– Computational Causal Inference at Netflix

– Tools for Ex-post Survey Data Harmonization

– How to pick more beautiful colors for your data visualizations

– Shiny in Production: App and Database Syncing

– Introduction to Causal Inference

– An Illustration of Decision Trees and Random Forests with an Application to the 2016 Trump Vote

– Key things to know about election polling in the United States

– State-of-the-art NLP models from R

– Introduction to Stan in R

– How to write your own R package and publish it on CRAN

– Bayesian Analysis for A/B Testing

– Estimating House Effects

– Heatmaps in ggplot2

– The Taboo Against Explicit Causal Inference in Nonexperimental Psychology

– Spreadsheet workflows in R

– A beginner’s guide to Shiny modules

– Dataviz Interview

– 10 Things to Know About Survey Experiments

– Applying Weights

– Creating effective interrupted time series graphs: Review and recommendations

– Lasso and the Methods of Causality

– How We Designed The Look Of Our 2020 Forecast

– Taking Control of Plot Scaling

– How to measure spatial diversity and segregation?

– 10+ Guidelines for Better Tables in R

– How maps in the media make us more negative about migrants

– Comparing two proportions in the same survey

– Quantitative Social Science Methods, I (Gov2001 at Harvard University)

– Introduction to Computational Thinking

– Creating R Packages with devtools

– Introduction to Statistical Learning in R

– Textrecipes series: Pretrained Word Embedding