Rationale
- R is an open-source, extremely versatile, and has packages relevant to every field of research.
- It is widely considered to be the future of statistical computing.
- Unlike commercial programs, which are very expensive and can only be accessed when enrolled at a university, R is free and accessible to anyone.
- R is always up to date with analysis techniques, with packages continually being added and revised to conduct specific analyses. This sets it above any other statistical packages, which are only updated every 5-10 years.
- Thus, R is a key tool for moving towards a knowledge-based economy.
Purpose
- To enhance learners’ knowledge and skills in analyzing quantitative data using R.
- To enable learners to identify and develop appropriate codes to run different univariate and multivariate data analyses using R and be able to summarize the outputs in graphs and tables.
Learning Outcomes
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- Demonstrate an understanding of basic statistical principles: Distributions and data types (categorical, ordinal, continuous); normality testing; summary statistics
- Demonstrate familiarity with what R is, how it is used, general navigation and understanding of coding. Includes factors vs vectors, subsampling of datasets and creating graphical plots
- Identify data types and the appropriate approach to testing or modeling
- Write code for creation of data objects, display of basic data and summary statistics
- Conduct univariate and multivariate statistical tests
- Interpret and export results to documents or vector/raster
Target Population
- Research and Academic Staff,
- Graduate Students,
- Government Employees,
- Independent Researchers
Delivery Modes
- Practical exercises with pre-existing datasets.
- Facilitation
- Direct observation
- Case studies
- Questioning (verbal or written) - ask relevant questions linked to the unit standard
- Provision of example code
- Encouragement to seek help through online platforms
Assessment
- Participation in class discussions
- Observation
- Simulation of activities
- Peer review sessions
- Feedback received from participants through daily evaluation forms
Recognition of Prior Learning
- Computer literacy and basic understanding of statistics
- Research/Industry experience
- Bachelor’s degree
Certification
- Attend 98% of the course to qualify for certificate
- Participate in class work, assignments, and practicum