1 About R
In this chapter, we will discuss the basics of R and RStudio, two essential tools in genomics data analysis. We will cover the advantages of using R and RStudio, how to set up RStudio, and the different panels of the RStudio interface.
1.1 What is R?
R is a programming language and software environment designed for statistical computing and graphics. It is widely used by statisticians, data scientists, and researchers for data analysis and visualization. R is an open-source language, which means it is free to use, modify, and distribute. Over the years, R has become particularly popular in the fields of genomics and bioinformatics, owing to its extensive libraries and powerful data manipulation capabilities.
The R language is a dialect of the S language, which was developed in the 1970s at Bell Laboratories. The first version of R was written by Robert Gentleman and Ross Ihaka and released in 1995 (see this slide deck for Ross Ihaka’s take on R’s history). Since then, R has been continuously developed by the R Core Team, a group of statisticians and computer scientists. The R Core Team releases a new version of R every year.
1.2 Why use R?
There are several reasons why R is a popular choice for data analysis, particularly in genomics and bioinformatics. These include:
- Open-source: R is free to use and has a large community of developers who contribute to its growth and development. What is “open-source”?
- Extensive libraries: There are thousands of R packages available for a wide range of tasks, including specialized packages for genomics and bioinformatics. These libraries have been extensively tested and ara available for free.
- Data manipulation: R has powerful data manipulation capabilities, making it easy (or at least possible) to clean, process, and analyze large datasets.
- Graphics and visualization: R has excellent tools for creating high-quality graphics and visualizations that can be customized to meet the specific needs of your analysis. In most cases, graphics produced by R are publication-quality.
- Reproducible research: R enables you to create reproducible research by recording your analysis in a script, which can be easily shared and executed by others. In addition, R does not have a meaningful graphical user interface (GUI), which renders analysis in R much more reproducible than tools that rely on GUI interactions.
- Cross-platform: R runs on Windows, Mac, and Linux (as well as more obscure systems).
- Interoperability with other languages: R can interfact with FORTRAN, C, and many other languages.
- Scalability: R is useful for small and large projects.
I can develop code for analysis on my Mac laptop. I can then install the same code on our 20k core cluster and run it in parallel on 100 samples, monitor the process, and then update a database (for example) with R when complete. In other words, R is a powerful tool that can be used for a wide range of tasks, from small-scale data analysis to large-scale genomics and omics data science projects.
1.3 Why not use R?
- R cannot do everything.
- R is not always the “best” tool for the job.
- R will not hold your hand. Often, it will slap your hand instead.
- The documentation can be opaque (but there is documentation).
- R can drive you crazy (on a good day) or age you prematurely (on a bad one).
- Finding the right package to do the job you want to do can be challenging; worse, some contributed packages are unreliable.]{}
- R does not have a meaningfully useful graphical user interface (GUI).
- Additional languages are becoming increasingly popular for bioinformatics and biological data science, such as Python, Julia, and Rust.
1.4 R License and the Open Source Ideal
R is free (yes, totally free!) and distributed under GNU license. In particular, this license allows one to:
- Download the source code
- Modify the source code to your heart’s content
- Distribute the modified source code and even charge money for it, but you must distribute the modified source code under the original GNU license.
This license means that R will always be available, will always be open source, and can grow organically without constraint.
1.5 Working with R
R is a programming language, and as such, it requires you to write code to perform tasks. This can be intimidating for beginners, but it is also what makes R so powerful. In R, you can write scripts to automate tasks, create functions to encapsulate complex operations, and use packages to extend the functionality of R.
R can be used interactively or as a scripting language. In interactive mode, you can enter commands directly into the R console and see the results immediately. In scripting mode, you can write a series of commands in a script file and then execute the entire script at once. This allows you to save your work, reuse code, and share your analysis with others.
In the next section, we will discuss how to set up RStudio, an integrated development environment (IDE) for R that makes it easier to write and execute R code. However, you can use R without RStudio if you prefer to work in the R console or another IDE. RStudio is not required to use R, but it does provide a more user-friendly interface and several useful features that can enhance your R programming experience.