Introduction To R and RStudio

RAdelaide 2025

Author

Affiliation

Dr Stevie Pederson

Black Ochre Data Labs
The Kids Research Institute Australia

A Brief Introduction to R

Why use R?

• Heavily used for analysis of biological data (along with Python)
  • Can handle extremely large datasets
    
  • Packages explicitly designed for complex analysis
  • Huge user base \(\implies\) internet resources
  • (Can be) very fast
• Very easy to dynamically interact with large datasets
  • Can also run as static scripts on HPC clusters
  • Low-level languages like C/C++ don’t allow for interactive analysis

I regularly work with data containing millions of lines

How do we use R?

• Never use drop-down menus or buttons \(\implies\) type every command
  • Complex processes are often implemented as ‘simple’ functions
• Considered a high-level language
  • Usually easy to read and understand then low-level languages (e.g. C)
  • Trade-off in regard to speed & memory management

How do we use R?

• Discuss column sorting with Simon although I believe it’s improved
• Mention paper claiming GLMs, but absolutely did not fit GLMs

• Reproducible Research!!!
  • Keep records (i.e. scripts) of every step of every analysis
  • Transparent methods for reviewers

• Avoids common Excel pitfalls (almost) never modify files on disk!
  \(\implies\) load data from a file into R and work with it there

Experience is the best teacher \(\implies\) please practice your skills

What is R?

• Derivative of S (Bell Labs, Chambers 1977)

What is R?

• Ross Ihaka is of NZ Maori descent
• Last commercial release of S-Plus in 2007
• Some of the code for GLMs was written by Prof Bill Venables (Head of Maths, UofA)

• Derivative of S (Bell Labs, Chambers 1977)
• R began to appear in 1996 (Ihaka and Gentleman 1996)
  • Ross Ihaka and Robert Gentleman (U of Auckland)
  • Disentangled some proprietary S code \(\implies\) open-source
• First official release (v1.0.0) in Feb 2000
  • Now estimated >2 million users (2012)
  • Nice history article here (Chambers 2020)

• My first exposure to R was 2002 (R v1.5)

What is R?

• Being open source creates headaches for University & Business IT departments
• There are identified security risks
• No guarantees of being virus free. Has inherent security flaws particularly using R data files (which are often part of packages)
• The community self-regulates
• Release Names are references to Peanuts cartoons

• Open source language
  • No corporate ownership \(\implies\) free software
  • Code is managed by the community of users
• R is formally run by volunteers \(\implies\) R Core
  • Mostly academics
  • John Chambers is still a member
• Annual release schedule + patches
  • Most recent is R 4.5.1 (Jun 14)

Extending R, Chambers (2016)

R Packages

• Packages are the key to R’s flexibility and power
  • Collections (or libraries) of related functions
  • ggplot2 \(\implies\) Create plots using the grammar of graphics
  • readr \(\implies\) Read files into R

R Packages

• Loading a package is like the Matrix where Trinity installs the set of skills to fly a helicopter
• Crap packages are generally identified by the users and just not-used
• Scientific rigour is usually checked during review of the accompanying publication
• Robert Gentleman was also a founder of Bioconductor before moving to 23&me

• \(>\) 22,000 packages are stored on CRAN (https://cran.r-project.org)
  • Not curated for statistical quality or documentation
  • Automated testing for successful installation

• Bioconductor is a secondary repository (https://www.bioconductor.org)
  • \(>\) 3000 packages with a more biological/genomics focus
  • Curated for language consistency & documentation

• Some packages also live on github or bitbucket
  • Will include latest (unstable) development versions

R Packages

• Installation of a package can be done through a variety of methods:
  • install.packages("packageName") will only install from CRAN

• Bioconductor also provide a CRAN package: BiocManager
  • BiocManager::install("packageName") installs from CRAN, Bioconductor & github

• The CRAN package pak also installs from multiple locations

Helpful Resources

Much of today is inspired by a two-day developers workshop I attended with Hadley Wickham. Also gave me an opportunity to have some great conversations with Winston Chang

https://r4ds.had.co.nz/

https://r-graphics.org/

Using R

The R Console

• This is the ‘ugly, old-school’ way of using R
• Still very useful, e.g. testing code on a HPC

• Let’s try using R as a standalone tool \(\implies\) open R NOT RStudio
  • On linux:
    Open a terminal then enter R
  • OSX: Click on your dock
  • Windows: Click in your Start Menu
• Do not open
• This is R at it’s ugliest (how I learned R)

