R Course

Course Description:

R Course Coordinator: Viresh Shah

Arrangements: Every Tuesday 4pm (1st years), 5pm (2nd years)

Teams Link: Join the team here!

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.

2020 Lectures (will be uploaded before session):

Lecture 1

Lecture 2

Lecture 3

Lecture 4

Lecture 5 

Lecture 6 (EconomistData.csv)

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 https://delvecchiomarco.com/ (these notes were the basis of Marco’s lectures for 16/17, of which more of his content is below)

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 Matthew’s course (2018/19):

R Course 1st Lecture

R Course 2nd lecture

R Course 3rd lecture

R course 4th lecture

R Course 5th lecture

R course 6th lecture

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