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Author : Katie Campbell, UCLA Introduction to R This session will cover the basics of R programming, from setting up your environment to basic data analysis. In this tutorial, you will: Become comfortable navigating your filesystem and environment from the R console Understanding, reading, and manipulating data structures Change or summarize datasets for basic analysis Introduce data visualization in RStudio Introduction to R programming R is a powerful programming language and software environment used for statistical computing, data analysis, and graphical representation of data. It is widely used among statisticians, data analysts, and researchers for data mining and statistical software development. Prerequisite: Files for today’s session Today’s session will utilize two input files, which we will use for practice. Start by creating a directory for the R session on your instance and download the files with the following commands: mkdir -p ~/workspace/intro_to_R cd ~/workspace/intro_to_R curl https://raw.githubusercontent.com/ParkerICI/MORRISON-1-public/refs/heads/main/RNASeq/RNA-CancerCell-MORRISON1-metadata.tsv > intro_r_metadata.tsv curl https://raw.githubusercontent.com/ParkerICI/MORRISON-1-public/refs/heads/main/RNASeq/data/RNA-CancerCell-MORRISON1-combat_batch_corrected-logcpm-all_samples.tsv.zip > intro_r_dataset.tsv.zip The downloaded intro_r_metadata.tsv file contains annotation of a set of RNAseq samples from patients with melanoma treated with immunotherapies. The intro_r_dataset.tsv.zip file contains batch effect-corrected gene expression values for all of the samples in this dataset. Why use R? Open Source: R is free to use and open-source. Extensive Packages: Thousands of packages available for various statistical techniques. Strong Community Support: Active community contributes to continuous improvement. Cross-Platform: Works on Windows, macOS, and Linux. Getting started We will launch R in our instance and be programming within the terminal direc

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