Executing native Java code in R: an approach based on a local server | Zendy

Mathieu Fortin | Zendy

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Executing native Java code in R: an approach based on a local server

Author(s) -

Mathieu Fortin

Publication year - 2020

Publication title -

peerj computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.806

H-Index - 24

ISSN - 2376-5992

DOI - 10.7717/peerj-cs.300

Subject(s) - computer science , java , programming language , strictfp , multithreading , operating system , vectorization (mathematics) , java applet , code (set theory) , generics in java , java annotation , real time java , parallel computing , interface (matter) , java concurrency , java modeling language , set (abstract data type) , bubble , maximum bubble pressure method , thread (computing)

The R language is widely used for data analysis. However, it does not allow for complex object-oriented implementation and it tends to be slower than other languages such as Java, C and C++. Consequently, it can be more computationally efficient to run native Java code in R. To do this, there exist at least two approaches. One is based on the Java Native Interface (JNI) and it has been successfully implemented in the rJava package. An alternative approach consists of running a local server in Java and linking it to an R environment through a socket connection. This alternative approach has been implemented in an R package called J4R. This article shows how this approach makes it possible to simplify the calls to Java methods and to integrate the R vectorization. The downside is a loss of performance. However, if the vectorization is used in conjunction with multithreading, this loss of performance can be compensated for.

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