Lessons from teaching R to non-programmers, loading a large and messy CSV file with data.table and command-line tools, Bayesian analyses with the brms package, and getting a better understanding of the tidyeval framework. Episode Links This week's curator: Jonathan Carroll (@carroll_jono (https://twitter.com/carroll_jono)) 6 Lessons I learned from teaching R to non-programmers (https://albert-rapp.de/post/2022-04-15-lessons-learned-from-teaching-nonprogrammers/) Loading a large, messy csv using data.table fread with cli tools (https://redwallanalytics.com/2022/04/21/loading-a-large-messy-csv-using-data-table-fread-with-cli-tools/) Bayesian analyses made easy: GLMMs in R package brms (https://oliviergimenez.github.io/blog/glmm-brms/) Not so standard evaluations - Getting a better understanding of the tidyeval framework (https://lukas-r.blog/posts/2022-04-20-not-so-standard-evaluations/) Entire issue available at rweekly.org/2022-W17 (https://rweekly.org/2022-W17.html) Supplement Resources Data science at the command line (https://datascienceatthecommandline.com) Learning Bayesian statistics podcast (https://www.learnbayesstats.com) Bayes Rules! An Introduction to Applied Bayesian Modeling (https://www.bayesrulesbook.com)