September 26, 2016

R Course

Course Description:

Sign ups are no longer available.

The lecture notes are available below. (Note: These notes will be updated over the coming weeks)

Host: Marco Del Vecchio. ( )

Office: IIPSI Building (Near Zeeman/MathStat building) third floor, desk number 41
Office Hours: Friday 10:00 – 11:00

Arrangements: The course will be held in A0.02 (PC room – Zeeman) during Term 1, and in R0.41 (PC room – Library) during Term 2. The course runs every Wednesday starting in Week 2 of Term 1 from 13:00pm to 15:00pm

Commitment: 12 lab lectures, 5 seminars, and 2 guest lectures.


  • Seminars:
    • Tidy data sets.
    • A Sneak Peek into Machine Learning: Support Vector Machines
    • The wonders of “foreach:” the joy and pain of parallel programming in R.
    • Web Scraping in R.
    • Reproducible Research: why it matters and how to do it.

Aims: This course offers a comprehensive and in-depth overview of the main aspects of the R language. This series of seminars and lab lectures are intended to provide the student with research-related tools, and examples of topics in computational statistics. All in all, this course will provide students with a strong background in R programming which will be beneficial when:

(a) taking academic modules that use R to illustrate practical applications
(b) embarking on a data analysis career.

Objectives: At the end of this course, students should have a broad understanding of the R language, and the ability to write concise code that can be used across many areas of data analysis. Specifically, students ought to be familiar with:

(a) R data types and the best way to perform operation on them
(b) functional programming and its role in data analysis and processing
(c) basic algorithm design.