Objectifs

The objective of this course is to provide an introduction to programming using the R language. It will also provide students with notions of data management, data manipulation and data analysis as well as of reproducible research, result-sharing and version control. At the end of the class, students should be able to construct their own R package, make it available via GitHub, document it using literate programming and render it visible by making a website.

Contenus

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
• Webscrapping: 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.

Références

This class is based on the textbook: “An Introduction to Statistical Programming Methods with R” , which is available here: http://r.smac-group.com.

The following texts will be heavily referenced:

• Recommended: Advanced R Programming by Hadley Wickham
• Recommended: R Packages Hadley Wickham by Hadley Wickham
• Recommended: An Introduction to R by W. N. Venables, D. M. Smith, and the R Core Team
• Recommended: blogdown: Creating Websites with R Markdown by Yihui Xie, Amber Thomas and Alison Presmanes Hill
• Recommended: R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund

Check the website of the course for more references.

Pré-requis

• strongly advised: Data Science in Business Analytics

This course is complementary to the Data Science in Business Analytics class. 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.

Evaluation

1ère tentative

Examen:

Sans examen (cf. modalités)  

Evaluation:

The learning outcomes are continuously assessed during the semester with the group homeworks and the group project (check this link for more details).

Rattrapage

Examen:

Sans examen (cf. modalités)  

Evaluation:

A second opportunity to pass the homeworks and/or the project is proposed to the failing students/groups.