graphics
   imageClass <- java.type('java.awt.image.BufferedImage')
   image <- new(imageClass, 450, 450, imageClass$TYPE_INT_RGB);
   graphics <- image$getGraphics()
   grDevices:::awt(image$getWidth(), image$getHeight(), graphics)

   # draw image
   pushViewport(plotViewport(margins = c(5.1, 4.1, 4.1, 2.1)))
   grid.xaxis(); grid.yaxis()
   grid.points(x = runif(10, 0, 1), y = runif(10, 0, 1),
        size = unit(0.01, "npc"))

   # open frame with image
   imageIcon <- new("javax.swing.ImageIcon", image)
   label <- new("javax.swing.JLabel", imageIcon)
   panel <- new("javax.swing.JPanel")
   frame <- new("javax.swing.JFrame")
                image$getWidth(), image$getHeight()))
   while (frame$isVisible()) Sys.sleep(1)

For more information on FastR interoperability with Java and other languages implemented with Truffle framework, refer to the Interoperability tutorial.

GraalVM implementation of R provides its own rJava compatible replacement package available at GitHub, which can be installed using:

$ R -e "install.fastr.packages('rJava')"

GraalVM R Engine Additional Features

Java Based Graphics

The GraalVM implementation of R includes its own Java based implementation of the grid package and the following graphics devices: png, jpeg, bmp, svg and awt (X11 is aliased to awt). The graphics package and most of its functions are not supported at the moment.

The awt device is based on the Java Graphics2D object and users can pass it their own Graphics2D object instance when opening the device using the awt function, as shown in the Java interop example. When the Graphics2D object is not provided to awt, it opens a new window similarly to X11.

The svg device in GraalVM implementation of R generates more lightweight SVG code than the svg implementation in GNU R. Moreover, functions tailored to manipulate the SVG device are provided: and svg.string. The SVG device is demonstrated in the following code sample. Please use the ?functionName syntax to learn more.

mtcars$cars <- rownames(mtcars)
print(barchart(cars~mpg, data=mtcars))
svgCode <-
In-Process Parallel Execution

GraalVM R engine adds a new cluster type SHARED for the parallel package. This cluster starts new jobs as new threads inside the same process. Example:

cl0 <- makeCluster(7, 'SHARED')
clusterApply(cl0, seq_along(cl0), function(i) i)

Worker nodes inherit the attached packages from the parent node with copy-on-write semantics, but not the global environment. This means that you do not need to load again R libraries on the worker nodes but values (including functions) from the global environment have to be transfered to the worker nodes, e.g., using clusterExport.

Note that unlike with the FORK or PSOCK clusters the child nodes in SHARED cluster are running in the same process, therefore, e.g., locking files with lockfile or flock will not work. Moreover, the SHARED cluster is based on an assumption that packages’ native code does not mutate shared vectors (which is a discouraged practice) and is thread safe and re-entrant on the C level.

If the code that you want to parallelize does not match these expectations, you can still use the PSOCK cluster with the GraalVM R engine. The FORK cluster and functions depending solely on forking (e.g., mcparallel) are not supported at the moment.

1 More technically, GraalVM implementation of R uses a fixed MRAN URL from $R_HOME/etc/DEFAULT_CRAN_MIRROR, which is a snapshot of the CRAN repository as it was visible at a given date from the URL string.

2 When this example is run for the first time, it installs the RcppArmadillo package, which may take few minutes. Note that this example can be run in both R executed with GraalVM and GNU R.