data-mining-techniques

Folders

• data: locate data files (e.g. .csv)
• original: original R scripts written by each author
• revised: revised R scripts with reformatting and refactoring
• chapters: add quarto documents to render html files that contains R code results
• final: final version to be included in the book

Environment

• R: version 4.3.2
• Packages
  • {renv}: version 1.0.3
  • other packages: from Posit Public Package Manager's CRAN snapshot as of 2024-01-12. Set repository address to be https://packagemanager.posit.co/cran/2024-01-12 with the following R script:

renv::init(repos = "https://packagemanager.posit.co/cran/2024-01-12")

Reformatting process

1. Copy R script file from original to revised
2. Run styler::style_file() with the file in revised folder. Use default tidyverse_style.
3. Remove code that creates side-effect, including but not limited to followings:
   1. setwd() to set work directory; all paths will be relative path starting from project directory as a root
   2. install.packages() to install packages; we will assume that required packages have been installed
4. Ensure that each R code does not miss any dependencies to include (e.g. file, library)
5. If it is possible and makes sense, remove dependency on {tidyverse}.
   1. If it makes sense to keep using {tidyverse}, minimize the use of {tidyverse} and provide a specific comment per each use if possible.