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

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  • 1Canadian Wood Fibre Centre, Canadian Forest Service, Ottawa, ON, Canada.

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This study explores integrating Java code within R for enhanced data analysis. The J4R package offers a socket-based approach, simplifying Java calls and R vectorization, with performance gains possible via multithreading.

Area of Science:

  • Computational statistics
  • Software engineering for data science

Background:

  • R is popular for data analysis but lacks complex object-oriented features and can be slow.
  • Integrating R with faster languages like Java can improve computational efficiency.

Purpose of the Study:

  • To introduce and evaluate the J4R package for running Java code within R.
  • To demonstrate simplified Java method calls and R vectorization integration using J4R.
  • To analyze the performance implications of the J4R approach.

Main Methods:

  • Implementation of a Java server and R client communication via socket connection.
  • Utilizing the J4R R package to manage the Java-R interaction.
  • Benchmarking performance with and without multithreading for vectorized operations.
Keywords:
InteroperabilityJava Native InterfaceJava local serverR vectorizationTCP/IP connection

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Main Results:

  • J4R simplifies Java method invocation and integrates R's vectorization capabilities.
  • A performance decrease is observed compared to native Java or JNI-based approaches.
  • Multithreading can mitigate the performance loss associated with J4R's vectorization.

Conclusions:

  • The J4R package provides a viable alternative for integrating Java and R, enhancing code simplicity and vectorization.
  • Careful consideration of performance trade-offs is necessary, with multithreading offering a potential solution.
  • J4R facilitates more complex computational tasks by leveraging Java's strengths within the R environment.