- 30 April - Advanced topics in R: An introduction to multivariate statistics - Register
- 1 May - Advanced topics in R: Working in the Tidyverse - Register
- 2 May - Advanced topics in R: An introduction to linear mixed-effects models in R - Register
For those of you interested in the introductory, week-long workshop, we anticipate that this will be offered the week of 16 September 2019. Registrations will open six-to-eight weeks before the workshop, but please express your interest here; this will not reserve a spot for you, it is only possible to reserve a spot once registration opens. You can also register your interest for other workshops on advanced topics.
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: