Скачать с ютуб Parallelizing R Scripts to Handle Large Files Efficiently в хорошем качестве

Parallelizing R Scripts to Handle Large Files Efficiently

Learn how to efficiently `parallelize your R scripts` to handle large datasets and reduce processing time with the help of the doParallel package. --- This video is based on the question https://stackoverflow.com/q/69632849/ asked by the user 'November2Juliet' ( https://stackoverflow.com/u/17005018/ ) and on the answer https://stackoverflow.com/a/69637833/ provided by the user 'Rui Barradas' ( https://stackoverflow.com/u/8245406/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Is there a way to parallelize running R script by input files? Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Parallelize R Scripts for Efficient Data Processing If you have a large number of files—like over seven thousand—that you need to read and process in R, you might be facing a significant time challenge. Processing each file individually can take an unmanageable amount of time, potentially hours or even days depending on their size. Luckily, there is a solution: parallel processing. In this guide, we’ll explore how to effectively parallelize your R scripts to handle large input files efficiently. The Problem You have a dataset consisting of multiple input files, each containing millions of rows. You've already worked through the data wrangling part of the job, but the challenge lies in the sheer volume of files. Running through each file in a sequential manner isn't feasible due to the extensive processing time required. The Goal The objective is to read all files, wrangle the data as needed, and then export each player’s data to different output files based on unique player IDs—all in a fraction of the time it normally would take. You want to utilize R's capacities for parallel processing to achieve this. Setting Up Parallel Processing in R To implement parallel processing in R, you can use the doParallel and parallel packages. Although doParallel has some restrictions on Windows, we can still leverage the parallel library for a more universal approach. Step 1: Install Required Packages Make sure you have the following packages installed: [[See Video to Reveal this Text or Code Snippet]] Step 2: Create a Function for Parallel Processing You'll need a custom function that will handle writing your output files for each player ID. Here's how you can set it up: [[See Video to Reveal this Text or Code Snippet]] Step 3: Set Up the Cluster You'll want to set up a cluster that allows R to run multiple processes at once. This is where you'll define how many cores to utilize: [[See Video to Reveal this Text or Code Snippet]] Step 4: Parallel File Writing Finally, you'll use parLapply to apply the function across the subsets of data. This step will manage the distribution of tasks across the cluster: [[See Video to Reveal this Text or Code Snippet]] Example Test Data To see how the above setup works, you can create a small test dataset: [[See Video to Reveal this Text or Code Snippet]] This will generate a manageable dataset to test your parallel writing function before scaling it up to your large data files. Conclusion Parallel processing in R can drastically reduce the time it takes to handle large datasets spread across many files. By using the parallel library and setting up clusters, you can leverage multiple CPU cores to process data efficiently and export output files without crashes or overwriting issues. Whether you’re a data scientist or a researcher, mastering these techniques will empower you to handle big data more adeptly. By following the instructions above, you should be well on your way to transforming your R data processing workflow. If you have any questions or need further assistance, feel free to leave a comment!