The R Console

• This is often referred to as the R Console
• At it’s simplest R is just a calculator (Press Enter)

> 1 + 1

[1] 2

> 2 * 2

[1] 4

> 2 ^ 3

[1] 8

The R Console

I never use a calculator program on my laptop, always R

• R has many standard functions
  • place the value inside the brackets after the function name

> sqrt(2)

[1] 1.414214

> log10(1000)

[1] 3

The R Console

We can create objects with names

> x <- 5

• We have just created an object called x
• The <- symbol is like an arrow i.e. “put the value 5 into x”

An APL Keyboard from the 1970s showing the arrow as a single key

Object names can be anything but should start with a letter not a number or special character

The R Console

• View the contents of the object by entering it’s name in the Console

> x

[1] 5

• The object x only exists in the R Environment

• Imagine your R Environment to be like an Excel Workbook
  • Each object is named just like each spreadsheet is named
    \(\implies\) objects don’t have to look like a spreadsheet

The R Console

Do R Objects Have Rules About Valid/Invalid Names

• The only “special characters” which can be used are . and _
  • This makes perfect sense when you realise +, /, *, - etc. are mathematical operators
  • Names cannot contain spaces \(\implies\) R will think there are two objects
• Object names must start with a letter, . or _.
  • Objects starting with a . are often hidden from view
• Common conventions are snake_case (my_object) or camelCase (myObject)
• Numbers can follow the initial character but cannot start a name
  • e.g. x1 is a valid name whilst 1x is not

The R Console

• We can pass objects to functions and perform operations on them

> x + 1

[1] 6

> sqrt(x)

[1] 2.236068

> x^2

[1] 25

> x > 1

[1] TRUE

The R Console

• Everything we’ve just done is trivial
• Real analysis isn’t
• If we perform a series of steps
  • Should we keep a copy of what we’ve done?
  • If so, how should we do that?

Recording Our R History

• Try using your up and down arrow keys in the R Console
• This will scroll through the history of commands you’ve entered
  • Can re-execute or modify any of these
• Saved in a file called .Rhistory in your working directory

• A common strategy is to record our code as an R Script as we write it
  • Just a plain text file with a record of our commands
• R Studio makes that easy & convenient

Exiting R

• To exit R type q()
• You will then be asked: Save workspace image? [y/n/c]:
  • Type n then hit Enter

• We can save our R environment with all of the objects we’ve created
  • By default this will be .RData in the folder we’ve been working in
• I personally never save .RData \(\implies\) can lead to dodgy analysis
  • The code to create objects & run analysis is the important part

R Studio

Introduction to RStudio

R and RStudio are two separate but connected things

• R is like the engine of your car

• RStudio is the ‘cabin’ we use to control the engine
  • Comes with extra features un-related to R that improve our ‘journey’
  • Known as an IDE (Integrated Development Environment)

• R does all the calculations, manages the data, generates plots
  • i.e. gets us to our destination

• RStudio helps manage our code, display the plots etc
  • i.e. makes our journey easier to navigate

What is RStudio

• RStudio/Posit is a corporation whilst R is an academic-led volunteer community. So far relatively good relationship
• Heard JJ Allaire present some of his latest work at a conference in 2023

• RStudio is product of a for profit company (Posit)
  • RStudio (Desktop) is free
  • RStudio Server (Posit Workbench) has annual licence fee of $’000s
• Posit employs many of the best & brightest package developers
  • e.g. tidyverse, bookdown, reticulate, roxygen2 etc.
  • The CEO (JJ Allaire) is still an active developer
• Other IDEs also exist (e.g. emacs, VSCode, Cursor, Positron)

Some very helpful features of RStudio

• We can write scripts and execute code interactively
• We can see everything we need (directories, plots, code, history etc.)

