– How To Read 2020 Polls Like A Pro – Visualizing Complex Science – What is machine learning, and how does it work? – Choosing Fonts for Your Data Visualization – Why linear mixed-effects models are probably not the solution to your missing data problems – Outstanding User Interfaces with Shiny – How I Taught […]

# Tag: Potpourri

### Potpourri: Statistics #65

– How The Economist presidential forecast works – GESIS Workshop: Applied Data Visualization – Introduction to R – tidyverse – Why Is It Called That Way?! – Origin and Meaning of R Package Names – PMAP 8921: Data Visualization – Visualising Odds Ratio – Exeter Q-Step Resources – tidymodels workflow with Bayesian optimisation – How […]

### Potpourri: Statistics #64

– Tidymodels: tidy machine learning in R – The Seven Key Things You Need To Know About dplyr 1.0.0 – Introduction to Data Science – When Is Anonymous Not Really Anonymous? – Empirical Papers for Teaching Causal Inference – Why log ratios are useful for tracking COVID-19 – Effect Sizes and Power for Interactions in […]

### Potpourri: Statistics #62

– Applied Bayesian Statistics Using Stan and R – Understanding Maximum Likelihood: An Interactive Visualization – Creating MS Word reports using the officer package – Shiny: Add/Removing Modules Dynamically – Pollsters got it wrong in the 2016 election. Now they want another shot. – Webscraping with R – from messy & unstructured to blisfully tidy […]

### Potpourri: Statistics #61

– Fairness and machine learning – How to Be a Statistical Detective – Overfitting: a guided tour – No Framework, No Problem! Structuring your project folder and creating custom Shiny components – Revisiting the Difference-in-Differences Parallel Trends Assumption: Part I Pre-Trend Testing – The Trouble with Crime Statistics – Data project checklist – Data Science […]

### Potpourri: Statistics #60

– Mining Social Media (also a good introduction to Python) – All The Economist Graphic Detail visualizations in one convenient PDF – R Cookbook, 2nd Edition – How to use Test Driven Development in a Data Science Workflow – Why are polls from different pollsters so different? – Difference-in-Differences – The list of 2019 visualization […]

### Potpourri: Statistics #59

– Quantitative Politics with R (version Nov 29) – Why scientists need to be better at data visualization – Coding habits for data scientists – Reducing frictions in writing with R Markdown for html and pdf – Reconstructing Images Using PCA – A ggplot2 Tutorial for Beautiful Plotting in R – (Re)introducing skimr v2 – […]

### Potpourri: Statistics #57

– Keep It Together: Using the tidyverse for machine learning – Learn to purrr – Mastering Shiny – A Comprehensive List of Handy R Packages – The challenges of using machine learning to identify gender in images – How is polling done around the world? – How to Get Better at Embracing Unknowns – Drawing […]

### Potpourri: Statistics #56

– Hands-on Machine Learning with R – The Truth About Linear Regression – Data Viz Book Reviews – Make Multi-point “dumbbell” Plots in ggplot2 – Storyline – City Intelligence Data Design Guidelines – shinyApp(), runApp(), shinyAppDir(), and a fourth option – Reordering and facetting for ggplot2 – R Docker tutorial – S4: a short guide […]

### Potpourri: Statistics #55

– What happens when a survey estimate doesn’t match a known benchmark – The Evolution of a ggplot (Ep. 1) – R Visualization – Dog breed popularity chart – 11,264 Regressions in One Tidy Plot – Getting Started with Multiple Imputation in R – The Permutation Test – R Best Practices – Tuning ggplot themes […]