Course description

This class is intended to introduce to the students a wide range of programming tools using the R language. Tentative list of topics that will be discussed in this class are listed below:

  • Reproducible research: knitr and rmarkdown
  • Version control: GitHub
  • Introduction to programming: Data structures, logical operators, control structures and functions
  • Visualizations: Exploratory data analysis with Base R and ggplot2
  • R packages: Construction of R-packages using devtools, roxygen2 and pkgdown
  • Communication: webiste creation via blogdown, Web application via shiny
  • Web scraping: Automatic extraction of data from websites using SelectorGadget, rvest and quantmod
  • High performance computing: R and C++ integration via Rcpp, parallel computing.

No IT background is assumed from the students but a strong will to learn useful and practical programming skills.

This course is complementary to the Data Science in Business Analytics class, taught in Spring 2018. Although not mandatory, we recommend the students to follow the Data Science in Business Analytics class prior to ours as it will facilitate they learning curve and diminish the importance of the workload that this class represents.