JuliaCall for Seamless Integration of R and Julia

[Table of Contents]

• JuliaCall for Seamless Integration of R and Julia
  • Installation
  • Basic Usage
  • Trouble Shooting and Way to Get Help
  • JuliaCall for R Package Developers
  • Suggestion and Issue Reporting
  • Interfaces between R and Julia

Package JuliaCall is an R interface to Julia, which is a high-level, high-performance dynamic programming language for numerical computing, see https://julialang.org/ for more information. Below is an image for Mandelbrot set. JuliaCall brings more than 100 times speedup of the calculation! See https://github.com/Non-Contradiction/JuliaCall/tree/master/example/mandelbrot for more information.

Installation

To use package JuliaCall, you first have to install Julia on your computer, you can download a generic binary from https://julialang.org/downloads/ and add it to your path, and then you can install JuliaCall just like any other R packages by

install.packages("JuliaCall")

Note that currently Julia v0.6.x, Julia v0.7.0 and Julia v1.0 are all supported by JuliaCall. But note that some Julia packages may not work on Julia v1.0 and you may need to use Julia v0.7.0 for transition purposes.

You can get the development version of JuliaCall by

devtools::install_github("Non-Contradiction/JuliaCall")

Basic Usage

library(JuliaCall)

## Do initial setup

julia <- julia_setup()
#> Julia version 1.0.0 at location /Applications/Julia-1.0.app/Contents/Resources/julia/bin will be used.
#> Loading setup script for JuliaCall...
#> Finish loading setup script for JuliaCall.

## If you want to use a julia at a specific location, you could do the following:
## julia_setup(JULIA_HOME = "the folder that contains julia binary"), 
## or you can set JULIA_HOME in command line environment or use `options(...)`

## Different ways for using Julia to calculate sqrt(2)

# julia$command("a = sqrt(2);"); julia$eval("a")
julia_command("a = sqrt(2);"); julia_eval("a")
#> [1] 1.414214
julia_eval("sqrt(2)")
#> [1] 1.414214
julia_call("sqrt", 2)
#> [1] 1.414214
julia_eval("sqrt")(2)
#> [1] 1.414214
julia_assign("x", sqrt(2)); julia_eval("x")
#> [1] 1.414214
julia_assign("rsqrt", sqrt); julia_call("rsqrt", 2)
#> [1] 1.414214
2 %>J% sqrt
#> [1] 1.414214

## You can use `julia$exists` as `exists` in R to test
## whether a function or name exists in Julia or not

julia_exists("sqrt")
#> [1] TRUE
julia_exists("c")
#> [1] FALSE

## Functions related to installing and using Julia packages

julia_install_package_if_needed("Optim")
julia_installed_package("Optim")
#> [1] "0.16.0"
julia_library("Optim")

Trouble Shooting and Way to Get Help

Julia is not found

Make sure the Julia installation is correct. JuliaCall is able to find Julia on PATH, and there are three ways for JuliaCall to find Julia not on PATH.

• Use julia_setup(JULIA_HOME = "the folder that contains julia binary")
• Use options(JULIA_HOME = "the folder that contains julia binary")
• Set JULIA_HOME in command line environment.

How to Get Help

• One way to get help for julia functions is just using julia$help like the following example:

