We will be covering the following broad topics:

• reproducible research in R,
• using git/github and Rstudio to track code development,
• R code style guidelines,
• loops, apply functions and vectorization in R,
• understanding and using the system of R classes: S3, S4, and R6,
• anatomy of an R package: writing your own package from scratch,
• code debugging, profiling and optimisation.
• working with tidyverse packages,
• efficient use of pipes,
• creating workflows in R,
• using the language of graphics, ggplot2,
• working with maps (ggmap),
• interactive plots,
• introduction to shiny web applications,
• statistical tests in R,
• models in R,
• some machine learning in R,
• biobase and other core parts of Bioconductor,
• practical use of Bioconductor packages (own data or provided example data).