Skip to Main Content
library logo banner

Research Software Learning Online: R and RStudio

Accessing R and RStudio

R Studio imageR is an open-source programming language that is used for programming, data analysis and data visualisation. It is widely used for statistical analysis. RStudio is a Graphical User Interface (GUI) for R. 

R and RStudio are available on all of the University-managed computers across campus and via UniDesk. Staff and students can install R and RStudio onto their University devices via the Software Centre. R is open-source software that you can download for free from the R Project website. There is an open-source version of RStudio available from the RStudio website.  There are instructions on installing R and RStudio onto your own device via the Data Carpentry website.

 

Image: CC BY-SA: https://commons.wikimedia.org/wiki/File:KnitR_in_RStudio.png

Please note that O'Reilly deletes content in December and June. If you are linking to courses on this platform we advise that you check that they are still running in January and July of each year. 

Getting started courses

These courses are suitable for researchers who are completely new to using R and RStudio.Getting started courses that require you to install R and RStudio will go through the steps to install the relevant programme. If you are using a University-managed PC please see the access information at the top of this page. 

Learn R Programming (1.5 hours)

This is a good introductory video course on R which will take you through setting up R and RStudio, the different data types and formats and some basic commands for data manipulation and summarising data. A good start to learning R from which you can engage with the longer, more in-depth courses. This course covers:

  • Getting started with R and R StudioLearn R Programming
  • Working with variables and functions
  • Working with different data formats (vectors, matrices, data frames)
  • Reading data into R and some summary statistics

R Programming Fundamentals (2.5 hours)

This is a good introductory video course for those who are new to R and new to coding. It covers the basics of R, introduces data visualisation and data management. Once you have completed this course you should feel more confident to take some of the more in-depth courses.  The course covers: 

  • Getting started with R and RStudioR programming fundamentals​
  • Variable types
  • Data import and export
  • Data visualisation
  • Data management 

R for Data Science: Analysis and Visualization (2.75 hours)

In this course you will learn the following:

  • How to install R, RStudio, and code packages to extend R's functionality.
  • Data modeling, visualization, and statistical analysis using R and RStudio.
  • Techniques for data visualization, including creating bar charts, histograms, and scatterplots.
  • Data wrangling skills such as selecting cases, recoding variables, and computing new variables.
  • Data analysis methods like computing frequencies, descriptives, correlations, linear regression, and contingency tables.

Intermediate and advanced courses

For those of you who are already familiar with R there are a wide range of courses that you can use to expand your knowledge and skills. Many of these can be dipped in- and out- of as needed. We have provided a selection of curated courses for you and you can search for other courses on O'Reilly Learning and LinkedIn Learning. 

R Programming (15 hours)

This 15-hour video teaches you how to program in R even if you are unfamiliar with statistical techniques. It starts with the basics of using R and progresses into data manipulation and model building. Users learn through hands-on practice with the code and techniques. New material covers chaining commands, faster data manipulation, new ways to read rectangular data into R, testing code, and the hot package Shiny.

Based on a course on R and Big Data taught by the author at Columbia

  • Designed from the ground up to help viewers quickly overcome R’s learning curve
  • Packed with hands-on practice opportunities and realistic, downloadable code examples
  • Presented by an author with unsurpassed experience teaching statistical programming and modeling to novices
  • For every potential R user: programmers, data scientists, DBAs, marketers, quants, scientists, policymakers, and many others

Complete Guide to R: Wrangling, Visualizing, and Modeling Data (8.25 hours)

In this course, you will learn how to utilize R for data analysis so you can use R to efficiently clean and analyze research data, create visualizations for presentations, and model data to extract valuable insights. Here are the key topics covered in the course:

  • Introduction to R and its applications in data analysis
  • Getting started with R and RStudio
  • Importing and exploring data
  • Visualizing data using ggplot2
  • Data wrangling techniques
  • Recoding and transforming data
  • Analyzing and predicting outcomes
  • Clustering and classifying cases
  • A case study on data science using R

R Programming for Statistics and Data Science (6.5 hours)

This is quite an in-depth video course that, for more experienced users of R, would be useful ‘how-to’ learning resource. The course covers: 

  • Getting started with R and RStudioR programming for statistics and data science​
  • Vectors and vector operations
  • Matrices and data frames
  • Fundamentals of programming in R
  • Manipulating and visualising data
  • Statistics: descriptive statistics, hypothesis testing and linear regression