Category: r
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Building a Command-Line Quiz Application in R
Building a Command-Line Quiz Application in R Practice control flow, input handling, and functions in R by creating an interactive quiz game. The post Building a Command-Line Quiz Application in R appeared first on Towards Data Science. Benjamin Nweke Go to original source
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The Hidden Trap of Fixed and Random Effects
The Hidden Trap of Fixed and Random Effects My lesson of how blindly over-controlling for noise can erase the effects you are measuring The post The Hidden Trap of Fixed and Random Effects appeared first on Towards Data Science. Ngoc Doan Go to original source
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How to Set the Number of Trees in Random Forest
How to Set the Number of Trees in Random Forest Scientific publication T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by determining the optimal number of trees. BMC bioinformatics, 26(1), 95. Follow this LINK to the original publication. Random Forest — A Powerful Tool for Anyone…
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Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware
Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware Summary of This Study Hardware choices – specifically hardware type and its quantity – along with training time, have a significant positive impact on energy, water, and carbon footprints during AI model training, whereas architecture-related factors do not. The interaction between…
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Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis
Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I…
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Harmonizing and Pooling Datasets for Health Research in R
Harmonizing and Pooling Datasets for Health Research in R R code to extract data from unique datasets and combine them in one harmonized dataset ready for seamless analysis Continue reading on Towards Data Science » Rodrigo M Carrillo Larco, MD, PhD Go to original source
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Effortless Data Handling: Find Variables Across Multiple Data Files with R
Effortless Data Handling: Find Variables Across Multiple Data Files with R A practical solution with code and workflow Lost in a maze of datasets and endless data dictionaries? Say goodbye to tedious variable hunting! Discover how to quickly identify and extract the variables you need from multiple SAS files using two simple R functions. Streamline your…