1 Resource materials

1.1 Additional readings

• Grolemund, H., & Wickham, H. (2017). R for Data Science. O’Reilly Media. https://r4ds.had.co.nz/

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer. https://www.statlearning.com/

• Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer. https://ggplot2-book.org/

• Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4th ed.). Springer. https://www.stats.ox.ac.uk/pub/MASS4/

• Peng, R. D. (2015). R Programming for Data Science. Leanpub. https://bookdown.org/rdpeng/rprogdatascience/

• Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer. https://topepo.github.io/caret/

• McKinney, W. (2017). Python for Data Analysis (2nd ed.). O’Reilly Media. https://www.oreilly.com/library/view/python-for-data/9781491957653/ [Note: While this book focuses on Python, it offers valuable insights into data manipulation and analysis that are transferable to R.]

1.2 Additional resources

• ggplot2 Gallery: https://exts.ggplot2.tidyverse.org/gallery/

• R Graphics Cookbook: https://r-graphics.org/

• Tidyverse Course: https://jhudatascience.org/tidyversecourse/get-data.html

• Tidyverse Cookbook: https://rstudio-education.github.io/tidyverse-cookbook/import.html

• Data Wrangling Cheatsheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

• R Markdown: The Definitive Guide: https://bookdown.org/yihui/rmarkdown/

• Quarto: https://quarto.org/