Appendix F — References

The book’s tidyverse approach draws on R for Data Science (Wickham, Çetinkaya-Rundel, and Grolemund 2023), its visualization teaching draws on the third edition of ggplot2: Elegant Graphics for Data Analysis (Wickham, Navarro, and Pedersen 2026), and the text-analysis case follows the tidy text framework (Silge and Robinson 2017).

The main investment data come from Norges Bank Investment Management (Norges Bank Investment Management 2026), while the employment case uses data published by Hong Kong’s University Grants Committee (University Grants Committee, Hong Kong 2026). The website and book publishing guidance refers to the official Quarto documentation (Posit Software, PBC 2026).

Norges Bank Investment Management. 2026. “All Investments.” 2026. https://www.nbim.no/en/investments/all-investments/.

Posit Software, PBC. 2026. “Quarto Documentation.” 2026. https://quarto.org/docs/.

Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. O’Reilly Media. https://www.tidytextmining.com/.

University Grants Committee, Hong Kong. 2026. “Graduate Employment Survey Statistics.” 2026. https://data.gov.hk/en-data/dataset/hk-ugc-ugc-student-ges-statistics.

Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. O’Reilly Media. https://r4ds.hadley.nz/.

Wickham, Hadley, Danielle Navarro, and Thomas Lin Pedersen. 2026. “ggplot2: Elegant Graphics for Data Analysis.” 2026. https://ggplot2-book.org/.