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Show Notes
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/