Learn R with Real Code Examples

Vectorized operations are faster than loops

Use data.table or dplyr for large datasets

Integrate compiled code (C/C++) for performance-critical sections

Use parallel processing packages when applicable

Avoid copying large objects unnecessarily

Be cautious with executing untrusted R scripts

Validate input data before processing

Use secure connections for remote data sources

Apply proper permissions for shared R environments

Sanitize outputs when deploying web apps (Shiny)

Measure memory and CPU usage

Monitor runtime of long computations

Log key metrics for reproducibility

Profile code with Rprof or profvis

Debug interactive sessions carefully

Use consistent naming conventions

Comment complex analyses

Leverage vectorized operations for efficiency

Follow tidyverse style guide for readability

Organize scripts into functions and modules