R was derived from the S language, which was initially developed as a purely statistical program designed to make data analysis easier. Later on, R was created to combine S strong features with developing features such as portability, computational efficiency, and memory managing.
Today, R is a free, open-source software program that honors the original S philosophy, it remains a powerful data analysis tool while also functioning as a programming language. R particularly excels in statistics and data visualization, making it a top choice across scientific disciplines.
In the field of bioinformatics, R has become one of the most used languages for analyzing complex biological datasets. R excels in documentation and active communities that keep with the most up-to-date scientific development. For example, the Bioconductor project is a dedicated open-source repository built on top of R, giving access to thousands of specialized packages for tasks such as genomic sequencing analysis, differential gene expression, single-cell RNA-seq, and proteomics. R’s robust statistical foundations make it especially well-suited for handling the high-dimensional, noisy data that is characteristic of biological experiments, while its visualization libraries allow users to generate publication-quality figures.
This course is a short R programming survey, aimed at those of you with some limited experience programming. If you are completely new to programming you will still be able to follow along. Because the training is short, we will focus on basic concepts only. More in depth training such as data analysis, publication ready figures, and version control training will be available later on. The training is split into two 2-hour sessions:
Thursday April 2nd & 3rd, 2026 10am to 12pm in Lab 5 room L5D23
Introduction to R Programming We go through the main parts of R programming syntax, data types, R objects & data wrangling. There are a few short interactive sections that can be done in your own laptop with R. You can download R thorugh your prefferred CRAN mirror, instructions can be found here: https://www.r-project.org/
Continued Introduction to R Programming, Packages & Libraries, R in HPC
We will continue our introduction to R programming using RStudio (R’s IDE), including R’s file system, how to create projects, how to install packages and load libraries, and we will end with a quick introduction on how to use R & RStudio IDE in Deigo.