Learn to identify those pesky speed bottlenecks in your R code. You will run a classic numerical operation in parallel and learn to check if it helps!

Should we parallelize?

When can you parallelize?

Using parLapply()

Parallelization in R

Reading files in parallel

Daily price ranges

Measuring the benefits

Bootstrapping the average maternal age

Can we vectorize?

Microbenchmark revenues

Introduction to Parallel Programming

Use parallelism in R for a variety of situations while efficiently managing dependencies. Turn those slow loops into smooth-running machines!

parLapply in real life

Running code on the cluster

A parallel filter

The parallel apply family

Healthcare spending with clusterMap()

Population growth rates with mclapply()

Column operations with trading volumes

Using foreach

Looping with foreach

foreach with variables and packages

Advanced foreach operations

Iterate and filter

Nested foreach loops for price trends

Parallel and foreach

Dive deep into the use of futures in parallel programming. Learn to process vectors, lists, and data frames in parallel, all the while keeping your code easy to maintain.

Futures

Delayed square roots

Futures for advertisement targets

Funding social sciences with futures

Mapping the future

Ranking stocks with furrr

Furrr type specification

Advanced operations with furrr

Processing multiple inputs

The furrr configuration

Do more with future_map()

Grouped bootstrap of stock prices

Row-binding future_map results

Parallel Futures

Learn to manage memory for parallel processes. Make your code reproducible, and add efficient debugging to your parallel programming toolkit.

Monitoring and managing memory

Memory profile of parLapply()

Chunking the bootstrap

Reproducibility in parallel

Reproducible lottery results

Reproduce the bootstrap

Debugging in parallel

Fixing parallel code

The debugging steps

Debugging correlations

Advanced debugging

The bootstrap error

Catch modeling errors

That's a wrap!

Troubleshooting in Parallel

The R programming language is a crucial component of the modern tech stack, but R code can sometimes take time to execute. This course on parallel programming can help you optimize your code by leveraging the multiple processors found in most modern computers. You'll grasp key parallel programming concepts and identify operations that can benefit from parallelization. You'll also explore established R packages (parallel, foreach, future) for parallelization and acquire the skills to reduce execution time, monitor, debug, and ensure reproducibility in parallelized code.

<h2>Speed Up Your Code with Parallel Programming</h2>
<br><br>
R programming language is a key part of the modern tech stack. But sometimes, R code takes a long time to run. The good news is that most modern computers have multiple processors. This course on parallel programming can help you speed up your code by harnessing the hardware you already have.
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<h2>Learn the Key Concepts</h2>
<br><br>
In this course, you will systematically learn the key concepts of parallel programming. You will profile and benchmark common computations like bootstraps and function mappings. You will also learn to identify operations that can benefit from parallelization.
<br><br>
<h2>Use R Packages to Parrallelize Operations</h2>
<br><br>
As you progress, you’ll explore a suite of mature R packages (parallel, foreach, future). You will learn to use these packages to parallelize operations with lists, matrices, and data frames. Working through a variety of tasks, you will gain the skills to rein in the execution time of nested for loops. You will also learn how to monitor, debug, and resolve reproducibility issues of parallelized code.
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<h2>Parallelize Your Existing Code</h2>
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With these tools under your belt, you will be able to write parallelized code that runs significantly faster. By the time you finish, you’ll have the skills to parallelize and maintain existing code in a principled manner.

Writing Efficient R Code

Introduction to the Tidyverse

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.

Parallel Programming in R Course | Boost Your Data Analysis Efficiency

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.


Parallel Programming in R

R Développeur

Make the results of the code reproducible

Run code sequentially on a small subset of input to make sure it works

The debugging steps

Parallel Programming in R

Exercice interactif pratique