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#rstats

171 posts144 participants24 posts today
Public
pglpm

Hi R people! Could you suggest some guides and links on how to set up parallel processing with `foreach` that can work both on Windows and Linux/Mac? I'm searching for guides on the net, but most of them seem to have become obsolete. Thank you!

#rstats#ParallelComputing
Public
useR! 2025 Conference

useR! 2025 has reduced fee waivers and opportunity scholarships available! Please apply by April 18th at midnight anywhere on Earth. Decisions will be sent by April 30th.

Reduced fee requests: bit.ly/useR2025-reduced-fee
Opportunity scholarships: bit.ly/useR2025-opp-scholar

user2025.r-project.org/register

useR! 2025 will be held from Friday, August 8th to Sunday, August 10th at Duke University in Durham, NC, USA.
#rstats #useR2025

Links and QR codes for the reduced fee and opportunity scholarship applications.
Public
Johann Joets

r-bloggers.com/2025/04/setting
#rstats #crossValidation #modeling

R-bloggers · Setting Up Cross-Validation (caret package) in R: A Step-by-Step Guide | R-bloggersIn this blog, we explored how to set up cross-validation in R using the caret package, a powerful tool for evaluating machine learning models. Here’s a quick recap of what we covered: Introduction to Cross-Validation: Cross-validation is a resampling technique that helps assess model performance and prevent overfitting by testing the model on multiple subsets … Continue reading Setting Up Cross-Validation (caret package) in R: A Step-by-Step GuideSetting Up Cross-Validation (caret package) in R: A Step-by-Step Guide was first posted on April 13, 2025 at 7:08 am.
Public
Johann Joets

r-bloggers.com/2025/04/data-sp
#rstats #sampling

R-bloggers · Data Splitting and Preprocessing (rsample) in R: A Step-by-Step Guide | R-bloggersData preprocessing is a crucial step in any machine learning workflow. It ensures that your data is clean, consistent, and ready for modeling. In this blog post, we’ll walk through the process of splitting and preprocessing data in R, using the rsample package for data splitting and saving the results for future use. Here’s what … Continue reading Data Splitting and Preprocessing (rsample) in R: A Step-by-Step GuideData Splitting and Preprocessing (rsample) in R: A Step-by-Step Guide was first posted on April 13, 2025 at 7:08 am.
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Hugh Graham

This vignette from {memuse} is jokes! cran.r-project.org/web/package

current favourite quotes:
" memuse() exists because I generally find it inappropriate to not have an object constructor of the same name as the object, and mu() exists because if I have to type more than 5 characters, I’m completely furious. I’m looking at you, suppressPackageStartupMessages(). . ."

" If you violate the terms of the GPL, Richard Stallman’s beard will sue you in internet court."

#rstats
Public
Ella Kaye

Yay Palmer Penguins! 🐧 Thanks @scottish for the shout-out for {basepenguins}. In #RStats 4.5.0 penguins data set has some shorter variable names. The {basepenguins} 📦 converts scripts that use {palmerpenguins} to use the native version, taking care of these differences. ellakaye.github.io/basepenguin datasci.social/@scottish/11431

ellakaye.github.ioConvert Files that Use palmerpenguins to Work with datasetsFrom R 4.5.0, the datasets package includes the penguins and penguins_raw data sets popularised in the palmerpenguins package. basepenguins takes files that use the palmerpenguins package and converts them to work with the versions from datasets (R >= 4.5.0). It does this by removing calls to library(palmerpenguins) and making the necessary changes to column names. Additionally, it provides helper functions to define new files paths for saving the output and a directory of example files to experiment with.
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