How to link Julia code with R using XRJulia
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R and Julia are programming languages that are widely used for data processing, data analysis, and data visualization. R finds its utility in extended data analytic functionality, while Julia is famous for its robust computational power. To increase the scope of the application of these languages, several packages are available to link both languages, with each package having its own pros and cons. In this Answer, we'll explore how to run Julia code in R using the XRJulia package.
Using Julia code in R
Julia code can be used in R through the XRJulia package, which provides an interface from R to Julia. It not only provides an interface to import variables from Julia to R without requiring any special programming but also allows the creation of objects in R that serve as proxies for Julia functions, thus allowing the user to use Julia functions within R.
Code example
Let's look at the following example:
library("XRJulia")
ev <- RJulia()
ev$Command("using StatsBase")
ev$Command("a = [3, 2, 4, 5, 6, 4, 1, 2]")
b <- ev$Eval("countmap(a)")
c <- juliaGet(b)
cat("Num | Count \n")
for (key in names(c)) {
cat(key, " |", c[[key]], "\n")
}Explanation
Line 1: The library
XRJuliais imported.Line 3: A new Julia session is started with the name
ev.Lines 4–6: The session
evis used to evaluate the occurrences of each element of an arrayausing thecountmap()function of Julia'sStatsBaselibrary.Lines 7–12: As
bis a Julia object, it is converted to an R object using thejuliaGet()function, and its value is printed usingforloop.
Note: If you want to learn about how to link Julia and R using
RCallpackage, visit this Educative Answer.
In conclusion, integrating R with Julia using XRJulia provides a powerful bridge between two different programming languages. With XRJulia, we can explore and experiment with data in a unified environment, improving the efficiency of our workflows and the quality of the analyses.
