Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

Calling R from Julia

R is a powerful language for statistical computing and machine learning. It also offers very potent plotting possibilities via theggplot2module. Julia offers seamless integration with R via theRCall.jlpackage.

Getting ready

RCallcan simply be installed with the Julia package manager. In the Julia command line (REPL), just press the] key and run the following command:

(v1.0) pkg> add RCall

The RCallinstaller will search for your local R installation. If it is not found,RCall will automatically install R for you using the inbuilt Python Anaconda (theRCallinstaller uses theConda.jlJulia module to installr-base Please note that the minimal required GNU R version forRCallis 3.4.0.


For ease of configuration and management, we recommend using an external R installation. The following three locations are checked when installingRCall:

  • TheENV["R_HOME"]environment variable
  • TheENV["PATH"]environment variable
  • The Windows registry (on Windows platforms...