# R doParallel: How to Parallelize R DataFrame Computations

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Parallelizing R dataframe computation is a guaranteed way to shave minutes or even hours from your data processing pipeline compute time. Sure, it adds more complexity to the code, but it can drastically reduce your computing bills, especially if you’re doing everything in the cloud.

R doParallel package provides a significant speed increase to your dataframe calculation while minimizing code changes. It has all you need and more to get your feet wet in the world of dataframe parallelization, and today you’ll learn all about it. After reading, you’ll know what changes you need to make to **run your code in parallel**, and how your **CPU core count** affects total compute time and overhead (initialization) time.

Complete beginner to parallel processing in R?Make sure to read our introduction guide to R doParallel first.

### Table of contents:

- How to Get Started with R doParallel
- Baseline – How Slow is Single-Threaded R?
- R doParallel in Action – How to Parallelize DataFrame Aggregations
- R DataFrame Parallelization – Does Compute Time Decrease with More CPU Cores?
- Summing Up R doParallel for DataFrames

## How to Get Started with R doParallel

Our introduction guide to parallelism already covered the basic theory and reasons you should care about the topic. Read that piece first if you’re not familiar with the concepts, as this article assumes you have a foundational understanding of R parallelism.

We won’t repeat ourselves here, but to recap:

- R doParallel package enables parallel computing by using the
`foreach`

package. This allows you to run foreach loops in parallel, and the computation will be split over multiple CPU cores. - For R dataframes, this means you’ll have to
**split them into chunks**, where the number of chunks is equal to the number of cores on which your doParallel cluster is running.

If you don’t have these packages installed, make sure to run the following from your R console:

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