Potential improvements/checklist for v2/2nd edition

[rudeboybert]

To be done for online edition of v2.1 or later

• Convert to Quarto
• Name all chunks
• Add recap/key concepts to each chapter AND/OR define as learning goals at the outset of each chapter
• In Ch7 Conclusion, consider adding a section on random sampling vs assignment?
• Add exercises at the end of each chapter online (or in the Appendices) and link to these sections
• Add diagrams for sampling and bootstrapping
• Update Learning Check solutions in the Appendix throughout the entire book
• Better split out the items in the YAML of index.Rmd into _output.yml for bs4_book and pdf_book when not applicable to both
• Add back in the image of the Second Edition of the book front cover to its webpage when finalized
• Link to 2nd edition in moderndive.com. 1st edition stays at moderndive.com

General

• Create release on GitHub for v2.0.0 when published in print
• Update cover-image logo path in index.Rmd
• Finalize 2e front cover
• Replace links to moderndive.com to be whatever is chosen for the 2e/v2 equivalent
• Check that regression-plane-ISLR2.html works for V2
• @avaldivi6: Check Learning Checks throughout the book for validity
• Create v2/2e deployment branch for Netlify with gitbook build from v2 branch
• Fix index so that all R package references appear under "R packages" (Only Ch9 remains)
• Replace references to specific Appendix with generic online Appendices
• Ensure all #### headers are unnumbered
• Replace "data set" with "dataset" throughout
• Add Arturo's bio and photo to Preface
• Update installation of moderndive to not come from GitHub
• Add Arturo's dedication to latex/before_body.tex.
• Think about moving regression chapters all to the end Keeping where they are to ensure some exploratory regression is done before inferential.
• Currently in v1 CLT is a subsection of Section 7.5. For v2, consider moving it to its own section to make CLT more prominent. Can do this by rebasing Switched section ordering and added to CLT #326 to eventual v2 branch that will get created.
• Refactor Section 7.3 to break down reading into more bite-sized pieces. Rebase Refactoring Ch7 sampling #324
• Per Katie’s suggestion, add discussion on R^2 and R^2-adjusted in Subsection 6.3.1 on Model selection. In particular somewhere after "visual model selection" we do. Can use moderndive::get_regression_summaries() wrapper to broom::augment() to get at these values easily. Can reuse fc4621d. Also per Will’s suggestion, add visualizations illustrating the breakdown of variances that go into R^2.
• Extend {infer} to more advanced use cases using fit() function
• Use base-pipe |> instead of %>%
• Address the warning message explicitly for group_by() in text and fix index.Rmd to remove options(dplyr.summarise.inform = FALSE)
• Add relocate() to end of Chapter 3
• Update preface with updates to 2nd edition
• Add almonds data to moderndive package
• Ask Kelly for an updated foreword
• Change font size of PDF to one point smaller
• Search for ? in PDF and online for any broken references
• @avaldivi6: Add references to Chapter 8 articles
• Update moderndive on CRAN
• Move Appendix E discussion to Preface and move package versions there as well (or move to conclusion of Chapter 11)

nycflights23

• Use the anyflights package to create an nycflights23 package to replace nycflights13 data.
• Update the early_january_weather and alaska_flights data frames in moderndive package to use '23 data
• Explain that nycflights23 is an updated version of nycflights13 using the anyflights package

@avaldivi6

• Erase Bowl from sections
• Footnote for almonds
• Make sure activity can be replicated by instructors (e.g. went to chemistry department to borrow scales). Be clear this refers to instructors (e.g, "If you are an instructor...")

[wmorgan485]

In Section 9.6.1, the discussion about the warning message to check that conditions have been met for the theoretical method {infer} might be rewritten for clarity. The warning message is generated by a visualize() command with method = "both". However, the discussion about the warning message follows a get_p_value() command using a simulation-based null distribution (null_distribution_movies_t), where this warning doesn't apply (right?). It might be clearer to discuss the warning message before getting the p-value. Or get_p_value might be run instead with a null distribution generated using assume(distribution = "t"), where the warning message would then apply.