Advanced topics in R: An introduction to multivariate statistics in R (1.5 days)
Monday 13 September, 9:30 – 16:30 (lunch break 12:30 – 13:30) and Tuesday 14 September, 9:30 – 12:30
Advanced topics in R: Analysis of Ecological Communities in R (1.5 days)
Tuesday 14 September, 13:30 – 16:30 and Wednesday 15 September, 9:30 – 16:30 (lunch break 12:30 – 13:30)
Advanced topics in R: An introduction to linear mixed-effects models in R (1.5 days)
Thursday 16 September, 9:30 – 16:30 (lunch break 12:30 – 13:30) and Friday 17 September, 9:30 – 12:30
Cost (per workshop):
> WSU students - $40 inc GST
> WSU staff - $60 inc GST
> External academic (students) - $200 inc GST
> External academic (non-students) - $250 inc GST
> Corporate - $500 inc GST
To be determined. To receive advanced notice and priority registration for the five-day introductory workshop or any other topic, please go to: https://forms.gle/nPG5vXLdmjhPRQAAA
Do you want to take your data analysis skills to the next level?
The R statistical computing environment has become a standard for scientific data analysis, visualization and reproducible research.
At the HIE, we offer an introductory course to help you climb the steep learning curve.
This five-day course is aimed at postgraduate students and staff, and is for newcomers to R, as well as intermediate users.
The course covers
- Basics of R (data entry, manipulation, tabulation, special data types)
- Visualisation (basic and advanced plotting of data)
- Statistical analyses (basic statistics, linear models)
- Project management (workflow and organization, principles of reproducible research).
The course will consist of short lectures with ample time for hands-on exercises.
The exercises are set up with different levels: more advanced students can work on more difficult problems.
We assume that you have completed some introductory statistics course at university level, but no previous programming experience is required.
Basics of R (Chapter 1 & 2)
Special Data Types (Chapter 3)
Visualizing Data – Part 1 (Chapter 4)
Basic Statistics (Chapter 5)
Summarizing and Tabulating Data (Chapter 6)
Linear Modelling (Chapter 7)
Functions, Lists and Loops (Chapter 8)
Visualizing Data – Part 2 (handout and web resource)
Project Management and Workflow (Chapter 9), Extra Topics
Time to work on own data, or self-study of a variety of additional topics.
The following resources are available for this course: