R-bloggers

ahead (R) 0.38.1 and its Python wrapper now install in a fraction of the time, by moving almost every heavy modeling dependency from Imports to Suggests and installing them at runtime, only when a function actually needs them.

Spatial machine learning has become a standard tool for producing environmental and geographic prediction maps. It is now relatively (technically) easy to combine field observations with remote sensing, climate, terrain, or other predictor layer...

R For SEO Part 10: SEO Reporting With Google Sheets & OpenRouter Welcome back, and we’re finally at the end of my R for SEO series, at least for now. We’ve gone through quite... This post was written by Ben Johnston on Ben Johnston

📊 Working through ROC-AUC from scratch, then poking at its blind spots — low prevalence, calibration, and finally Decision Curve Analysis. Mostly notes to myself on what I learned (and got confused by) along the way. 🤔📈 Motivations We see ROC-AUC so often with classification models, we know the higher the better, but ...

Introduction Portfolio construction often relies on mean–variance optimization or factor models. Yet, recent research highlights the importance of skewness—the third statistical moment—as a driver of asset returns. Assets with lottery-like payoffs (high positive skewness) tend to be overpriced, while negatively skewed assets are often underpriced. A 66‑page ...

Introduction Data Aim and hypotheses Assumptions Variable type and design Independence between subjects Normality Sphericity Outliers Repeated measures ANOVA in R With the {rstatix} package With base R Interpretations Post-hoc tests Summary Ref...

This post estimates potential labor market transition pathways based on the similarity of skills, abilities and knowledge required by occupations. The approach broadly follows Bratanova et al. (2026). Like their research, the considered transition pathways are for workers currently employed as truck drivers. Unlike their approach, we've used a more recent ...

Shiny and Streamlit dashboards depend on a live R or Python process, which makes vanilla deployments slow to load. Observable Framework compiles your data pipeline at build time and ships a static, JavaScript-only site — here's how it works, with a small restaurant-tips dashboard as an example.

I really only wanted to spill some fountain pen ink. Not literally. That would be expensive. 🖋️ I wanted to take colours inspired by Pilot’s Iroshizuku fountain pen inks, drop them somewhere on a blank canvas, and let them flow. That led me to f...

Zhenguo Zhang's Blog https://fortune9.netlify.app/2026/08/30/r-understanding-position-dodge-and-position-dodge2-in-ggplot2/ - In ggplot2, when displaying grouped data along categorical axes (such as grouped bar charts, boxplots, or error bars), horiz...

One tiny dataset, another rabbit hole 🐇🕳️ In the previous post, I turned 24 Pilot Iroshizuku fountain pen inks into a tiny shop using ggplot2. But while sorting the inks by colour, I started wondering what it would look like if I could rearrang...

Three hundred fifty-three new packages were submitted to CRAN in July. Here are my Top 40 picks in nineteen categories: Causal Inference, Chemistry, Climate Studies, Computational Methods, Ecology, Economics, Econometrics, Genomics, Geomorphomet...

Programmatic Access to Data and Statistics from the World Bank API