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Non-Contradiction
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JuliaCall for Seamless Integration of R and Julia

R build
status CRAN\_Status\_Badge DOI

[Table of Contents] <!-- Table of contents generated generated by http://tableofcontent.eu -->

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

You can install

JuliaCall
just like any other R packages by
install.packages("JuliaCall")

To use

JuliaCall
you must have a working installation of Julia. This can be easily done via:
library(JuliaCall)
install_julia()

which will automatically install and setup a version of Julia specifically for use with JuliaCall. Or you can do

library(JuliaCall)
julia_setup(installJulia = TRUE)

which will invoke

install_julia
automatically if Julia is not found and also do initialization of
JuliaCall
.

You can also setup Julia manually by downloading a generic binary from https://julialang.org/downloads/ and add it to your path. Currently

Julia v0.6.x
and the
Julia v1.x
releases are all supported by
JuliaCall
.

You can get the development version of

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

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.
library(JuliaCall)
julia  Julia version 1.5.0 at location C:\Users\lch34\AppData\Local\JULIAC~1\JULIAC~1\julia\V15~1.0\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 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] "0.22.0" julia_library("Optim")

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)

ERROR: could not load library "/usr/lib/x86_64-linux-gnu/../bin/../lib/x86_64-linux-gnu/julia/sys.so"

This error happens when Julia is built/installed with

MULTIARCH_INSTALL=1
, as it is on e.g. Debian. It is caused by the bindir-locating code in jl_init not being multiarch-aware. To work around it, try setting
JULIA_BINDIR=/usr/bin
in
.Renviron
.

How to Get Help

  • One way to get help for Julia functions is just using
    julia$help
    as 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 `v` 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 -81.0:
#> 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 real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to compute the square root.   For such matrices, eigenvalues  that appear to be slightly negative due to roundoff errors are treated as if they were zero More precisely, matrices with all eigenvalues `= -rtol*(max ||)` are treated as semidefinite (yielding a Hermitian square root), with negative eigenvalues taken to be zero. `rtol` is a keyword argument to `sqrt` (in the Hermitian/real-symmetric case only) that defaults to machine precision scaled by `size(A,1)`.
#> 
#> 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/jump-dev/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 [email protected] or [email protected].

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 [email protected] or [email protected]. 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.

License

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.

Citing

If you use

JuliaCall
in research that resulted in publications, then please cite the
JuliaCall
paper using the following BibTeX entry:
@Article{JuliaCall,
    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},
  }

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