Subject
12 entries
R
Bookmarks
Regression and Other Stories
Regression and Other Stories by Gelman, Hill, and Vehtari is a practical statistics textbook covering regression modeling from basics through causal inference — grounded in real data examples and the Bayesian workflow. The modern standard for applied regression.
An Introduction to Statistical Learning (2nd Edition)
The second edition of James, Witten, Hastie, and Tibshirani's canonical intro-level statistical learning textbook, updated in 2021 to include deep learning, survival analysis, and multiple testing. It sits between undergraduate statistics and the more demanding Elements of Statistical Learning — the best entry point for practitioners who want rigorous but accessible ML foundations.
benchm-ml: ML Algorithm Benchmark Comparison
A systematic benchmark of machine learning algorithms across platforms and implementations — comparing gradient boosting, random forests, neural networks, and others on speed and accuracy. One of the best empirical references for choosing between ML tools in 2015, when the xgboost vs sklearn debate was live.
Multiple A/B/n Tests in Marketing with ANOVA and R
Marketing Distillery's practical guide to running multiple A/B/n tests using ANOVA in R — extending the standard two-group t-test to handle multiple variants simultaneously without inflating false positive rates.
Togaware: One Page R — A Survival Guide to Data Science with R
Togaware's One Page R is a survival guide to data science with R — a dense, practical reference document covering the R ecosystem from data loading to modeling to visualization. The 'one page' is ironic; it's comprehensive.
Advanced R — Hadley Wickham
Hadley Wickham's Advanced R — the definitive guide to R's unusual object systems, functional programming patterns, environments, and performance profiling. Essential reading for anyone who wants to move from R user to R programmer.
The Homogenization of Scientific Computing: Why Python Is Eating Other Languages' Lunch
R-Bloggers post arguing that Python was converging on R and MATLAB's territory in scientific computing in 2014. The key insight: Python didn't win by being better at any one thing, but by being good enough at everything while sharing one ecosystem.
R vs Python — Round 1
The Swarm Lab's side-by-side comparison of R and Python on a data analysis task — first in a series. Both languages solve the same problem, revealing stylistic and ecosystem differences rather than a clear winner.
Introducing R
Alyssa Frazee's introduction to R for people who don't yet know they need it — a gentle, motivated tour of why R is the right tool for statistical computing. Written by a biostatistician who uses it daily.
K-Means Clustering 86 Single Malt Scotch Whiskies
Clustering 86 single malt Scotch whiskies by flavor profile using k-means in R — a fun worked example that makes clustering tangible. Shows how to choose k and interpret results when the data has real-world meaning.
Python Displacing R As The Programming Language For Data Science
ReadWrite article on Python displacing R as the primary data science language — part of the 2013 wave of coverage tracking the Python/R competition. Python's software engineering strengths and growing ML ecosystem were tipping the balance.
Variable Importance in Neural Networks
R-bloggers post on measuring variable importance in neural networks — techniques like the Garson algorithm and Olden's method for attributing prediction contributions to input features. An early attempt at neural network interpretability before SHAP, LIME, and modern explainability tools existed.