R Code Optimization IV: Practical Tools and Workflow
The fourth and final post in the series, covering profiling with profvis, benchmarking with microbenchmark and bench, and the iterative optimization workflow that brings it all together.
R Code Optimization III: Hardware Utilization and Performance
The third post in a four-part series on code optimization, covering vectorization, parallelization, and memory management techniques to maximize computational efficiency.
R Code Optimization II: Language, Design, and Readability
The second post in a series on code optimization, exploring how programming languages, clean code principles, and how algorithm design shapes code efficiency.
R Code Optimization I: Foundations and Principles
The first post in a series focused on code optimization, establishing the foundational principles and decision framework for when to optimize code.
Analyzing Epidemiological Time Series With The R Package {distantia}
Tutorial on the applications of the R package {distantia} to the analysis of epidemiological time series.
Coding a Minimalistic Dynamic Time Warping Library with R
Tutorial on how to implement dynamic time warping in R
A Gentle Intro to Dynamic Time Warping
Brief introduction to Dynamic Time Warping with a conceptual step-by-step break down.
My Reading List: Data Science
Live post with a curated list of high-quality data science posts and videos I found enlightening.
Mapping Categorical Predictors to Numeric With Target Encoding
Target encoding is commonly used to map categorical variables to numeric with the objective of facilitating exploratory data analysis and machine learning modeling. This post covers the basics of this method, and explains how and when to use it.
