Getting Started

ANU BDSI
workshop Introduction to R programming

Emi Tanaka

Biological Data Science Institute

3rd April 2024

Welcome 👋

Teaching team

  • Academic statistician passionate about data science and open source software
  • Currently, Deputy Director of ANU Biological Data Science Institute and Executive Editor of R Journal
  • PhD in Statistical Bioinformatics
  • BSci (Adv Maths) with major in Mathematics and Statistics
  • Loves data and coding

        https://emitanaka.org     @statsgen     fosstodon.org/@emitanaka

  • 2nd year of PhD student at ANU, working on phylogenomics methods to study hybridisation
  • BSci in Bioinformatics
  • Enjoy hiking and science
  • jeremiasivan
  • 3rd Year PhD student @ Linde Lab, E&E Division, RSB, ANU
  • Investigating the evolution of the Australian orchid flora and associated funga
  • MScSt (Biodiversity Science)
  • Loves playing music, reading, nature
  • @rpodonnell
  • rpodonnell.github.io
  • 2nd Year PhD student @ Sequeira Lab, E&E Division, RSB, ANU
  • Identifying social, collective, or coordinated movement behaviour patterns in sharks using tracking data
  • MSc (Marine Biology), BSc (Biology)
  • Loves triathlon, hiking and the ocean
  • @nilskreuter
  • https://linktr.ee/nilskreuter

Your turn! Tell us about yourself 👋



pollev.com/emitanaka

Workshop materials

All materials will be hosted at
https://anu-bdsi.github.io/workshop-intro-R/

🕙 Schedule

Time Content
10:00–10:30 Getting Started
10:30–10:45 Exercise 1
10:45–11:15 Working with objects
11:15–11:30 Exercise 2
11:30–11:40 Break
11:40–12:00 Reading and writing data
12:00–12:10 Exercise 3
12:10–12:40 Functions, loops and conditional statements
12:40–12:50 Exercise 4
12:50–13:00 Wrap-up

Learning objectives

  • Conduct elementary arithmetic operations using R
  • Navigate the RStudio interactive development environment (IDE)
  • Install external packages in R to extend functionality
  • Comprehend various object types in R
  • Manipulate lists, matrices, and vectors in R
  • Compute basic summary statistics including mean, median, quartiles, and standard deviation using R
  • Grasp the concept of missing values within the R environment
  • Import and export data in R
  • Create basic functions, employ conditional statements, and utilize for loops in R
  • Decipher error messages and do basic troubleshooting

Current learning objectives

  • Conduct elementary arithmetic operations using R
  • Navigate the RStudio interactive development environment (IDE)
  • Install external packages in R to extend functionality
  • -Comprehend various object types in R
  • -Manipulate lists, matrices, and vectors in R
  • -Compute basic summary statistics including mean, median, quartiles, and standard deviation using R
  • -Grasp the concept of missing values within the R environment
  • -Import and export data in R
  • -Create basic functions, employ conditional statements, and utilize for loops in R
  • -Decipher error messages and do basic troubleshooting

What is R?

  • R is a programming language predominately for data analysis.
  • RStudio Desktop is an integrated development environment (IDE) that helps you to use R.

  • Visual Studio Code is another popular IDE (we don’t use this).

Why learn R?

  • R is one of the top programming languages for statistics or data science.
    • Python is also a good alternative language for data science.
    • Better to have a mastery of at least one language rather than none.
  • R was initially developed by statisticians for statisticians.
    • State-of-the-art statistical methods are more readily available in R.
  • R has a very active and friendly community.
  • R is a free and open source software (FOSS) and is a cross-platform language:
    • free = money is not a barrier to use it,
    • open source software = transparency,
    • cross-platform = can be used on Windows, Mac, and Linux.

Rapid development

  • Access to Open AI (known for ChatGPT) API is already available in R!
library(openai)
out <- create_image("A data scientist with a laptop and 
                     statistical software in an anime style")

Rich graphical outputs

  • R also has a wide range of packages for creating beautiful and interactive graphics.

Try clicking on a bar on the left plot.

Dynamic documents

  • Create beautiful documents with better reproducible practices and workflow!
  • This slide and the website is made using a combination of R and Quarto.
scroll down

Interactively working with R

  • You can use R like a calculator:

How do you use R?

  • RStudio Desktop (or RStudio IDE) is the most common way to use R.

Live demo

Customise Global Options

  • Go to RStudio > Tools > Global Options…
  • Under the General tab, make sure the “Restore .RData into workspace at startup” is unticked.
  • This avoids unexpectedly loading (old) data into your workspace and making your code only work in your workspace, but not for others (which is bad reproducible practice).

Arithmetics

Vectors

Functions

  • There are many functions in R.
  • You can look at the documentation on how to use it:

Finding functions

  • To find indexed functions for a package:
  • Google it with a good set of keywords.
  • The recent trend is ask generative AI.

Base R

  • R has 7 packages, collectively referred to as the “base R”, that are loaded automatically when you launch it.
  • The functions in the base packages are generally well-tested and trustworthy.

Contributed R Packages

  • R packages are community developed extensions to R (much like apps on your mobile).
  • The Comprehensive R Archive Network (CRAN) is a volunteer maintained repository that hosts submitted R packages that are approved (much like an app store).
  • There are close to 20,000 packages available on CRAN but the qualities of R packages vary.
  • There are other repositories that host R packages, e.g. Bioconductor for bioinformatics, R Universe, R-Forge, GitHub (we won’t cover these).

Photo by Sara Kurfeß on Unsplash

Using packages on CRAN

  • If the package (say praise) is on CRAN, you can install it by:
install.packages("praise")
  • You only need to install.packages() once!
  • Loading exported functions from a package:
  • Use package::function() for without loading package:

Summary

RStudio Desktop (or RStudio IDE)

Console or Source


  • Use install.packages() to install a package (only once).
  • Use library() to load a package.
  • Use package::function() to use a function from a package without loading it.

RStudio Desktop Cheatsheet

RStudio Desktop Cheatsheet

Exercise time

15:00

anu-bdsi.github.io/workshop-intro-R/