julia_help("sqrt")
#> ```
#> sqrt(x)
#> ```
#> 
#> Return $\sqrt{x}$. Throws [`DomainError`](@ref) for negative [`Real`](@ref) arguments. Use complex negative arguments instead. The prefix operator `√` is equivalent to `sqrt`.
#> 
#> # Examples
#> 
#> ```jldoctest; filter = r"Stacktrace:(\n \[[0-9]+\].*)*"
#> julia> sqrt(big(81))
#> 9.0
#> 
#> julia> sqrt(big(-81))
#> ERROR: DomainError with -8.1e+01:
#> NaN result for non-NaN input.
#> Stacktrace:
#>  [1] sqrt(::BigFloat) at ./mpfr.jl:501
#> [...]
#> 
#> julia> sqrt(big(complex(-81)))
#> 0.0 + 9.0im
#> ```
#> 
#> ```
#> sqrt(A::AbstractMatrix)
#> ```
#> 
#> If `A` has no negative real eigenvalues, compute the principal matrix square root of `A`, that is the unique matrix $X$ with eigenvalues having positive real part such that $X^2 = A$. Otherwise, a nonprincipal square root is returned.
#> 
#> If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to compute the square root. Otherwise, the square root is determined by means of the Björck-Hammarling method [^BH83], which computes the complex Schur form ([`schur`](@ref)) and then the complex square root of the triangular factor.
#> 
#> [^BH83]: Åke Björck and Sven Hammarling, "A Schur method for the square root of a matrix", Linear Algebra and its Applications, 52-53, 1983, 127-140. [doi:10.1016/0024-3795(83)80010-X](https://doi.org/10.1016/0024-3795(83)80010-X)
#> 
#> # Examples
#> 
#> ```jldoctest
#> julia> A = [4 0; 0 4]
#> 2×2 Array{Int64,2}:
#>  4  0
#>  0  4
#> 
#> julia> sqrt(A)
#> 2×2 Array{Float64,2}:
#>  2.0  0.0
#>  0.0  2.0
#> ```

• The GitHub Pages for this repository host the documentation for the development version of JuliaCall: https://non-contradiction.github.io/JuliaCall/.

• And you are more than welcome to contact me about JuliaCall at lch34677@gmail.com or cxl508@psu.edu.

JuliaCall for R Package Developers

If you are interested in developing an R package which is an interface for a Julia package, JuliaCall is an ideal choice for that!

Basically you only need to find the Julia function or Julia module you want to have in R and then just using the module and call the function. Several examples are:

• diffeqr is a package for solving differential equations in R. It utilizes DifferentialEquations.jl for its core routines to give high performance solving of ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), and differential-algebraic equations (DAEs) directly in R.
• convexjlr is an R package for Disciplined Convex Programming (DCP) by providing a high level wrapper for Julia package Convex.jl. convexjlr can solve linear programs, second order cone programs, semidefinite programs, exponential cone programs, mixed-integer linear programs, and some other DCP-compliant convex programs through Convex.jl.
• ipoptjlr provides an R interface to the Ipopt nonlinear optimization solver. It provides a simple high-level wrapper for Julia package [Ipopt.jl] (https://github.com/JuliaOpt/Ipopt.jl).
• Julia MixedModels from R illustrates how to use JuliaCall and Julia package MixedModels.jl to build mixed models in R.
• autodiffr provides automatic differentiation to native R functions by wrapping Julia packages ForwardDiff.jl and ReverseDiff.jl through JuliaCall, which is a work in progress.

If you have any issues in developing an R package using JuliaCall, you may report it using the link: https://github.com/Non-Contradiction/JuliaCall/issues/new. Or email me at lch34677@gmail.com or cxl508@psu.edu.

Suggestion and Issue Reporting

JuliaCall is under active development now. Any suggestion or issue reporting is welcome! You may report it using the link: https://github.com/Non-Contradiction/JuliaCall/issues/new. Or email me at lch34677@gmail.com or cxl508@psu.edu.

And if you encounter some issues which crash R or RStudio, then you may have met segfault errors. I am very sorry. And I am trying my best to remove errors like that. It will be much appreciated if you can

• download the source of JuliaCall from Github,
• open JuliaCall.Rproj in your RStudio or open R from the directory where you download the source of JuliaCall,
• run devtools::check() to see the result of R CMD check for JuliaCall on your machine,
• and paste the result to the issue report.

Interfaces between R and Julia

• RCall.jl is a Julia package which embeds R in Julia. JuliaCall is inspired by RCall.jl and depends on RCall.jl for many functionalities like much of the type conversion between R and Julia.
• XRJulia is an R package based on John Chambers’ XR package and allow for structured integration of R with Julia. It connects to Julia and uses JSON to transfer data between Julia and R.
• RJulia is an R package which embeds Julia in R as well as JuliaCall. It is not on CRAN yet and I haven’t tested it.