# Why writing vectorized code in R is a good idea

**Revolutions**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

As a language for statistical computing, R has always had a bias towards linear algebra, and is optimized for operations dealing in complete vectors and matrixes. This can be surprising to programmers coming to R from lower-level languages, where iterative programming (looping over the elements of a vector or matrix) is more natural and often more efficient. That's not the case with R, though: Noam Ross explains why vectorized programming in R is a good idea:

If you can express what you want to do in R in a line or two, with just a few function calls that are actually calling compiled code, it’ll be more efficient than if you write long program, with the added overhead of many function calls. This is not the case in all other languages. Often, in compiled languages, you want to stick with lots of very simple statements, because it allows the compiler to figure out the most efficient translation of the code.

Read Noam's complete article at the link below for a bunch of useful tips and tricks for writing efficient and clear code in the R langauge using vectorized programming.

Noam Ross: Vectorization in R: Why?

**leave a comment**for the author, please follow the link and comment on their blog:

**Revolutions**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.