Julia is a powerful, high-level, dynamic programming language. It is also a general-purpose language used to write applications. It has many features like being open-source, simple, efficient, and fast. But one feature that makes it stand out is interoperability.
Interoperability can be described as the capacity of two or more languages to interact with each other for effective data transmission in any system. One of Julia’s features is its ability to communicate and call language into itself while writing the Julia syntax.
To inter-operate with the R language, the RCall package is used. This package is built by RCall package, we can harness the strengths of both languages, enhancing the flexibility and capabilities of the data analysis or scientific computing workflows.
Let's look at the following example:
using RCall
@rlibrary stats
N = 10
x = 1:10
mean = 10
sd = 5
a = R"rnorm($N, mean=$mean, sd=$sd)"
y = dlnorm(x, meanlog=mean, sdlog=sd)
println("List of random numbers")
for i = 1:length(a)
println("$i \t $(a[i])")
end
println("\nLog normal probability density function")
println(y)Line 1: The RCall library is imported in the Julia file.
Line 2: The R library stats is imported in Julia using the @rlibrary macro.
Lines 4–7: Different variables are defined in Julia which will be used for random number generation and a log-normal probability density function.
Line 9: An R script is used to generate random numbers with N values, a mean of mean, and a standard distribution of sd.
Line 10: The R function stats.dlnorm() is used to generate a log-normal probability density function with the independent variable array x, a log mean of mean, and a log standard distribution of sd.
Lines 12–18: The resulting arrays are printed.
Note: If you want to learn about how to link Julia code in R using
XRJuliapackage, visit this Educative Answer.