Interoperability with Java

The GraalVM R runtime provides the built-in interoperability with Java. Java class objects can be obtained via java.type(...). In order to run R with the Java interoperability features, the R or Rscript commands have to be started with the --jvm option.

R --jvm

Note: All of the following examples are meant to be executed in the R REPL; no additional Java dependencies are necessary.

• The standard new function interprets String arguments as a Java class if such class exists.
• new also accepts Java types returned from java.type.
• The 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 Graphics Interoperability.

The following example creates a new Java BufferedImage object, plots random data to it using R’s grid package, and shows an 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.

library(grid)
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()
   graphics$setBackground(java.type('java.awt.Color')$white);
   grDevices:::awt(image$getWidth(), image$getHeight(), graphics)

   # draw image
   grid.newpage()
   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")
   panel$add(label)
   frame <- new("javax.swing.JFrame")
   frame$setMinimumSize(new("java.awt.Dimension",
                image$getWidth(), image$getHeight()))
   frame$add(panel)
   frame$setVisible(T)
   while (frame$isVisible()) Sys.sleep(1)
}
openJavaWindow()

GraalVM’s R runtime provides its own rJava-compatible replacement package available at GitHub, which can be installed using:

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

In order for third party Java libraries to be accessed, they have to be placed on R’s class path:

> java.addToClasspath("/foo/bar.jar")
> java.addToClasspath(c("/foo/bar.jar", "/foo/bar2.jar"))

Getting a Java Class

The access to a Java type is achieved by providing a fully qualified class name to the java.type function:

> calendarClass <- java.type('java.util.GregorianCalendar')

The returned value is a polyglot object representing a Java type.

The respective Java class is then available through the class property:

> calendarClass$class

The same works also for static class members:

> calendarClass$getInstance()

Every requested class has to be on the R classpath. The JDK classes, like GregorianCalendar used above, work out of the box.

Creating a New Java Object

A new Java object can be created by providing a Java type to the new function:

> calendar <- new(calendarClass)

It is also possible to pass over additional constructor arguments:

> calendar <- new(calendarClass, year=2042L, moth=3L, day=1L)

Alternately, you can use just a class name:

calendar <- new("java.util.GregorianCalendar")
calendar <- new("java.util.GregorianCalendar", year=2042L, moth=3L, day=1L)

Accessing Fields and Methods

The access to static and instance fields and methods is provided by the $ and [ operators.

To access Java fields:

> calendarClass$SUNDAY
> calendarClass["SUNDAY"]

To invoke Java methods:

> currentTime <- calendar$getTime()
> currentTime["toString"]()
> calendar$setTime(currentTime)

Polyglot objects returned from a field or method, or created via new, are either automatically converted into corresponding R values or they live on as polyglot objects in the GraalVM R runtime. If necessary, they can be passed over to Java:

> cet <- java.type("java.util.TimeZone")$getTimeZone("CET")
> cetCalendar <- new(calendarClass, cet)

Handling of Java Primitives

The returned Java primitives, primitive wrappers, and String instances are automatically converted into corresponding R values and map as follows:

• R integer values map directly to Java int/Integer
• R numeric to Java double/Double
• R logical to Java boolean/Boolean
• R character to Java String
• If necessary R integer and double are converted to the expected Java type

Inspecting Polyglot Objects

The names function can be used to obtain a list of instance and static members from a polyglot Java object or Java class:

> names(calendar)
> names(calendarClass)

Code completion works as well:

> calendar$a<TAB>

Working with Java Arrays

The need for Java arrays appears when they have to be passed over to java as arguments.

You can create an array by creating an array class and instantiating an array from it:

> arrayClass <- java.type('int[]')
> intArray <- new(arrayClass, 3)

The component type names of primitive arrays are boolean, byte, char, double, float, int, long, and short – the same as in each particular primitive wrapper TYPE constant (see, e.g., Integer.TYPE.getName()). Note that it is possible to pass an R vector into a Java method in case the expected Java array is of a primitive component type or String. Then, the conversion happens automatically in the background.

> integerArray <- new(java.type('java.lang.Integer[]'), 3L)
> integer2DimArray <- new('java.lang.Integer[][]', c(2L, 3L))
> stringArray <- new(java.type('java.lang.String[]'), 3L)

The access to array elements is provided by the [ operator:

> stringArray[1] <- 'a'
> string2DimArray[1,1] <- 'a'
> element <- stringArray[1]
> element <- string2DimArray[1,1]

Converting Java Arrays into R Objects

Unlike Java primitives or their wrappers, Java arrays are not automatically converted into an R vector. Nevertheless, when appropriate, they can be handled by R builtin functions the same way as native R objects:

> sapply(intArray, function(e) { e })
> length(stringArray)
> length(string2DimArray[1])

Explicit Java Array Conversion

A Java array conversion can be done explicitly by providing a Java array to the as.vector function:

> intVec <- as.vector(intArray)

Arrays having a Java primitive component type are converted into an R vector. Otherwise a list containing the array elements is created:

> characterVector <- as.character(intArray)
> logicalVector <- as.logical(intArray)
> ...

Java Iterable Interface

When appropriate, Java objects implementing java.lang.Iterable are handled in the same way as Java arrays when passed as arguments to functions:

> javaList <- new(java.type('java.util.ArrayList'))
> javaList$add(0);
> javaList$add(1)
> length(javaList)
> as.integer(javaList)
> as.logical(javaList)

Compatibility with rJava

The GraalVM R runtime comes with an rJava compatibility layer based on the Java interoperability features. Most of the officially documented rJava functions are supported. For more information, see the rJava CRAN documentation.

• You can install the GraalVM R runtime’s rJava replacement using install.packages("rJava"). The install.packages function in R has special handling for some packages, including rJava, and it downloads rJava from the source repository on GitHub instead of from MRAN.
• As with any other R package, before executing any rJava functions, the package has to be loaded first:

  > library(rJava)
  

Supported rJava features:

• The $ and [ operators work the same as described above.

Java Graphics Interoperability

The GraalVM R runtime 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 to 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 similar to X11.

The svg device in GraalVM R runtime generates more lightweight SVG code than the svg implementation in GNU R. Moreover, functions tailored to manipulate the SVG device are provided: svg.off and svg.string. The SVG device is demonstrated in the following code sample:

library(lattice)
svg()
mtcars$cars <- rownames(mtcars)
print(barchart(cars~mpg, data=mtcars))
svgCode <- svg.off()
cat(svgCode)

To learn more, see the ?functionName syntax.