Getting Started with Julia's SciML for the R User

If you're an R user who has looked into Julia, you're probably wondering where all of the scientific computing packages are. How do I solve ODEs? Solve f(x)=0 for x? Etc. SciML is the ecosystem for doing this with Julia.

Why SciML? High-Level Workflow Reasons

• Performance - The key reason people are moving from R to Julia's SciML in droves is performance. Even simple ODE solvers are much faster!, demonstrating orders of magnitude performance improvements for differential equations, nonlinear solving, optimization, and more. And the performance advantages continue to grow as more complex algorithms are required.
• Composable Library Components - In R environments, every package feels like a silo. Functions made for one file exchange library cannot easily compose with another. SciML's generic coding with JIT compilation these connections create new optimized code on the fly and allow for a more expansive feature set than can ever be documented. Take new high-precision number types from a package and stick them into a nonlinear solver. Take a package for Intel GPU arrays and stick it into the differential equation solver to use specialized hardware acceleration.
• A Global Harmonious Documentation for Scientific Computing - R's documentation for scientific computing is scattered in a bunch of individual packages where the developers do not talk to each other! This not only leads to documentation differences, but also “style” differences: one package uses tol while the other uses atol. With Julia's SciML, the whole ecosystem is considered together, and inconsistencies are handled at the global level. The goal is to be working in one environment with one language.
• Easier High-Performance and Parallel Computing - With Julia's ecosystem, CUDA will automatically install of the required binaries and cu(A)*cu(B) is then all that's required to GPU-accelerate large-scale linear algebra. MPI is easy to install and use. Distributed computing through password-less SSH. Multithreading is automatic and baked into many libraries, with a specialized algorithm to ensure hierarchical usage does not oversubscribe threads. Basically, libraries give you a lot of parallelism for free, and doing the rest is a piece of cake.
• Mix Scientific Computing with Machine Learning - Want to automate the discovery of missing physical laws using neural networks embedded in differentiable simulations? Julia's SciML is the ecosystem with the tooling to integrate machine learning into the traditional high-performance scientific computing domains, from multiphysics simulations to partial differential equations.

In this plot, deSolve in blue represents R's most commonly used solver:

Need Help Translating from R to Julia?

The following resources can be particularly helpful when adopting Julia for SciML for the first time:

• The Julia Manual's Noteworthy Differences from R page
• Tutorials on Data Wrangling and Plotting in Julia (Sections 4 and 5) written for folks with a background in R.
• Double-check your results with deSolveDiffEq.jl (automatically converts and runs ODE definitions with R's deSolve solvers)
• Use RCall.jl to more incrementally move code to Julia.
• Comparisons between R and Julia from the DataFrames package. And an accessible starting point for Julia's DataFrames.

R to Julia SciML Functionality Translations

The following chart will help you get quickly acquainted with Julia's SciML Tools:

Want to See the Power of Julia?

Check out this R-Bloggers blog post on diffeqr, a package which uses ModelingToolkit to translate R code to Julia, and achieves 350x acceleration over R's popular deSolve ODE solver package. But when the solve is done purely in Julia, it achieves 2777x acceleration over deSolve!