Potpourri: Statistics #57

545. Keep It Together: Using the tidyverse for machine learning

546. Learn to purrr

547. Mastering Shiny

548. A Comprehensive List of Handy R Packages

549. The challenges of using machine learning to identify gender in images

550. How is polling done around the world?

551. How to Get Better at Embracing Unknowns

552. Drawing maps in R

553. Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics

554. Visualizing Locke and Mill: a tidytext analysis

555. Tutorial: Cleaning UK Office for National Statistics data in R

556. Transitioning into the tidyverse: part 1, part 2

557. Your Friendly Guide to Colors in Data Visualisation

558. Optimising your R code – a guided example

559. Learning data visualization

560. Reference Collection to push back against “Common Statistical Myths”

561. mutate_all(), select_if(), summarise_at()... what's the deal with scoped verbs?!

562. Tools for Exploring and Comparing Data Frames

563. Tom’s Cookbook for Better Viz

564. Themes to Improve Your ggplot Figures

565. Lesser Known R Features

566. What Statistics Can and Can't Tell Us About Ourselves

567. A Graphical Introduction to tidyr's pivot_*()

568. n() cool #dplyr things

569. Bayesian Linear Mixed Models: Random Intercepts, Slopes, and Missing Data

570. Prepping data for #rstats #tidyverse and a priori planning

571. NYT-style urban heat island maps