September 26, 2016

R Course

Course Description:

R Course Coordinator: Matthew Exact

Arrangements: The course will be held every Wednesday in Term 2

Sign ups: Registration will be available at the end of Term 1

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.

R Course 1st Lecture

R Course 2nd lecture

R Course 3rd lecture

R course 4th lecture

R Course 5th lecture

Useful resources

R cheat sheet (useful once you’re familiar with R as it lists useful functions)

Marco Del Vecchio’s very thorough notes – All Credit to Marco (these notes were the basis of Marco’s lectures for 16/17, of which more of his content is below) Very useful course from UCLA (In particular the R Basics and Data management chapters) useful course from Coursera

Content from Marco’s course (2016/17):

  • 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.