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.
https://stats.idre.ucla.edu/r/seminars/intro/ Very useful course from UCLA
https://en.wikibooks.org/wiki/R_Programming (In particular the R Basics and Data management chapters)
https://www.coursera.org/learn/r-programming useful course from Coursera
Content from Marco’s course (2016/17):
- Lab lectures:
- 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.