InfoGuides: Software for Digital Scholarship: R

The R Project for Statistical Computing is a free, open source statistical computing language that is popular among researchers in many fields.

The learning curve for true understanding is steep, but specific tasks (e.g., importing files from other statistical software) are often quite easy because of packages that can be created by anybody. R undergoes rapid development and improvement. Tutorials even a year old may be out of date.

Note that RStudio is currently in the process of changing names to Posit. Various tutorials may reference one or the other, but both are acceptable at this time.

R for Data Management
R for Statistical Analysis
R for Data Visualization

See the slides from our Getting Started with R (pdf) workshop for an overview and recommendations.

Access to R & RStudio

FREE to install on personal computers
Available in all ITS Labs and the DiSC Lab
On the Virtual Computing Lab

Start Here

Quick Start

No installations needed; runs in your web browser.

RStudio Cloud Primers - Videos and try-it-yourself examples from the very basics
1. Programming Basics - functions/packages, arguments, objects, data types and structures
  - Data basics - more on dataframes (+tibbles), and data types
2. The Tidyverse - a group of packages for data management
  The following 3 primers in Work with Data
  1. Working with Tibbles - tibbles, overview of the tidyverse package
  2. Isolating Data with dplyr - the functions select, filter, arrange, and the pipe ($>$)
    1. More on filter
  3. Deriving Information with dplyr - the functions summarize, group_by, and mutate
    1. More on summarize, and mutate
  - a longer alternative to #2 & #3 Wrangling penguins, a dplyr primer by Allison Horst
3. Visualization with ggplot2 (8 tutorials) - various chart and plot types
  - the single primer Visualization Basics requires no prior knowledge, great for building motivation.
4. Set Up - extra help to install R, RStudio, and Packages on your own computer
  - R Portable (SourceForge)

Starting a Project

Data Science with R and RStudio (UQ Library)
- R with RStudio: Getting Started (~1.5 hrs): Covers the basics of starting and organizing a process, opening a data file and doing basic data management and visualization. Markdown document with the code
- R data manipulation with RStudio and dplyr (~1hr): covers the basics of dplyr and the tidyverse.

Full Course

R for Data Science by Garrett Grolemund; Hadley Wickham Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle--transform your datasets into a form convenient for analysis Program--learn powerful R tools for solving data problems with greater clarity and ease Explore--examine your data, generate hypotheses, and quickly test them Model--provide a low-dimensional summary that captures true "signals" in your dataset Communicate--learn R Markdown for integrating prose, code, and results
Call Number: Available Online; Non-circulating copy in the DiSC lab
ISBN: 9781491910399
Publication Date: 2017-01-05

See also R for Data Science: Exercise Solutions by Jeffrey B. Arnold

2nd Edition in progress

R Infoguide

Learn R Infoguide: Table of Contents

Overview
General Tutorials
Statistical Analysis - See materials for teaching and learning statistics with R
Data Management - Go here for packages and tutorials on preparing data
Graphics - R excels at making graphics, see the many options and galleries
R Programming - Links for programmers who want to know R well
Good Practices
Get Help - Need more help? See your options