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.