Evaluating & Optimizing Code Performance In R

If you're working with large and complex data sets, optimizing your R code is essential to improving performance. That's where packages like tidyverse and data.table come into play. In this RStudio tutorial from Enterprise DNA, you'll learn how to evaluate and optimize your R code's performance using these packages and others.

Using the read.csv() function as an example, you'll see how to measure the time it takes for RStudio to read a large CSV file. From there, you'll explore techniques for improving performance, including optimizing data types and operations and using parallel processing.

With the insights gained from this tutorial, you'll be equipped to write R scripts and programs that run more efficiently, even with large data sets or real-time applications. Check out the full tutorial on the Enterprise DNA blog to learn more.

Continue reading Evaluating & Optimizing Code Performance In R on the Enterprise DNA blog.