Design principles for data analysis, unraveling pipeline analyses with {Unravel}, and visualizing simulated environmental changes in western Canada with Shiny. Episode Links This week's curator: Eric Nantz Design Principles for Data Analysis {Unravel} - A fluent code explorer for R Case Study: Simulating Environment Change Agents on Species in Canada's Western Boreal Forests Entire issue available at rweekly.org/2022-W40 Supplement Resources Casual Inference Podcast: https://casualinfer.libsyn.com Not So Standard Deviations Podcast: https://nssdeviations.com/ Designing for Analytics Podcast: https://designingforanalytics.com/experiencing-data-podcast/ Elements and Principles for Characterizing Variation between Data Analyses (preprint) https://arxiv.org/abs/1903.07639 Stephanie Hicks' thread on the preprint: https://twitter.com/stephaniehicks/status/1108462768099856384 Lucy D'Agostino McGowan's presentation at JSM 2022: https://www.lucymcgowan.com/talk/asa_joint_statistical_meeting_2022 Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results https://journals.sagepub.com/doi/10.1177/2515245917747646 Unravel presentation at UIST 2021 https://www.youtube.com/watch?v=wJ77e39XVEs ShinyWBI https://wbi-nwt.analythium.app/apps/nwt/