R for data science

This course is intermediate level and built on the content from our R for beginners course. We covered content from programming to machine learning.

The course covered content for these learning objectives:

  1. To write your own custom functions
  2. To use for loops, apply functions and if statements
  3. To analyse large matrices of data in a semi-automated way
  4. To normalise data
  5. To quantify and correct batch effect
  6. To undertake the most common clustering algorithms including k-means and hierarchical
  7. To perform variable selection and present these results in different plots including heatmaps
  8. To do 2-way ANOVA
  9. To undertake multivariate modelling
  10. A brief introduction to machine learning.

The course included brief theoretical introductions followed by hands on exercises based on real life research examples. In the future we plan to split this course in 2, one to cover programming topics and another for data analyses.