# Content

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, and R6,
- 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,