How to install V8 on Ubuntu without breaking?

For easiest setup, set the environment variable DOWNLOAD_STATIC_LIBV8=1 before installing to automatically download a static libv8 build. Alternatively, install libv8-dev via apt, but this may require handling system dependencies manually.

Can I use npm packages in R with V8?

Yes, via browserify as detailed in the vignette, allowing you to bundle npm modules for use in R. You can also source libraries directly from CDNs using ctx$source() for simpler cases.

V8 or reticulate for running external code in R?

V8 is for JavaScript and WebAssembly, while reticulate is for Python. Choose V8 if you need JavaScript libraries or WebAssembly; for Python data science tools, reticulate is the better fit.

How to pass data between R and JavaScript in V8?

Use ctx$assign() to send data from R to JavaScript and ctx$get() to retrieve results. For complex objects, serialize as JSON, but be mindful of performance with large datasets.

Is V8 good for Shiny apps?

Yes, it can run JavaScript logic server-side in Shiny, but manage contexts carefully to avoid memory leaks in long sessions. It's not for client-side browser interactivity.

What JavaScript features does V8 support?

Support depends on the linked libv8 version; check the package documentation for specifics. Older versions might lack latest ECMAScript features, so verify compatibility for your use case.

Open-Awesome

V8

NOASSERTIONC++v8.0.0

An R interface to Google's V8 JavaScript and WebAssembly engine for executing JavaScript code within R.

Visit Website GitHub

207 stars30 forks0 contributors

What is V8?

V8 is an R package that provides an interface to Google's V8 JavaScript and WebAssembly engine, allowing R users to execute JavaScript code directly within their R environment. It solves the problem of integrating JavaScript libraries, data processing scripts, or WebAssembly modules into R workflows without needing separate runtime setups.

Target Audience

R developers and data scientists who need to incorporate JavaScript libraries, run JavaScript-based data transformations, or leverage WebAssembly modules within their R projects.

Value Proposition

Developers choose V8 for its seamless integration of JavaScript execution into R, eliminating the need for external Node.js setups and enabling direct use of JavaScript ecosystems within R scripts.

Overview

Embedded JavaScript Engine for R

Use Cases

Best For

Running JavaScript data processing scripts within R workflows
Integrating JavaScript visualization libraries (e.g., D3.js) into R applications
Using npm packages in R via browserify or CDN sources
Executing WebAssembly modules for performance-critical computations in R
Prototyping or testing JavaScript code alongside R analysis
Embedding JavaScript-based logic in R packages or Shiny apps

Not Ideal For

Projects requiring full browser environments with DOM APIs for front-end development
High-performance computing tasks where native R or C++ extensions are more efficient
Teams already using Node.js extensively without a need for R integration
Applications needing real-time JavaScript debugging with IDE tooling

Pros & Cons

Pros

Cross-Platform Simplicity

Binary packages are available directly from CRAN for OS-X and Windows, making installation straightforward for most users without external dependencies.

External Library Integration

Supports sourcing JavaScript libraries from CDNs and using npm packages via browserify, as shown in vignettes, enabling direct use of ecosystems like D3.js in R.

WebAssembly Support

Leverages V8's WebAssembly engine to run compiled code within R, allowing for performance-critical computations without leaving the R environment.

Context Isolation

Provides managed JavaScript contexts to isolate state and prevent conflicts, essential for running multiple scripts or in Shiny apps.

Cons

Linux Installation Complexity

Requires manual setup of libv8 or environment variables for static builds, which can be cumbersome and error-prone compared to other platforms.

Limited Debugging Tools

Lacks integrated debugging support for JavaScript code within R, making it difficult to trace errors or inspect execution without external workarounds.

Performance Overhead

Introduces overhead for data transfer between R and JavaScript contexts, which can impact performance for large datasets or frequent calls.

Frequently Asked Questions

Related Projects

JuliaCall

Embed Julia in R

Stars287

Forks42

Last commit2 months ago

RJulia

R package which integrating R and Julia

Stars145

Forks23

Last commit9 years ago

runr

Run external programs from R (experimental)

Stars88

Forks25