# Programming resources

## R

A lot of genomics/transcriptomics/proteomics packages are written in the R programming language. If you are coming from the Python world, I am very sure you will hate the language at first. I hated it too. But as you dive deeper, you will form an unbreakable bond with the language.

# Quick R primer

A notebook with some useful operations is available here.

## Familiarizing yourself with R

To familiarize yourself with R, you can make use of the following two resources:

**Recommended:** Swirl is an R learning package that will walk you through R programming language in a hands-on manner. Swirl will introduce and explore various topics in the form of interactive programming assignments within the Rstudio window. This is a completely free and open source package: https://swirlstats.com/students.html

*Additional*: R for Data Science: learning R to analyze your data. “The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R” https://r4ds.had.co.nz/index.html

## Python

A noetbook with useful commands is available here.