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.