1748. Data Vis Dispatch: March 7, March 14, March 21, March 28
1749. Lists of curated books
1750. Beware the Propensity Score: It’s a Collider
1751. Are {tidyverse} function names getting longer?
1752. What to do with “null” results: Part I: Nonsignificant and underpowered, Part II: Nonsignificant but with sufficient data, Part III: Underpowered study, but significant results
1753. Tidyverse vs. Base-R: How To Choose The Best Framework For You
1754. Distribution regression in R
1755. Plot moving average in R using ggplot2
1756. Render parameterized reports with Quarto
1757. The Power of Minimalist Maps
1758. Non-representative samples! What could possibly go wrong?
1759. How Discord Stores Trillions of Messages
1760. The Polls Nailed The 2022 Election
1761. Easy Graph Mistakes to Avoid
1762. Design-Based Inference for Multi-arm Bandits
1763. Organising R scripts
1764. The Shortcomings of Standardized Regression Coefficients
1765. Sample size and confirmation bias:
1766. Teaching material for Causal ML
1767. Bias vs. Consistency
1768. Scraping London Marathon data with {rvest}
1769. What Are Word and Sentence Embeddings?
1770. How to customise the style of your {shinydashboard} Shiny app
1771. Probit Regression in R: Interpretation & Examples
1772. How to set up an R-based AWS Lambda to write to AWS S3 on a schedule
1773. Getting Started with Simple Slopes Analysis
1774. Some good practices for research with R
1775. How old was Aragorn in regular human years?
1776. Awesome Computational Social Science
1777. Do parties zig-zag?
1778. The tidymodels is getting a whole lot faster
1779. An Introduction to Statistical Learning
1780. Reading Remote Data Files
1781. Some love for Base R: Part 1, Part 2, Part 3
1782. Alt Text in R: Plots, Reports, and Shiny
1783. Missing data: An update on the state of the art
1784. Five charts that changed the world
1785. Deep learning with Keras using MNIST dataset
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