The goal of these lessons is twofold: I have tried to cover all the contents you asked for (at least, those pertaining R programming), while introducing you to state of the art libraries and tools we actually use at work. When I was enrolled in your master, the course load focused heavily on basic or vanilla R, which falls short in frequent tasks such as splitting columns, group-apply operations or data reshaping. Moreover, the go-to library for scientific graphics in R, ggplot2, favors long data frames and tidy data, where each column is a variable and each row, an observation. Therefore, it is critical for you to master the art of data tidying.
Due to the limited time we have, I have narrowed down the contents to the absolute bare-bones of the vast amount of resources available in the tidyverse. In this regard, I'm drawing heavily from my soon-to-be 2 years of experience working with large matrices and data frames as part of CNIO's (Spanish National Cancer Research Centre) Bioinformatics Unit.
Finally, I'll be more than happy to answer any questions our doubts you send me at [email protected].