Introduction to Coding in R

The key learning outcomes from this course are: 

• An understanding of data science workflows.
• The ability to install, run, and use a coding environment, including the handling of libraries and datasets.
• Confidence in cleaning and preparing data for analysis. 
• An understanding of how to perform statistical tests efficiently in a coding environment. 
• An ability to use both supervised and unsupervised data science methods. 
• The ability to prepare journal or thesis-ready data visualisations, plots, and graphs. 

Topics to be covered include: 

• Introduction to the R environment and R Studio 
• Data science workflows 
• Principles of data handling 
• Statistical tests and packages 
• Effective data visualisation 
• Data exploration and dimensionality reduction 
• Machine learning with R. 

The course will run over two weeks. The first week will be held in-person, in a computer lab on the Manor Park, University of Surrey campus. The structure for this first week will follow a format of an initial face-to-face lecture for each topic to introduce key concepts and examples, followed by interactive workshops to apply these concepts using the R coding language. Subsequently, students will spend one week completing a code-based project and submit this for assessment. Students may choose to carry out the project on their own data, or they may select from a number of available datasets and topics. Additional learning materials will be provided ahead of the course, as well as following the face-to-face week, for independent study.