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,
• writing own functions – best practices,
• understanding and using the system of R classes: S3, S4, R6 and RC,
• anatomy of an R package: writing your own package from scratch,
• code debugging, profiling and optimisation,
• working with tidyverse family of packages,
• efficient use of %>% pipes,
• elements of functional programming in R using purr,
• using the language of graphics, ggplot2,
• working with maps (ggmap2),
• interactive plots,
• introduction to shiny web applications,
• biostatistical models in R,
• statistical and machine learning in R,
• introduction to deep learning using R and keras,
• introduction to Bioconductor.