• Predictive auto-completion
• Integration with Github Co-Pilot
• Integration with other languages
  • markdown, \(\LaTeX\), bash, python, C++, git etc.
• Numerous add-ons to simplify larger tasks

• Posit is now developing Positron to better enable a variety of languages

Create an R Project

I use R Projects to manage each analysis

1. Create a directory on your computer for today’s material
   • We recommend RAdelaide25 in your home directory

2. Now open RStudio
   • RStudio will always open in a directory somewhere
   • Look in the Files pane (bottom-right) to see where it’s looking
     (Or type getwd() in the Console pane)
   • This is the current working directory for R

Create an R Project

We want RStudio to be looking in our new directory (RAdelaide25)
\(\implies\)R Projects make this easy

• File > New Project > Existing Directory
• Browse to your RAdelaide25 directory \(\implies\) Create Project

Create an R Project

• R Projects are simply a wrapper for keeping an analysis organised
  • Will always open in the R Project directory
  • You can easily navigate to a directory with all scripts and data
  • Makes managing file paths from your code very simple
• R Projects can be particularly helpful when loading external files
• Also when saving/exporting lots of files as part of your analysis

• The R Project name is always the directory name
• Not essential, but good practice and extremely useful
• The Project Menu is in the top-right of RStudio

• Enables us to work on multiple analyses/datasets
• Just open the relevant project \(\implies\) you’re ready to go

Create An Empty R Script

1. File > New File > R Script
2. Save As DataImport.R

RStudio

This is the basic layout we often work with

We’ll come back to the script window in the next section

The R Console

• This is the R Console within the RStudio IDE
• We’ve already explored this briefly

• In the same pane we also have two other tabs
  • Terminal: An approximation of a bash terminal (or PowerShell for Windows)
  • Background Jobs shows progress when compiling RMarkdown & Quarto \(\implies\) Not super relevant

The R Environment

Like we did earlier, in the R Console type:

> x <- 5

Where have we created the object x?

• Is it on your hard drive somewhere?
• Is it in a file somewhere?

• We have placed x in our R Environment
• Formally known as your Global Environment

The R Environment

• The R Environment is like your desktop
• We keep all our relevant objects here
  • Multiple objects are usually created during an analysis
  • Can save all the objects in your environment as a single .RData object
  • R can be set to automatically save your environment on exit
  • Unlike Excel: We usually save our code not our environment

The History Tab

• Next to the Environment Tab is the History Tab
• Keeps a record of the last ~200 lines of code
  • Very useful for remembering steps during exploration
  • Best practice is to enter + execute code from the Script Window

• We can generally ignore the Connections and any other tabs
  • A git tab will also appear for those who use git in their project

Accessing Help

• May be issues with URL '/help/library/base/html/00Index.html' not found
• The examples in this help page are a bit rubbish…

> ?sqrt

• This will take you to the Help Tab for the sqrt() function
  • Contents may look confusing at this point but will become clearer

• Many inbuilt functions are organised into a package called base
  • Packages group similar/related functions together
  • base is always installed and loaded with R

Additional Sources For Help

As a package author, I’m always reading my own help pages. I simply can’t remember everything I’ve written

• Help pages in R can be hit & miss
  • Some are excellent and informative \(\implies\) some aren’t
  • I regularly read my own help pages

• Bioconductor has a support forum for Bioconductor packages
  • https://support.bioconductor.org
  • All packages have a vignette (again varying quality)

• Google is your friend \(\implies\) maybe ChatGPT?

The Plots Pane

• We’ve already seen the Files Tab
• Plots appear in the Plots Tab

> plot(cars)

Other Panes

• The Packages Tab is a bad idea
  • Can be disabled by popular request (I always do)
  • Temptation to click is strong
  • Very bad for reproducible research!!!

• Viewer Tab is used when compiling HTML documents from RMarkdown

• Every tab can be minimised/maximised using the buttons on the top right
• Window separators can be be moved to resize panes manually

Cheatsheet and Shortcuts

Help > Cheatsheets > RStudio IDE Cheat Sheet

Page 2 has lots of hints:

• Ctrl + 1 places focus on the Script Window
• Ctrl + 2 places focus on the Console
• Ctrl + 3 places focus on the Help Tab

Conclusion

• R is the engine, driving everything we do
• RStudio brings together multiple features for easy coding
• Can easily access Help, Plots, Scripts etc
• Integrated Development Environment (IDE)

• Other IDEs do exist (e.g. VSCode, Positron) \(\implies\) beyond scope of course

References

Chambers, John M. 1977. Computational Methods for Data Analysis. New York: Wiley.

———. 2020. “S, r, and Data Science.” Proc. ACM Program. Lang. 4 (HOPL): 1–17.

Ihaka, Ross, and Robert Gentleman. 1996. “R: A Language for Data Analysis and Graphics.” J. Comput. Graph. Stat. 5 (3): 299–314.