We conclude Week 2 with introduction to R Programming. This module takes an estimated 1.5 hours to complete.
R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.
R is one of the most widely-used and powerful programming languages in bioinformatics. R especially shines where a variety of statistical tools are required (e.g. RNA-Seq, population genomics, etc.) and in the generation of publication-quality graphs and figures.
By the end of week 1, you should:
- Know how to download and install R and R Studio on Mac and Windows
- Be familiar with R workspace and files
- Know the basic data types in R