JuliaCall v0.17.1


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Seamless Integration Between R and 'Julia'

Provides 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. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'.


JuliaCall for Seamless Integration of R and Julia

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[Table of Contents]

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.


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


Currently Julia v0.6.x, Julia v1.0.x, and Julia v1.1 are all supported by JuliaCall.

You can get the development version of JuliaCall by


Basic Usage

Before using JuliaCall, you need to do initial setup by function julia_setup() for automatic type conversion, Julia display systems, etc. It is necessary for every new R session to use the package. If not carried out manually, it will be invoked automatically before other julia_xxx functions. Solutions to some common error in julia_setup() are documented in the troubleshooting section.

julia <- julia_setup()
#> Julia version 1.0.3 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 `Julia` at a specific location, you could do the following:
## julia_setup(JULIA_HOME = "the folder that contains Julia binary").
## You can also set JULIA_HOME in command line environment or use `options(...)`.

## Different ways of 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
#> [1] 1.414214
julia_call("sqrt", 2)
#> [1] 1.414214
#> [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

#> [1] TRUE
#> [1] FALSE

## Functions related to installing and using Julia packages

#> [1] "0.18.1"

Troubleshooting and Ways to Get Help

Julia is not found

Make sure the Julia installation is correct. JuliaCall can 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.

libstdc++.so.6: version `GLIBCXX_3.4.xx’ not found

Such problems are usually on Linux machines. The cause for the problem is that R cannot find the libstdc++ version needed by Julia. To deal with the problem, users can export “TheFolderContainsJulia/lib/julia” to R_LD_LIBRARY_PATH.

RCall not properly installed

The issue is usually caused by updates in R, and it can be typically solved by setting rebuild argument to TRUE in julia_setup() as follows.

JuliaCall::julia_setup(rebuild = TRUE)

How to Get Help

  • One way to get help for Julia functions is just using julia$help as the following example:
#> ```
#> 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
#> ```

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. You only need to find the Julia function or Julia module you want to have in R, using the module, and julia_call the function. There are some examples:

  • 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).
  • FixedEffectjlr uses the Julia package FixedEffectModels.jl to estimate large fixed effects models in R.
  • 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, Issue Reporting, and Contributing

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. You are welcome to use the issue template and the pull request template. The contributing guide provides some guidance for making contributions.

Checking JuliaCall Package

To check and test the JuliaCall package, you need to have the source package. You can

  • download the source of JuliaCall from Github,
  • open JuliaCall.Rproj in your RStudio or open R from the downloaded directory,
  • run devtools::test() to see the result of the test suite.
  • run devtools::check() or click the Check button in the RStudio Build panel in the upper right to see the result of R CMD check.

Other 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 type conversion between R and Julia.
  • XRJulia is an R package based on John Chambers’ XR package and allows for structured integration of R with Julia. It connects to Julia and uses JSON to transfer data between Julia and R. A simple performance comparison between XRJulia and Julia can be found in JuliaCall JOSS paper.
  • 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.


JuliaCall is licensed under MIT.

Code of Conduct

Please note that the JuliaCall project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.


If you use JuliaCall in research that resulted in publications, then please cite the JuliaCall paper using the following BibTeX entry:

    author = {Changcheng Li},
    title = {{JuliaCall}: an {R} package for seamless integration between {R} and {Julia}},
    journal = {The Journal of Open Source Software},
    publisher = {The Open Journal},
    year = {2019},
    volume = {4},
    number = {35},
    pages = {1284},
    doi = {10.21105/joss.01284},

Functions in JuliaCall

Name Description
plotsViewer Julia plots viewer in R.
julia_markdown_setup Do setup for JuliaCall in RMarkdown documents and notebooks.
eng_juliacall Julia language engine in R Markdown
call Call julia functions.
julia_help Get help for a julia function.
julia_package Using julia packages.
autowrap Use automatic wrapper for julia type.
julia_notebook_setup (Deprecated) Do setup for julia chunks in RMarkdown notebooks.
JuliaObjectFields JuliaObject Fields.
julia_command Evaluate string commands in julia and (may) invoke the julia display system.
JuliaObject Convert an R Object to Julia Object.
julia_console Open julia console.
JuliaCall JuliaCall: Seamless Integration Between R and Julia.
julia_eval Evaluate string commands in julia and get the result back in R.
julia_pkg_wrap Wrap julia functions and packages the easy way.
julia_setup Do initial setup for the JuliaCall package.
julia_exists Check whether a julia object with the given name exists or not.
%>J% Language piper for julia language.
julia_assign Assign a value to a name in julia.
julia_source Source a julia source file.
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Type Package
Date 2019-11-26
License MIT + file LICENSE
URL https://github.com/Non-Contradiction/JuliaCall
BugReports https://github.com/Non-Contradiction/JuliaCall/issues
Encoding UTF-8
LazyData true
RoxygenNote 7.0.0
LinkingTo Rcpp
NeedsCompilation yes
ByteCompile yes
SystemRequirements Julia >= 0.6.0, RCall.jl
VignetteBuilder knitr
Packaged 2019-11-27 01:29:01 UTC; lch34
Repository CRAN
Date/Publication 2019-11-27 07:20:10 UTC

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