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,