Introduction to R

Successful students will be able to:

# Foundational knowledge (information and ideas)

  • Describe how R differs from spreadsheets and point-and-click software,

  • Prepare data for use in R,

  • Import and export various formats of data,

  • Describe the different types of objects and data,

  • Describe the use of functions and function arguments,

  • Identify what function to use for specific idea, test, or analysis,

  • Describe best practice in data management, analysis, and visualisation.

# Application (skills, thinking and project management)

  • Be confident managing, describing, analysing, and visualising data in R,

  • Write your own functions,

  • Generate publication-quality graphics,

  • Run statistical tests and develop models to analyse data,

  • Produce results suitable for a publication.

  • Describe what someone else’s R code does.

# Integration (connecting ideas)

  • Understand the different ways humans can interact with computers (CLI vs GUI),

  • Understand the pros and cons of open-source software,

  • Understand the pros and cons of reproducible research,

  • Correct and improve someone else’s code.

# Human dimension (learning about oneself and others)

  • Learn a new way of working with and looking at data,

  • Appreciate what learning a new complex skill feels like, and overcoming the frustration.

# Values (developing new feelings, interests and values)

  • Critique published analyses and graphics,

  • Support the community endeavour of science and open-source software.

# Learning how to learn (becoming a better student and self-directed learner)

  • Work iteratively within R to develop analyses and figures,

  • Use online and other resources to address specific problems,

  • Learn to discern good advice from bad.