Trouble rjava in r portable#
Trouble rjava in r code#

Trouble rjava in r code#

The JNI can be used both ways: it allows running native code within a Java environment or running Java code in a foreign environment. This package makes it possible to start a JVM embedded in the R environment and to execute native Java code using the well-known Java Native Interface (JNI, see Liang, 1999). The rJava package is a low-level R to Java interface that has been available for over a decade ( Urbanek, 2020). Some statistical packages, such as nlme and lme4, rely on these tools for tedious mathematical operations in order to reduce the computational time ( Pinheiro, Bates & R Core Team, 2017 Bates et al., 2019). The basic installation of R already provides tools that can run native C and C++ code. Interoperability has the major advantage of avoiding code replication across different languages and thereby it decreases the maintenance effort, since only one version of the source code exists. This idea of making calls between different languages is referred to as interoperability ( Epperly et al., 2012). When dealing with complex models, invoking native Java or C++ code directly in R can result in a significant gain in computational time compared to translating the code and running it directly in the R environment. One thing for sure, these two languages are faster than R, mainly because of their implementation ( Wickham, 2014, Ch. However, the JVM technology has improved over time and the two languages now show a comparable performance in some contexts ( Vivanco & Pizzi, 2002 Costanza, Herzeel & Verachtert, 2019), even though it is still widely accepted that C++ is slightly faster than Java. In terms of performance, the first versions of Java were considered to be slower than C++. In practice, the Java Virtual Machine (JVM) comes with the Java installation package. This virtual machine makes the interface between the operating system (OS) and the Java code.

Trouble rjava in r portable#

Compared to other languages, Java has the advantage of being highly portable thanks to a virtual machine technology. According to different surveys, Java counts among the five most popular languages in computer programming ( GitHub, 2018 Stack Overflow, 2018 Tiobe, 2020). Object-oriented languages such as C++ and Java are better suited for this task since they allow for encapsulation and polymorphism ( Schildt, 2007, Ch. Some scientific problems, especially those involving modelling, require complex object-oriented implementation and computational performance that can hardly be achieved in R. 16) and it is now widely used by the scientific community.

This interpreted language was primarily designed for data analysis and statistics ( Wickham, 2014, Ch. It has been recently ranked the tenth most popular language according to the TIOBE index ( Tiobe, 2020). The R language ( Venables & Smith, 2020) has gained in popularity over the last decades.