#MonthOfJulia Day 25: Interfacing with Other Languages

September 30, 2015

(This article was first published on Exegetic Analytics » R, and kindly contributed to R-bloggers)


Julia has native support for calling C and FORTRAN functions. There are also add on packages which provide interfaces to C++, R and Python. We’ll have a brief look at the support for C and R here. Further details on these and the other supported languages can be found on github.

Why would you want to call other languages from within Julia? Here are a couple of reasons:

  • to access functionality which is not implemented in Julia;
  • to exploit some efficiency associated with another language.

The second reason should apply relatively seldom because, as we saw some time ago, Julia provides performance which rivals native C or FORTRAN code.


C functions are called via ccall(), where the name of the C function and the library it lives in are passed as a tuple in the first argument, followed by the return type of the function and the types of the function arguments, and finally the arguments themselves. It’s a bit klunky, but it works!

julia> ccall((:sqrt, "libm"), Float64, (Float64,), 64.0)

It makes sense to wrap a call like that in a native Julia function.

julia> csqrt(x) = ccall((:sqrt, "libm"), Float64, (Float64,), x);
julia> csqrt(64.0)

This function will not be vectorised by default (just try call csqrt() on a vector!), but it’s a simple matter to produce a vectorised version using the @vectorize_1arg macro.

julia> @vectorize_1arg Real csqrt;
julia> methods(csqrt)
# 4 methods for generic function "csqrt":
csqrt{T<:Real}(::AbstractArray{T<:Real,1}) at operators.jl:359
csqrt{T<:Real}(::AbstractArray{T<:Real,2}) at operators.jl:360
csqrt{T<:Real}(::AbstractArray{T<:Real,N}) at operators.jl:362
csqrt(x) at none:6

Note that a few extra specialised methods have been introduced and now calling csqrt() on a vector works perfectly.

julia> csqrt([1, 4, 9, 16])
4-element Array{Float64,1}:


I’ll freely admit that I don’t dabble in C too often these days. R, on the other hand, is a daily workhorse. So being able to import R functionality into Julia is very appealing. The first thing that we need to do is load up a few packages, the most important of which is RCall. There’s great documentation for the package here.

julia> using RCall
julia> using DataArrays, DataFrames

We immediately have access to R’s builtin data sets and we can display them using rprint().

julia> rprint(:HairEyeColor)
, , Sex = Male

Hair    Brown Blue Hazel Green
  Black    32   11    10     3
  Brown    53   50    25    15
  Red      10   10     7     7
  Blond     3   30     5     8

, , Sex = Female

Hair    Brown Blue Hazel Green
  Black    36    9     5     2
  Brown    66   34    29    14
  Red      16    7     7     7
  Blond     4   64     5     8

We can also copy those data across from R to Julia.

julia> airquality = DataFrame(:airquality);
julia> head(airquality)
6x6 DataFrame
| Row | Ozone | Solar.R | Wind | Temp | Month | Day |
| 1   | 41    | 190     | 7.4  | 67   | 5     | 1   |
| 2   | 36    | 118     | 8.0  | 72   | 5     | 2   |
| 3   | 12    | 149     | 12.6 | 74   | 5     | 3   |
| 4   | 18    | 313     | 11.5 | 62   | 5     | 4   |
| 5   | NA    | NA      | 14.3 | 56   | 5     | 5   |
| 6   | 28    | NA      | 14.9 | 66   | 5     | 6   |

rcopy() provides a high-level interface to function calls in R.

julia> rcopy("runif(3)")
3-element Array{Float64,1}:

However, for some complex objects there is no simple way to translate between R and Julia, and in these cases rcopy() fails. We can see in the case below that the object of class lm returned by lm() does not diffuse intact across the R-Julia membrane.

julia> "fit <- lm(bwt ~ ., data = MASS::birthwt)" |> rcopy
ERROR: `rcopy` has no method matching rcopy(::LangSxp)
 in rcopy at no file
 in map_to! at abstractarray.jl:1311
 in map_to! at abstractarray.jl:1320
 in map at abstractarray.jl:1331
 in rcopy at /home/colliera/.julia/v0.3/RCall/src/sexp.jl:131
 in rcopy at /home/colliera/.julia/v0.3/RCall/src/iface.jl:35
 in |> at operators.jl:178

But the call to lm() was successful and we can still look at the results.

julia> rprint(:fit)

lm(formula = bwt ~ ., data = MASS::birthwt)

(Intercept)          low          age          lwt         race  
    3612.51     -1131.22        -6.25         1.05      -100.90  
      smoke          ptl           ht           ui          ftv  
    -174.12        81.34      -181.95      -336.78        -7.58 

You can use R to generate plots with either the base functionality or that provided by libraries like ggplot2 or lattice.

julia> reval("plot(1:10)");                # Will pop up a graphics window...
julia> reval("library(ggplot2)");
julia> rprint("ggplot(MASS::birthwt, aes(x = age, y = bwt)) + geom_point() + theme_classic()")
julia> reval("dev.off()")                  # ... and close the window.

Watch the videos below for some other perspectives on multi-language programming with Julia. Also check out the complete code for today (including examples with C++, FORTRAN and Python) on github.

The post #MonthOfJulia Day 25: Interfacing with Other Languages appeared first on Exegetic Analytics.

To leave a comment for the author, please follow the link and comment on their blog: Exegetic Analytics » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)