Run R with GraalVM Enterprise

GraalVM Enterprise 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.

Installing R

The R language runtime is not provided by default, can be added to GraalVM Enterprise using the functional gu utility:

gu install r

Please note, the installation of the R language component is possible only from GitHub catalog for both GraalVM Community and Enterprise users. See gu --help for more information.


The R language runtime requires the OpenMP runtime library and GFortran 3 runtime libraries to be installed on the target system. Following commands should install those dependencies.

On macOS it is necessary to run $GRAALVM_HOME/bin/configure_fastr. This script will attempt to locate the necessary runtime libraries on your computer and will fine-tune the the GraalVM R installation according to your system. On Linux systems, this script will check that the necessary libraries are installed, and if not, it will suggest how to install them.

Moreover, to install R packages that contain C/C++ or Fortran code, compilers for those languages must be present on the target system. Following packages satisfy the dependencies of the most common R packages:

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 $GRAALVM_HOME/etc/Renviron file.

Running R Code

Run R code with the R and Rscript commands:

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

The R language runtime 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:

Note: unlike other GraalVM languages, R does not yet ship with GraalVM Enterprise 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:


Running R Extensions

The GraalVM R engine can run R extensions in two modes:

The native mode is better suited for code that does not extensively interact with the R API, for example, plain C or Fortran numerical computations working on primitive arrays. The llvm mode provides significantly better performance for extensions that frequently call between R and the C/C++ code, because GraalVM LLVM interpreter is also partially evaluated by the Truffle library like the R code, both can be inlined and optimized as one compilation unit. Moreover, GraalVM LLVM is supported by tools shipped with GraalVM Enterprise which allows, for instance, to debug R and C code together.

In one GraalVM R process, any R package can be loaded in either mode. That is, GraalVM R supports mixing packages loaded in the native mode with packages loaded in the llvm mode in one process.

Generating LLVM Bitcode

The GraalVM R engine is configured to use the LLVM toolchain to compile R packages native code. This toolchain produces standard executable binaries for a given system, but it also embeds the corresponding LLVM bitcode into them. The binaries produced by the LLVM toolchain can be loaded in both modes: native or llvm.

The GraalVM R engine can be reconfigured to use your system default compilers when installing R packages by running:

# use local installation of GGC:
R -e 'fastr.setToolchain("native")'
# to revert back to using the GraalVM's LLVM toolchain:
R -e 'fastr.setToolchain("llvm")'

Using the system default compilers may be more reliable, but you loose the ability to load the R packages built with the LLVM toolchain in the llvm mode, because they will not contain the embedded bitcode. Moreover, mixing packages built by the local system default compilers and packages built by the LLVM toolchain in one GraalVM R process may cause linking issues.

Choosing the Running Mode

Starting from the version 19.3.0, the GraalVM R engine uses the following defaults:

To enable the llvm mode for loading the packages, use --R.BackEnd=llvm. You can also enable each mode selectively for given R packages by using:

High Performance

GraalVM runtime optimizes R code that runs for extended periods of time. The speculative optimizations based on the runtime behaviour 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.

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:

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 R Engine Compatibility

GraalVM implementation of R is based on GNU R and reuses the base packages. It is currently based on GNU-R 3.7.4, 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 extensions. 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 repository. This behaviour 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:

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:

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 Write Polyglot Programs and the Embed Languages 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 the Java Based Graphics section below.

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)

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 transferred 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.