– 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