PNG0088, Understanding and coding the R programming language, 3.0 Hp
Print syllabus

Syllabus

Level

Third cycle

Grading Scale

The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.

Course language

English

Entry Requirements

Objectives

The aim of the course is to help each student overcome the initial steep learning curve that is associated with learning R, and how to think in a structured and logical way to make programming easier. By the end of the course students will:

1. Know the differences between data structure types and why these are used
2. Be able to create data structures and extract information from these
3. Understand how functions work in R and be able to create their own
4. Use specific programming methods to automate repetitive processes
5. Create publication-quality figures from data
6. Implement and extract information from statistical objects
7. Write code in a series of logical steps to create complex outputs using combinations of simple functions

Content

The course is about R as a language, to allow participants to understand the code to read and write. It will start from a very basic level and teaches many of the principles that are necessary to be able to write your own programs in R but are usually skipped over in the rush to do some ‘stats’. Each day is a combination of lectures and exercises with an in-class computer exercise introduced after every new concept. During these exercises the teachers will work with the students to help them achieve the objective of each task, and to answer any questions regarding the concepts.

Examination Formats and Requirements for Passing the Course

There will be no formal examination to pass this course. Instead, during the course students will demonstrate during in-class exercises the following programming skills and concepts of R. 1. Creating vector and matrix structures 2. Importing and manipulating datasets using csv files created from excel 3. The 3 vector principles in R (recycling, vectorisation & indexing) and how these relate to data objects and functions 4. if-else statements, iterative loops and writing functions 5. Manipulating base graphics to produce publication-quality figures 6. Extracting, storing and plotting data from statistical objects 7. Combining the principles from 1-6 to solve complex problems

Responsible Department/Equivalent

Department of Ecology

Supplementary information